Effects of Temporal and Spatial Context Within the Macaque Face-Processing System

Temporal and spatial context play a key role in vision as a whole, and in face perception specifically. However, little is known about the neurophysiological mechanisms by which contextual cues exert their effects. Anatomically distinct face patches in the macaque brain analyze facial form, and studies of the activity within these patches have begun to clarify the neural machinery that underlies facial perception. This system provides a uniquely valuable opportunity to study how context affects the perception of form. We used functional magnetic resonance imaging (fMRI) to investigate the brain activity of macaque monkeys while they viewed faces placed in either temporal or spatial context. Facial motion transmits rich and ethologically vital information, but the way that the brain interprets such natural temporal context is poorly understood. Facial motion activates the face patches and surrounding areas, yet it is not known whether this motion is processed by its own specialized neural machinery, and if so, what that machinery’s organization might be. To address these questions, we monitored the brain activity of macaque monkeys while they viewed low- and high-level motion and form stimuli. We found that, beyond classical motion areas and the known face patch system, moving faces recruited a heretofore-unrecognized face patch. Although all face patches displayed distinctive selectivity for face motion over object motion, only two face patches preferred naturally moving faces, while three others preferred randomized, rapidly varying sequences of facial form. This functional divide was anatomically specific, segregating dorsal from ventral face patches, thereby revealing a new organizational principle of the macaque face-processing system. Like facial motion, bodies can provide valuable social context, revealing emotion and identity. Little is known about the joint processing of faces and bodies, even though there is reason to believe that their neural representations are intertwined. To identify interaction between the neural representations of face and body, we monitored the brain activity of the same monkeys while they viewed pictures of whole monkeys, isolated monkey heads, and isolated monkey bodies. We found that certain areas, including anterior face patches, responded more to whole monkeys than would be predicted by summing the separate responses to isolated heads and isolated bodies. The supralinear response was specific to viewing the conjunction of head and body; heads placed atop nonbody objects did not evoke this activity signature. However, a supralinear context response was elicited by pixelated, ambiguous faces presented on bodies. The size of this response suggests that the supralinear signal in this case did not result from the disambiguation of the ambiguous faces. These studies of contextually evoked activity within the macaque face processing system deepen our understanding of the cortical organization of both visual context and face processing, and identify promising sites for future research into the mechanisms underlying these critical aspects of perception

A Methodology for Extracting Human Bodies from Still Images

Monitoring and surveillance of humans is one of the most prominent applications of today and it is expected to be part of many future aspects of our life, for safety reasons, assisted living and many others. Many efforts have been made towards automatic and robust solutions, but the general problem is very challenging and remains still open. In this PhD dissertation we examine the problem from many perspectives. First, we study the performance of a hardware architecture designed for large-scale surveillance systems. Then, we focus on the general problem of human activity recognition, present an extensive survey of methodologies that deal with this subject and propose a maturity metric to evaluate them. One of the numerous and most popular algorithms for image processing found in the field is image segmentation and we propose a blind metric to evaluate their results regarding the activity at local regions. Finally, we propose a fully automatic system for segmenting and extracting human bodies from challenging single images, which is the main contribution of the dissertation. Our methodology is a novel bottom-up approach relying mostly on anthropometric constraints and is facilitated by our research in the fields of face, skin and hands detection. Experimental results and comparison with state-of-the-art methodologies demonstrate the success of our approach

Contributions to Medical Image Segmentation and Signal Analysis Utilizing Model Selection Methods

