Saxe, D.
* 1996: Toward robust skin identification in video images
* 1999: Distributed output encoding for multi-class pattern recognition
Saxe, S.[Samuel]
* 2019: Gravity Recovery and Climate Experiment (GRACE) Storage Change Characteristics (2003-2016) over Major Surface Basins and Principal Aquifers in the Conterminous United States
Saxen, F.[Frerk]
* 2013: FPGA-Based Real-Time Pedestrian Detection on High-Resolution Images
* 2014: Color-based skin segmentation: An evaluation of the state of the art
* 2015: Handling Data Imbalance in Automatic Facial Action Intensity Estimation
* 2017: Landmark based head pose estimation benchmark and method
* 2017: Real vs. Fake Emotion Challenge: Learning to Rank Authenticity from Facial Activity Descriptors
* 2018: How the Region of Interest Impacts Contact Free Heart Rate Estimation Algorithms
* 2019: Detecting Arbitrarily Rotated Faces for Face Analysis
* 2020: Facial Action Unit Recognition in the Wild with Multi-Task CNN Self-Training for the EmotioNet Challenge
* 2023: Classification networks for continuous automatic pain intensity monitoring in video using facial expression on the X-ITE Pain Database
Includes: Saxen, F.[Frerk] Saxen, F.
9 for Saxen, F.
Saxena, A.[Ashutosh]
* 2007: 3-D Reconstruction from Sparse Views using Monocular Vision
* 2007: Learning 3-D Scene Structure from a Single Still Image
* 2008: 3-D Depth Reconstruction from a Single Still Image
* 2008: On optimal royalty costs for video compression
* 2009: Make3D: Learning 3D Scene Structure from a Single Still Image
* 2011: Mode dependent DCT/DST for intra prediction in block-based image/video coding
* 2012: Jointly Optimized Spatial Prediction and Block Transform for Video and Image Coding
* 2012: Learning the right model: Efficient max-margin learning in Laplacian CRFs
* 2012: On secondary transforms for prediction residual
* 2012: Toward Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models
* 2013: 3D-Based Reasoning with Blocks, Support, and Stability
* 2013: DCT/DST-Based Transform Coding for Intra Prediction in Image/Video Coding
* 2013: Hallucinated Humans as the Hidden Context for Labeling 3D Scenes
* 2013: Infinite Latent Conditional Random Fields
* 2013: Low complexity image matching using color based SIFT
* 2013: Low Latency Secondary Transforms for Intra/Inter Prediction Residual
* 2013: Mid-level feature based local descriptor selection for image search
* 2013: On secondary transforms for scalable video coding
* 2014: Nearest-neighbor intra prediction for screen content video coding
* 2014: On prediction techniques for palette coding
* 2014: Physically Grounded Spatio-temporal Object Affordances
* 2015: 360 degrees video coding using region adaptive smoothing
* 2015: 3D Reasoning from Blocks to Stability
* 2015: Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
* 2015: Low-complexity separable multiplier-less loop filter for video coding
* 2015: Motion estimation and compensation for fisheye warped video
* 2015: Unsupervised Semantic Parsing of Video Collections
* 2015: Watch-n-patch: Unsupervised understanding of actions and relations
* 2016: Anticipating Human Activities Using Object Affordances for Reactive Robotic Response
* 2016: Improving Intra Prediction in High-Efficiency Video Coding
* 2016: Modeling 3D Environments through Hidden Human Context
* 2016: Structural-RNN: Deep Learning on Spatio-Temporal Graphs
* 2018: Watch-n-Patch: Unsupervised Learning of Actions and Relations
* 2021: Remote Sensing Imagery Segmentation: A Hybrid Approach
* 2023: A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis
* 2024: Noise level estimation using locality preserving natural image statistics
Includes: Saxena, A.[Ashutosh] Saxena, A.[Ankur] Saxena, A. Saxena, A.[Amit] Saxena, A.[Apoorv]
36 for Saxena, A.
