Learning recognition and segmentation of 3-D objects from 2-D images

  title={Learning recognition and segmentation of 3-D objects from 2-D images},
  author={Juyang Weng and Narendra Ahuja and Thomas S. Huang},
  journal={1993 (4th) International Conference on Computer Vision},
A framework called Cresceptron is introduced for automatic algorithm design through learning of concepts and rules, thus deviating from the traditional mode in which humans specify the rules constituting a vision algorithm. [...] Key Method The Cresceptron uses a hierarchical structure to grow networks automatically, adaptively, and incrementally through learning. The Cresceptron makes it possible to generalize training exemplars to other perceptually equivalent items. Experiments with a variety of real-world…Expand
Learning Recognition and Segmentation Using the Cresceptron
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An Automated Perceptual Learning Algorithm for Determining Structure-Based Visual Prototypes of Objects from Internet-Scale Data
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Learning object recognition models from images
  • Arthur R. Pope, D. Lowe
  • Mathematics, Computer Science
  • 1993 (4th) International Conference on Computer Vision
  • 1993
The authors show how to learn a model from a series of training images depicting a class of objects, producing a model that represents a probability distribution over the variation in object appearance that can recognize objects as similar in general appearance while distinguishing them by their detailed features. Expand
SHOSLIF: a framework for object recognition from images
  • J. Weng
  • Computer Science
  • Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)
  • 1994
A new framework called self-organizing hierarchical optimal subspace learning and inference framework (SHOSLIF) is introduced for recognizing and segmenting real-world objects from images. ItExpand
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Cresceptron and Shoslif: toward Comprehensive Visual Learning 1
Comprehensive visual learning concerns a uniied theory and methodology for computer vision systems to comprehensively learn the visual world with only minimal hand-crafted rules about the world. ThisExpand
Hierarchical Discriminant Analysis for Image Retrieval
  • D. Swets, J. Weng
  • Computer Science
  • IEEE Trans. Pattern Anal. Mach. Intell.
  • 1999
The self-organizing hierarchical optimal subspace learning and inference framework (SHOSLIF) system uses the theories of optimal linear projection for optimal feature derivation and a hierarchical structure to achieve logarithmic retrieval complexity. Expand
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  • A. Hoogs, R. Bajcsy
  • Computer Science
  • Proceedings of 13th International Conference on Pattern Recognition
  • 1996
Results indicate that the inclusion of the segmentation information significantly improves pose adjustment accuracy over using purely geometric information for model appearance. Expand


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