This thesis presents contributions to model selection techniques, especially based on information theoretic criteria, with the goal of solving problems appearing in signal analysis and in medical image representation, segmentation, and compression.The field of medical image segmentation is wide and is quickly developing to make use of higher available computational power. This thesis concentrates on several applications that allow the utilization of parametric models for image and signal representation. One important application is cell nuclei segmentation from histological images. We model nuclei contours by ellipses and thus the complicated problem of separating overlapping nuclei can be rephrased as a model selection problem, where the number of nuclei, their shapes, and their locations define one segmentation. In this thesis, we present methods for model selection in this parametric setting, where the intuitive algorithms are combined with more principled ones, namely those based on the minimum description length (MDL) principle. The results of the introduced unsupervised segmentation algorithm are compared with human subject segmentations, and are also evaluated with the help of a pathology expert.Another considered medical image application is lossless compression. The objective has been to add the task of image segmentation to that of image compression such that the image regions can be transmitted separately, depending on the region of interest for diagnosis. The experiments performed on retinal color images show that our modeling, in which the MDL criterion selects the structure of the linear predictive models, outperforms publicly available image compressors such as the lossless version of JPEG 2000.For time series modeling, the thesis presents an algorithm which allows detection of changes in time series signals. The algorithm is based on one of the most recent implementations of the MDL principle, the sequentially normalized maximum likelihood (SNML) models.This thesis produces contributions in the form of new methods and algorithms, where the simplicity of information theoretic principles are combined with a rather complex and problem dependent modeling formulation, resulting in both heuristically motivated and principled algorithmic solutions

Tracking the emergence of visual recognition through multivariate approaches

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 121-130).The visual system is a dynamic entity whose response properties depend on context and experience. In this thesis, I examine how the brain changes as we learn to see - what changes occur during the onset of recognition, in the mature visual system on the one hand, and in a developmentally nascent one, on the other? Working with normal adults, I focus on the processes that underlie the interpretation of images as meaningful entities. This interpretation is greatly facilitated by prior information about a stimulus. What are the neural sites that exhibit experience dependent changes? Using multivariate decoding techniques, I find pervasive evidence of such changes throughout the visual system. Critically, cortical regions previously implicated in such learning are not the same loci as sites of increased information. Examining the temporal mechanisms of recognition, I identify the perceptual state transitions corresponding to the onset of meaning in an observed image. Furthermore, decoding techniques reveal the flow of information during this 'eureka moment.' I find feedback processing when a degraded image is first meaningfully interpreted, and then a rapid transition into feed-forward processing for more coherent images. Complementing the studies with mature subjects, my work with developmentally nascent observers explores the genesis of visual interpretation. What neural changes accompany the earliest stages of visual learning? I show that children treated for congenital blindness exhibit significant cortical re-organization after sight onset, in contrast to the classical notion of a critical period for visual plasticity. The specific kind of reorganization suggests that visual experience enhances information coding efficiency in visual cortex. Additionally, I present evidence of rapid development of functionally specialized cortical regions. Overall, the thesis presents two complementary perspectives on the genesis of visual meaning. The results help advance our understanding of how short-term experience, as well as developmental history, shapes our interpretation of the complex visual world.by Scott Gorlin.Ph.D

Image morphological processing

Mathematical Morphology with applications in image processing and analysis has been becoming increasingly important in today\u27s technology. Mathematical Morphological operations, which are based on set theory, can extract object features by suitably shaped structuring elements. Mathematical Morphological filters are combinations of morphological operations that transform an image into a quantitative description of its geometrical structure based on structuring elements. Important applications of morphological operations are shape description, shape recognition, nonlinear filtering, industrial parts inspection, and medical image processing. In this dissertation, basic morphological operations, properties and fuzzy morphology are reviewed. Existing techniques for solving corner and edge detection are presented. A new approach to solve corner detection using regulated mathematical morphology is presented and is shown that it is more efficient in binary images than the existing mathematical morphology based asymmetric closing for corner detection. A new class of morphological operations called sweep mathematical morphological operations is developed. The theoretical framework for representation, computation and analysis of sweep morphology is presented. The basic sweep morphological operations, sweep dilation and sweep erosion, are defined and their properties are studied. It is shown that considering only the boundaries and performing operations on the boundaries can substantially reduce the computation. Various applications of this new class of morphological operations are discussed, including the blending of swept surfaces with deformations, image enhancement, edge linking and shortest path planning for rotating objects. Sweep mathematical morphology is an efficient tool for geometric modeling and representation. The sweep dilation/erosion provides a natural representation of sweep motion in the manufacturing processes. A set of grammatical rules that govern the generation of objects belonging to the same group are defined. Earley\u27s parser serves in the screening process to determine whether a pattern is a part of the language. Finally, summary and future research of this dissertation are provided