Saxena, D.[Divya]
* 2021: Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
* 2022: Electric Vehicle Trip Chain Information-Based Hierarchical Stochastic Energy Management With Multiple Uncertainties
* 2022: Multi-Constraint Adversarial Networks for Unsupervised Image-to-Image Translation
* 2023: Re-GAN: Data-Efficient GANs Training via Architectural Reconfiguration
Includes: Saxena, D.[Divya] Saxena, D.[Dipti]
Saxena, G.[Gaurav]
* 2022: Pansharpening scheme using spatial detail injection-based convolutional neural networks
Saxena, M.[Mohit]
* 2015: Graph-theory-based spectral feature selection for computer aided diagnosis of Parkinson's disease using T1-weighted MRI
Saxena, N.[Nidhi]
* 2017: Pansharpening approach using Hilbert vibration decomposition
* 2018: Offensive and Defensive Exposition of Wearable Computing, An
* 2018: Pansharpening scheme using filtering in two-dimensional discrete fractional Fourier transform
* 2022: Pansharpening scheme using spatial detail injection-based convolutional neural networks
* 2022: Siamese-SR: A Siamese Super-Resolution Model for Boosting Resolution of Digital Rock Images for Improved Petrophysical Property Estimation
* 2023: YOLOv8 Based Deep Learning Method for Potholes Detection
Includes: Saxena, N.[Nidhi] Saxena, N.[Nitesh] Saxena, N.[Nishank]
Saxena, P.
* 2018: CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction
* 2020: One-Shot learning based classification for segregation of plastic waste
* 2022: RIChEx: A Robust Inter-Frame Change Exposure for Segmenting Moving Objects
Includes: Saxena, P. Saxena, P.[Paresh] Saxena, P.[Prafulla]
Saxena, R.[Rajiv]
* 2011: optimized transmultiplexer using combinational window functions, An
* 2018: Towards a polyalgorithm for land use change detection
Includes: Saxena, R.[Rajiv] Saxena, R.[Rishu]
Saxena, S.[Shobhit]
* 2008: Crowd Behavior Recognition for Video Surveillance
* 2009: Google Newspaper Search: Image Processing and Analysis Pipeline
* 2013: Determination of network vulnerability factor using rough set
* 2013: Language Adaptive Methodology for Handwritten Text Line Segmentation
* 2015: Coordinated Local Metric Learning
* 2016: Heterogeneous Face Recognition with CNNs
* 2019: First steps toward CNN based source classification of document images shared over messaging app
* 2020: Pre-trained convolutional neural networks as feature extractors for diagnosis of breast cancer using histopathology
* 2021: Breast cancer histopathology image classification using kernelized weighted extreme learning machine
* 2021: Retinal Vessel Segmentation Using Blending-Based Conditional Generative Adversarial Networks
* 2023: Generalist Framework for Panoptic Segmentation of Images and Videos, A
Includes: Saxena, S.[Shobhit] Saxena, S.[Sanklan] Saxena, S.[Subhra] Saxena, S.[Shreyas] Saxena, S.[Suraj] Saxena, S.[Shweta] Saxena, S.[Saurabh]
11 for Saxena, S.
Saxena, S.C.
* 2005: Locally adaptive wavelet domain bayesian processor for denoising medical ultrasound images using speckle modelling based on rayleigh distribution
* 2006: Adaptive compression of medical ultrasound images
* 2011: Image enhancement by wavelet-based thresholding neural network with adaptive learning rate
* 2012: PSO-based learning of sub-band adaptive thresholding function for image denoising
Saxena, T.
* 1995: Computing Invariants Using Elimination Methods
* 1996: Using Elimination Methods to Compute Thermophysical Algebraic Invariants from Infrared Imagery
* 1997: Cubic Rational Polynomial Camera Model, The
* 1998: Geometric, Algebraic, and Thermophysical Techniques for Object Recognition in IR Imagery
* 1998: Recognizing Objects in Cluttered Images Using Subgraph Isomorphism
Includes: Saxena, T. Saxena, T.[Tushar]
Saxena, V.
* 2005: noninvasive, multimodality approach based on MRS and MRI techniques for monitoring intracranial brain tumor angiogenesis, A
* 2005: PerfectDoc: a ground truthing environment for complex documents
* 2010: Remote Sensing Image Registration Techniques: A Survey
* 2015: Advanced free-form deformation and Kullback-Lieblier divergence measure for digital elevation model registration
Includes: Saxena, V. Saxena, V.[Vinay] Saxena, V.[Vikas]
Saxena, V.N.[Vineeta Nigam]
* 2021: Breast cancer diagnosis from mammographic images using optimized feature selection and neural network architecture