19.6.3.7 Agriculture, Inspection -- Food Products, Plants, Farms

Chapter Contents (Back)
Real Time Vision. Application, Inspection. Inspection, Food. Food Inspection. Plant Inspection. See also Weed Detection.

Dipix Technologies,
2001
WWW Link. Vendor, Inspection. Industrial inspection systems for food industry.

Ellips B.V.,
1989.
WWW Link. Vendor, Inspection. Industrial inspection systems for food industry.

JLI Vision,
1985. Founded as Jřrgen Lćssře Ingeniřrfirma Aps
WWW Link. Vendor, Inspection. Industrial inspection systems for food industry.

Tillett and Hague Technology Ltd,
2005.
WWW Link. Vendor, Inspection. Automation for agriculture, e.g. visual guided spraying.

Buhler Sortex,
2010
WWW Link. Vendor, Inspection. Food inspection and sorting using images.

Plant Phenotyping Datasets for Computer Vision,
2016
WWW Link. Dataset, Plants. We present a collection of benchmark datasets in the context of plant phenotyping. We provide annotated imaging data and suggest suitable evaluation criteria for plant/leaf segmentation, detection, tracking as well as classification and regression problems. The figure symbolically depicts the data available together with ground truth segmentations and further annotations and metadata. Article in press. See also Finely-grained annotated datasets for image-based plant phenotyping.

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Chen, Z.[Zikuan], Tao, Y.[Yang],
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Davies, E.R.,
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Davies, E.R., Bateman, M., Mason, D.R., Chambers, J., Ridgway, C.,
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Elsevier DOI 0211
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Casasent, D.[David], Chen, X.W.[Xue-Wen],
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Elsevier DOI 0211
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Codrea, C.M., Aittokallio, T., Keränen, M., Tyystjärvi, E., Nevalainen, O.S.,
Feature learning with a genetic algorithm for fluorescence fingerprinting of plant species,
PRL(24), No. 15, November 2003, pp. 2663-2673.
Elsevier DOI 0308
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Pandit, R.B.[Ram Bhuwan], Tang, J.[Juming], Liu, F.[Frank], Mikhaylenko, G.[Galina],
A computer vision method to locate cold spots in foods in microwave sterilization processes,
PR(40), No. 12, December 2007, pp. 3667-3676.
Elsevier DOI 0709
Color values; Computer vision; Image processing; Chemical marker; Heating patterns; Microwave sterilization; Process validation; IMAQ vision builder; Cold spot BibRef

Meade, R.[Ronald],
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And: US_Patent7,222,726, May 29, 2007
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Pajares, G., Tellaeche, A., Burgosartizzu, X.P., Ribeiro, A.,
Design of a computer vision system for a differential spraying operation in precision agriculture using hebbian learning,
IET-CV(1), No. 3-4, December 2007, pp. 93-99.
DOI Link 0905
BibRef

Sun, D.W.[Da-Wen], (Ed.)
Computer Vision Technology for Food Quality Evaluation,
ElsevierInc., 2008. ISBN: 978-0-12-373642-0
WWW Link. Buy this book: Computer Vision Technology for Food Quality Evaluation (Food Science and Technology) BibRef 0800

Bossu, J.[Jérémie], Gee, C.[Christelle], Truchetet, F.[Frederic],
Development of a machine vision system for a real time precision sprayer,
ELCVIA(7), No. 3, 2008, pp. xx-yy.
DOI Link 0909
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Galleguillos, C.[Carolina], Belongie, S.J.[Serge J.],
Context based object categorization: A critical survey,
CVIU(114), No. 6, June 2010, pp. 712-722.
Elsevier DOI 1006
Object recognition; Context; Object categorization; Computer vision systems Appearance alone is not enough. Incorporate various kinds of context. BibRef

McFee, B.[Brian], Galleguillos, C.[Carolina], Lanckriet, G.R.G.[Gert R.G.],
Contextual Object Localization With Multiple Kernel Nearest Neighbor,
IP(19), No. 2, February 2011, pp. 570-585.
IEEE DOI 1102
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Galleguillos, C.[Carolina], McFee, B.[Brian], Lanckriet, G.R.G.[Gert R.G.],
Iterative Category Discovery via Multiple Kernel Metric Learning,
IJCV(108), No. 1-2, May 2014, pp. 115-132.
Springer DOI 1405
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Galleguillos, C.[Carolina], McFee, B.[Brian], Belongie, S.J.[Serge J.], Lanckriet, G.R.G.[Gert R.G.],
From region similarity to category discovery,
CVPR11(2665-2672).
IEEE DOI 1106
BibRef
Earlier:
Multi-class object localization by combining local contextual interactions,
CVPR10(113-120).
IEEE DOI 1006
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Rabinovich, A.[Andrew], Vedaldi, A.[Andrea], Galleguillos, C.[Carolina], Wiewiora, E.[Eric], Belongie, S.J.[Serge J.],
Objects in Context,
ICCV07(1-8).
IEEE DOI 0710
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Merler, M.[Michele], Galleguillos, C.[Carolina], Belongie, S.J.[Serge J.],
Recognizing Groceries in situ Using in vitro Training Data,
SLAM07(1-8).
IEEE DOI 0706
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Galleguillos, C.[Carolina], Faymonville, P.[Peter], Belongie, S.J.[Serge J.],
BUBL: An effective region labeling tool using a hexagonal lattice,
Emergent09(2072-2079).
IEEE DOI 0910
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Galleguillos, C.[Carolina], Babenko, B.[Boris], Rabinovich, A.[Andrew], Belongie, S.J.[Serge J.],
Weakly Supervised Object Localization with Stable Segmentations,
ECCV08(I: 193-207).
Springer DOI 0810
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Galleguillos, C.[Carolina], Rabinovich, A.[Andrew], Belongie, S.J.[Serge J.],
Object categorization using co-occurrence, location and appearance,
CVPR08(1-8).
IEEE DOI 0806
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Mantelli Neto, S.L.[Sylvio Luiz], Besen de Aguiar, D.[Daniel], Sens dos Santos, B.[Bianca], von Wangenheim, A.[Aldo],
Multivariate Bayesian cognitive modeling for unsupervised quality control of baked pizzas,
MVA(23), No. 3, May 2012, pp. 491-499.
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Subramanian, R.[Ram], Spalding, E.P.[Edgar P.], Ferrier, N.J.[Nicola J.],
A high throughput robot system for machine vision based plant phenotype studies,
MVA(24), No. 3, April 2013, pp. 619-636.
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Oliveira, L.[Luciano], Costa, V.[Victor], Neves, G.[Gustavo], Oliveira, T.[Talmai], Jorge, E.[Eduardo], Lizarraga, M.[Miguel],
A mobile, lightweight, poll-based food identification system,
PR(47), No. 5, 2014, pp. 1941-1952.
Elsevier DOI 1412
Food identification BibRef

Ljungqvist, M.G.[Martin Georg], Nielsen, O.H.A.[Otto Hřjager Attermann], Frosch, S.[Stina], Nielsen, M.E.[Michael Engelbrecht], Clemmensen, L.H.[Line Harder], Ersbřll, B.K.[Bjarne Kjćr],
Hyperspectral imaging based on diffused laser light for prediction of astaxanthin coating concentration,
MVA(25), No. 2, February 2014, pp. 327-343.
Springer DOI 1412
Fish feed pellet coating. BibRef

Pujari, J.D.[Jagadeesh D.], Yakkundimath, R.[Rajesh], Byadgi, A.S.[Abdulmunaf S.],
Detection and classification of fungal disease with Radon transform and support vector machine affected on cereals,
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Koenig, K.[Kristina], Höfle, B.[Bernhard], Hämmerle, M.[Martin], Jarmer, T.[Thomas], Siegmann, B.[Bastian], Lilienthal, H.[Holger],
Comparative classification analysis of post-harvest growth detection from terrestrial LiDAR point clouds in precision agriculture,
PandRS(104), No. 1, 2015, pp. 112-125.
Elsevier DOI 1505
Terrestrial laser scanning BibRef

