A novel approach to robot vision using a hexagonal grid and spiking neural networks

Many robots use range data to obtain an almost 3-dimensional description of their environment. Feature driven segmentation of range images has been primarily used for 3D object recognition, and hence the accuracy of the detected features is a prominent issue. Inspired by the structure and behaviour of the human visual system, we present an approach to feature extraction in range data using spiking neural networks and a biologically plausible hexagonal pixel arrangement. Standard digital images are converted into a hexagonal pixel representation and then processed using a spiking neural network with hexagonal shaped receptive fields; this approach is a step towards developing a robotic eye that closely mimics the human eye. The performance is compared with receptive fields implemented on standard rectangular images. Results illustrate that, using hexagonally shaped receptive fields, performance is improved over standard rectangular shaped receptive fields

TOWARDS A COMPUTATIONAL MODEL OF RETINAL STRUCTURE AND BEHAVIOR

Human vision is our most important sensory system, allowing us to perceive our surroundings. It is an extremely complex process that starts with light entering the eye and ends inside of the brain, with most of its mechanisms still to be explained. When we observe a scene, the optics of the eye focus an image on the retina, where light signals are processed and sent all the way to the visual cortex of the brain, enabling our visual sensation. The progress of retinal research, especially on the topography of photoreceptors, is often tied to the progress of retinal imaging systems. The latest adaptive optics techniques have been essential for the study of the photoreceptors and their spatial characteristics, leading to discoveries that challenge the existing theories on color sensation. The organization of the retina is associated with various perceptive phenomena, some of them are straightforward and strictly related to visual performance like visual acuity or contrast sensitivity, but some of them are more difficult to analyze and test and can be related to the submosaics of the three classes of cone photoreceptors, like how the huge interpersonal differences between the ratio of different cone classes result in negligible differences in color sensation, suggesting the presence of compensation mechanisms in some stage of the visual system. In this dissertation will be discussed and addressed issues regarding the spatial organization of the photoreceptors in the human retina. A computational model has been developed, organized into a modular pipeline of extensible methods each simulating a different stage of visual processing. It does so by creating a model of spatial distribution of cones inside of a retina, then applying descriptive statistics for each photoreceptor to contribute to the creation of a graphical representation, based on a behavioral model that determines the absorption of photoreceptors. These apparent color stimuli are reconstructed in a representation of the observed scene. The model allows the testing of different parameters regulating the photoreceptor's topography, in order to formulate hypothesis on the perceptual differences arising from variations in spatial organization

Time-efficient sparse analysis of histopathological Whole Slide Images

International audienceHistopathological examination is a powerful method for the prognosis of critical diseases. But, despite significant advances in high-speed and high-resolution scanning devices or in virtual exploration capabilities, the clinical analysis of Whole Slide Images (WSI) largely remains the work of human experts. We propose an innovative platform in which multi-scale computer vision algorithms perform fast analysis of a histopathological WSI. It relies on specific high and generic low resolution image analysis algorithms embedded in a multi-scale framework to rapidly identify the high power fields of interest used by the pathologist to assess a global grading. GPU technologies as well speed up the global time-efficiency of the system. In a sense, sparse coding and sampling is the keystone of our approach. In terms of validation, we are designing a computer-aided breast biopsy analysis application based on histopathology images and designed in collaboration with a pathology department. The current ground truth slides correspond to about 36,000 high magnification (40X) high power fields. The time processing to achieve automatic WSI analysis is on a par with the pathologist's performance (about ten minutes a WSI), which constitutes by itself a major contribution of the proposed methodology

Developing a new generation of neuro-prosthetic interfaces: structure-function correlates of viable retina-CNT biohybrids

PhD ThesisOne of the many challenges in the development of neural prosthetic devices is the choice of electrode material. Electrodes must be biocompatible, and at the same time, they must be able to sustain repetitive current injections in a highly corrosive physiological environment. We investigated the suitability of carbon nanotube (CNT) electrodes for retinal prosthetics by studying prolonged exposure to retinal tissue and repetitive electrical stimulation of retinal ganglion cells (RGCs). Experiments were performed on retinal wholemounts isolated from the Cone rod homeobox (CRX) knockout mouse, a model of Leber congenital amaurosis. Retinas were interfaced at the vitreo-retinal juncture with CNT assemblies and maintained in physiological conditions for up to three days to investigate any anatomical (immunohistochemistry and electron microscopy) and electrophysiological changes (multielectrode array stimulation and recordings; electrodes were made of CNTs or commercial titanium nitride). Anatomical characterisation of the inner retina, including RGCs, astrocytes and Müller cells as well as cellular matrix and inner retinal vasculature, provide strong evidence of a gradual remodelling of the retina to incorporate CNT assemblies, with very little indication of an immune response. Prolonged electrophysiological recordings, performed over the course of three days, demonstrate a gradual increase in signal amplitudes, lowering of stimulation thresholds and an increase in cellular recruitment for RGCs interfaced with CNT electrodes, but not with titanium nitride electrodes. These results provide for the first time electrophysiological, ultrastructural and cellular evidence of the time-dependent formation of strong and viable bio-hybrids between the RGC layer and CNT arrays in intact retinas. We conclude that CNTs are a promising material for inclusion in retinal prosthetic devices

