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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.

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.

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.

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.

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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.
	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.
	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.
	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.