Candiago, S.[Sebastian], Remondino, F.[Fabio], de Giglio, M.[Michaela], Dubbini, M.[Marco], Gattelli, M.[Mario],
Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images,
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DOI Link 1505
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Baravalle, R.[Rodrigo], Delrieux, C.[Claudio], Gomez, J.[Juan],
Multifractal characterisation and classification of bread crumb digital images,
JIVP(2015), No. 1, 2015, pp. 9.
DOI Link 1505
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Story, D.[David], Kacira, M.[Murat],
Design and implementation of a computer vision-guided greenhouse crop diagnostics system,
MVA(26), No. 4, May 2015, pp. 495-506.
Springer DOI 1506
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Minervini, M., Scharr, H., Tsaftaris, S.,
Image Analysis: The New Bottleneck in Plant Phenotyping,
SPMag(32), No. 4, July 2015, pp. 126-131.
IEEE DOI 1506
[Applications Corner] Agriculture BibRef

Chi, J.N.[Jian-Ning], Eramian, M.G.,
Enhancement of Textural Differences Based on Morphological Component Analysis,
IP(24), No. 9, September 2015, pp. 2671-2684.
IEEE DOI 1506
image enhancement BibRef

Yi, X.[Xin], Eramian, M.G.[Mark G.], Wang, R.J.[Ruo-Jing], Neufeld, E.[Eric],
Identification of Morphologically Similar Seeds Using Multi-kernel Learning,
CRV14(143-150)
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Accuracy BibRef

Ivanov, S.[Stepan], Bhargava, K.[Kriti], Donnelly, W.[William],
Precision Farming: Sensor Analytics,
IEEE_Int_Sys(30), No. 4, July 2015, pp. 76-80.
IEEE DOI 1506
Data integration BibRef

Xu, R., Herranz, L., Jiang, S., Wang, S., Song, X., Jain, R.,
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IEEE DOI 1506
Accuracy. Food dishes. Context. BibRef

Impoco, G., Tuminello, L.,
Incremental learning to segment micrographs,
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Elsevier DOI 1509
Image analysis BibRef

Impoco, G.[Gaetano], Licitra, G.[Giuseppe],
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CAIP09(914-921).
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Vidovic, I.[Ivan], Cupec, R.[Robert], Hocenski, Ž.[Željko],
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Agricultural automation BibRef

Minervini, M.[Massimo], Fischbachb, A.[Andreas], Scharrb, H.[Hanno], Tsaftarisa, S.A.[Sotirios A.],
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PDF File. The dataset: See also Plant Phenotyping Datasets for Computer Vision. BibRef 1600

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Food recognition BibRef

Tatsuma, A.[Atsushi], Aono, M.[Masaki],
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Springer DOI 1608
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Earlier: A1, A2, A3, A5, A6, A7, Only:
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Springer DOI 1504
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Scharr, H.[Hanno], Dee, H.[Hannah], French, A.P.[Andrew P.], Tsaftaris, S.A.[Sotirios A.],
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Springer DOI 1608
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Benoit, L.[Landry], Benoit, R.[Romain], Belin, É.[Étienne], Vadaine, R.[Rodolphe], Demilly, D.[Didier], Chapeau-Blondeau, F.[François], Rousseau, D.[David],
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Springer DOI 1608
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Augustin, M.[Marco], Haxhimusa, Y.[Yll], Busch, W.[Wolfgang], Kropatsch, W.G.[Walter G.],
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Springer DOI 1608
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Golbach, F.[Franck], Kootstra, G.[Gert], Damjanovic, S.[Sanja], Otten, G.[Gerwoud], van de Zedde, R.[Rick],
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Springer DOI 1608
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Kelly, D.[Derek], Vatsa, A.[Avimanyou], Mayham, W.[Wade], Ngô, L.[Linh], Thompson, A.[Addie], Kazic, T.[Toni],
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Springer DOI 1608
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Santos, T.T.[Thiago T.], Rodrigues, G.C.[Gustavo C.],
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Cruz, J.A.[Jeffrey A.], Yin, X.[Xi], Liu, X.M.[Xiao-Ming], Imran, S.M.[Saif M.], Morris, D.D.[Daniel D.], Kramer, D.M.[David M.], Chen, J.[Jin],
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Pound, M.P.[Michael P.], French, A.P.[Andrew P.], Fozard, J.A.[John A.], Murchie, E.H.[Erik H.], Pridmore, T.P.[Tony P.],
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Zheng, P.[Peng], Zhao, Z.Q.[Zhong-Qiu], Gao, J.[Jun], Wu, X.D.[Xin-Dong],
Image set classification based on cooperative sparse representation,
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Sparse representation BibRef

Zhao, Z.Q.[Zhong-Qiu], Hong, Y.[Yan], Zheng, P.[Peng], Wu, X.D.[Xin-Dong],
Plant identification using triangular representation based on salient points and margin points,
ICIP15(1145-1149)
IEEE DOI 1512
Feature extraction BibRef

Herranz, L.[Luis], Jiang, S.Q.[Shu-Qiang], Xu, R.H.[Rui-Han],
Modeling Restaurant Context for Food Recognition,
MultMed(19), No. 2, February 2017, pp. 430-440.
IEEE DOI 1702
Which restaurant helps reduce the possible foods. BibRef

Bell, J.[Jonathan], Dee, H.M.[Hannah M.],
Watching plants grow: a position paper on computer vision and Arabidopsis thaliana,
IET-CV(11), No. 2, March 2017, pp. 113-121.
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Dehais, J., Anthimopoulos, M., Shevchik, S., Mougiakakou, S.,
Two-View 3D Reconstruction for Food Volume Estimation,
MultMed(19), No. 5, May 2017, pp. 1090-1099.
IEEE DOI 1704
Calibration BibRef

Min, W., Jiang, S., Sang, J., Wang, H., Liu, X., Herranz, L.,
Being a Supercook: Joint Food Attributes and Multimodal Content Modeling for Recipe Retrieval and Exploration,
MultMed(19), No. 5, May 2017, pp. 1100-1113.
IEEE DOI 1704
Correlation BibRef

Li, D.[Dawei], Xu, L.[Lihong], Tang, X.S.[Xue-Song], Sun, S.Y.[Shao-Yuan], Cai, X.[Xin], Zhang, P.[Peng],
3D Imaging of Greenhouse Plants with an Inexpensive Binocular Stereo Vision System,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
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Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei],
Generalized Aggregation of Sparse Coded Multi-Spectra for Satellite Scene Classification,
IJGI(6), No. 6, 2017, pp. xx-yy.
DOI Link 1706
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Kusumoto, R.[Riko], Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei],
Hybrid Aggregation of Sparse Coded Descriptors for Food Recognition,
ICPR14(1490-1495)
IEEE DOI 1412
Encoding BibRef

Pandey, P., Deepthi, A., Mandal, B., Puhan, N.B.,
FoodNet: Recognizing Foods Using Ensemble of Deep Networks,
SPLetters(24), No. 12, December 2017, pp. 1758-1762.
IEEE DOI 1712
convolution, food products, image recognition, neural nets, FoodNet, Indian food image database, automatic food recognition system, food recognition BibRef

Patrick, A.[Aaron], Li, C.[Changying],
High Throughput Phenotyping of Blueberry Bush Morphological Traits Using Unmanned Aerial Systems,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
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Asaari, M.S.M.[Mohd Shahrimie Mohd], Mishra, P.[Puneet], Mertens, S.[Stien], Dhondt, S.[Stijn], Inzé, D.[Dirk], Wuyts, N.[Nathalie], Scheunders, P.[Paul],
Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform,
PandRS(138), 2018, pp. 121-138.
Elsevier DOI 1804
Close-range hyperspectral imaging, Linear reflectance model, Standard normal variate, Spectral similarity measure, Plant stress BibRef

Zheng, J.N.[Jian-Nan], Zou, L.[Liang], Wang, Z.J.[Z. Jane],
Mid-level deep Food Part mining for food image recognition,
IET-CV(12), No. 3, April 2018, pp. 298-304.
DOI Link 1804
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Heravi, E.J.[Elnaz Jahani], Aghdam, H.H.[Hamed Habibi], Puig, D.[Domenec],
An optimized convolutional neural network with bottleneck and spatial pyramid pooling layers for classification of foods,
PRL(105), 2018, pp. 50-58.
Elsevier DOI 1804
Food classification, Convolutional neural networks, Neural network visualization, Deep learning, Spatial pyramid pooling BibRef

