Real time architectures for the scale Invariant feature transform algorithm

Feature extraction in digital image processing is a very intensive task for a CPU. In order to achieve real time image throughputs, hardware parallelism must be exploited. The speed-up of the system is constrained by the degree of parallelism of the implementation and this one at the same time, by programmable device size and the power dissipation. In this work, issues related to the synthesis of the Scale-Invariant Feature Transform (SIFT) algorithm on a FPGA to obtain target processing rates faster than 50 frames per second for VGA images, are analyzed. In order to increase the speedup of the algorithm, the work includes the analysis of feasible simplifications of the algorithm for a tracking application and the results are synthesized on an FPGA.This work has been partially funded by Spanish government projects TEC2015-66878-C3-2-R (MINECO/FEDER, UE) and TEC2015- 66878-C3-3-R (MINECO/FEDER, UE)

Intestinal content detection in capsule endoscopy using robust features

This work covers two aspects. First, it generally compares and summarizes the similarities and differences of state of the art feature detector and descriptor and second it presents a novel approach of detecting intestinal content (in particular bubbles) in capsule endoscopy images. Feature detectors and descriptors providing invariance to change of perspective, scale, signal-noise-ratio and lighting conditions are important and interesting topics in current research and the number of possible applications seems to be numberless. After analysing a selection of in the literature presented approaches, this work investigates in their suitability for applications information extraction in capsule endoscopy images. Eventually, a very good performing detector of intestinal content in capsule endoscopy images is presented. A accurate detection of intestinal content is crucial for all kinds of machine learning approaches and other analysis on capsule endoscopy studies because they occlude the field of view of the capsule camera and therefore those frames need to be excluded from analysis. As a so called "byproduct" of this investigation a graphical user interface supported Feature Analysis Tool is presented to execute and compare the discussed feature detectors and descriptor on arbitrary images, with configurable parameters and visualized their output. As well the presented bubble classifier is part of this tool and if a ground truth is available (or can also be generated using this tool) a detailed visualization of the validation result will be performed.Nota: Aquest document conté originàriament altre material i/o programari només consultable a la Biblioteca de Ciència i Tecnologia

ON FPGA BASED ACCELERATION OF IMAGE PROCESSING IN MOBILE ROBOTICS

In visual navigation tasks, a lack of the computational resources is one of the main limitations of micro robotic platforms to be deployed in autonomous missions. It is because the most of nowadays techniques of visual navigation relies on a detection of salient points that is computationally very demanding. In this paper, an FPGA assisted acceleration of image processing is considered to overcome limitations of computational resources available on-board and to enable high processing speeds while it may lower the power consumption of the system. The paper reports on performance evaluation of the CPU–based and FPGA–based implementations of a visual teach-and-repeat navigation system based on detection and tracking of the FAST image salient points. The results indicate that even a computationally efficient FAST algorithm can benefit from a parallel (low–cost) FPGA–based implementation that has a competitive processing time but more importantly it is a more power efficient

Robotic Assembly Using 3D and 2D Computer Vision

The content of this thesis concerns the development and evaluation of a robotic cell used for automated assembly. The automated assembly is made possible by a combination of an eye-inhand 2D camera and a stationary 3D camera used to automatically detect objects. Computer vision, kinematics and programming is the main topics of the thesis. Possible approaches to object detection has been investigated and evaluated in terms of performance. The kinematic relation between the cameras in the robotic cell and robotic manipulator movements has been described. A functioning solution has been implemented in the robotic cell at the Department of Production and Quality Engineering laboratory. Theory with significant importance to the developed solution is presented. The methods used to achieve each part of the solution is anchored in theory and presented with the decisions and guidelines made throughout the project work in order to achieve the final solution. Each part of the system is presented with associated results. The combination of these results yields a solution which proves that the methods developed to achieve automated assembly works as intended. Limitations, challenges and future possibilities and improvements for the solution is then discussed. The results from the experiments presented in this thesis demonstrates the performance of the developed system. The system fulfills the specifications defined in the problem description and is functioning as intended considering the instrumentation used

Intelligent Behavioral Action Aiding for Improved Autonomous Image Navigation

In egomotion image navigation, errors are common especially when traversing areas with few landmarks. Since image navigation is often used as a passive navigation technique in Global Positioning System (GPS) denied environments; egomotion accuracy is important for precise navigation in these challenging environments. One of the causes of egomotion errors is inaccurate landmark distance measurements, e.g., sensor noise. This research determines a landmark location egomotion error model that quantifies the effects of landmark locations on egomotion value uncertainty and errors. The error model accounts for increases in landmark uncertainty due to landmark distance and image centrality. A robot then uses the error model to actively orient to position landmarks in image positions that give the least egomotion calculation uncertainty. Two actions aiding solutions are proposed: (1) qualitative non-evaluative aiding action, and (2) quantitative evaluative aiding action with landmark tracking. Simulation results show that both action aiding techniques reduce the position uncertainty compared to no action aiding. Physical testing results substantiate simulation results. Compared to no action aiding, non-evaluative action aiding reduced egomotion position errors by an average 31.5%, while evaluative action aiding reduced egomotion position errors by an average 72.5%. Physical testing also showed that evaluative action aiding enables egomotion to work reliably in areas with few features, achieving 76% egomotion position error reduction compared to no aiding

Embedded landmark acquisition system for visual slam using star identification based stereo correspondence descriptor

