The aim of this project is to develop and perform a first evaluation of a software, based on the active contour, which automatically computes the cross-section area of the internal jugular veins through a sequence of 90 ultrasound images. The software is intended to be useful in future research in the field of intra cranial pressure and its associated diseases. The biomechanics of the internal jugular veins and its relationship to the intra cranial pressure is studied with ultrasound. It generates data in the form of ultrasound sequences shot in seven different body positions, supine to upright. Vein movements in cross section over the cardiac cycle are recorded for all body positions. From these films, it is interesting to know how the cross-section area varies over the cardiac cycle and between body positions, in order to estimate the pressure. The software created was semi-automatic, where the operator loads each individual sequence and sets the initial contour on the first frame. It was evaluated in a test by comparing its computed areas with manually estimated areas.  The test showed that the software was able to track and compute the area with a satisfactory accuracy for a variety of sequences. It is also faster and more consistent than manual measurements. The most difficult sequences to track were small vessels with narrow geometries, fast moving walls, and blurry edges. Further development is required to correct a few bugs in the algorithm. Also, the improved algorithm should be evaluated on a larger sample of sequences before using it in research.

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

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.

<p>Today, tool center point calibration is mostly done by a manual procedure. The method is very time consuming and the result may vary due to how skilled the operators are.</p><p>This thesis proposes a new automated iterative method for tool center point calibration of industrial robots, by making use of computer vision and image processing techniques. The new method has several advantages over the manual calibration method. Experimental verifications have shown that the proposed method is much faster, still delivering a comparable or even better accuracy. The setup of the proposed method is very easy, only one USB camera connected to a laptop computer is needed and no contact with the robot tool is necessary during the calibration procedure.</p><p>The method can be split into three different parts. Initially, the transformation between the robot wrist and the tool is determined by solving a closed loop of homogeneous transformations. Second an image segmentation procedure is described for finding point correspondences on a rotation symmetric robot tool. The image segmentation part is necessary for performing a measurement with six degrees of freedom of the camera to tool transformation. The last part of the proposed method is an iterative procedure which automates an ordinary four point tool center point calibration algorithm. The iterative procedure ensures that the accuracy of the tool center point calibration only depends on the accuracy of the camera when registering a movement between two positions.</p>

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.

In this thesis, a novel and efficient numerical method is presented for the computation of the L² optimal mass transport mapping in two and three dimensions. The method uses a direct variational approach. A new projection to the constraint technique has been formulated that can yield a good starting point for the method as well as a second order accurate discretization to the problem. The numerical experiments demonstrate that the algorithm yields accurate results in a relatively small number of iterations that are mesh independent. In the first part of the thesis, the theory and implementation details of the proposed method are presented. These include the reformulation of the Monge-Kantorovich problem using a variational approach and then using a consistent discretization in conjunction with the "discretize-then-optimize" approach to solve the resulting discrete system of differential equations. Advanced numerical methods such as multigrid and adaptive mesh refinement have been employed to solve the linear systems in practical time for even 3D applications. In the second part, the methods efficacy is shown via application to various image processing tasks. These include image registration and morphing. Application of (OMT) to registration is presented in the context of medical imaging and in particular image guided therapy where registration is used to align multiple data sets with each other and with the patient. It is shown that an elastic warping methodology based on the notion of mass transport is quite natural for several medical imaging applications where density can be a key measure of similarity between different data sets e.g. proton density based imagery provided by MR. An application is also presented of the two dimensional optimal mass transport algorithm to compute diffeomorphic correspondence maps between curves for geometric interpolation in an active contour based visual tracking application.