Min, W., Bao, B.K., Mei, S., Zhu, Y., Rui, Y., Jiang, S.,
You Are What You Eat: Exploring Rich Recipe Information for Cross-Region Food Analysis,
MultMed(20), No. 4, April 2018, pp. 950-964.
IEEE DOI 1804
Analytical models, Computers, Cultural differences, Metadata, Pattern analysis, Probabilistic logic, Visualization, topic model BibRef

Kim, D.W.[Dong-Wook], Yun, H.S.[Hee Sup], Jeong, S.J.[Sang-Jin], Kwon, Y.S.[Young-Seok], Kim, S.G.[Suk-Gu], Lee, W.S.[Won Suk], Kim, H.J.[Hak-Jin],
Modeling and Testing of Growth Status for Chinese Cabbage and White Radish with UAV-Based RGB Imagery,
RS(10), No. 4, 2018, pp. xx-yy.
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Ege, T.[Takumi], Yanai , K.[Keiji],
Image-Based Food Calorie Estimation Using Recipe Information,
IEICE(E101-D), No. 5, May 2018, pp. 1333-1341.
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Xu, J.[Jing], Ma, L.[Long], Wu, J.[Jie], Xu, X.M.[Xiao-Meng], Sun, Y.[Ye], Liu, Q.A.[Qi-Ang], Pan, L.Q.[Lei-Qing], Tu, K.[Kang],
Applications of hyperspectral and optical scattering imaging technique in the detection of food microorganism,
IJCVR(8), No. 3, 2018, pp. 267-282.
DOI Link 1807
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Strickland, E.,
3 sensors to track every bite and gulp,
Spectrum(55), No. 7, July 2018, pp. 9-10.
IEEE DOI 1807
[News item] health care, medical computing, mobile computing, bite, calories, diets, dozens BibRef

De Sousa Ribeiro, F., Gong, L., Calivá, F., Swainson, M., Gudmundsson, K., Yu, M., Leontidis, G., Ye, X., Kollias, S.,
An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging,
ICIP18(2376-2380)
IEEE DOI 1809
Feature extraction, Geometry, Computer architecture, Machine learning, Food packaging, maximally stable extremal regions BibRef

Horiguchi, S., Amano, S., Ogawa, M., Aizawa, K.,
Personalized Classifier for Food Image Recognition,
MultMed(20), No. 10, October 2018, pp. 2836-2848.
IEEE DOI 1810
feature extraction, food technology, image classification, image recognition, class mean classifier, deep feature BibRef

Yu, Q., Anzawa, M., Amano, S., Ogawa, M., Aizawa, K.,
Food Image Recognition by Personalized Classifier,
ICIP18(171-175)
IEEE DOI 1809
Feature extraction, Image recognition, Optimization, Databases, Artificial neural networks, Training, Adaptation models, classifier adaptation BibRef


Chen, Y., Ribera, J., Delp, E.J.,
Estimating Plant Centers Using A Deep Binary Classifier,
Southwest18(105-108)
IEEE DOI 1809
Unmanned aerial vehicles, Agriculture, Image segmentation, Shape, Chemicals, Image analysis, Genetics, Plant Phenotyping, CNN BibRef

Fang, S., Liu, C., Tahboub, K., Zhu, F., Delp, E.J., Boushey, C.J.,
cTADA: The Design of a Crowdsourcing Tool for Online Food Image Identification and Segmentation,
Southwest18(25-28)
IEEE DOI 1809
Image segmentation, Tools, Noise measurement, Crowdsourcing, Task analysis, Systematics, Training data, Dietary Assessment, Groundtruth Segmentation BibRef

Fang, S., Shao, Z., Mao, R., Fu, C., Delp, E.J., Zhu, F., Kerr, D.A., Boushey, C.J.,
Single-View Food Portion Estimation: Learning Image-to-Energy Mappings Using Generative Adversarial Networks,
ICIP18(251-255)
IEEE DOI 1809
Estimation, Image segmentation, Generative adversarial networks, Gallium nitride, Task analysis, Image-to-Energy Mapping BibRef

Belan, P.A.[Peterson A.], de Macedo, R.A.G.[Robson A. G.], Pereira, M.M.A.[Marihá M. A.], Alves, W.A.L.[Wonder A. L.], de Araújo, S.A.[Sidnei A.],
A Fast and Robust Approach for Touching Grains Segmentation,
ICIAR18(482-489).
Springer DOI 1807
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Galati, R., Reina, G., Messina, A., Gentile, A.,
Survey and navigation in agricultural environments using robotic technologies,
AVSS17(1-6)
IEEE DOI 1806
agriculture, farming, image fusion, intelligent robots, mobile robots, robot vision, telerobotics, Wheels BibRef

Bhosle, K., Musande, V.,
Stress Monitoring of Mulberry Plants By Finding Rep Using Hyperspectral Data,
Hannover17(383-386).
DOI Link 1805
BibRef

Chen, H., Wang, J., Qi, Q., Li, Y., Sun, H.,
Bilinear CNN Models for Food Recognition,
DICTA17(1-6)
IEEE DOI 1804
computer vision, feature extraction, feedforward neural nets, image classification, learning (artificial intelligence), Image recognition BibRef

Galloway, A., Taylor, G.W., Ramsay, A., Moussa, M.,
The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environment,
CRV17(361-366)
IEEE DOI 1804
aquaculture, computer vision, feedforward neural nets, image annotation, image segmentation, Ciona17 dataset, semantic segmentation BibRef

Rezende Silva, G.[Gustavo], Cunha Escarpinati, M.[Mauricio], Duarte Abdala, D.[Daniel], Rezende Souza, I.[Iuri],
Definition of Management Zones Through Image Processing for Precision Agriculture,
WVC17(150-154)
IEEE DOI 1804
agriculture, autonomous aerial vehicles, crops, farming, remotely operated vehicles, robot vision, vegetation mapping, NDVI, k-means clustering BibRef

Moghadam, P., Ward, D., Goan, E., Jayawardena, S., Sikka, P., Hernandez, E.,
Plant Disease Detection Using Hyperspectral Imaging,
DICTA17(1-8)
IEEE DOI 1804
agriculture, crops, feature extraction, hyperspectral imaging, image classification, learning (artificial intelligence), Vegetation mapping BibRef

Wang, Y., Zhu, F., Boushey, C.J., Delp, E.J.,
Weakly supervised food image segmentation using class activation maps,
ICIP17(1277-1281)
IEEE DOI 1803
Cancer, Image segmentation, Kernel, Semantics, Supervised learning, Task analysis, Training, dietary assessment, graph model, weakly supervised learning BibRef

Gao, K., White, T., Palaniappan, K., Warmund, M., Bunyak, F.,
Museed: A mobile image analysis application for plant seed morphometry,
ICIP17(2826-2830)
IEEE DOI 1803
Image analysis, Image edge detection, Image segmentation, Kernel, Mobile applications, Shape, image analysis, mobile application, plant seed morphometry BibRef

Ming, Z.Y.[Zhao-Yan], Chen, J.[Jingjing], Cao, Y.[Yu], Forde, C.[Ciarán], Ngo, C.W.[Chong-Wah], Chua, T.S.[Tat Seng],
Food Photo Recognition for Dietary Tracking: System and Experiment,
MMMod18(II:129-141).
Springer DOI 1802
BibRef

Alves, W.A.L.[Wonder A. L.], Gobber, C.F.[Charles F.], Hashimoto, R.F.[Ronaldo F.],
Plant Bounding Box Detection from Desirable Residues of the Ultimate Levelings,
ICIAR18(474-481).
Springer DOI 1807
BibRef
Earlier: A2, A1, A3:
Ultimate Leveling Based on Mumford-Shah Energy Functional Applied to Plant Detection,
CIARP17(220-228).
Springer DOI 1802
BibRef

Tanno, R.[Ryosuke], Ege, T.[Takumi], Yanai, K.[Keiji],
AR DeepCalorieCam: An iOS App for Food Calorie Estimation with Augmented Reality,
MMMod18(II:352-356).
Springer DOI 1802
BibRef

