SVCL - Research

Home People Research Publications Demos

News Jobs Prospective
Students About Internal

Research Projects

Research at the Statistical Visual Computing Lab covers a wide range of subjects in the areas of computer vision, image processing, machine learning, and multimedia. Below are some descriptions of on-going or past projects. Please check the publications page too, as we currently do not have a web page for some of the projects.

Scene Classification with Low-dimensional Semantic Spaces: A novel approach to scene categorization is proposed. An intermediate space is introduced, based on a low dimensional semantic "theme" image representation, learned with weak supervision from casual image annotations.

Discriminant Hypothesis for Visual Saliency: a decision-theoretic formulation of visual saliency, its biological plausibility, and applications to computer vision.

Bottom-up Saliency and Its Biological Plausibility: biological plausibility of bottom-up saliency by combination of the discriminant hypothesis and center-surround operators.

Top-down Discriminant Saliency: learning discriminant salient features for visual recognition.

Understanding Video of Crowded Environments : motion segmentation and motion classification in video of crowded environments, such as pedestrian scenes and highway traffic.

Dynamic Textures: A family of generative stochastic dynamic texture models for analyzing motion.

Background Subtraction: Background subtraction in dynamic scenes.

Query by Semantic Example: image retrieval in a query-by-example fashion on semantic spaces.
Semantic Image Annotation and Retrieval: automatically labeling images with content-based keywords, and image retrieval via automatic annotations.

Pedestrian Crowd Counting: estimate the size of moving crowds in a privacy preserving manner, i.e. without people models or tracking.

Classification and Retrieval of Traffic Video: classification of traffic video using a generative probabilistic motion model and probabilistic kernel classifiers.

Motion Segmentation: robust segmentation of motion in video.

Classifier Loss Function Design: The design and theory of Bayes consistent loss functions and classifiers with applications.

Real-Time Object Detection Cascades: Real-time face, car, pedestrian and logo detection in images.

Real-Time EEG Surprise Signal Detection: Cost sensitive boosting and real-time detection cascades for EEG surprise signal detection.

Cost Sensitive Learning: The design and theory of cost sensitive classifiers with applications.

Image compression using Object-based Regions of Interest: learning ROI masks for image and video coding at very low bit-rates

Optimal Features for Large-scale Visual Recognition: learning algorithms for feature design that are optimal, in the minimum probability of error sense, and scalable in the number of visual classes.

Probabilistic Kernel Classifiers: design of kernels functions between probability densities.

Minimum Probability of Error Image Retrieval: optimal search of large image collections with content-based queries.

Semantic Image Classification: augmenting retrieval systems with understanding of image semantics.

Learning Mixture Hierarchies: learning hierarchical mixture models for efficient classifier design, image indexing, and semantic classification hierarchies.

Measuring Image Manifold Distances: image similarity measures that are invariant to spatial transformations.

Motion Analysis: motion models and estimation algorithms for segmentation, mosaicking, and layered representations.

Modeling the Structure of Video: statistical models of video structure and Bayesian inference procedures for improved parsing and semantic classification.

Home	People	Research	Publications	Demos

News	Jobs	Prospective Students	About	Internal

Research Projects
Research at the Statistical Visual Computing Lab covers a wide range of subjects in the areas of computer vision, image processing, machine learning, and multimedia. Below are some descriptions of on-going or past projects. Please check the publications page too, as we currently do not have a web page for some of the projects.
	Scene Classification with Low-dimensional Semantic Spaces: A novel approach to scene categorization is proposed. An intermediate space is introduced, based on a low dimensional semantic "theme" image representation, learned with weak supervision from casual image annotations.
	Discriminant Hypothesis for Visual Saliency: a decision-theoretic formulation of visual saliency, its biological plausibility, and applications to computer vision.
	Bottom-up Saliency and Its Biological Plausibility: biological plausibility of bottom-up saliency by combination of the discriminant hypothesis and center-surround operators.
	Top-down Discriminant Saliency: learning discriminant salient features for visual recognition.
	Understanding Video of Crowded Environments : motion segmentation and motion classification in video of crowded environments, such as pedestrian scenes and highway traffic.
	Dynamic Textures: A family of generative stochastic dynamic texture models for analyzing motion.
	Background Subtraction: Background subtraction in dynamic scenes.
	Query by Semantic Example: image retrieval in a query-by-example fashion on semantic spaces.
	Semantic Image Annotation and Retrieval: automatically labeling images with content-based keywords, and image retrieval via automatic annotations.
	Pedestrian Crowd Counting: estimate the size of moving crowds in a privacy preserving manner, i.e. without people models or tracking.
	Classification and Retrieval of Traffic Video: classification of traffic video using a generative probabilistic motion model and probabilistic kernel classifiers.
	Motion Segmentation: robust segmentation of motion in video.
	Classifier Loss Function Design: The design and theory of Bayes consistent loss functions and classifiers with applications.
	Real-Time Object Detection Cascades: Real-time face, car, pedestrian and logo detection in images.
	Real-Time EEG Surprise Signal Detection: Cost sensitive boosting and real-time detection cascades for EEG surprise signal detection.
	Cost Sensitive Learning: The design and theory of cost sensitive classifiers with applications.
	Image compression using Object-based Regions of Interest: learning ROI masks for image and video coding at very low bit-rates
	Optimal Features for Large-scale Visual Recognition: learning algorithms for feature design that are optimal, in the minimum probability of error sense, and scalable in the number of visual classes.
	Probabilistic Kernel Classifiers: design of kernels functions between probability densities.
	Minimum Probability of Error Image Retrieval: optimal search of large image collections with content-based queries.
	Semantic Image Classification: augmenting retrieval systems with understanding of image semantics.
	Learning Mixture Hierarchies: learning hierarchical mixture models for efficient classifier design, image indexing, and semantic classification hierarchies.
	Measuring Image Manifold Distances: image similarity measures that are invariant to spatial transformations.
	Motion Analysis: motion models and estimation algorithms for segmentation, mosaicking, and layered representations.
	Modeling the Structure of Video: statistical models of video structure and Bayesian inference procedures for improved parsing and semantic classification.