Locating target at high speed using image decimation decomposition processing

We develop a decimation-decomposition processing technique that consists of judiciously selecting certain decimation-decomposed components of an image and then performing inter-component processing. For a (kx,ky)-decimation decomposition, there may be up to kxky decimation-decomposed components. The minimal surviving and maximal non-surviving lengths associated with inter-component processing algorithm allows for clutter suppression. By removing detection redundancies, one can locate the target at high speed. A “where-then-what” model is proposed for target tracking and recognition. It locates the target by-image decimation-decomposition processing first and then recognizes the target in question using a suitable image recognition technique

Multiresolutional Fault-Tolerant Sensor Integration and Object Recognition in Images.

This dissertation applies multiresolution methods to two important problems in signal analysis. The problem of fault-tolerant sensor integration in distributed sensor networks is addressed, and an efficient multiresolutional algorithm for estimating the sensors\u27 effective output is proposed. The problem of object/shape recognition in images is addressed in a multiresolutional setting using pyramidal decomposition of images with respect to an orthonormal wavelet basis. A new approach to efficient template matching to detect objects using computational geometric methods is put forward. An efficient paradigm for object recognition is described

Maximum likelihood parameter estimation of mixture models and its application to image segmentation and restoration

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 79-82).by Mohammed Saeed.M.S

Document image processing using irregular pyramid structure

Ph.DDOCTOR OF PHILOSOPH

IMAGE SEGMENTATION BY A MULTIRESOLUTION APPROACH

Multiresolution approaches to computer vision are able to rapidly detect and extract global structures from an image. In this paper we present (a) a pyramid-based algorithm that can detect the bimodality of the population of pixels in a grey level digital image and (b) a pyramid-based algorithm that maps the values of a bimodal population into two constant values which are approximately the means of the two component subpopulations. A population is considered bimodal if it can be divided into two component subpopulations whose variances are small relative to the population variance. An improvement to the above algorithm, which uses an iterative scheme, is also given, as well as some examples of segmented images. Both algorithms require processing times on the order of the logarithm of the population size