Open AccessProceedings Article
Trainable Visual Models for Object Class Recognition.
Andrew Zisserman
- 01 Jan 2004
pp 690
TL;DR: A number of successes have been achieved by using ideas and algorithms from statistical learning theory, where visual models are trained using positive and negative examples of the class.
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Abstract: Recognizing object classes, such as cars, planes or elephants, in an image or a video remains one of the most challenging problems in Computer Vision. However, recently a number of successes have been achieved by using ideas and algorithms from statistical learning theory, where visual models are trained using positive and negative examples of the class.
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Citations
•Journal Article
Generic model abstraction from examples
Yakov Keselman,Sven Dickinson +1 more
TL;DR: In this article, the authors address the problem of automatically acquiring a generic 2D view-based class model from a set of images, each containing an exemplar object belonging to that class.
72
•Dissertation
Exploiting object dynamics for recognition and control
Philipp Robbel
- 01 Jan 2007
TL;DR: A recognition system that extends invariant local features (shape contexts) into the time domain by integration of the motion model and an entropy-based view selection scheme is presented that allows the vision system to “skip ahead” to highly discriminative views.
4
References
Combined Object Categorization and Segmentation With an Implicit Shape Model
Bastian Leibe,Ales Leonardis,Bernt Schiele +2 more
- 01 Jan 2004
TL;DR: Results for articulated objects, which show that the proposed method can categorize and segment unfamiliar objects in differ- ent articulations and with widely varying texture patterns, even under significant partial occlusion.
•Journal Article
Generic model abstraction from examples
Yakov Keselman,Sven Dickinson +1 more
TL;DR: In this article, the authors address the problem of automatically acquiring a generic 2D view-based class model from a set of images, each containing an exemplar object belonging to that class.
72
Workshop on Generic Object Recognition and Categorization
Sven Dickinson,Ales Leonardis,Bernt Schiele +2 more
- 27 Jun 2004
TL;DR: It is argued that high-level, volumetric part-based descriptions are essential in the process of recognizing objects that might never have been observed before, and for which no exact geometric model is available.
Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns.
TL;DR: Intrinsic signal imaging from inferotemporal cortex revealed that visually presented objects activated patches in a distributed manner, suggesting that an object is represented by a combination of cortical columns, each of which represents a visual feature (feature column).
Analyzing vision at the complexity level
TL;DR: This analysis of visual search performance in terms of attentional influences on visual information processing and complexity satisfaction allows a large body of neurophysiological and psychological evidence to be tied together.