Orientador : Prof. Dr. Eduardo TodtDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 13/04/2015Inclui referênciasResumo: O uso de câmeras como sensores principais em Localização e Mapeamento Simultâneos (Simultaneous Localization and Mapping), o que é denominado SLAM Visual (Visual SLAM), tem crescido recentemente devido à queda nos preços das câmeras. Ao mesmo tempo em que imagens trazem informações mais ricas do que outros sensores típicos empregados em aplicações SLAM, como lasers e sonares, há um custo adicional de processamento significativo quando elas são utilizadas. A informação de profundidade adicional proveniente de configurações estéreo de câmeras às fazem mais interessantes para aplicações SLAM. Nesta abordagem em especial, grande parte do custo de processamento adicional vem da extração de pontos únicos ou pedaços em ambas as imagens em estéreo e da solução do problema de correspondência entre eles. Com posse dessa informação, a disparidade horizontal entre o par de imagens pode ser utilizada para recuperar a informação de profundidade. Esse trabalho explora a utilização de uma plataforma embarcada do tipo system-ona- chip (SoC) que integra um processador ARM multinúcleo com lógica FPGA como um módulo de processamento para visão estéreo. O detector de cantos Harris e Stephens (Harris & Stephens, 1988) é usado para encontrar pontos de interesse (Points of Interest, POIs) em imagens estéreo em um coprocessador soft sintetizado no FPGA para acelerar a extração de características e livrar o processador principal deste processo altamente paralelizável. As tarefas restantes tais como correção das imagens pela calibração de câmeras, encontrar um descritor único para as características detectadas e a correspondência entre os POIs no par de imagens estéreo são solucionadas em software executando no processador principal. A arquitetura proposta para o coprocessador permite que a tarefa de extração de cantos seja executada em aproximadamente metade do tempo necessário pelo processador principal sem auxílio algum. Após encontrar os POIs, para cada um dos pontos um descritor único é necessário para que seja possível encontrar o POI correspondente na outra imagem. Esse trabalho também propõe um descritor inovador que considera o relacionamento espacial bidimensional global entre os pontos detectados para descrevê-los individualmente. Para cada imagem, cada ponto da nuvem de pontos detectada pelo algoritmo de Harris e Stephens é descrito considerando-se apenas as posições relativas entre ele e seus vizinhos. Quando somente a posição é considerada, um padrão de céu estrelado noturno é formado pelos POIs. Com o padrão de POIs sendo considerado como estrelas, descritores já utilizados em problemas de identificação de estrelas podem ser reaplicados para identificar unicamente POIs. Um protótipo do descritor baseado do algoritmo de grade de Padgett e KreutzDelgado (Padgett & KreutzDelgado, 1997) é escrito e seus resultados comparados com os descritores normalmente utilizados para este propósito, mostrando que a informação espacial bidimensional pode ser utilizada por si só para resolver o problema de correspondência. O número de correspondências úteis é comparável ao atingido com o SIFT, o descritor com melhor desempenho neste quesito, enquanto a velocidade foi superior ao BRIEF, o descritor mais rápido utilizado na comparação, na plataforma ARM, com um speedup de 1,64 e 1,40 nas bases de dados dos testes. Palavras-chave: Harris; FPGA; SLAM; Hardware Reconfigurável; VHDL; Processamento de Imagem; Visão Estéreo; Computer Vision; Arquitetura Híbrida; Sistemas Embarcados; Pontos de Interesse; Keypoints; Correspondência; Correspondência Estéreo; Identificação de Estrelas; Descrição de Características; Percepção de Profundidade.Abstract: The use of cameras as the main sensors in Simultaneous Localization and Mapping, what is called Visual SLAM, has risen recently due to the fall in camera prices. While images bring richer information than other typical SLAM sensors, such as lasers and sonars, there is significant extra processing cost when they are used. The extra depth information available from stereo camera setups makes them preferable for SLAM applications. In this particular approach, great part of the added processing cost comes from extracting unique points or image patches in both stereo images and solving the correspondence problem between them. With this information, the horizontal disparity between the pair can be used to retrieve depth information. This work explores the use of an embedded system-on-a-chip (SoC) platform that integrates a multicore ARM processor with FPGA fabric as a stereo vision processing module. The Harris and Stephens corner detector (Harris & Stephens, 1988) is used to find Point of Interests (POIs) in stereo images in a hardware soft co-processor synthesized in the FPGA to speed up feature extraction and relieve this highly parallelizable process from the main embedded processor. Remaining tasks such as image correction from camera calibration, finding unique descriptor for the detected features and the correspondence between POIs in the stereo pair are solved in software running on the main processor. The proposed architecture for the co-processor enabled the corner extraction task to be performed in about half the time taken by the main processor without aid. After finding the POIs, for each point a unique descriptor is needed for finding the correspondent POI in the other image. This work also proposes an innovative descriptor that considers a global two-dimensional spatial relationship between the detected points to describe them individually. In each image, every point in the cloud of points detected by the Harris and Stephens algorithm is described by considering only the relative position between it and its neighbors. When position alone is considered, a starry night pattern is formed by the POIs. With the POI pattern being considered as stars, the descriptors already used in star identification problems can be reapplied to uniquely identify POIs. A prototype of the descriptor based on the Padgett and KreutzDelgado's grid algorithm (Padgett & KreutzDelgado, 1997) is written and the results compared with common descriptors used for this purpose, showing that two-dimensional spatial information alone can be used to solve the correspondence problem. The number of useful matches was comparable to what was obtained with SIFT, the best performing descriptor in this matter, while the speed was superior to BRIEF, the fastest descriptor used in the comparison, on the ARM platform, with a speedup of 1.64 and 1.40 on the tested datasets. Keywords: Harris; FPGA; SLAM; Reconfigurable Hardware; VHDL; Image Processing; Stereo Vision; Computer Vision; Hybrid Architecture; Embedded Systems; Point Of Interest; Keypoints; Matching; Stereo Correspondence; Star Identification; Feature Description; Depth Perception