Shape Processing across Lateral Occipital Cortex

There are two predominant means of identifying visual areas in the human brain; retinotopy (exploiting maps of the visual field) and localisers (exploiting functional selectivity). This thesis aimed to bridge those two approaches, assessing the roles of LO-1 and LO-2; two retinotopically-defined regions that show overlap with the functionally-defined (shape selective) Lateral Occipital Complex (LOC). More generally, we asked what is the nature of the shape representation across Lateral Occipital cortex? We first probed the functional roles of LO-1 and LO-2, finding that LO-2 is the more shape-sensitive region of the pair and will respond to second order shape stimuli, whereas LO-1 may process more local cues (perhaps orientation information). Our later work then assessed neural shape representations across visual cortex, identifying two discrete representations; ‘Shape-profile’ (essentially retinotopic responses) and ‘Shape-complexity’ (responses based upon the complexity of a shape’s contour). The latter dimension captured variance in LOC, and surprisingly LO-2. This indicates that even explicit visual field maps can respond to non‑retinotopic attributes such as curvature complexity. Intriguingly, a transition between dimensions occurred around LO-1 and LO-2. Finally, we explicitly tested whether the ‘Shape-complexity’ representation may be curvature based. Our results implied that radial shape protrusions are highly salient features for Lateral Occipital cortex, but it is not necessarily the points of maximal curvature that are being responded to. Instead, we hypothesise that it is the convergent lines comorbid with curvature that neurons may be attuned to, as such lines likely represent the most salient or characteristic features in a given shape. In sum, we argue for an evolving shape representation across visual cortex, with some degree of shape sensitivity first emerging around LO-1 and LO-2. These maps may then be acting as preliminary processing stages for more selective shape tunings in LOC

3D Object Recognition Based On Constrained 2D Views

The aim of the present work was to build a novel 3D object recognition system capable of classifying man-made and natural objects based on single 2D views. The approach to this problem has been one motivated by recent theories on biological vision and multiresolution analysis. The project's objectives were the implementation of a system that is able to deal with simple 3D scenes and constitutes an engineering solution to the problem of 3D object recognition, allowing the proposed recognition system to operate in a practically acceptable time frame. The developed system takes further the work on automatic classification of marine phytoplank- (ons, carried out at the Centre for Intelligent Systems, University of Plymouth. The thesis discusses the main theoretical issues that prompted the fundamental system design options. The principles and the implementation of the coarse data channels used in the system are described. A new multiresolution representation of 2D views is presented, which provides the classifier module of the system with coarse-coded descriptions of the scale-space distribution of potentially interesting features. A multiresolution analysis-based mechanism is proposed, which directs the system's attention towards potentially salient features. Unsupervised similarity-based feature grouping is introduced, which is used in coarse data channels to yield feature signatures that are not spatially coherent and provide the classifier module with salient descriptions of object views. A simple texture descriptor is described, which is based on properties of a special wavelet transform. The system has been tested on computer-generated and natural image data sets, in conditions where the inter-object similarity was monitored and quantitatively assessed by human subjects, or the analysed objects were very similar and their discrimination constituted a difficult task even for human experts. The validity of the above described approaches has been proven. The studies conducted with various statistical and artificial neural network-based classifiers have shown that the system is able to perform well in all of the above mentioned situations. These investigations also made possible to take further and generalise a number of important conclusions drawn during previous work carried out in the field of 2D shape (plankton) recognition, regarding the behaviour of multiple coarse data channels-based pattern recognition systems and various classifier architectures. The system possesses the ability of dealing with difficult field-collected images of objects and the techniques employed by its component modules make possible its extension to the domain of complex multiple-object 3D scene recognition. The system is expected to find immediate applicability in the field of marine biota classification

Shape analysis of a synthetic diamond

Two-dimensional images of synthetic industrial diamond particles were obtained using a camera, framegrabber and PC-based image analysis software. Various methods for shape quantification were applied, including two-dimensional shape factors, Fourier series expansion of radius as a function of angle, boundary fractal analysis, polygonal harmonics, and corner counting methods. The shape parameter found to be the most relevant was axis ratio, defined as the ratio of the minor axis to the major axis of the ellipse with the same second moments of area as the particle. Axis ratio was used in an analysis of the sorting of synthetic diamonds on a vibrating table. A model was derived based on the probability that a particle of a given axis ratio would travel to a certain bin. The model described the sorting of bulk material accurately but it was found not to be applicable if the shape mix of the feed material changed dramatically. This was attributed to the fact that the particle-particle interference was not taken into account. An expert system and a neural network were designed in an attempt to classify particles by a combination of four shape parameters. These systems gave good results when discriminating between particles from bin I and bin 9 but not for neighbouring bins or for more than two classes. The table sorting process was discussed in light of the findings and it was demonstrated that the shape distributions of sorted diamond fractions can be quantified in a useful and meaningful way

The transfer and persistence of environmental trace indicators, and methods for digital data acquisition from photographs and micrographs: applications for forensic science research

