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cvpr-2004.pdf
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🔗 Source: leon.bottou.com
📊 Size: 752 KB
📄 Pages: 8 pages
⬇️ Downloads: 80

Summary

Yann LeCun, Fu Jie Huang, and Léon Bottou assessed learning methods for generic object recognition with invariance to pose and lighting. A large dataset of 194,400 images of 50 uniform-colored toys under various conditions was collected. The objects belonged to 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. Nearest Neighbor methods, Support Vector Machines, and Convolutional Networks were tested, with Convolutional Nets yielding the best results, including a 7% test error rate for unseen object instances and a 14% error rate for a segmentation/recognition task with cluttered images. A real-time version of the system was implemented, detecting and classifying objects in natural scenes at 10 frames per second.

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Description

Yann LeCun, Fu Jie Huang, and Léon Bottou assessed learning methods for generic object recognition with invariance to pose and lighting.

Technical Information

  • File Format: PDF
  • File Size: 752 KB
  • Pages: 8
  • Language: EN
  • Total Downloads: 80
  • Last Updated: 1 month ago

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