Recognizing objects in a natural environment: a contextual vision system (CVS)
Proceedings of a workshop on Image understanding workshop
Natural object recognition
World model driven recognition of natural scenes.
World model driven recognition of natural scenes.
Active Object Recognition: Looking for Differences
International Journal of Computer Vision - Special issue: Research at McGill University
Contextual Priming for Object Detection
International Journal of Computer Vision
Competitive Segmentation: A Struggle for Image Space
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A Vision System for Environment Representation: From Landscapes to Landmarks
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SketchREAD: a multi-domain sketch recognition engine
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Artificial Intelligence Review
International Journal of Computer Vision
Segmentation and description of natural outdoor scenes
Image and Vision Computing
An adaptive focus-of-attention model for video surveillance and monitoring
Machine Vision and Applications
SketchREAD: a multi-domain sketch recognition engine
ACM SIGGRAPH 2007 courses
International Journal of Remote Sensing
Searching Eye Centers Using a Context-Based Neural Network
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Categorizing Perceptions of Indoor Rooms Using 3D Features
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Tracking the soccer ball using multiple fixed cameras
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Control structures for incorporating picture-specific context in image interpretation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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Communications of the ACM
Using the forest to see the trees: exploiting context for visual object detection and localization
Communications of the ACM
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IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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Expert Systems with Applications: An International Journal
A framework of context-aware object recognition for smart home
ICOST'07 Proceedings of the 5th international conference on Smart homes and health telematics
Context based object categorization: A critical survey
Computer Vision and Image Understanding
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Approximate world models: incorporating qualitative and linguistic information into vision systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Context-based search for 3D models
ACM SIGGRAPH Asia 2010 papers
Context modeling in computer vision: techniques, implications, and applications
Multimedia Tools and Applications
Context-Based scene recognition using bayesian networks with scale-invariant feature transform
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Activity-Object bayesian networks for detecting occluded objects in uncertain indoor environment
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Bag of spatio-visual words for context inference in scene classification
Pattern Recognition
Using text N-grams for model suggestions in 3D scenes
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Graspable parts recognition in man-made 3d shapes
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
ACM Transactions on Graphics (TOG)
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Results from an ongoing project concerned with recognizing objects in complex scene domains, especially in the domain that includes the natural outdoor world, are described. Traditional machine recognition paradigms assume either that all objects of interest are definable by a relatively small number of explicit shape models or that all objects of interest have characteristic, locally measurable features. The failure of both assumptions has a dramatic impact on the form of an acceptable architecture for an object recognition system. In this work, the use of the contextual information is a central issue, and a system is explicitly designed to identify and use context as an integral part of recognition that eliminates the traditional dependence on stored geometric models and universal image partitioning algorithms. This paradigm combines the results of many simple procedures that analyze monochrome, color, stereo, or 3D range images. Interpreting the results along with relevant contextual knowledge makes it possible to achieve a reliable recognition result, even when using imperfect visual procedures. Initial experimentation with the system on ground-level outdoor imagery has demonstrated competence beyond what is attainable with other vision systems.