Flying Target Detection and Recognition by Feature Fusion

This paper presents a near-realtime visual detection and recognition approach for flying target detection and recognition. Detection is based on fast and robust background modeling and shape extraction, while recognition of target classes is based on shape and texture fused querying on a-priori built real datasets. Main application areas are passive defense and surveillance scenarios

Multi-Technique Fusion for Shape-Based Image Retrieval

Content-based image retrieval (CBIR) is still in its early stages, although several attempts have been made to solve or minimize challenges associated with it. CBIR techniques use such visual contents as color, texture, and shape to represent and index images. Of these, shapes contain richer information than color or texture. However, retrieval based on shape contents remains more difficult than that based on color or texture due to the diversity of shapes and the natural occurrence of shape transformations such as deformation, scaling and orientation. This thesis presents an approach for fusing several shape-based image retrieval techniques for the purpose of achieving reliable and accurate retrieval performance. An extensive investigation of notable existing shape descriptors is reported. Two new shape descriptors have been proposed as means to overcome limitations of current shape descriptors. The first descriptor is based on a novel shape signature that includes corner information in order to enhance the performance of shape retrieval techniques that use Fourier descriptors. The second descriptor is based on the curvature of the shape contour. This invariant descriptor takes an unconventional view of the curvature-scale-space map of a contour by treating it as a 2-D binary image. The descriptor is then derived from the 2-D Fourier transform of the 2-D binary image. This technique allows the descriptor to capture the detailed dynamics of the curvature of the shape and enhances the efficiency of the shape-matching process. Several experiments have been conducted in order to compare the proposed descriptors with several notable descriptors. The new descriptors not only speed up the online matching process, but also lead to improved retrieval accuracy. The complexity and variety of the content of real images make it impossible for a particular choice of descriptor to be effective for all types of images. Therefore, a data- fusion formulation based on a team consensus approach is proposed as a means of achieving high accuracy performance. In this approach a select set of retrieval techniques form a team. Members of the team exchange information so as to complement each other’s assessment of a database image candidate as a match to query images. Several experiments have been conducted based on the MPEG-7 contour-shape databases; the results demonstrate that the performance of the proposed fusion scheme is superior to that achieved by any technique individually

Haptic robot-environment interaction for self-supervised learning in ground mobility

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de ComputadoresThis dissertation presents a system for haptic interaction and self-supervised learning mechanisms to ascertain navigation affordances from depth cues. A simple pan-tilt telescopic arm and a structured light sensor, both fitted to the robot’s body frame, provide the required haptic and depth sensory feedback. The system aims at incrementally develop the ability to assess the cost of navigating in natural environments. For this purpose the robot learns a mapping between the appearance of objects, given sensory data provided by the sensor, and their bendability, perceived by the pan-tilt telescopic arm. The object descriptor, representing the object in memory and used for comparisons with other objects, is rich for a robust comparison and simple enough to allow for fast computations. The output of the memory learning mechanism allied with the haptic interaction point evaluation prioritize interaction points to increase the confidence on the interaction and correctly identifying obstacles, reducing the risk of the robot getting stuck or damaged. If the system concludes that the object is traversable, the environment change detection system allows the robot to overcome it. A set of field trials show the ability of the robot to progressively learn which elements of environment are traversable

Video metadata extraction in a videoMail system

Currently the world swiftly adapts to visual communication. Online services like YouTube and Vine show that video is no longer the domain of broadcast television only. Video is used for different purposes like entertainment, information, education or communication. The rapid growth of today’s video archives with sparsely available editorial data creates a big problem of its retrieval. The humans see a video like a complex interplay of cognitive concepts. As a result there is a need to build a bridge between numeric values and semantic concepts. This establishes a connection that will facilitate videos’ retrieval by humans. The critical aspect of this bridge is video annotation. The process could be done manually or automatically. Manual annotation is very tedious, subjective and expensive. Therefore automatic annotation is being actively studied. In this thesis we focus on the multimedia content automatic annotation. Namely the use of analysis techniques for information retrieval allowing to automatically extract metadata from video in a videomail system. Furthermore the identification of text, people, actions, spaces, objects, including animals and plants. Hence it will be possible to align multimedia content with the text presented in the email message and the creation of applications for semantic video database indexing and retrieving

Biological object representation for identification

This thesis is concerned with the problem of how to represent a biological object for computerised identification. Images of biological objects have been generally characterised by shapes and colour patterns in the biology domain and the pattern recognition domain. Thus, it is necessary to represent the biological object using descriptors for the shape and the colour pattern. The basic requirements which a description method should satisfy are those such as invariance of scale, location and orientation of an object; direct involvement in the identification stage; easy assessment of results. The major task to deal with in this thesis was to develop a shape-description method and a colour-pattern description method which could accommodate all of the basic requirements and could be generally applied in both domains. In the colour-pattern description stage, an important task was to segment a colour image into meaningful segments. The most efficient method for this task is to apply Cluster Analysis. In the image analysis and pattern recognition domains, the majority of approaches to this method have been constrained by the problem of dealing with inordinate amounts of data, i.e. a large number of pixels of an image. In order to directly apply Cluster Analysis to the colour image segmentation, data structure, the Auxiliary Means is developed in this thesis