Chen, Y., Ribera, J., Boomsma, C., Delp, E.J.[Edward J.],
Locating Crop Plant Centers from UAV-Based RGB Imagery,
CVPPP17(2030-2037)
IEEE DOI 1802
Greenhouses, Plants (biology), Training data, Unmanned aerial vehicles BibRef

Choudhury, S.D., Goswami, S., Bashyam, S., Awada, T., Samal, A.,
Automated Stem Angle Determination for Temporal Plant Phenotyping Analysis,
CVPPP17(2022-2029)
IEEE DOI 1802
Cameras, Colored noise, Image color analysis, Image segmentation, Image sequences, Junctions, Skeleton BibRef

Uchiyama, H., Sakurai, S., Mishima, M., Arita, D., Okayasu, T., Shimada, A., Taniguchi, R.I.,
An Easy-to-Setup 3D Phenotyping Platform for KOMATSUNA Dataset,
CVPPP17(2038-2045)
IEEE DOI 1802
Cameras, Image color analysis, Indoor environments, Lighting, Soil, Tools BibRef

Pound, M.P., Atkinson, J.A., Wells, D.M., Pridmore, T.P., French, A.P.,
Deep Learning for Multi-task Plant Phenotyping,
CVPPP17(2055-2063)
IEEE DOI 1802
Agriculture, Computer vision, Ear, Image resolution, Image segmentation, Machine learning, Training BibRef

Christ, P.F., Schlecht, S., Ettlinger, F., Grün, F., Heinle, C., Tatavatry, S., Ahmadi, S.A., Diepold, K., Menze, B.H.,
Diabetes60: Inferring Bread Units From Food Images Using Fully Convolutional Neural Networks,
ACVR17(1526-1535)
IEEE DOI 1802
Cameras, Computer vision, Diabetes, BibRef

Pawara, P.[Pornntiwa], Okafor, E.[Emmanuel], Schomaker, L.[Lambert], Wiering, M.[Marco],
Data Augmentation for Plant Classification,
ACIVS17(615-626).
Springer DOI 1712
BibRef

Aguilar, E.[Eduardo], Bolańos, M.[Marc], Radeva, P.[Petia],
Food Recognition Using Fusion of Classifiers Based on CNNs,
CIAP17(II:213-224).
Springer DOI 1711
BibRef

Fiorucci, M.[Marco], Fratton, M.[Marco], Dulecha, T.G.[Tinsae G.], Pelillo, M.[Marcello], Pravato, A.[Alberto], Roncato, A.[Alessandro],
A Computer Vision System for the Automatic Inventory of a Cooler,
CIAP17(I:575-585).
Springer DOI 1711
BibRef

Ahmad, N.M.[Norul Maslissa], Ali, N.M.[Nazlena Mohamad], Baharin, H.[Hanif],
MyRedList: Virtual Application for Threatened Plant Species,
IVIC17(445-454).
Springer DOI 1711
BibRef

Razali, M.N.[Mohd Norhisham], Manshor, N.[Noridayu], Halin, A.A.[Alfian Abdul], Yaakob, R.[Razali], Mustapha, N.[Norwati],
Food Category Recognition Using SURF and MSER Local Feature Representation,
IVIC17(212-223).
Springer DOI 1711
BibRef

Salvador, A., Hynes, N., Aytar, Y., Marin, J., Ofli, F., Weber, I., Torralba, A.B.,
Learning Cross-Modal Embeddings for Cooking Recipes and Food Images,
CVPR17(3068-3076)
IEEE DOI 1711
Data models, Image representation, Semantics, Tools, Training BibRef

Carstensen, J.M.,
Fast, versatile, and non-destructive biscuit inspection system using spectral imaging,
MVA17(502-505)
DOI Link 1708
Image color analysis, Imaging, Indexes, Moisture, Moisture measurement, Reflectivity, Training BibRef

Bhugra, S., Anupama, A., Chaudhury, S., Lall, B., Chugh, A.,
Phenotyping of xylem vessels for drought stress analysis in rice,
MVA17(428-431)
DOI Link 1708
Feature extraction, Image segmentation, Microscopy, Morphology, Principal component analysis, Shape, Stress BibRef

Ege, T., Yanai, K.,
Simultaneous estimation of food categories and calories with multi-task CNN,
MVA17(198-201)
DOI Link 1708
Correlation, Estimation, Image recognition, MISO, Organizations, Standards, Training BibRef

Bindlish, E., Abbott, A.L., Balota, M.,
Assessment of Peanut Pod Maturity,
WACV17(688-696)
IEEE DOI 1609
Agriculture, Calibration, Color, Image color analysis, Imaging, Soil, Visualization BibRef

Hansen, M.A.E.[Michael A.E.], Kannan, A.S.[Ananda S.], Lund, J.[Jacob], Thorn, P.[Peter], Sasic, S.[Srdjan], Carstensen, J.M.[Jens M.],
State Estimation of the Performance of Gravity Tables Using Multispectral Image Analysis,
SCIA17(II: 471-480).
Springer DOI 1706
Gravity tables are machines that separate dense grains from lighter ones. BibRef

Einarsson, G.[Gudmundur], Jensen, J.N.[Janus N.], Paulsen, R.R.[Rasmus R.], Einarsdottir, H.[Hildur], Ersbřll, B.K.[Bjarne K.], Dahl, A.B.[Anders B.], Christensen, L.B.[Lars Bager],
Foreign Object Detection in Multispectral X-ray Images of Food Items Using Sparse Discriminant Analysis,
SCIA17(I: 350-361).
Springer DOI 1706
BibRef

Bolańos, M., Radeva, P.,
Simultaneous food localization and recognition,
ICPR16(3140-3145)
IEEE DOI 1705
Cameras, Computer vision, Image recognition, Kernel, Pattern recognition, Proposals, Training BibRef

Moulos, I.[Ioannis], Maramis, C.[Christos], Ioakimidis, I.[Ioannis], van den Boer, J.[Janet], Nolstam, J.[Jenny], Mars, M.[Monica], Bergh, C.[Cecilia], Maglaveras, N.[Nicos],
Objective and Subjective Meal Registration via a Smartphone Application,
MADiMa15(409-416).
Springer DOI 1511
BibRef

Caon, M.[Maurizio], Carrino, S.[Stefano], Prinelli, F.[Federica], Ciociola, V.[Valentina], Adorni, F.[Fulvio], Lafortuna, C.[Claudio], Tabozzi, S.[Sarah], Serrano, J.[José], Condon, L.[Laura], Khaled, O.A.[Omar Abou], Mugellini, E.[Elena],
Towards an Engaging Mobile Food Record for Teenagers,
MADiMa15(417-424).
Springer DOI 1511
BibRef

Waltner, G.[Georg], Schwarz, M.[Michael], Ladstätter, S.[Stefan], Weber, A.[Anna], Luley, P.[Patrick], Bischof, H.[Horst], Lindschinger, M.[Meinrad], Schmid, I.[Irene], Paletta, L.[Lucas],
MANGO: Mobile Augmented Reality with Functional Eating Guidance and Food Awareness,
MADiMa15(425-432).
Springer DOI 1511
BibRef

Mendiola-Lau, V.[Victor], Silva Mata, F.J.[Francisco José], Martínez-Díaz, Y.[Yoanna], Bustamante, I.T.[Isneri Talavera], de Marsico, M.[Maria],
Automatic Classification of Herbal Substances Enhanced with an Entropy Criterion,
CIARP16(233-240).
Springer DOI 1703
BibRef

Marin, R.D.C.[Ricardo D. C.], Green, R.D.[Richard D.],
A Hidden Markov Model for modeling and extracting vine structure in images,
ICVNZ15(1-6)
IEEE DOI 1701
feature extraction BibRef

Nguyen, C.V.[Chuong V.], Fripp, J.[Jurgen], Lovell, D.R.[David R.], Furbank, R.[Robert], Kuffner, P.[Peter], Daily, H.[Helen], Sirault, X.[Xavier],
3D Scanning System for Automatic High-Resolution Plant Phenotyping,
DICTA16(1-8)
IEEE DOI 1701
Australia BibRef

