5 research outputs found
Stereo Vision Matching using Characteristics Vectors
Stereo vision is a usual method to obtain depth information from images. The problems encountered when applying the majority of well established algorithms to provide this information are due to the high computational load required. This occurs in both the block matching and graphical cues (such as edges) matching. In this article we address this issue by performing an image analysis which considers each pixel only once, thus enhancing the efficiency of the image processing. Additionally, when matching is carried out over statistical descriptors of the image regions, commonly referred to as characteristic vectors, whose number of these vectors is, by definition, lower than the possible block matching possibilities, the algorithm achieves an improved level of performance. In this paper we present a new algorithm which has been specifically designed to solve the commonly observed problems which arise from other well know techniques. This algorithm was designed using a previous work carried out by the authors in this area to determine the descriptors extraction processes. The complete analysis has been carried out over gray scale images. The results obtained from both real and synthetic images are presented in terms of matching quality and time consumption and compared to other published results. Finally, a discussion is provided on additional features related to the matching process
Direct extraction of tau information for use in ego-motion
Avoidance collisions with obstacles is a critical function of any autonomous vehicle. This thesis considers the problem of utilising information about time to contact available in the ambient optic array. Motion-from-smear (W.G. Chen, Nandhakumar, & Martin, 1994; Geisler, 1999) is used to aid judgment of global tau (Kaiser & Mowafy, 1993; D. N. Lee, 1974, 1976). A robotic system employing motion-from smear was tested in a task requiring judgment of global tau and found to provide adequate accuracy (mean error= -0.52s) but poor precision (SD= 1.52s). Motion from-smear is also discussed with respect to its application to a novel formulation for composite tau and a use of motion parallax in stair descent
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Depth Estimation from a Single Holoscopic 3D Image and Image Up-sampling with Deep-learning
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London3D depth information is widely utilized in industries such as security, autonomous vehicles, robotics, 3D printing, AR/VR entertainment, cinematography and medical science. However, state-of-the-art imaging and 3D depth-sensing technologies are rather complicated or expensive and still lack scalability and interoperability. The research identified, entails the development of an innovative technique for reliable and efficient 3D depth estimation that deliver better accuracy. The proposed (1) multilayer Holoscopic 3D encoding technique reduces the computational cost of extracting viewpoint images from complex structured Holoscopic 3D data by 95%, by using labelled multilayer elemental images. It also addresses misplacement of elemental image pixels due to lens distortion error. The multilayer Holoscopic 3D encoding computing efficiency leads to the implementation of real-time 3D depth-dependent applications. Also, (2) an innovative approach of a deep learning-based single image super-resolution framework is developed and evaluated. It identified that learning-based image up-sampling techniques could be used regardless of inadequate 3D training data, as 2D training data can yield the same results.
(3) The research is extended further by implementation of an H3D depth disparity -based framework, where a Holoscopic content adaptation technique for extracting semi-segmented stereo viewpoint image is introduced, and the design of a smart 3D depth mapping technique is proposed. Particularly, it provides a somewhat accurate 3D depth estimation from H3D images in near real-time. Holoscopic 3D image has thousands of perspective elemental images from omnidirectional viewpoint images and (4) a novel 3D depth estimation technique is developed to estimates 3D depth information directly from a single Holoscopic 3D image without the loss of any angular information and the introduction of unwanted artefacts. The proposed 3D depth measurement techniques are computationally efficient and robust with high accuracy; these can be incorporated in real-time applications of autonomous vehicles, security and AR/VR for real-time interaction
Ayuda técnica para la autonomía en el desplazamiento
The project developed in this thesis involves the design, implementation and evaluation of a
new technical assistance aiming to ease the mobility of people with visual impairments. By
using processing and sounds synthesis, the users can hear the sonification protocol (through
bone conduction) informing them, after training, about the position and distance of the
various obstacles that may be on their way, avoiding eventual accidents.
In this project, surveys were conducted with experts in the field of rehabilitation, blindness
and techniques of image processing and sound, which defined the user requirements that
served as guideline for the design.
The thesis consists of three self-contained blocks: (i) image processing, where 4 processing
algorithms are proposed for stereo vision, (ii) sonification, which details the proposed sound
transformation of visual information, and (iii) a final central chapter on integrating the above
and sequentially evaluated in two versions or implementation modes (software and
hardware).
Both versions have been tested with both sighted and blind participants, obtaining qualitative
and quantitative results, which define future improvements to the project. ---------------------------------------------------------------------------------------------------------------------------------------------El proyecto desarrollado en la presente tesis doctoral consiste en el diseño, implementación y
evaluación de una nueva ayuda técnica orientada a facilitar la movilidad de personas con
discapacidad visual.
El sistema propuesto consiste en un procesador de estereovisión y un sintetizador de sonidos,
mediante los cuales, las usuarias y los usuarios pueden escuchar un código de sonidos
mediante transmisión ósea que les informa, previo entrenamiento, de la posición y distancia
de los distintos obstáculos que pueda haber en su camino, evitando accidentes.
En dicho proyecto, se han realizado encuestas a expertos en el campo de la rehabilitación, la
ceguera y en las técnicas y tecnologías de procesado de imagen y sonido, mediante las cuales
se definieron unos requisitos de usuario que sirvieron como guía de propuesta y diseño.
La tesis está compuesta de tres grandes bloques autocontenidos: (i) procesado de imagen,
donde se proponen 4 algoritmos de procesado de visión estéreo, (ii) sonificación, en el cual se
detalla la propuesta de transformación a sonido de la información visual, y (iii) un último
capítulo central sobre integración de todo lo anterior en dos versiones evaluadas
secuencialmente, una software y otra hardware.
Ambas versiones han sido evaluadas con usuarios tanto videntes como invidentes, obteniendo
resultados cualitativos y cuantitativos que permiten definir mejoras futuras sobre el proyecto
finalmente implementado