Dans cette thèse, nous nous intéressons au problème de la segmentation de portraits numériques. Nous appelons portrait numérique la photographie d’une personne avec un cadre allant grossièrement du gros plan au plan poitrine. Le problème abordé dans ce travail est un cas spécifique de la segmentation d’images où il s’agit notamment de définir précisément la frontière de la région « cheveux ». Ce problème est par essence très délicat car les attributs de la région « cheveux » (géométrie, couleur, texture) présentent une grande variabilité à la fois entre les personnes et au sein de la région. Notre cadre applicatif est un système d’« essayage virtuel » de lunettes à destination du grand public, il n’est pas possible de contrôler les conditions de prise de vue comme l’éclairage de la scène ou la résolution des images, ce qui accroît encore la diculté du problème. L’approche proposée pour la segmentation de portraits numériques est une approche du plus grossier au plus fin procédant par étapes successives. Nous formulons le problème comme celui d’une segmentation multi-régions, en introduisant comme « régions secondaires », les régions adjacentes à la région « cheveux » , c.-à-d. les régions « peau » et « fond ». La méthode est fondée sur l’apparence (appearance-based method) et a comme spécificité le fait de déterminer les descripteurs de régions les plus adaptés à partir d’une base d’images d’apprentissage et d’outils statistiques. À la première étape de la méthode, nous utilisons l’information contextuelle d’un portrait numérique — connaissances a priori sur les relations spatiales entre régions— pour obtenir des échantillons des régions « cheveux », « peau » et « fond ». L’intérêt d’une approche fondée sur l’apparence est de pouvoir s’adapter à la fois aux conditions de prises de vue ainsi qu’aux attributs de chaque régions. Au cours de cette étape, nous privilégions les modèles de forme polygonaux couplés aux contours actifs pour assurer la robustesse du modèle. Lors de la seconde étape, à partir des échantillons détectés à l’étape précédente, nous introduisons un descripteur prenant en compte l’information de couleur et de texture. Nous proposons une segmentation grossière par classification en nous appuyant à nouveau sur l’information contextuelle : locale d’une part grâce aux champs de Markov, globale d’autre part grâce à un modèle a priori de segmentation obtenu par apprentissage qui permet de rendre les résultats plus robustes. La troisième étape ane les résultats en définissant la frontière des « cheveux » comme une région de transition. Cette dernière contient les pixels dont l’apparence provient du mélange de contributions de deux régions (« cheveux »et « peau » ou «fond »). Ces deux régions de transition sont post-traitées par un algorithme de «démélange » (digital matting) pour estimer les coecients de transparence entre « cheveux » et « peau », et entre « cheveux » et « fond ». À l’issue de ces trois étapes, nous obtenons une segmentation précise d’un portrait numérique en trois « calques », contenant en chaque pixel l’information de transparence entre les régions « cheveux », « peau » et « fond ». Les résultats obtenus sur une base d’images de portraits numériques ont mis en évidence les bonnes performances de notre méthode. / In this thesis, we are interested in the problem of the segmentation of digital portraits. We call digital portrait the photography of a person with a frame roughly ranging from the close-up to the chest plane. The problem addressed in this work is a specific case of the segmentation of images where it is especially necessary to define precisely the border of the "hair" region. This problem is inherently very delicate because the attributes of the "hair" region (geometry, color, texture) present an important variability between people and within the region. Our application is a system of "virtual fitting" of glasses for the general audience, it is not possible to control the shooting conditions such as stage lighting or image resolution, which increases the difficulty of the problem. The approach proposed for the segmentation of digital portraits is an approach « coarse to fine », proceeding in successive stages. We formulate the problem as a multi-region segmentation, introducing as "secondary regions" regions adjacent to the "hair" region, ie, the "skin" and "background" regions. The method is based on appearance-based method and has as a specificity the determination of the descriptors of regions most adapted from a database of learning and statistical tools. In the first step of the method, we use the contextual information of a Digital portrait - a priori knowledge about the spatial relations between regions - to obtain samples of the regions "hair", "skin" and "background". The value of an appearance-based approach is to be able to adapt to both the shooting conditions and the attributes of each region. During this stage, we prefer polygonal shape models coupled with active contours to ensure the robustness of the model. In the second step, from the samples detected in the previous step, we introduce a descriptor taking into account the color and texture information. We propose a rough segmentation by classification by relying on the contextual information: local on the one hand thanks to the Markov fields, global on the other hand thanks to an a priori model of segmentation obtained by learning which il allow to obtain robust results. The third stage refines the results by defining the border of "hair" as a transition region. This région contains the pixels whose appearance comes from the mixture of contributions of two regions ("hair" and "skin" or "background"). These two transition regions are post-processed by a digital matting algorithm to estimate the coefficients of transparency between "hair" and "skin", and between "hair" and "background". At the end of these three steps, we obtain a precise segmentation of a digital portrait into three "layers", containing in each pixel the information of transparency between the regions "hair", "skin" and "background". The results obtained on the basis of images of digital portraits have highlighted the good performance of our method.

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.

This diploma thesis aims to implement an active contour method for ultrasound image segmentation. Properties of ultrasound images, basic segmentation approaches and a~principle of choosen active contour methods are described within theoretical part. Two different groups of active contour methods exists, methods with use of gradient and without use of gradient as image feature. For comparision, one method of each group is implemented in practical part and subsequently, segmentation efficiency and properties of methods are compared in evaluation part.

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