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Computer Vision and Image Processing

A variety of problems in low- and high-level vision are studied.

The low-level vision (i.e. image processing) problems being addressed are edge detection, stereo correlation, contour grouping, image segmentation, and figure-ground discrimination. Various computational approaches such as genetic algorithms, simulated annealing, neural networks, and parallel and distributed processing are being investigated in the context of these low-level vision problems.

In high-level vision, the current research is focused on the identification and localization of objects in range and intensity images from prestored CAD models. Efficient recognition and localization algorithms based on graph theory such as subgraph isomorphism and hypergraph monomorphism are being investigated.

Issues related to efficient retrieval from large object model databases are also being addressed. In particular, hierarchical index and hash structures well suited for object models represented as attributed relational hypergraphs are being investigated.

The research in low- and high-level vision is being applied to several application areas such as automated industrial inspection, geographic information systems and multi-media systems.


Heterogeneous Multi-Robot Systems Wireless Ad Hoc Robotic Networks Search and Rescue Robotics Human-Robot Interfaces for Intelligent Teleoperation Robotics in Precision Agriculture

Computer Vision • Machine Learning • Deep Neural Networks • Learning With Limited Labels • Image Understanding in 3D Geometry • Human Analysis • Face Analysis and Synthesis • Generative Models • Object Detection and Scene Understanding • Context Modeling •…

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