Chen, J.J.[Jing-Jing], Pang, L.[Lei], Ngo, C.W.[Chong-Wah],
Cross-Modal Recipe Retrieval: How to Cook this Dish?,
MMMod17(I: 588-600).
Springer DOI 1701
BibRef

Yang, H.X.[Hai-Xiang], Zhang, D.[Dong], Lee, D.J.[Dah-Jye], Huang, M.J.[Min-Jie],
A Sparse Representation Based Classification Algorithm for Chinese Food Recognition,
ISVC16(II: 3-10).
Springer DOI 1701
BibRef

Myers, A., Johnston, N., Rathod, V., Korattikara, A., Gorban, A., Silberman, N., Guadarrama, S., Papandreou, G., Huang, J., Murphy, K.,
Im2Calories: Towards an Automated Mobile Vision Food Diary,
ICCV15(1233-1241)
IEEE DOI 1602
Cameras BibRef

Martinel, N., Foresti, G.L., Micheloni, C.,
Wide-Slice Residual Networks for Food Recognition,
WACV18(567-576)
IEEE DOI 1806
computer vision, feature extraction, food technology, image classification, image representation, Visualization BibRef

Martinel, N., Piciarelli, C., Micheloni, C., Foresti, G.L.,
A Structured Committee for Food Recognition,
ACVR15(484-492)
IEEE DOI 1602
Diseases BibRef

Kawasaki, Y.[Yusuke], Uga, H.[Hiroyuki], Kagiwada, S.[Satoshi], Iyatomi, H.[Hitoshi],
Basic Study of Automated Diagnosis of Viral Plant Diseases Using Convolutional Neural Networks,
ISVC15(II: 638-645).
Springer DOI 1601
BibRef

Li, Y.[Ying], Sheopuri, A.[Anshul],
Applying image analysis to assess food aesthetics and uniqueness,
ICIP15(311-314)
IEEE DOI 1512
Computational aesthetics BibRef

Wang, Y.[Yu], He, Y.[Ye], Zhu, F.Q.[Feng-Qing], Boushey, C.[Carol], Delp, E.J.[Edward J.],
The Use of Temporal Information in Food Image Analysis,
MADiMa15(317-325).
Springer DOI 1511
BibRef

Knez, S.[Simon], Šajn, L.[Luka],
Food Object Recognition Using a Mobile Device: State of the Art,
MADiMa15(366-374).
Springer DOI 1511
BibRef

Pouladzadeh, P.[Parisa], Yassine, A.[Abdulsalam], Shirmohammadi, S.[Shervin],
FooDD: Food Detection Dataset for Calorie Measurement Using Food Images,
MADiMa15(441-448).
Springer DOI 1511
BibRef

Han, S.[Simeng], Cointault, F.[Frédéric], Salon, C.[Christophe], Simon, J.C.[Jean-Claude],
Automatic Detection of Nodules in Legumes by Imagery in a Phenotyping Context,
CAIP15(II:134-145).
Springer DOI 1511
BibRef

Brilhador, A.[Anderson], Serrarens, D.A.[Daniel A.], Lopes, F.M.[Fabrício M.],
A Computer Vision Approach for Automatic Measurement of the Inter-plant Spacing,
CIARP15(219-227).
Springer DOI 1511
BibRef

Ciocca, G.[Gianluigi], Napoletano, P.[Paolo], Schettini, R.[Raimondo],
Food Recognition and Leftover Estimation for Daily Diet Monitoring,
MADiMa15(334-341).
Springer DOI 1511
BibRef

Matsunaga, H.[Hiroki], Doman, K.[Keisuke], Hirayama, T.[Takatsugu], Ide, I.[Ichiro], Deguchi, D.[Daisuke], Murase, H.[Hiroshi],
Tastes and Textures Estimation of Foods Based on the Analysis of Its Ingredients List and Image,
MADiMa15(326-333).
Springer DOI 1511
BibRef

Mazzei, A.[Alessandro], Anselma, L.[Luca], de Michieli, F.[Franco], Bolioli, A.[Andrea], Casuu, M.[Matteo], Gerbrandy, J.[Jelle], Lunardi, I.[Ivan],
Mobile Computing and Artificial Intelligence for Diet Management,
MADiMa15(342-349).
Springer DOI 1511
BibRef

Kagaya, H.[Hokuto], Aizawa, K.[Kiyoharu],
Highly Accurate Food/Non-Food Image Classification Based on a Deep Convolutional Neural Network,
MADiMa15(350-357).
Springer DOI 1511
BibRef

Farinella, G.M.[Giovanni Maria], Moltisanti, M.[Marco], Battiato, S.[Sebastiano],
Food Recognition Using Consensus Vocabularies,
MADiMa15(384-392).
Springer DOI 1511
BibRef

Liang, B.[Bing], Song, G.X.[Gu-Xin], Li, G.L.[Gong-Li],
Discussion about the effect of digital plants library on the plants landscape restoration in Yuanmingyuan,
CIPA15(43-48).
DOI Link 1508
BibRef

Ávila, M.M.[M. Mar], Caballero, D.[Daniel], Durán, M.L.[M. Luisa], Caro, A.[Andrés], Pérez-Palacios, T.[Trinidad], Antequera, T.[Teresa],
Including 3D-textures in a Computer Vision System to Analyze Quality Traits of Loin,
CVS15(456-465).
Springer DOI 1507
BibRef

Chaudhury, A.[Ayan], Ward, C.[Christopher], Talasaz, A.[Ali], Ivanov, A.G.[Alexander G.], Huner, N.P.A.[Norman P.A.], Grodzinski, B.[Bernard], Patel, R.V.[Rajni V.], Barron, J.L.[John L.],
Computer Vision Based Autonomous Robotic System for 3D Plant Growth Measurement,
CRV15(290-296)
IEEE DOI 1507
Image reconstruction BibRef

Lam, A., Kuno, Y., Sato, I.,
Evaluating freshness of produce using transfer learning,
FCV15(1-4)
IEEE DOI 1506
agricultural products BibRef

Skytte, J.[Jacob], Mřller, F.[Flemming], Abildgaard, O.[Otto], Dahl, A.[Anders], Larsen, R.[Rasmus],
Discriminating Yogurt Microstructure Using Diffuse Reflectance Images,
SCIA15(187-198).
Springer DOI 1506
BibRef

Kawano, Y.[Yoshiyuki], Yanai, K.[Keiji],
Automatic Expansion of a Food Image Dataset Leveraging Existing Categories with Domain Adaptation,
TASKCV14(3-17).
Springer DOI 1504
BibRef

Farinella, G.M.[Giovanni Maria], Allegra, D.[Dario], Stanco, F.[Filippo], Battiato, S.[Sebastiano],
On the Exploitation of One Class Classification to Distinguish Food Vs Non-Food Images,
MADiMa15(375-383).
Springer DOI 1511
BibRef
And: A1, A2, A3, Only:
A Benchmark Dataset to Study the Representation of Food Images,
ACVR14(584-599).
Springer DOI 1504
BibRef

Ward, B.[Ben], Bastian, J.[John], van den Hengel, A.[Anton], Pooley, D.[Daniel], Bari, R.[Rajendra], Berger, B.[Bettina], Tester, M.[Mark],
A Model-Based Approach to Recovering the Structure of a Plant from Images,
PlantType14(215-230).
Springer DOI 1504
BibRef

van den Hengel, A.J.[Anton J.], Russell, C.[Chris], Dick, A.[Anthony], Bastian, J.[John], Pooley, D.[Daniel], Fleming, L.[Lachlan], Agapito, L.[Lourdes],
Part-based modelling of compound scenes from images,
CVPR15(878-886)
IEEE DOI 1510
BibRef

Santos, T.T.[Thiago Teixeira], Koenigkan, L.V.[Luciano Vieira], Barbedo, J.G.A.[Jayme Garcia Arnal], Rodrigues, G.C.[Gustavo Costa],
3D Plant Modeling: Localization, Mapping and Segmentation for Plant Phenotyping Using a Single Hand-held Camera,
PlantType14(247-263).
Springer DOI 1504
BibRef

