Video content analysis for intelligent forensics

The networks of surveillance cameras installed in public places and private territories continuously record video data with the aim of detecting and preventing unlawful activities. This enhances the importance of video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis. In this thesis, the primary focus is on four key aspects of video content analysis, namely; 1. Moving object detection and recognition, 2. Correction of colours in the video frames and recognition of colours of moving objects, 3. Make and model recognition of vehicles and identification of their type, 4. Detection and recognition of text information in outdoor scenes. To address the first issue, a framework is presented in the first part of the thesis that efficiently detects and recognizes moving objects in videos. The framework targets the problem of object detection in the presence of complex background. The object detection part of the framework relies on background modelling technique and a novel post processing step where the contours of the foreground regions (i.e. moving object) are refined by the classification of edge segments as belonging either to the background or to the foreground region. Further, a novel feature descriptor is devised for the classification of moving objects into humans, vehicles and background. The proposed feature descriptor captures the texture information present in the silhouette of foreground objects. To address the second issue, a framework for the correction and recognition of true colours of objects in videos is presented with novel noise reduction, colour enhancement and colour recognition stages. The colour recognition stage makes use of temporal information to reliably recognize the true colours of moving objects in multiple frames. The proposed framework is specifically designed to perform robustly on videos that have poor quality because of surrounding illumination, camera sensor imperfection and artefacts due to high compression. In the third part of the thesis, a framework for vehicle make and model recognition and type identification is presented. As a part of this work, a novel feature representation technique for distinctive representation of vehicle images has emerged. The feature representation technique uses dense feature description and mid-level feature encoding scheme to capture the texture in the frontal view of the vehicles. The proposed method is insensitive to minor in-plane rotation and skew within the image. The capability of the proposed framework can be enhanced to any number of vehicle classes without re-training. Another important contribution of this work is the publication of a comprehensive up to date dataset of vehicle images to support future research in this domain. The problem of text detection and recognition in images is addressed in the last part of the thesis. A novel technique is proposed that exploits the colour information in the image for the identification of text regions. Apart from detection, the colour information is also used to segment characters from the words. The recognition of identified characters is performed using shape features and supervised learning. Finally, a lexicon based alignment procedure is adopted to finalize the recognition of strings present in word images. Extensive experiments have been conducted on benchmark datasets to analyse the performance of proposed algorithms. The results show that the proposed moving object detection and recognition technique superseded well-know baseline techniques. The proposed framework for the correction and recognition of object colours in video frames achieved all the aforementioned goals. The performance analysis of the vehicle make and model recognition framework on multiple datasets has shown the strength and reliability of the technique when used within various scenarios. Finally, the experimental results for the text detection and recognition framework on benchmark datasets have revealed the potential of the proposed scheme for accurate detection and recognition of text in the wild

Text Segmentation in Web Images Using Colour Perception and Topological Features

The research presented in this thesis addresses the problem of Text Segmentation in Web images. Text is routinely created in image form (headers, banners etc.) on Web pages, as an attempt to overcome the stylistic limitations of HTML. This text however, has a potentially high semantic value in terms of indexing and searching for the corresponding Web pages. As current search engine technology does not allow for text extraction and recognition in images, the text in image form is ignored. Moreover, it is desirable to obtain a uniform representation of all visible text of a Web page (for applications such as voice browsing or automated content analysis). This thesis presents two methods for text segmentation in Web images using colour perception and topological features. The nature of Web images and the implicit problems to text segmentation are described, and a study is performed to assess the magnitude of the problem and establish the need for automated text segmentation methods. Two segmentation methods are subsequently presented: the Split-and-Merge segmentation method and the Fuzzy segmentation method. Although approached in a distinctly different way in each method, the safe assumption that a human being should be able to read the text in any given Web Image is the foundation of both methods’ reasoning. This anthropocentric character of the methods along with the use of topological features of connected components, comprise the underlying working principles of the methods. An approach for classifying the connected components resulting from the segmentation methods as either characters or parts of the background is also presented

Automated Low-Cost Malaria Detection System in Thin Blood Slide Images Using Mobile Phones

Malaria, a deadly disease which according to the World Health Organisation (WHO) is responsible for the fatal illness in 200 million people around the world in 2010, is diagnosed using peripheral blood examination. The work undertaken in this research programme aims to develop an automated malaria parasite-detection system, using microscopic-image processing, that can be incorporated onto mobile phones. In this research study, the main objective is to achieve the performance equal to or better than the manual microscopy, which is the gold standard in malaria diagnosis, in order to produce a reliable automated diagnostic platform without expert intervention, for the effective treatment and eradication of the deadly disease. The work contributed to the field of mathematical morphology by proposing a novel method called the Annular Ring Ratio transform for blood component identification. It has also proposed an automated White Blood Cell and Red Blood Cell differentiation algorithm, which when combined with ARR transform method, has wide applications not only for malaria diagnosis but also for many blood related analysis involving microscopic examination. The research has undertaken investigations on infected cell identification which aids in the calculation of parasitemia, the measure of infection. In addition, an automated diagnostic tool to detect the sexual stage (gametocytes) of the species P.falciparum for post-treatment malaria diagnosis was developed. Furthermore, a parallel investigation was carried out on automated malaria diagnosis on fluorescent thin blood films and a WBC and infected cell differentiation algorithm was proposed. Finally, a mobile phone application based on the morphological image processing algorithms proposed in this thesis was developed. A complete malaria diagnostic unit using the mobile phones attached to a portable microscope was set up which has enormous potential not only for malaria diagnosis but also for the blood parasitological field where advancement in medical diagnostics using cellular smart phone technology is widely acknowledged

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

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

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

On Multi-Resolution 3D Orbital Imagery and Visualisation for Mars Geological Analysis

Mars Science Laboratory has revealed a dynamic history of water as the rover has ascended the mysterious Mount Sharp in Gale crater. Because rovers only “see” their local environment, planetary scientists rely on satellite-based orbital imagery to understand the regional geology of Gale crater. However, orbital imagery is map-view—viewed from above, lacking perspective—which presents challenges to interpretation of stratigraphy. 3D visualisation is an emerging opportunity to study orbital images in more intuitive, field-like environments, but has had limited application to Mars. In this work, I formulate and analyse 3D orbital imagery over Gale crater, Mars to investigate the stratigraphy of Mount Sharp 700 m above and 40 km away from MSL. First, I process orbital imagery from the HRSC, CTX, and HiRISE cameras into 3D digital terrain models (DTMs). I then co-register and evaluate these DTMs using statistical tools and existing products to build a new, validated, multi-resolution basemap tied down to MOLA. Sakarya Vallis, a 400-m deep canyon on Mount Sharp, was then analysed in a 3D environment at 1 m/px. From measurements of exposed rock layers, I construct cross-sections, stratigraphic logs, and a geological unit map to capture this geology. Seven geological units are interpreted across 1 km of exposure, varying in thicknesses (10–174 m) and dips (3–12º). These units may reveal a cyclic depositional environment; a progradational sequence and channel; and unconformities. This work therefore suggests two periods of sub-aqueous deposition in this region during the Late Noachian to Early Hesperian. These results further provide geological context of Gale crater as MSL ascends Mount Sharp, and future inputs for palaeoenvironmental models of Gale crater