An Optoelectronic Stimulator for Retinal Prosthesis

Retinal prostheses require the presence of viable population of cells in the inner retina. Evaluations of retina with Age-Related Macular Degeneration (AMD) and Retinitis Pigmentosa (RP) have shown a large number of cells remain in the inner retina compared with the outer retina. Therefore, vision loss caused by AMD and RP is potentially treatable with retinal prostheses. Photostimulation based retinal prostheses have shown many advantages compared with retinal implants. In contrary to electrode based stimulation, light does not require mechanical contact. Therefore, the system can be completely external and not does have the power and degradation problems of implanted devices. In addition, the stimulating point is flexible and does not require a prior decision on the stimulation location. Furthermore, a beam of light can be projected on tissue with both temporal and spatial precision. This thesis aims at fi nding a feasible solution to such a system. Firstly, a prototype of an optoelectronic stimulator was proposed and implemented by using the Xilinx Virtex-4 FPGA evaluation board. The platform was used to demonstrate the possibility of photostimulation of the photosensitized neurons. Meanwhile, with the aim of developing a portable retinal prosthesis, a system on chip (SoC) architecture was proposed and a wide tuning range sinusoidal voltage-controlled oscillator (VCO) which is the pivotal component of the system was designed. The VCO is based on a new designed Complementary Metal Oxide Semiconductor (CMOS) Operational Transconductance Ampli er (OTA) which achieves a good linearity over a wide tuning range. Both the OTA and the VCO were fabricated in the AMS 0.35 µm CMOS process. Finally a 9X9 CMOS image sensor with spiking pixels was designed. Each pixel acts as an independent oscillator whose frequency is controlled by the incident light intensity. The sensor was fabricated in the AMS 0.35 µm CMOS Opto Process. Experimental validation and measured results are provided

Egocentric Computer Vision and Machine Learning for Simulated Prosthetic Vision

Las prótesis visuales actuales son capaces de proporcionar percepción visual a personas con cierta ceguera. Sin pasar por la parte dañada del camino visual, la estimulación eléctrica en la retina o en el sistema nervioso provoca percepciones puntuales conocidas como “fosfenos”. Debido a limitaciones fisiológicas y tecnológicas, la información que reciben los pacientes tiene una resolución muy baja y un campo de visión y rango dinámico reducido afectando seriamente la capacidad de la persona para reconocer y navegar en entornos desconocidos. En este contexto, la inclusión de nuevas técnicas de visión por computador es un tema clave activo y abierto. En esta tesis nos centramos especialmente en el problema de desarrollar técnicas para potenciar la información visual que recibe el paciente implantado y proponemos diferentes sistemas de visión protésica simulada para la experimentación.Primero, hemos combinado la salida de dos redes neuronales convolucionales para detectar bordes informativos estructurales y siluetas de objetos. Demostramos cómo se pueden reconocer rápidamente diferentes escenas y objetos incluso en las condiciones restringidas de la visión protésica. Nuestro método es muy adecuado para la comprensión de escenas de interiores comparado con los métodos tradicionales de procesamiento de imágenes utilizados en prótesis visuales.Segundo, presentamos un nuevo sistema de realidad virtual para entornos de visión protésica simulada más realistas usando escenas panorámicas, lo que nos permite estudiar sistemáticamente el rendimiento de la búsqueda y reconocimiento de objetos. Las escenas panorámicas permiten que los sujetos se sientan inmersos en la escena al percibir la escena completa (360 grados).En la tercera contribución demostramos cómo un sistema de navegación de realidad aumentada para visión protésica ayuda al rendimiento de la navegación al reducir el tiempo y la distancia para alcanzar los objetivos, incluso reduciendo significativamente el número de colisiones de obstáculos. Mediante el uso de un algoritmo de planificación de ruta, el sistema encamina al sujeto a través de una ruta más corta y sin obstáculos. Este trabajo está actualmente bajo revisión.En la cuarta contribución, evaluamos la agudeza visual midiendo la influencia del campo de visión con respecto a la resolución espacial en prótesis visuales a través de una pantalla montada en la cabeza. Para ello, usamos la visión protésica simulada en un entorno de realidad virtual para simular la experiencia de la vida real al usar una prótesis de retina. Este trabajo está actualmente bajo revisión.Finalmente, proponemos un modelo de Spiking Neural Network (SNN) que se basa en mecanismos biológicamente plausibles y utiliza un esquema de aprendizaje no supervisado para obtener mejores algoritmos computacionales y mejorar el rendimiento de las prótesis visuales actuales. El modelo SNN propuesto puede hacer uso de la señal de muestreo descendente de la unidad de procesamiento de información de las prótesis retinianas sin pasar por el análisis de imágenes retinianas, proporcionando información útil a los ciegos. Esté trabajo está actualmente en preparación.<br /