A Proposal Concerning the Analysis of Shadows in Images by an Active Observer (Dissertation Proposal)

Shadows occur frequently in indoor scenes and outdoors on sunny days. Despite the information inherent in shadows about a scene\u27s geometry and lighting conditions, relatively little work in image understanding has addressed the important problem of recognizing shadows. This is an even more serious failing when one considers the problems shadows pose for many visual techniques such as object recognition and shape from shading. Shadows are difficult to identify because they cannot be infallibly recognized until a scene\u27s geometry and lighting are known. However, there are a number of cues which together strongly suggest the identification of a shadow. We present a list of these cues and methods which can be used by an active observer to detect shadows. By an active observer, we mean an observer that is not only mobile, but can extend a probe into its environment. The proposed approach should allow the extraction of shadows in real time. Furthermore, the identification of a shadow should improve with observing time. In order to be able to identify shadows without or prior to obtaining information about the arrangement of objects or information about the spectral properties of materials in the scene, we provide the observer with a probe with which to cast its own shadows. Any visible shadows cast by the probe can be easily identified because they will be new to the scene. These actively obtained shadows allow the observer to experimentally determine the number and location of light sources in the scene, to locate the cast shadows, and to gain information about the likely spectral changes due to shadows. We present a novel method for locating a light source and the surface on which a shadow is cast. It takes into account errors in imaging and image processing and, furthermore, it takes special advantage of the benefits of an active observer. The information gained from the probe is of particular importance in effectively using the various shadow cues. In the course of identifying shadows, we also present a new modification on an image segmentation algorithm. Our modification provides a general description of color images in terms of regions that is particularly amenable to the analysis of shadows

Towards an efficient, unsupervised and automatic face detection system for unconstrained environments

Nowadays, there is growing interest in face detection applications for unconstrained environments. The increasing need for public security and national security motivated our research on the automatic face detection system. For public security surveillance applications, the face detection system must be able to cope with unconstrained environments, which includes cluttered background and complicated illuminations. Supervised approaches give very good results on constrained environments, but when it comes to unconstrained environments, even obtaining all the training samples needed is sometimes impractical. The limitation of supervised approaches impels us to turn to unsupervised approaches. In this thesis, we present an efficient and unsupervised face detection system, which is feature and configuration based. It combines geometric feature detection and local appearance feature extraction to increase stability and performance of the detection process. It also contains a novel adaptive lighting compensation approach to normalize the complicated illumination in real life environments. We aim to develop a system that has as few assumptions as possible from the very beginning, is robust and exploits accuracy/complexity trade-offs as much as possible. Although our attempt is ambitious for such an ill posed problem-we manage to tackle it in the end with very few assumptions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Motion Segmentation Aided Super Resolution Image Reconstruction

This dissertation addresses Super Resolution (SR) Image Reconstruction focusing on motion segmentation. The main thrust is Information Complexity guided Gaussian Mixture Models (GMMs) for Statistical Background Modeling. In the process of developing our framework we also focus on two other topics; motion trajectories estimation toward global and local scene change detections and image reconstruction to have high resolution (HR) representations of the moving regions. Such a framework is used for dynamic scene understanding and recognition of individuals and threats with the help of the image sequences recorded with either stationary or non-stationary camera systems. We introduce a new technique called Information Complexity guided Statistical Background Modeling. Thus, we successfully employ GMMs, which are optimal with respect to information complexity criteria. Moving objects are segmented out through background subtraction which utilizes the computed background model. This technique produces superior results to competing background modeling strategies. The state-of-the-art SR Image Reconstruction studies combine the information from a set of unremarkably different low resolution (LR) images of static scene to construct an HR representation. The crucial challenge not handled in these studies is accumulating the corresponding information from highly displaced moving objects. In this aspect, a framework of SR Image Reconstruction of the moving objects with such high level of displacements is developed. Our assumption is that LR images are different from each other due to local motion of the objects and the global motion of the scene imposed by non-stationary imaging system. Contrary to traditional SR approaches, we employed several steps. These steps are; the suppression of the global motion, motion segmentation accompanied by background subtraction to extract moving objects, suppression of the local motion of the segmented out regions, and super-resolving accumulated information coming from moving objects rather than the whole scene. This results in a reliable offline SR Image Reconstruction tool which handles several types of dynamic scene changes, compensates the impacts of camera systems, and provides data redundancy through removing the background. The framework proved to be superior to the state-of-the-art algorithms which put no significant effort toward dynamic scene representation of non-stationary camera systems

State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

Multi-camera cooperative scene interpretation

In our society, video processing has become a convenient and widely used tool to assist, protect and simplify the daily life of people in areas such as surveillance and video conferencing. The growing number of cameras, the handling and analysis of these vast amounts of video data enable the development of multi-camera applications that cooperatively use multiple sensors. In many applications, bandwidth constraints, privacy issues, and difficulties in storing and analyzing large amounts of video data make applications costly and technically challenging. In this thesis, we deploy techniques ranging from low-level to high-level approaches, specifically designed for multi-camera networks. As a low-level approach, we designed a novel low-level foreground detection algorithm for real-time tracking applications, concentrating on difficult and changing illumination conditions. The main part of this dissertation focuses on a detailed analysis of two novel state-of-the-art real-time tracking approaches: a multi-camera tracking approach based on occupancy maps and a distributed multi-camera tracking approach with a feedback loop. As a high-level application we propose an approach to understand the dynamics in meetings - so called, smart meetings - using a multi-camera setup, consisting of fixed ambient and portable close-up cameras. For all method, we provided qualitative and quantitative results on several experiments, compared to state-of-the-art methods

Multi-camera object segmentation in dynamically textured scenes using disparity contours

This thesis presents a stereo-based object segmentation system that combines the simplicity and efficiency of the background subtraction approach with the capacity of dealing with dynamic lighting and background texture and large textureless regions. The method proposed here does not rely on full stereo reconstruction or empirical parameter tuning, but employs disparity-based hypothesis verification to separate multiple objects at different depths.The proposed stereo-based segmentation system uses a pair of calibrated cameras with a small baseline and factors the segmentation problem into two stages: a well-understood offline stage and a novel online one. Based on the calibrated parameters, the offline stage models the 3D geometry of a background by constructing a complete disparity map. The online stage compares corresponding new frames synchronously captured by the two cameras according to the background disparity map in order to falsify the hypothesis that the scene contains only background. The resulting object boundary contours possess a number of useful features that can be exploited for object segmentation.Three different approaches to contour extraction and object segmentation were experimented with and their advantages and limitations analyzed. The system demonstrates its ability to extract multiple objects from a complex scene with near real-time performance. The algorithm also has the potential of providing precise object boundaries rather than just bounding boxes, and is extensible to perform 2D and 3D object tracking and online background update

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas