Spatch based active partitions with linguistically formulated energy

The present paper shows the method of cognitive hierarchical active partitions that can be applied to creation of automatic image understanding systems. The approach, which stems from active contours techniques, allows one to use not only the knowledge contained in an image, but also any additional expert knowledge. Special emphasis is put on the effcient way of knowledge retrieval, which could minimise the necessity to render information expressed in a natural language into a description convenient for recognition algorithms and machine learning

Active dictionary models: A framework for non-linear shape modeling

Proyecto de Graduación (Maestría en Ingeniería en Electrónica) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería en Electrónica, 2015.Shape modeling has applications in science and industry fields. The existing algorithms are based on linear methods and on unimodal normal distributions not appropriate to model deformations present in natural signals. This work presents a novel shape model based on dictionary learning which is capable of representing these deformations. First a dictionary is trained through K-SVD and OMP. Then it is used as a model to represent shapes using a sparse weighting vector. The denoising properties of the model are shown for additive noise, but with the limitation that it can also represent invalid shapes. Afterwards, in order to compensate for the dictionary model limitation, a non-linear denoising method is developed based on orthogonal manifold projections. This extension ensures that the output is always a valid shape. Finally the complete iterative algorithm is presented. In this stage, the application o↵ers an initial approximation of the shape to segment. The shape is modeled using the dictionary and projected to the manifold whereby a valid shape is ensured. This process is repeated until an established convergence criteria is met. It is shown how the proposed method is capable of modeling both linear and non-linear deformations with high success

Part-based Grouping and Recognition: A Model-Guided Approach

Institute of Perception, Action and BehaviourThe recovery of generic solid parts is a fundamental step towards the realization of general-purpose vision systems. This thesis investigates issues in grouping, segmentation and recognition of parts from two-dimensional edge images. A new paradigm of part-based grouping of features is introduced that bridges the classical grouping and model-based approaches with the purpose of directly recovering parts from real images, and part-like models are used that both yield low theoretical complexity and reliably recover part-plausible groups of features. The part-like models used are statistical point distribution models, whose training set is built using random deformable superellipse. The computational approach that is proposed to perform model-guided part-based grouping consists of four distinct stages. In the first stage, codons, contour portions of similar curvature, are extracted from the raw edge image. They are considered to be indivisible image features because they have the desirable property of belonging either to single parts or joints. In the second stage, small seed groups (currently pairs, but further extension are proposed) of codons are found that give enough structural information for part hypotheses to be created. The third stage consists in initialising and pre-shaping the models to all the seed groups and then performing a full fitting to a large neighbourhood of the pre-shaped model. The concept of pre-shaping to a few significant features is a relatively new concept in deformable model fitting that has helped to dramatically increase robustness. The initialisations of the part models to the seed groups is performed by the first direct least-square ellipse fitting algorithm, which has been jointly discovered during this research; a full theoretical proof of the method is provided. The last stage pertains to the global filtering of all the hypotheses generated by the previous stages according to the Minimum Description Length criterion: the small number of grouping hypotheses that survive this filtering stage are the most economical representation of the image in terms of the part-like models. The filtering is performed by the maximisation of a boolean quadratic function by a genetic algorithm, which has resulted in the best trade-off between speed and robustness. Finally, images of parts can have a pronounced 3D structure, with ends or sides clearly visible. In order to recover this important information, the part-based grouping method is extended by employing parametrically deformable aspects models which, starting from the initial position provided by the previous stages, are fitted to the raw image by simulated annealing. These models are inspired by deformable superquadrics but are built by geometric construction, which render them two order of magnitudes faster to generate than in previous works. A large number of experiments is provided that validate the approach and, since several new issues have been opened by it, some future work is proposed

Segmentation of Brain Tissue from Magnetic Resonance Images

Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. Brain tissue is a particularly complex structure, and its segmentation is an important step for studies in temporal change detection of morphology, as well as for 3D visualization in surgical planning. In this paper, we present a method for segmentation of brain tissue from magnetic resonance images that is a combination of three existing techniques from the Computer Vision literature: EM segmentation, binary morphology, and active contour models. Each of these techniques has been customized for the problem of brain tissue segmentation in a way that the resultant method is more robust than its components. Finally, we present the results of a parallel implementation of this method on IBM's supercomputer Power Visualization System for a database of 20 brain scans each with 256x256x124 voxels and validate those against segmentations generated by neuroanatomy experts