Environmental forms of trace evidence (such as mineral grains, pollen grains, algae, and sediment) can offer valuable insights within forensic casework. An issue facing forensic science as a whole, and these environmental indicators specifically, is a relative dearth of empirical research which would underpin the interpretation of such indicators when attempting forensic reconstruction. This thesis aims to address this lacuna, undertaking experiments to: (1) Explore variables which affect the rates of transfer and persistence, with specific focus upon quartz grains (a terrestrial indicator) and diatom valves (an aquatic indicator) upon footwear materials (a substrate that has been under-represented in past studies); (2) Conduct research into the effects of particle size and morphology upon transfer and persistence; (3) Develop and adapt methodologies to undertake this research. Accordingly, the outputs of this thesis are: (1) The creation of new datasets which could inform the interpretation of these trace indicators within forensic investigations and crime reconstruction scenarios and (2) The development of novel methodologies which could be employed in future research to attempt to accelerate data collection and analysis, without compromising on accuracy. This research is interdisciplinary, combining theory from forensic science, analytical techniques from the environmental sciences, and some elements of image processing and analysis. This research was funded by the Engineering and Physical Sciences Research Council of the United Kingdom through the Security Science Doctoral Training Research Centre (UCL SECReT) based at University College London (EP/G037264/1)

Local user mapping via multi-modal fusion for social robots

User detection, recognition and tracking is at the heart of Human Robot Interaction, and yet, to date, no universal robust method exists for being aware of the people in a robot surroundings. The presented work aims at importing into existing social robotics platforms different techniques, some of them classical, and other novel, for detecting, recognizing and tracking human users. These algorithms are based on a variety of sensors, mainly cameras and depth imaging devices, but also lasers and microphones. The results of these parallel algorithms are then merged so as to obtain a modular, expandable and fast architecture. This results in a local user mapping thanks to multi-modal fusion. Thanks to this user awareness architecture, user detection, recognition and tracking capabilities can be easily and quickly given to any robot by re-using the modules that match its sensors and its processing performance. The architecture provides all the relevant information about the users around the robot, that can then be used for end-user applications that adapt their behavior to the users around the robot. The variety of social robots in which the architecture has been successfully implemented includes a car-like mobile robot, an articulated flower and a humanoid assistance robot. Some modules of the architecture are very lightweight but have a low reliability, others need more CPU but the associated confidence is higher. All configurations of modules are possible, and fit the range of possible robotics hardware configurations. All the modules are independent and highly configurable, therefore no code needs to be developed for building a new configuration, the user only writes a ROS launch file. This simple text file contains all wanted modules. The architecture has been developed with modularity and speed in mind. It is based on the Robot Operating System (ROS) architecture, a de facto software standard in robotics. The different people detectors comply with a common interface called PeoplePoseList Publisher, while the people recognition algorithms comply with an interface called PeoplePoseList Matcher. The fusion of all these different modules is based on Unscented Kalman Filter techniques. Extensive benchmarks of the sub-components and of the whole architecture, using both academic datasets and data acquired in our lab, and end-user application samples demonstrate the validity and interest of all levels of the architecture.La detección, el reconocimiento y el seguimiento de los usuarios es un problema clave para la Interacción Humano-Robot. Sin embargo, al día de hoy, no existe ningún método robusto universal para para lograr que un robot sea consciente de la gente que le rodea. Esta tesis tiene como objetivo implementar, dentro de robots sociales, varias técnicas, algunas clásicas, otras novedosas, para detectar, reconocer y seguir a los usuarios humanos. Estos algoritmos se basan en sensores muy variados, principalmente cámaras y fuentes de imágenes de profundidad, aunque también en láseres y micrófonos. Los resultados parciales, suministrados por estos algoritmos corriendo en paralelo, luego son mezcladas usando técnicas probabilísticas para obtener una arquitectura modular, extensible y rápida. Esto resulta en un mapa local de los usuarios, obtenido por técnicas de fusión de datos. Gracias a esta arquitectura, las habilidades de detección, reconocimiento y seguimiento de los usuarios podrían ser integradas fácil y rápidamente dentro de un nuevo robot, reusando los módulos que corresponden a sus sensores y el rendimiento de su procesador. La arquitectura suministra todos los datos útiles sobre los usuarios en el alrededor del robot y se puede usar por aplicaciones de más alto nivel en nuestros robots sociales de manera que el robot adapte su funcionamiento a las personas que le rodean. Los robots sociales en los cuales la arquitectura se pudo importar con éxito son: un robot en forma de coche, una flor articulada, y un robot humanoide asistencial. Algunos módulos de la arquitectura son muy ligeros pero con una fiabilidad baja, mientras otros requieren más CPU pero son más fiables. Todas las configuraciones de los módulos son posibles y se ajustan a las diferentes configuraciones hardware que puede tener el robot. Los módulos son independientes entre ellos y altamente configurables, por lo que no hay que desarrollar código para una nueva configuración. El usuario sólo tiene que escribir un fichero launch de ROS. Este sencillo fichero de texto contiene todos los módulos que se quieren lanzar. Esta arquitectura se desarrolló teniendo en mente que fuese modular y rápida. Se basa en la arquitectura Robot Operating System (ROS), un estándar software de facto en la robótica. Todos los detectores de personas tienen una interfaz común llamada PeoplePoseList Publisher, mientras los algoritmos de reconocimiento siguen una interfaz llamada PeoplePoseList Matcher. La fusión de todos estos módulos se basa en técnicas de filtros de Kalman no lineares (Unscented Kalman Filters). Se han realizado pruebas exhaustivas de precisión y de velocidad de cada componente y de la arquitectura completa (realizadas sobre ambos bases de datos académicas además de sobre datos grabados en nuestro laboratorio), así como prototipos sencillos de aplicaciones finales. Así se comprueba la validez y el interés de la arquitectura a todos los niveles.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Fernando Torres Medina.- Secretario: María Dolores Blanco Rojas.- Vocal: Jorge Manuel Miranda Día