Early visual learning
Featuring contributions from experts in the field of computer vision, Early Visual Learning represents the cutting edge of research in this field. The editors focus on learning techniques that are applied more or less directly to the signals provided by vision sensors. The emphasis is on low-level visual learning techniques that draw on results in the fields of statistics, pattern recognition and neural networks. This book will be of interest to researchers and has potential as a graduate level text in a visual learning course.
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Probabilistic Visual Learning for Object Representation
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3D objects analysis appearance approach approximation attribute basis functions cells classification clustering complex Computer Vision Conf coordinates corresponding cortex curves database density estimation described detection detector dimensionality distractor eigenface eigenspace eigenvalues eigenvectors error examples experiments F-space face recognition feature vector Figure filter Gaussian genetic algorithm IEEE illumination image feature image segmentation image set inferior temporal cortex input image interpolation invariance learning-from-examples linear Machine Learning manifold match quality measure matrix method model feature model graph monkeys neural network neurons nodes object recognition optimal orientation output pairings parameters pattern recognition performance pixels Poggio pose position probability distribution problem Proc recognition rate recognition system recognize represent representation robot rotation samples scale scheme segmentation quality selective sensor shape SHOSLIF shown space structure supervised learning target task techniques template test images texture tion training images training set values view-based viewpoint transformation visual learning volcanoes
Learning-Based Robot Vision: Principles and Applications
No preview available - 2001