Imaging, Vision, and Pattern Recognition

Pattern Recognition and Machine Vision

Suppose we have a probability density function for some continuous random variable: e.g. a Gaussian or Normal distribution. p(x) = 1 √ 2πσ 2 exp − (x − µ) 2 2σ 2 (1) Usually when we use this function to calculate the probability of getting the value x in the range x to x to x+δ x, we assume that the parameters σ and µ are known or given. To emphasize this point of view, the probability density ...

Pattern Recognition and Machine Vision: Stereo Vision and Depth Reconstruction

Let us now consider the case where the solution is overdetermined. Now there are more sets of equations than there are unknowns, which is manifested by A being rectangular, with more rows than columns (i.e. tall and thin). In general, there will be no vector x that satisfies all of the equations. In the absence of a perfect solution, we aim to find a solution that minimizes the square error bet...

Advances in Computer Vision and Pattern Recognition

Peripheral vision and pattern recognition: a review.

We summarize the various strands of research on peripheral vision and relate them to theories of form perception. After a historical overview, we describe quantifications of the cortical magnification hypothesis, including an extension of Schwartz's cortical mapping function. The merits of this concept are considered across a wide range of psychophysical tasks, followed by a discussion of its l...

Vision and Pattern Recognition , San Francisco , 1996 . Pattern Rejection

The e ciency of pattern recognition is particularly crucial in two scenarios; whenever there are a large number of classes to discriminate, and, whenever recognition must be performed a large number of times. We propose a single technique, namely, pattern rejection, that greatly enhances e ciency in both cases. A rejector is a generalization of a classi er, that quickly eliminates a large fract...