Калмановская фильтрация одного класса изображений динамических объектов

We discuss the problem of estimating the state of a dynamic object by using observed images generated by an optical system. The work aims to implement a novel approach that would ensure improved accuracy of dynamic object tracking using a sequence of images. We utilize a vector model that describes the object image as a limited number of vertexes (reference points). Upon imaging, the object of interest is assumed to be retained at the center of each frame, so that the motion parameters can be considered as projections onto the axes of a coordinate system matched with the camera's optical axis. The novelty of the approach is that the observed parameters (the distance along the optical axis and angular attitude) of the object are calculated using the coordinates of specified points in the object images. For estimating the object condition, a Kalman-Bucy filter is constructed on the assumption that the dynamic object motion is described by a set of equations for the translational motion of the center of mass along the optical axis and variations in the angular attitude relative to the image plane. The efficiency of the proposed method is illustrated by an example of estimating the object's angular attitude.Рассматривается задача оценивания состояния динамического объекта по наблюдаемым изображениям, сформированным оптической системой. Цель исследования состоит в реализации нового подхода, обеспечивающего повышение точности автономного слежения за динамическим объектом по последовательности изображений. Используется векторная модель изображения объекта в виде ограниченного количества вершин (базовых точек). Предполагается, что в процессе регистрации объект удерживается в центральной области каждого кадра, поэтому параметры движения могут описываться в виде проекций на оси системы координат, связанной с оптической осью камеры. Новизна подхода состоит в том, что наблюдаемые параметры (расстояние вдоль оптической оси и угловое положение) объекта вычисляются по координатам заданных точек на изображениях объекта. Для оценки состояний объекта строится фильтр Калмана-Бьюси в предположении, что движение динамического объекта описывается совокупностью уравнений поступательного движения центра масс вдоль оптической оси и изменений углового положения относительно плоскости изображения. Приведен пример оценивания углового положения объекта, иллюстрирующий работоспособность предложенного метода

Intelligent Interfaces to Empower People with Disabilities

Severe motion impairments can result from non-progressive disorders, such as cerebral palsy, or degenerative neurological diseases, such as Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), or muscular dystrophy (MD). They can be due to traumatic brain injuries, for example, due to a traffic accident, or to brainste

A Study of Segmentation and Normalization for Iris Recognition Systems

Iris recognition systems capture an image from an individual's eye. The iris in the image is then segmented and normalized for feature extraction process. The performance of iris recognition systems highly depends on segmentation and normalization. For instance, even an effective feature extraction method would not be able to obtain useful information from an iris image that is not segmented or normalized properly. This thesis is to enhance the performance of segmentation and normalization processes in iris recognition systems to increase the overall accuracy. The previous iris segmentation approaches assume that the boundary of pupil is a circle. However, according to our observation, circle cannot model this boundary accurately. To improve the quality of segmentation, a novel active contour is proposed to detect the irregular boundary of pupil. The method can successfully detect all the pupil boundaries in the CASIA database and increase the recognition accuracy. Most previous normalization approaches employ polar coordinate system to transform iris. Transforming iris into polar coordinates requires a reference point as the polar origin. Since pupil and limbus are generally non-concentric, there are two natural choices, pupil center and limbus center. However, their performance differences have not been investigated so far. We also propose a reference point, which is the virtual center of a pupil with radius equal to zero. We refer this point as the linearly-guessed center. The experiments demonstrate that the linearly-guessed center provides much better recognition accuracy. In addition to evaluating the pupil and limbus centers and proposing a new reference point for normalization, we reformulate the normalization problem as a minimization problem. The advantage of this formulation is that it is not restricted by the circular assumption used in the reference point approaches. The experimental results demonstrate that the proposed method performs better than the reference point approaches. In addition, previous normalization approaches are based on transforming iris texture into a fixed-size rectangular block. In fact, the shape and size of normalized iris have not been investigated in details. In this thesis, we study the size parameter of traditional approaches and propose a dynamic normalization scheme, which transforms an iris based on radii of pupil and limbus. The experimental results demonstrate that the dynamic normalization scheme performs better than the previous approaches

Improved facial feature fitting for model based coding and animation

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Intensity based methodologies for facial expression recognition.

