PS Penev, JJ Atick - Network: computation in neural systems, 1996 - Taylor & Francis
Low-dimensional representations of sensory signals are key to solving many of the computational problems encountered in high-level vision. Principal component analysis …
M Weber, M Welling, P Perona - … ECCV 2000: 6th European Conference on …, 2000 - Springer
We present a method to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. We focus on a particular type of …
We develop statistical methods which allow effective visual detection, categorization, and tracking of objects in complex scenes. Such computer vision systems must be robust to wide …
We investigate a method for learning object categories in a weakly supervised manner. Given a set of images known to contain the target category from a similar viewpoint, learning …
S Ioffe - Computer Vision–ECCV 2006: 9th European …, 2006 - Springer
Linear dimensionality reduction methods, such as LDA, are often used in object recognition for feature extraction, but do not address the problem of how to use these features for …
MI Miller, U Grenander, JA OSullivan… - IEEE Transactions on …, 1997 - ieeexplore.ieee.org
Proposes a framework for simultaneous detection, tracking, and recognition of objects via data fused from multiple sensors. Complex dynamic scenes are represented via the …
I Fasel, B Fortenberry, J Movellan - Computer Vision and Image …, 2005 - Elsevier
We formulate a probabilistic model of image generation and derive optimal inference algorithms for finding objects and object features within this framework. The approach …
The shape variation displayed by a class of objects can be represented as probability density function, allowing us to determine plausible and implausible examples of the class …
MC Burl, M Weber, P Perona - Computer Vision—ECCV'98: 5th European …, 1998 - Springer
Many object classes, including human faces, can be modeled as a set of characteristic parts arranged in a variable spatial configuration. We introduce a simplified model of a …