A multi-scale topological shape model for single and multiple component shapes

A novel shape model of multi-scale topological features is proposed which considers those features relating to connected components and holes. This is achieved by considering the \textit{persistent homology} of a pair of sublevel set functions corresponding to a pair of distance functions defined on the ambient space. The model is applicable to both single and multiple component shapes and, to the authors knowledge, is the first shape model to consider multi-scale topological features of multiple component shapes. It is demonstrated, both qualitatively and quantitatively, that the proposed model models useful multi-scale topological features and outperforms a commonly used benchmark models with respect to the task of multiple component shape retrieval

Multi-scale active shape description in medical imaging

Shape description in medical imaging has become an increasingly important research field in recent years. Fast and high-resolution image acquisition methods like Magnetic Resonance (MR) imaging produce very detailed cross-sectional images of the human body - shape description is then a post-processing operation which abstracts quantitative descriptions of anatomically relevant object shapes. This task is usually performed by clinicians and other experts by first segmenting the shapes of interest, and then making volumetric and other quantitative measurements. High demand on expert time and inter- and intra-observer variability impose a clinical need of automating this process. Furthermore, recent studies in clinical neurology on the correspondence between disease status and degree of shape deformations necessitate the use of more sophisticated, higher-level shape description techniques. In this work a new hierarchical tool for shape description has been developed, combining two recently developed and powerful techniques in image processing: differential invariants in scale-space, and active contour models. This tool enables quantitative and qualitative shape studies at multiple levels of image detail, exploring the extra image scale degree of freedom. Using scale-space continuity, the global object shape can be detected at a coarse level of image detail, and finer shape characteristics can be found at higher levels of detail or scales. New methods for active shape evolution and focusing have been developed for the extraction of shapes at a large set of scales using an active contour model whose energy function is regularized with respect to scale and geometric differential image invariants. The resulting set of shapes is formulated as a multiscale shape stack which is analysed and described for each scale level with a large set of shape descriptors to obtain and analyse shape changes across scales. This shape stack leads naturally to several questions in regard to variable sampling and appropriate levels of detail to investigate an image. The relationship between active contour sampling precision and scale-space is addressed. After a thorough review of modem shape description, multi-scale image processing and active contour model techniques, the novel framework for multi-scale active shape description is presented and tested on synthetic images and medical images. An interesting result is the recovery of the fractal dimension of a known fractal boundary using this framework. Medical applications addressed are grey-matter deformations occurring for patients with epilepsy, spinal cord atrophy for patients with Multiple Sclerosis, and cortical impairment for neonates. Extensions to non-linear scale-spaces, comparisons to binary curve and curvature evolution schemes as well as other hierarchical shape descriptors are discussed

Vehicle make and model recognition for intelligent transportation monitoring and surveillance.

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS), such as autonomous navigation, traffic analysis, traffic surveillance and security systems. A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multi-class classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicles makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system. In this dissertation, facing the growing importance of make and model recognition of vehicles, we present a VMMR system that provides very high accuracy rates and is robust to several challenges. We demonstrate that the VMMR problem can be addressed by locating discriminative parts where the most significant appearance variations occur in each category, and learning expressive appearance descriptors. Given these insights, we consider two data driven frameworks: a Multiple-Instance Learning-based (MIL) system using hand-crafted features and an extended application of deep neural networks using MIL. Our approach requires only image level class labels, and the discriminative parts of each target class are selected in a fully unsupervised manner without any use of part annotations or segmentation masks, which may be costly to obtain. This advantage makes our system more intelligent, scalable, and applicable to other fine-grained recognition tasks. We constructed a dataset with 291,752 images representing 9,170 different vehicles to validate and evaluate our approach. Experimental results demonstrate that the localization of parts and distinguishing their discriminative powers for categorization improve the performance of fine-grained categorization. Extensive experiments conducted using our approaches yield superior results for images that were occluded, under low illumination, partial camera views, or even non-frontal views, available in our real-world VMMR dataset. The approaches presented herewith provide a highly accurate VMMR system for rea-ltime applications in realistic environments.\\ We also validate our system with a significant application of VMMR to ITS that involves automated vehicular surveillance. We show that our application can provide law inforcement agencies with efficient tools to search for a specific vehicle type, make, or model, and to track the path of a given vehicle using the position of multiple cameras