| dc.description.abstract | An innate desire of many vision researchers IS to unravel the mystery of human
visual perception Such an endeavor, even ~f it were not wholly successful, is expected to yield byproducts of considerable significance to industrial applications
Based on the current understanding of the neurophysiological and computational
processes in the human bran, it is believed that visual perception can be decomposed into distinct modules, of which feature / contour extraction and recognition / classification of the features corresponding to the objects play an important role. A remarkable characteristic of human visual expertise is its invariance to rotation shift, and scaling of objects in a scene
Researchers concur on the relevance of imitating as many properties as we have
knowledge of, of the human vision system, in order to devise simple solutions to
the problems in computational vision. The inference IS that this can be more
efficiently achieved by invoking neural architectures with specific characteristics
(similar to those of the modules in the human brain), and conforming to rules of
an appropriate mathematical baas As a first step towards the development of
such a framework, we make explicit (1) the nature of the images to be analyzed,
(11) the features to be extracted, (111) the relationship among features, contours,
and shape, and (iv) the exact nature of the problems To this end, we formulate
explicitly the problems considered in this thesis as follows
Problem 1
Given an Image localize and extract the boundary (contours) of the object of
Interest in lt
Problem 2
Recognize the shape of the object characterized by that contour employing a
suitable coder-recognizer such that ~t IS unaffected by rotation scaling and
translation of the objects
Problem 3
Gwen a stereo-pair of Images (1) extract the salient contours from the Images,
(ii)establish correspondence between the points in them and (111) estimate the depth associated with the points
We present a few algorithm as practical solutions to the above problems. The main contributions of the thesis are:
• A new algorithm for extraction of contours from images: and
• A novel method for invariantly coding shapes as pulses to facilitate their recognition.
The first contribution refers to a new active contour model, which is a neural network designed to extract the nearest salient contour in a given image by deforming itself to match the boundary of the object. The novelty of the model consists in the exploitation of the principles of spatial isomorphism and self organization in order to create flexible contours characterizing shapes in images. It turns out that the theoretical basis for the proposed model can be traced to the extensive literature on:
• Gestalt perception in which the principles of psycho-physical isomorphism plays a role; and
• Early processing in the human visual system derived from neuro-anatomical and neuro-physiological properties.
The initially chosen contour is made to undergo deformation by a locally co-operative, globally competitive scheme, in order to enable it to cling to the nearest salient contour in the test image. We illustrate the utility and versatility of the model by applying to the problems of boundary extraction, stereo vision, and bio-medical image analysis (including digital libraries).
The second contribution of the thesis is relevant to the design and development of a machine vision system in which the required contours are first to be extracted from a given set of images. Then follows the stage of recognizing the shape of the object characterized by that contour. It should, however, be noted that the latter problem is to be resolved in such a way that the system is unaffected by translation, relation, and scaling of images of objects under consideration. To this end, we develop some novel schemes:
• A pulse-coding scheme for an invariant representation of shapes; and
• A neural architecture for recognizing the encoded shapes.
The first (pulse-encoding) scheme is motivated by the versatility of the human visual system, and utilizes the properties of complex logarithmic mapping (CLM) which transforms rotation and scaling (in its domain) to shifts (in its range). In order to handle this shift, the encoder converts the CLM output to a sequence of
pulses These pulses are then fed to a novel multi-layered neural recognizer which
(1) invokes template matching with a distinctly implemented architecture, and (11)
achieves robustness (to noise and shape deformation) by virtue of its overlapping
strategy for code classification The proposed encoder-recognizer system (a) is
hardware implementable by a high-speed electronic switching circuit, and (b) can
add new patterns on-line to the existing ones Examples are given to illustrate
the proposed schemes.
The them is organized as follows:
Chapter 2 deals with the problem of extraction of salient contours from a
given gray level image, using a neural network-based active contour model
It explains the need for the use of active contour models, along with a brief
survey of the existing models, followed by two possible psycho-physiological
theories to support the proposed model After presenting the essential characteristics
of the model, the advantages and applications of the proposed
approach are demonstrated by some experimental results.
Chapter 3 is concerned with the problem of coding shapes and recognizing
them To this end, we describe a pulse coder for generating pulses invariant
to rotation, scaling and shift The code thus generated IS then fed to a
recognizer which classifies shapes based on the pulse code fed to it The
recognizer can also add new shapes to its 'knowledge-base' on-line. The
recognizer's properties are then discussed, thereby bringing out its advantages
with respect to various related architectures found in the literature.
Experimental results are then presented to Illustrate some prominent characteristics
of the approach.
Chapter 4 concludes the thesis, summarizing the overall contribution of the
thesis, and describing possible future directions | en_US |
