Euclidean distance transformation is a fundamental technique in image understanding and computer vision. Some important characteristics in image analysis such as skeleton and object boundary are based upon the distance transformation computation. In this thesis, we compare our method of computing Euclidean distance transformations with the method of Chamfer distance transformation. Our method is faster and more accurate than the Chamfer method. The boundary detection is an interesting and challenging task in computer vision. We integrate distance transform, watershed transform and active contour model to achieve boundary detection. Our method can successfully separate the touching objects, so as to facilitate the subsequent image processing for obtaining the geometric, and texture characteristics of objects. These features are useful for further medical images applications.

In this dissertation, several GA-based approaches for fractal image compression and active contour model are proposed. The main drawback of the classical fractal image compression is the long encoding time. Two methods are proposed in this dissertation to solve this problem. First, a schema genetic algorithm (SGA), in which the Schema Theorem is embedded in GA, is proposed to reduce the encoding time. In SGA, the genetic operators are adapted according to the Schema Theorem in the evolutionary process performed on the range blocks. We find that such a method can indeed speedup the encoder and also preserve the image quality. Moreover, based on the self-similarity characteristic of the natural image, a spatial correlation genetic algorithm (SC-GA) is proposed to further reduce the encoding time. There are two stages in the SC-GA method. The first stage makes use of spatial correlations in images for both the domain pool and the range pool to exploit local optima. The second stage is operated on the whole image to explore more adequate similarities if the local optima are not satisfactory. Thus not only the encoding speed is accelerated further, but also the higher compression ratio is achieved, because the search space is limited relative to the positions of the previously matched blocks, fewer bits are required to record the offset of the domain block instead of the absolute position. The experimental results of comparing the two methods with the full search, traditional GA, and other GA search methods are provided to demonstrate that they can indeed reduce the encoding time substantially. The main drawback of the traditional active contour model (ACM) for extracting the contour of a given object is that the snake cannot converge to the concave region of the object under consideration. An improved ACM algorithm is proposed in this dissertation to solve this problem. The algorithm is composed of two stages. In the first stage, the ACM with traditional energy function guides the snake to converge to the object boundary except the concave regions. In the second stage, for the control points which stay outside the concave regions, a proper energy template are chosen and are added in the external energy. The modified energy function is applied so as to move the snake toward the concave regions. Therefore, the object of interest can be completely extracted. The experimental results show that, by using this method, the snake can indeed completely extract the boundary of the given object, while the extra cost is very low. In addition, for the problem that the snake cannot precisely extract the object contour when the number of the control points on the snake is not enough, a GA-based ACM algorithm is presented to deal with such a problem. First the improved ACM algorithm is used to guide the snake to approximately extract the object boundary. By utilizing the evolutionary strategy of GA, we attempt to extract precisely the object boundary by adding a few control points into the snake. Similarly, some experimental results are provided to show the performance of the method.

In this dissertation, particle swarm optimization (PSO) is utilized for fractal image compression (FIC) and active contour model (ACM). The dissertation is divided into two parts. The first part is concerned with the FIC and the second part with ACM. FIC is promising both theoretically and practically for image compression. However, since the encoding speed of the traditional full search method is very time-consuming, FIC with full search is unsuitable for real-time applications. In this dissertation, several novel PSO-based approaches incorporating the edge property of the image blocks are proposed to speedup the encoder and preserve the image quality. Instead of the full search, a direction map is built according to the edge type of the image blocks, which directs the particles in the swarm to regions consisting of candidates of higher similarity. Therefore, the searching space is reduced and the speedup can be achieved. Also, since the strategy is performed according to the edge property, better visual effect can be preserved. Experimental results show that the visual-based particle swarm optimization speeds up the encoder 125 times faster with only 0.89 dB decay of image quality in comparison to the full search method. The second part of the dissertation is concerned with the active contour model for automatic object boundary identification. In the traditional methods for ACM, each control point searches its new position in a small nearby window. Consequently, the boundary concavities cannot be searched accurately. Some improvements have been made in the past to enlarge the searching space, yet they are still time-consuming. To overcome these drawbacks, a novel multi-population PSO technique is adopted in this dissertation to enhance the concavity searching capability and reduce the search time but in a larger searching window. In the proposed scheme, to each control point in the contour there is a corresponding swarm of particles with the best swarm particle as the new control point. The proposed optimizer not only inherits the spirit of the original PSO in each swarm but also shares information of the surrounding swarms. Experimental results demonstrate that the proposed method can improve the search of object concavities without extra computation time.

A DSP-based system is developed in this thesis for tracking ¡§an arbitrary-shaped object¡¨. It uses CCD camera to capture images, and detects in the video sequence. When we want to track a target that we interest, we can make the target in the view of camera. If the target move, the system will lock it and extract its contour by using active contour model. After extracting contour, the system will start to track target and shows the locked image on the LCD screen. The tracking system includes three sub-systems : ¡§Moving Object Detection¡¨, ¡§Active Contour Model¡¨, and ¡§Contour Matching¡¨. From the results of experiment, it can meet the expectation and gain good performance and robustness.

Driving while drowsy is a major cause behind road accidents, and exposes the driver to a much higher crash risk compared to driving while alert. Therefore, the use of assistive systems that monitor a driver’s level of vigilance and alert the fatigue driver can be significant in the prevention of accidents. This thesis introduces three different methods towards the detection of drivers’ drowsiness based on yawning measurement. All three approaches involve several steps, including the real time detection of the driver’s face, mouth and yawning. The last approach, which is the most accurate, is based on the Viola-Jones theory for face and mouth detection and the back projection theory for measuring both the rate and the amount of changes in the mouth for yawning detection. Test results demonstrate that the proposed system can efficiently measure the aforementioned parameters and detect the yawning state as a sign of a driver’s drowsiness.

Driving while drowsy is a major cause behind road accidents, and exposes the driver to a much higher crash risk compared to driving while alert. Therefore, the use of assistive systems that monitor a driver’s level of vigilance and alert the fatigue driver can be significant in the prevention of accidents. This thesis introduces three different methods towards the detection of drivers’ drowsiness based on yawning measurement. All three approaches involve several steps, including the real time detection of the driver’s face, mouth and yawning. The last approach, which is the most accurate, is based on the Viola-Jones theory for face and mouth detection and the back projection theory for measuring both the rate and the amount of changes in the mouth for yawning detection. Test results demonstrate that the proposed system can efficiently measure the aforementioned parameters and detect the yawning state as a sign of a driver’s drowsiness.

Driving while drowsy is a major cause behind road accidents, and exposes the driver to a much higher crash risk compared to driving while alert. Therefore, the use of assistive systems that monitor a driver’s level of vigilance and alert the fatigue driver can be significant in the prevention of accidents. This thesis introduces three different methods towards the detection of drivers’ drowsiness based on yawning measurement. All three approaches involve several steps, including the real time detection of the driver’s face, mouth and yawning. The last approach, which is the most accurate, is based on the Viola-Jones theory for face and mouth detection and the back projection theory for measuring both the rate and the amount of changes in the mouth for yawning detection. Test results demonstrate that the proposed system can efficiently measure the aforementioned parameters and detect the yawning state as a sign of a driver’s drowsiness.

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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