We present an unsupervised technique for visual learning, which is based on density
estimation in high-dimensional spaces using an eigenspace decomposition. Two types of …

Low-dimensional representations of sensory signals are key to solving many of the
computational problems encountered in high-level vision. Principal component analysis …

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 …

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 …

Proposes a framework for simultaneous detection, tracking, and recognition of objects via
data fused from multiple sensors. Complex dynamic scenes are represented via the …

We formulate a probabilistic model of image generation and derive optimal inference
algorithms for finding objects and object features within this framework. The approach …

We present a probabilistic latent-variable framework for data visu (cid: 173) alisation, a key
feature of which is its applicability to binary and categorical data types for which few …

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 …