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

Image segmentation for defect detection on veneer surfaces

Machine vision is widely used in scientific areas and non-wood using industries, but the extreme variability of wood has limited its adoption by forest products industries. However, it is now becoming a key factor in further automation of the forest products industry. As a very important part of machine vision, developing image segmentation algorithms that can be used for wood products is an ambitious undertaking. The focus of this research was to adapt existing and develop some new segmentation algorithms which could be used to detect defects on veneer surfaces. Nine algorithms covering three segmentation technique categories were explored. Three existing edge detection algorithms were modified for use on veneer images, and four existing thresholding algorithms were adapted in both global and local versions. Two new region extraction algorithms were developed specifically for defect detection on veneer surfaces. The performances of these nine algorithms were tested and compared under the combinations of two camera resolutions (5-bit and 8-bit), three color spaces (RGB, Lab, and gray-scale), and seven surface features (clear wood, blue stain, loose knot, pitch pocket, pitch streak, tight knot, and wane). Ten sample images for each of seven surface features on Douglas-fir veneer [Pseudotsuga menziesii] were used. Ten measures were proposed for performance evaluation. A multi-factor factorial ANOVA was used in the performance tests and comparisons. The best combinations of camera resolution and color space for each of the algorithms were determined. The 5-bit and 8-bit camera resolutions were not significantly different for the three edge detection and two region extraction algorithms, but the 8-bit camera resolution was better for all but one of the thresholding algorithms. That exception was the global Otsu thresholding algorithm, for which the 5-bit camera resolution was better. The RGB color space was the best for all algorithms. Overall, the two region extraction algorithms were the best. Under the best combination of factors, those two algorithms provided the highest defect detection accuracies of 91% for pitch streak samples and over 95% for loose knot, tight knot, and pitch pocket samples. These results were accomplished while still providing clear wood accuracies of over 95%. The one performance exception was blue stain, for which no satisfactory algorithm was found

Object Recognition

Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs

Computational models for image contour grouping

Contours are one dimensional curves which may correspond to meaningful entities such as object boundaries. Accurate contour detection will simplify many vision tasks such as object detection and image recognition. Due to the large variety of image content and contour topology, contours are often detected as edge fragments at first, followed by a second step known as {u0300}{u0300}contour grouping'' to connect them. Due to ambiguities in local image patches, contour grouping is essential for constructing globally coherent contour representation. This thesis aims to group contours so that they are consistent with human perception. We draw inspirations from Gestalt principles, which describe perceptual grouping ability of human vision system. In particular, our work is most relevant to the principles of closure, similarity, and past experiences. The first part of our contribution is a new computational model for contour closure. Most of existing contour grouping methods have focused on pixel-wise detection accuracy and ignored the psychological evidences for topological correctness. This chapter proposes a higher-order CRF model to achieve contour closure in the contour domain. We also propose an efficient inference method which is guaranteed to find integer solutions. Tested on the BSDS benchmark, our method achieves a superior contour grouping performance, comparable precision-recall curves, and more visually pleasant results. Our work makes progresses towards a better computational model of human perceptual grouping. The second part is an energy minimization framework for salient contour detection problem. Region cues such as color/texture homogeneity, and contour cues such as local contrast, are both useful for this task. In order to capture both kinds of cues in a joint energy function, topological consistency between both region and contour labels must be satisfied. Our technique makes use of the topological concept of winding numbers. By using a fast method for winding number computation, we find that a small number of linear constraints are sufficient for label consistency. Our method is instantiated by ratio-based energy functions. Due to cue integration, our method obtains improved results. User interaction can also be incorporated to further improve the results. The third part of our contribution is an efficient category-level image contour detector. The objective is to detect contours which most likely belong to a prescribed category. Our method, which is based on three levels of shape representation and non-parametric Bayesian learning, shows flexibility in learning from either human labeled edge images or unlabelled raw images. In both cases, our experiments obtain better contour detection results than competing methods. In addition, our training process is robust even with a considerable size of training samples. In contrast, state-of-the-art methods require more training samples, and often human interventions are required for new category training. Last but not least, in Chapter 7 we also show how to leverage contour information for symmetry detection. Our method is simple yet effective for detecting the symmetric axes of bilaterally symmetric objects in unsegmented natural scene images. Compared with methods based on feature points, our model can often produce better results for the images containing limited texture