Benoit, L.[Landry], Semaan, G.[Georges], Franconi, F.[Florence], Belin, É.[Étienne], Chapeau-Blondeau, F.[François], Demilly, D.[Didier], Rousseau, D.[David],
3D Multimodal Simulation of Image Acquisition by X-Ray and MRI for Validation of Seedling Measurements with Segmentation Algorithms,
PlantType14(131-139).
Springer DOI 1504
BibRef

Pound, M.P.[Michael P.], French, A.P.[Andrew P.], Murchie, E.H.[Erik H.], Pridmore, T.P.[Tony P.],
Surface Reconstruction of Plant Shoots from Multiple Views,
PlantType14(158-173).
Springer DOI 1504
BibRef

Klodt, M.[Maria], Cremers, D.[Daniel],
High-Resolution Plant Shape Measurements from Multi-view Stereo Reconstruction,
PlantType14(174-184).
Springer DOI 1504
BibRef

Beijbom, O.[Oscar], Joshi, N.[Neel], Morris, D.[Dan], Saponas, S.[Scott], Khullar, S.[Siddharth],
Menu-Match: Restaurant-Specific Food Logging from Images,
WACV15(844-851)
IEEE DOI 1503
Computer vision BibRef

Bettadapura, V.[Vinay], Thomaz, E.[Edison], Parnami, A.[Aman], Abowd, G.D.[Gregory D.], Essa, I.[Irfan],
Leveraging Context to Support Automated Food Recognition in Restaurants,
WACV15(580-587)
IEEE DOI 1503
Cameras BibRef

He, Y.[Ye], Xu, C.[Chang], Khanna, N.[Nitin], Boushey, C.J.[Carol J.], Delp, E.J.[Edward J.],
Analysis of food images: Features and classification,
ICIP14(2744-2748)
IEEE DOI 1502
Accuracy BibRef

Skoien, K.R.[Kristoffer Rist], Alver, M.O.[Morten Omholt], Alfredsen, J.A.[Jo Arve],
A computer vision approach for detection and quantification of feed particles in marine fish farms,
ICIP14(1648-1652)
IEEE DOI 1502
Aquaculture BibRef

Farinella, G.M.[Giovanni Maria], Moltisanti, M.[Marco], Battiato, S.[Sebastiano],
Classifying food images represented as Bag of Textons,
ICIP14(5212-5216)
IEEE DOI 1502
Accuracy BibRef

Yokoya, N.[Naoto], Kokawa, M.[Mito], Sugiyama, J.[Junichi],
Spectral unmixing of fluorescence fingerprint imagery for visualization of constituents in pie pastry,
ICIP14(679-683)
IEEE DOI 1502
Dairy products BibRef

Afridi, M.J.[Muhammad Jamal], Liu, X.M.[Xiao-Ming], McGrath, J.M.[J. Mitchell],
An Automated System for Plant-Level Disease Rating in Real Fields,
ICPR14(148-153)
IEEE DOI 1412
Diseases BibRef

Neumann, M.[Marion], Hallau, L.[Lisa], Klatt, B.[Benjamin], Kersting, K.[Kristian], Bauckhage, C.[Christian],
Erosion Band Features for Cell Phone Image Based Plant Disease Classification,
ICPR14(3315-3320)
IEEE DOI 1412
Cameras BibRef

Djuricic, A., Weinmann, M., Jutzi, B.,
Potentials of small, lightweight and low cost Multi-Echo Laser Scanners for detecting Grape Berries,
CloseRange14(211-216).
DOI Link 1411
BibRef

Dubosclard, P.[Pierre], Larnier, S.[Stanislas], Konik, H.[Hubert], Herbulot, A.[Ariane], Devy, M.[Michel],
Automatic Method for Visual Grading of Seed Food Products,
ICIAR14(I: 485-495).
Springer DOI 1410
BibRef

Nggada, S.H.[Shawulu Hunira], Muyingi, H.N.N.[Hippolyte N'Sung-Nza], Dheedan, A.[Amer], Gorejena, M.[Marshal],
Farmer Assisted Mobile Framework for Improving Agricultural Products,
ICISP14(647-657).
Springer DOI 1406
BibRef

Tarry, C.[Cole], Wspanialy, P.[Patrick], Veres, M.[Matthew], Moussa, M.[Medhat],
An Integrated Bud Detection and Localization System for Application in Greenhouse Automation,
CRV14(344-348)
IEEE DOI 1406
Cameras BibRef

Botterill, T., Green, R.D., Mills, S.,
Finding a vine's structure by bottom-up parsing of cane edges,
IVCNZ13(112-117)
IEEE DOI 1412
edge detection. Vine pruning robot. BibRef

Floriello, D., Botterill, T., Green, R.D.,
Defining a geometric probability measure in correspondence problems for branched structures,
IVCNZ13(311-316)
IEEE DOI 1412
computer vision BibRef

Marin, R.D.C., Botterill, T., Green, R.D.,
Split-and-merge EM for vine image segmentation,
IVCNZ13(270-275)
IEEE DOI 1412
Gaussian processes BibRef

McCulloch, J., Green, R.,
Detecting wires in the canopy of grapevines using neural networks: A robust and heuristic free approach,
IVCNZ13(334-339)
IEEE DOI 1412
manipulators BibRef

Wang, B.[Bin], Gao, Y.S.[Yong-Sheng], Sun, C.M.[Chang-Ming], Blumenstein, M.[Michael], La Salle, J.[John],
A Local Scale Selection Scheme for Multiscale Area Integral Invariants,
DICTA16(1-6)
IEEE DOI 1701
Australia BibRef

Quevedo, R.[Roberto], Valencia, E.[Emir], Bastías, J.M.[José Miguel], Cárdenas, S.[Stefany],
Description of the Enzymatic Browning in Avocado Slice Using GLCM Image Texture,
PSIVTWS13(93-101).
Springer DOI 1412
BibRef

Xu, C.[Chang], He, Y.[Ye], Khanna, N.[Nitin], Boushey, C.J.[Carol J.], Delp, E.J.[Edward J.],
Model-based food volume estimation using 3D pose,
ICIP13(2534-2538)
IEEE DOI 1412
BibRef
Earlier: A2, A1, A3, A4, A5:
Context based food image analysis,
ICIP13(2748-2752)
IEEE DOI 1412
3D model rendering. Contextual Information BibRef

Sharifzadeh, S.[Sara], Clemmensen, L.H.[Line H.], Lřje, H.[Hanne], Ersbřll, B.K.[Bjarne K.],
Statistical Quality Assessment of Pre-fried Carrots Using Multispectral Imaging,
SCIA13(620-629).
Springer DOI 1311
BibRef

Kawano, Y.[Yoshiyuki], Yanai, K.[Keiji],
Offline 1000-Class Classification on a Smartphone,
IWMV14(193-194)
IEEE DOI 1409
BibRef

Kawano, Y.[Yoshiyuki], Yanai, K.[Keiji],
FoodCam: A Real-Time Mobile Food Recognition System Employing Fisher Vector,
MMMod14(II: 369-373).
Springer DOI 1405
BibRef
Earlier:
Rapid Mobile Object Recognition Using Fisher Vector,
ACPR13(476-480)
IEEE DOI 1408
BibRef
And:
Real-Time Mobile Food Recognition System,
IWMV13(1-7)
IEEE DOI 1309
Android application;food recognition;mobile image recognition image classification. BibRef

Matsuda, Y.[Yuji], Yanai, K.[Keiji],
Multiple-food recognition considering co-occurrence employing manifold ranking,
ICPR12(2017-2020).
WWW Link. 1302
BibRef

Joutou, T.[Taichi], Yanai, K.[Keiji],
A food image recognition system with Multiple Kernel Learning,
ICIP09(285-288).
IEEE DOI 0911
BibRef

Masoudian, A.[Alireza], Mcisaac, K.A.[Kenneth A.],
Application of Support Vector Machine to Detect Microbial Spoilage of Mushrooms,
CRV13(281-287)
IEEE DOI 1308
Accuracy BibRef

Elibol, A.[Armagan], Posch, S.[Stefan], Maurer, A.[Andreas], Pillen, K.[Klaus], Möller, B.[Birgit],
Vision-Based 3D-Reconstruction of Barley Plants,
IbPRIA13(406-415).
Springer DOI 1307
BibRef