by Hok Chun Lo.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 136-143).Abstracts in English and Chinese.LIST OF FIGURES --- p.viiiLIST OF TABLES --- p.xChapter 1. --- INTRODUCTION --- p.1Chapter 2. --- PREVIOUS WORK ON FACIAL EXPRESSION RECOGNITION --- p.9Chapter 2.1. --- Active Deformable Contour --- p.9Chapter 2.2. --- Facial Feature Points and B-spline Curve --- p.10Chapter 2.3. --- Optical Flow Approach --- p.11Chapter 2.4. --- Facial Action Coding System --- p.12Chapter 2.5. --- Neural Network --- p.13Chapter 3. --- EIGEN-ANALYSIS BASED METHOD FOR FACIAL EXPRESSION RECOGNITION --- p.15Chapter 3.1. --- Related Topics on Eigen-Analysis Based Method --- p.15Chapter 3.1.1. --- Terminologies --- p.15Chapter 3.1.2. --- Principal Component Analysis --- p.17Chapter 3.1.3. --- Significance of Principal Component Analysis --- p.18Chapter 3.1.4. --- Graphical Presentation of the Idea of Principal Component Analysis --- p.20Chapter 3.2. --- EigenFace Method for Face Recognition --- p.21Chapter 3.3. --- Eigen-Analysis Based Method for Facial Expression Recognition --- p.23Chapter 3.3.1. --- Person-Dependent Database --- p.23Chapter 3.3.2. --- Direct Adoption of EigenFace Method --- p.24Chapter 3.3.3. --- Multiple Subspaces Method --- p.27Chapter 3.4. --- Detail Description on Our Approaches --- p.29Chapter 3.4.1. --- Database Formation --- p.29Chapter a. --- Conversion of Image to Column Vector --- p.29Chapter b. --- "Preprocess: Scale Regulation, Orientation Regulation and Cropping." --- p.30Chapter c. --- Scale Regulation --- p.31Chapter d. --- Orientation Regulation --- p.32Chapter e. --- Cropping of images --- p.33Chapter f. --- Calculation of Expression Subspace for Direct Adoption Method --- p.35Chapter g. --- Calculation of Expression Subspace for Multiple Subspaces Method. --- p.38Chapter 3.4.2. --- Recognition Process for Direct Adoption Method --- p.38Chapter 3.4.3. --- Recognition Process for Multiple Subspaces Method --- p.39Chapter a. --- Intensity Normalization Algorithm --- p.39Chapter b. --- Matching --- p.44Chapter 3.5. --- Experimental Result and Analysis --- p.45Chapter 4. --- DEFORMABLE TEMPLATE MATCHING SCHEME FOR FACIAL EXPRESSION RECOGNITION --- p.53Chapter 4.1. --- Background Knowledge --- p.53Chapter 4.1.1. --- Camera Model --- p.53Chapter a. --- Pinhole Camera Model and Perspective Projection --- p.54Chapter b. --- Orthographic Camera Model --- p.56Chapter c. --- Affine Camera Model --- p.57Chapter 4.1.2. --- View Synthesis --- p.58Chapter a. --- Technique Issue of View Synthesis --- p.59Chapter 4.2. --- View Synthesis Technique for Facial Expression Recognition --- p.68Chapter 4.2.1. --- From View Synthesis Technique to Template Deformation --- p.69Chapter 4.3. --- Database Formation --- p.71Chapter 4.3.1. --- Person-Dependent Database --- p.72Chapter 4.3.2. --- Model Images Acquisition --- p.72Chapter 4.3.3. --- Templates' Structure and Formation Process --- p.73Chapter 4.3.4. --- Selection of Warping Points and Template Anchor Points --- p.77Chapter a. --- Selection of Warping Points --- p.78Chapter b. --- Selection of Template Anchor Points --- p.80Chapter 4.4. --- Recognition Process --- p.81Chapter 4.4.1. --- Solving Warping Equation --- p.83Chapter 4.4.2. --- Template Deformation --- p.83Chapter 4.4.3. --- Template from Input Images --- p.86Chapter 4.4.4. --- Matching --- p.87Chapter 4.5. --- Implementation of Automation System --- p.88Chapter 4.5.1. --- Kalman Filter --- p.89Chapter 4.5.2. --- Using Kalman Filter for Trakcing in Our System --- p.89Chapter 4.5.3. --- Limitation --- p.92Chapter 4.6. --- Experimental Result and Analysis --- p.93Chapter 5. --- CONCLUSION AND FUTURE WORK --- p.97APPENDIX --- p.100Chapter I. --- Image Sample 1 --- p.100Chapter II. --- Image Sample 2 --- p.109Chapter III. --- Image Sample 3 --- p.119Chapter IV. --- Image Sample 4 --- p.135BIBLIOGRAPHY --- p.13