Development of an Active Shape Model Using the Discrete Cosine Transform

Facial recognition systems have been successfully applied in security, law-enforcement and human identification application, for automatically identifying a human in a digital image or a video frame. In a feature-based face recognition system using a set of features extracted from each of the prominent facial components, automatic and accurate localization of facial features is an essential pre-processing step. The active shape model (ASM) is a flexible shape model that was originally proposed to automatically locate a set of landmarks representing the facial features. Various improved versions of this model for facial landmark annotation have been developed for increasing the shape fitting accuracy at the expense of significantly increased computational complexity. This thesis is concerned with developing a low-complexity active shape model by incorporating the energy compaction property of the discrete cosine transform (DCT). Towards this goal, the proposed ASM, which utilizes a 2-D profile based on the DCT of the local grey-level gradient pattern around a landmark, is first developed. The ASM is then utilized in a scheme of facial landmark annotation for locating facial features of the face in an input image. The proposed ASM provides two distinct advantages: (i) the use of a smaller number of DCT coefficients in building a compressed DCT profile significantly reduces the computational complexity, and (ii) the process of choosing the low-frequency DCT coefficients filters out the noise contained in the image. Simulations are performed to demonstrate the superiority of the proposed ASM over other improved versions of the original active shape model in terms of the fitting accuracy as well as in terms of the computational complexity. It is shown that the use of the proposed model in the application of facial landmark annotation significantly reduces the execution time without affecting the accuracy of the facial shape fitting

Automated Facial Anthropometry Over 3D Face Surface Textured Meshes

The automation of human face measurement means facing mayor technical and technological challenges. The use of 3D scanning technology is widely accepted in the scientific community and it offers the possibility of developing non-invasive measurement techniques. However, the selection of the points that form the basis of the measurements is a task that still requires human intervention. This work introduces digital image processing methods for automatic localization of facial features. The first goal was to examine different ways to represent 3D shapes and to evaluate whether these could be used as representative features of facial attributes, in order to locate them automatically. Based on the above, a non-rigid registration procedure was developed to estimate dense point-to-point correspondence between two surfaces. The method is able to register 3D models of faces in the presence of facial expressions. Finally, a method that uses both shape and appearance information of the surface, was designed for automatic localization of a set of facial features that are the basis for determining anthropometric ratios, which are widely used in fields such as ergonomics, forensics, surgical planning, among othersResumen : La automatización de la medición del rostro humano implica afrontar grandes desafíos técnicos y tecnológicos. Una alternativa de solución que ha encontrado gran aceptación dentro de la comunidad científica, corresponde a la utilización de tecnología de digitalización 3D con lo cual ha sido posible el desarrollo de técnicas de medición no invasivas. Sin embargo, la selección de los puntos que son la base de las mediciones es una tarea que aún requiere de la intervención humana. En este trabajo se presentan métodos de procesamiento digital de imágenes para la localización automática de características faciales. Lo primero que se hizo fue estudiar diversas formas de representar la forma en 3D y cómo estas podían contribuir como características representativas de los atributos faciales con el fin de poder ubicarlos automáticamente. Con base en lo anterior, se desarrolló un método para la estimación de correspondencia densa entre dos superficies a partir de un procedimiento de registro no rígido, el cual se enfocó a modelos de rostros 3D en presencia de expresiones faciales. Por último, se plantea un método, que utiliza tanto información de la forma como de la apariencia de las superficies, para la localización automática de un conjunto de características faciales que son la base para determinar índices antropométricos ampliamente utilizados en campos tales como la ergonomía, ciencias forenses, planeación quirúrgica, entre otrosDoctorad

Compression of 4D medical image and spatial segmentation using deformable models

Ph.DDOCTOR OF PHILOSOPH

Modal matching : a method for describing, comparing, and manipulating digital signals

Thesis (Ph. D.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1995.Includes bibliographical references (leaves 134-144).by Stanley Edward Sclaroff.Ph.D

Statistical shape modelling: automatic shape model building

Statistical Shape Models (SSM) have wide applications in image segmentation, surface registration and morphometry. This thesis deals with an important issue in SSM, which is establishing correspondence between a set of shape surfaces on either 2D or 3D. Current methods involve either manual annotation of the data (current ‘gold standard’); or establishing correspondences by using segmentation or registration algorithms; or using an information technique, Minimum Description Length (MDL), as an objective function that measures the utility of a model (the state-of-the-art). This thesis presents in principle another framework for establishing correspondences completely automatically by treating it as a learning process. Shannon theory is used extensively to develop an objective function, which measures the performance of a model along each eigenvector direction, and a proper weighting is automatically calculated for each energy component. Correspondence finding can then be treated as optimizing the objective function. An efficient optimization method is also incorporated by deriving the gradient of the cost function. Experimental results on various data are presented on both 2D and 3D. In the end, a quantitative evaluation between the proposed algorithm and MDL shows that the proposed model has better Generalization Ability, Specificity and similar Compactness. It also shows a good potential ability to solve the so-called “Pile Up” problem that exists in MDL. In terms of application, I used the proposed algorithm to help build a facial contour classifier. First, correspondence points across facial contours are found automatically and classifiers are trained by using the correspondence points found by the MDL, proposed method and direct human observer. These classification schemes are then used to perform gender prediction on facial contours. The final conclusion for the experiments is that MEM found correspondence points built classification scheme conveys a relatively more accurate gender prediction result. Although, we have explored the potential of our proposed method to some extent, this is not the end of the research for this topic. The future work is also clearly stated which includes more validations on various 3D datasets; discrimination analysis between normal and abnormal subjects could be the direct application for the proposed algorithm, extension to model-building using appearance information, etc