Bin, W.[Wang], Zhuo, W.[Wang], Yuan, M.Z.[Ming-Zhe],
Intelligent control based on case-based reasoning for outlet tobacco moisture percentage of loosening resurgence machine,
ICARCV12(1728-1732).
IEEE DOI 1304
BibRef

Hazisawa, T.[Takeshi], Toda, M.[Masashi], Sakoil, T.[Teruvasu], Matumural, K.[Kazuhiro], Fukuda, M.[Masahito],
Image analysis method for grading raw shiitake mushrooms,
FCV13(46-52).
IEEE DOI 1304
BibRef

Roomi, S.M.M.[S. Mohamed Mansoor], Priya, R.J.[R. Jyothi], Bhumesh, S., Monisha, P.,
Classification of mangoes by object features and contour modeling,
IMVIP12(165-168).
IEEE DOI 1302
BibRef

Romeijn, H., Sheth, F., Pettit, C.J.,
An Evaluative Review of Simulated Dynamic Smart 3d Objects,
AnnalsPRS(I-4), No. 2012, pp. 125-130.
HTML Version. 1209
3D models of plants. BibRef

Marchant, R.[Ross], Jackway, P.T.[Paul T.],
Generalised Hilbert Transforms for the Estimation of Growth Direction in Coral Cores,
DICTA11(660-665).
IEEE DOI 1205
BibRef

Patil, N.K., Yadahalli, R.M.,
The Effect of Block Size, Training Set and K-Value in the Classification of Food Grains Using HSI Color Model,
NCVPRIPG11(50-53).
IEEE DOI 1205
BibRef

Paproki, A., Fripp, J., Salvado, O., Sirault, X., Berry, S., Furbank, R.,
Automated 3D Segmentation and Analysis of Cotton Plants,
DICTA11(555-560).
IEEE DOI 1205
BibRef

Cohen, C.J.[Charles J.], Haanpaa, D.[Doug], Zott, J.P.[James P.],
Machine vision algorithms for robust animal species identification,
AIPR15(1-7)
IEEE DOI 1605
Haar transforms BibRef

Cohen, C.J.[Charles J.], Haanpaa, D.[Doug], Rowe, S.[Steve], Zott, J.P.[James P.],
Vision algorithms for automated census of animals,
AIPR11(1-5).
IEEE DOI 1204
BibRef

Salo, H.[Heikki], Tirronen, V.[Ville], Neri, F.[Ferrante],
Evolutionary Regression Machines for Precision Agriculture,
EvoIASP(356-365).
Springer DOI 1204
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Patel, J.J.[Jalpa J.], Modi, C.K.[Chintan K.], Jain, K.R.[Kavindra R.],
Quality evaluation of Foeniculum vulgare (Fennel) seeds using colorization,
ICIIP11(1-6).
IEEE DOI 1112
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Yeh, M.C.[Mei-Chen], Tai, J.[Jason],
A Hierarchical Approach to Practical Beverage Package Recognition,
PSIVT11(I: 348-357).
Springer DOI 1111
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Moonrinta, J., Chaivivatrakul, S., Dailey, M.N., Ekpanyapong, M.,
Fruit detection, tracking, and 3D reconstruction for crop mapping and yield estimation,
ICARCV10(1181-1186).
IEEE DOI 1109
BibRef

Palacharla, P.K.[Pavan K.], Durbha, S.S.[Surya S.], King, R.L.[Roger L.], Gokaraju, B.[Balakrishna], Lawrence, G.W.[Gary W.],
A hyperspectral reflectance data based model inversion methodology to detect reniform nematodes in cotton,
MultiTemp11(249-252).
IEEE DOI 1109
BibRef

Buus, O.T.[Ole Thomsen], Jřrgensen, J.R.[Johannes Ravn], Carstensen, J.M.[Jens Michael],
Analysis of Seed Sorting Process by Estimation of Seed Motion Trajectories,
SCIA11(273-284).
Springer DOI 1105
BibRef

Nielsen, O.H.A.[Otto Hřjager Attermann], Dahl, A.L.[Anders Lindbjerg], Larsen, R.[Rasmus], Mřller, F.[Flemming], Nielsen, F.D.[Frederik Donbćk], Thomsen, C.L.[Carsten L.], Aanćs, H.[Henrik], Carstensen, J.M.[Jens Michael],
Supercontinuum Light Sources for Hyperspectral Subsurface Laser Scattering: Applications for Food Inspection,
SCIA11(327-337).
Springer DOI 1105
BibRef

Song, Y.[Yu], Glasbey, C.A.[Chris A.], van der Heijden, G.W.A.M.[Gerie W.A.M.], Polder, G.[Gerrit], Dieleman, J.A.[J. Anja],
Combining Stereo and Time-of-Flight Images with Application to Automatic Plant Phenotyping,
SCIA11(467-478).
Springer DOI 1105
BibRef

Nohara, S.[Sachiyo], Kato, K.[Kunihito], Yamamoto, K.[Kazuhiko], Yoshimura, W.[Wakako], Kasamatsu, C.[Chinatsu],
A deliciousness information extraction method by controlling of image information,
FCV11(1-6).
IEEE DOI 1102
Visual evaluation of food for taste. BibRef

Nakajima, C.[Chikahito], Nogata, Y.[Yasuyuki], Sugimoto, M.[Masaaki],
Autodetection of barnacle larvae at power plants,
FCV11(1-4).
IEEE DOI 1102
BibRef

Bosch, M.[Marc], Zhu, F.Q.[Feng-Qing], Khanna, N.[Nitin], Boushey, C.J.[Carol J.], Delp, E.J.[Edward J.],
Combining global and local features for food identification in dietary assessment,
ICIP11(1789-1792).
IEEE DOI 1201
BibRef
Earlier: A2, A1, A4, A5, Only:
An image analysis system for dietary assessment and evaluation,
ICIP10(1853-1856).
IEEE DOI 1009
Image based. BibRef

Perciano, T.[Talita], Hirata, R.[Roberto], de Castro Jorge, L.A.[Lúcio André],
Parameter Estimation for Ridge Detection in Images with Thin Structures,
CIARP10(386-393).
Springer DOI 1011
BibRef

Villette, S., Gee, C., Piron, E., Martin, R., Miclet, D., Paindavoine, M.,
An efficient vision system to measure granule velocity and mass flow distribution in fertiliser centrifugal spreading,
IPTA10(543-548).
IEEE DOI 1007
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Yang, S.L.[Shulin Lynn], Chen, M.[Mei], Pomerleau, D.[Dean], Sukthankar, R.[Rahul],
Food recognition using statistics of pairwise local features,
CVPR10(2249-2256).
IEEE DOI Video of talk:
WWW Link. 1006
BibRef

Chen, L.J.[Li-Jun], Ren, W.T.[Wen-Tao], Li, Y.K.[Yong-Kui],
Fast location of corn images based on position features,
IASP10(272-275).
IEEE DOI 1004
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Wei, Z.B.[Zhen-Bo], Wang, J.[Jun],
Discrimination of Honeys by Electronic Tongue and Different Analytical Techniques,
CISP09(1-5).
IEEE DOI 0910
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Dissing, B.S.[Bjorn S.], Clemmesen, L.H.[Line H.], Loje, H.[Hanne], Ersboll, B.K.[Bjarne K.], Adler-Nissen, J.[Jens],
Temporal reflectance changes in vegetables,
CRICV09(1917-1922).
IEEE DOI 0910
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Chen, M.[Mei], Dhingra, K.[Kapil], Wu, W.[Wen], Yang, L.[Lei], Sukthankar, R.[Rahul], Yang, J.[Jie],
PFID: Pittsburgh fast-food image dataset,
ICIP09(289-292).
IEEE DOI 0911
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Xun, Y.[Yi], Yang, Q.H.[Qing-Hua], Bao, G.[Guanjun], Gao, F.[Feng], Li, W.[Wei],
Recognition of Broken Corn Seeds Based on Contour Curvature,
CISP09(1-5).
IEEE DOI 0910
BibRef

Zhang, Z.Y.[Zhuo-Yong], Wang, F.X.[Feng-Xia], de B Harrington, P.,
Two-Dimensional Mid- and Near-Infrared Correlation Spectroscopy for Rhubarb Identification,
CISP09(1-6).
IEEE DOI 0910
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Liu, Q.S.[Qing-Sheng], Liu, G.H.[Gao-Huan],
Using Tasseled Cap Transformation of CBERS-02 Images to Detect Dieback or Dead Robinia Pseudoacacia Plantation,
CISP09(1-5).
IEEE DOI 0910
BibRef

Sun, X., Gong, H.J., Zhang, F., Chen, K.J.,
A Digital Image Method for Measuring and Analyzing Color Characteristics of Various Color Scores of Beef,
CISP09(1-6).
IEEE DOI 0910
BibRef

Backes, A.R.[André R.], de M. Sá Junior, J.J.[Jarbas J.], Kolb, R.M.[Rosana M.], Bruno, O.M.[Odemir M.],
Plant Species Identification Using Multi-scale Fractal Dimension Applied to Images of Adaxial Surface Epidermis,
CAIP09(680-688).
Springer DOI 0909
See also Shape Skeleton Classification Using Graph and Multi-scale Fractal Dimension. BibRef

Larsen, R.[Rasmus], Arngren, M.[Morten], Hansen, P.W.[Per Waaben], Nielsen, A.A.[Allan Aasbjerg],
Kernel Based Subspace Projection of Near Infrared Hyperspectral Images of Maize Kernels,
SCIA09(560-569).
Springer DOI 0906
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Portman, N.[Nataliya], Grenander, U.[Ulf], Vrscay, E.R.[Edward R.],
Direct Estimation of Biological Growth Properties from Image Data Using the 'GRID' Model,
ICIAR09(832-843).
Springer DOI 0907
BibRef

Ma, W.[Wei], Zha, H.B.[Hong-Bin],
Convenient reconstruction of natural plants by images,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Xie, N.H.[Nian-Hua], Li, X.[Xi], Zhang, X.Q.[Xiao-Qin], Hu, W.M.[Wei-Ming], Wang, J.Z.[James Z.],
Boosted cannabis image recognition,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Taouil, K.[Khaled], Chtourou, Z.[Zied], Kamoun, L.[Lotfi],
Machine Vision Based Quality Monitoring in Olive Oil Conditioning,
IPTA08(1-4).
IEEE DOI 0811
BibRef

Belhumeur, P.N.[Peter N.], Chen, D.Z.[Dao-Zheng], Feiner, S.[Steven], Jacobs, D.W.[David W.], Kress, W.J.[W. John], Ling, H.B.[Hai-Bin], Lopez, I.[Ida], Ramamoorthi, R.[Ravi], Sheorey, S.[Sameer], White, S.[Sean], Zhang, L.[Ling],
Searching the World's Herbaria: A System for Visual Identification of Plant Species,
ECCV08(IV: 116-129).
Springer DOI 0810
BibRef

Lumme, J., Karjalainen, M., Kaartinen, H., Kukko, A., Hyyppä, J., Hyyppä, H., Jaakkola, A., Kleemola, J.,
Terrestrial Laser Scanning of Agricultural Crops,
ISPRS08(B5: 563 ff).
PDF File. 0807
BibRef

Šeatovic, D.[Dejan],
A Segmentation Approach in Novel Real Time 3D Plant Recognition System,
CVS08(xx-yy).
Springer DOI 0805
BibRef

Song, Y.[Yu], Wilson, R.G.[Roland G.], Edmondson, R.[Rodney], Parsons, N.[Nick],
Surface Modelling of Plants from Stereo Images,
3DIM07(312-319).
IEEE DOI 0708
BibRef

Mizuno, S.J.[Shin-Ji], Noda, K.[Keiichi], Ezaki, N.[Nobuo], Takizawa, H.[Hotaka], Yamamoto, S.J.[Shin-Ji],
Detection of Wilt by Analyzing Color and Stereo Vision Data of Plant,
MIRAGE07(400-411).
Springer DOI 0703
BibRef

Guo, M.[Mingen], Ou, Z.Y.[Zong-Ying], Wei, H.L.[Hong-Lei],
Inspecting Ingredients of Starches in Starch-Noodle based on Image Processing and Pattern Recognition,
ICPR06(II: 877-880).
IEEE DOI 0609
BibRef

Dahl, A.B.[Anders Bjorholm], Aanćs, H.[Henrik], Larsen, R.[Rasmus], Ersbřll, B.K.[Bjarne K.],
Classification of Biological Objects Using Active Appearance Modelling and Color Cooccurrence Matrices,
SCIA07(938-947).
Springer DOI 0706
Active Appearance Models. AAM for logs and vegetables. BibRef

Kita, N., Kita, Y., Yang, H.Q.[Hai-Quan],
Archiving technology for plant inspection images captured by mobile active cameras '4D visible memory',
3DPVT02(208-213). 0206
BibRef

Wiwart, M.[Marian], Koczowska, I.[Irena], Borusiewicz, A.[Andrzej],
Estimation of Fusarium Head Blight of Triticale Using Digital Image Analysis of Grain,
CAIP01(563 ff.).
Springer DOI 0210
BibRef

Tadeo, F., Matia, D., Laya, D., Santos, F., Alvarez, T., Gonzalez, S.,
Detection of Phases in Sugar Crystallization Using Wavelets,
ICIP01(III: 178-181).
IEEE DOI 0108
BibRef

Rodenacker, K., Gais, P., Juetting, U., Hense, B.A.,
(Semi-) Automatic Recognition of Microorganisms in Water,
ICIP01(III: 30-33).
IEEE DOI 0108
BibRef

Davies, R.[Roger], Heleno, P.[Paulo], Correia, B.A.B.[Bento A. Brázio], Dinis, J.[Joăo],
VIP3D: An Application of Image Processing Technology for Quality Control in the Food Industry,
ICIP01(I: 293-296).
IEEE DOI 0108
BibRef

Mallant, J.P.,
Visual Inspection in the Food Industry,
SCIA99(Invited Talk). BibRef 9900

Jones, R.[Ronald], Frydendal, I.[Ib],
Segmentation of Sugar Beets Using Image and Graph Processing,
ICPR98(Vol II: 1697-1699).
IEEE DOI 9808
BibRef

Hahn, F.[Federico], Mota, R.[Rafael],
Nobel Chile Jalapeno sorting using structured laser and neural network classifiers,
CIAP97(II: 517-523).
Springer DOI 9709
BibRef

Gregori, M., Lombardi, L., Savini, M., Scianna, A.,
Autonomous plant inspection and anomaly detection,
CIAP97(II: 509-516).
Springer DOI 9709
BibRef

Bolle, R.M., Connell, J.H., Haas, N., Mohan, R., and Taubin, G.,
VeggieVision: A Produce Recognition System,
WACV96(244-251).
IEEE DOI 9609
BibRef

Garcia-Consuegra, J., Cisneros, G., Martinez, A.,
A methodology for woody crop location and discrimination in remote sensing,
CIAP99(810-815).
IEEE DOI 9909
BibRef

Samal, A., Peterson, B., Holliday, D.J.,
Recognizing plants using stochastic L-systems,
ICIP94(I: 183-187).
IEEE DOI 9411
BibRef

Dobrusin, Y.[Yuri], Edan, Y.[Yael], Grinshpun, J.[Joseph], Peiper, U.M.[Uri M.], Wolf, I.[Isaac], Hetzroni, A.[Amots],
Computer image analysis to locate targets for an agricultural robot,
CAIP93(775-779).
Springer DOI 9309
BibRef

Berke, J.[József], Gyorffy, K.[Katalin], Fischl, G.[Géza], Kárpáti, L.[László], Bakonyi, J.[József],
The application of digital image processing in the evaluation of agricultural experiments,
CAIP93(780-787).
Springer DOI 9309
BibRef

Belaid, A.,
Metrology in quality control of nuts,
ICPR90(I: 636-638).
IEEE DOI 9006
BibRef

Fox, J.S., Weldon, Jr., E., and Ang, M.,
Machine Vision Techniques for Finding Sugarcane Seedeyes,
CVPR85(653-655). (Univ. of Hawaii) Hough. Feature Computation. Interesting use of pyramids and hough transform. BibRef 8500

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Weed Detection .


Last update:Nov 17, 2018 at 09:12:27