Algorithms for VLSI stereo vision circuits applied to autonomous robots

Since the inception of Robotics, visual information has been incorporated in order to allow the robots to perform tasks that require an interaction with their environment, particularly when it is a changing environment. Depth perception is a most useful information for a mobile robot to navigate in its environment and interact with its surroundings. Among the different methods capable of measuring the distance to the objects in the scene, stereo vision is the most advantageous for a small, mobile robot with limited energy and computational power. Stereoscopy implies a low power consumption because it uses passive sensors and it does not require the robot to move. Furthermore, it is more robust, because it does not require a complex optic system with moving elements. On the other hand, stereo vision is computationally intensive. Objects in the scene have to be detected and matched across images. Biological sensory systems are based on simple computational elements that process information in parallel and communicate among them. Analog VLSI chips are an ideal substrate to mimic the massive parallelism and collective computation present in biological nervous systems. For mobile robotics they have the added advantage of low power consumption and high computational power, thus freeing the CPU for other tasks. This dissertation discusses two stereoscopic methods that are based on simple, parallel cal- culations requiring communication only among neighboring processing units (local communication). Algorithms with these properties are easy to implement in analog VLSI and they are also very convenient for digital systems. The first algorithm is phase-based. Disparity, i.e., the spatial shift between left and right images, is recovered as a phase shift in the spatial-frequency domain. Gábor functions are used to recover the frequency spectrum of the image because of their optimum joint spatial and spatial-frequency properties. The Gábor-based algorithm is discussed and tested on a Khepera miniature mobile robot. Two further approximations are introduced to ease the analog VLSI and digital implementations. The second stereoscopic algorithm is difference-based. Disparity is recovered by a simple calculation using the image differences and their spatial derivatives. The algorithm is simulated on a digital system and an analog VLSI implementation is proposed and discussed. The thesis concludes with the description of some tools used in this research project. A stereo vision system has been developed for the Webots mobile robotics simulator, to simplify the testing of different stereo algorithms. Similarly, two stereo vision turrets have been built for the Khepera robot

The Effect of Applying 2D Enhancement Algorithms on 3D Video Content

abstract: Enhancement algorithms are typically applied to video content to increase their appeal to viewers. Such algorithms are readily available in the literature and are already widely applied in, for example, commercially available TVs. On the contrary, not much research has been done on enhancing stereoscopic 3D video content. In this paper, we present research focused on the effect of applying enhancement algorithms used for 2D content on 3D side-by-side content. We evaluate both offline enhancement of video content based on proprietary enhancement algorithms and real-time enhancement in the TVs. This is done using stereoscopic TVs with active shutter glasses, viewed both in their 2D and 3D viewing mode. The results of this research show that 2D enhancement algorithms are a viable first approach to enhance 3D content. In addition to video quality degradation due to the loss of spatial resolution as a consequence of the 3D video format, brightness reduction inherent to polarized or shutter glasses similarly degrades video quality. We illustrate the benefit of providing brightness enhancement for stereoscopic displays.View the article as published at https://www.hindawi.com/journals/jece/2014/601392

A Future for Integrated Diagnostic Helping

International audienceMedical systems used for exploration or diagnostic helping impose high applicative constraints such as real time image acquisition and displaying. A large part of computing requirement of these systems is devoted to image processing. This chapter provides clues to transfer consumers computing architecture approaches to the benefit of medical applications. The goal is to obtain fully integrated devices from diagnostic helping to autonomous lab on chip while taking into account medical domain specific constraints.This expertise is structured as follows: the first part analyzes vision based medical applications in order to extract essentials processing blocks and to show the similarities between consumer’s and medical vision based applications. The second part is devoted to the determination of elementary operators which are mostly needed in both domains. Computing capacities that are required by these operators and applications are compared to the state-of-the-art architectures in order to define an efficient algorithm-architecture adequation. Finally this part demonstrates that it's possible to use highly constrained computing architectures designed for consumers handled devices in application to medical domain. This is based on the example of a high definition (HD) video processing architecture designed to be integrated into smart phone or highly embedded components. This expertise paves the way for the industrialisation of intergraded autonomous diagnostichelping devices, by showing the feasibility of such systems. Their future use would also free the medical staff from many logistical constraints due the deployment of today’s cumbersome systems

Neuromorphic stereo vision: A survey of bio-inspired sensors and algorithms

Any visual sensor, whether artificial or biological, maps the 3D-world on a 2D-representation. The missing dimension is depth and most species use stereo vision to recover it. Stereo vision implies multiple perspectives and matching, hence it obtains depth from a pair of images. Algorithms for stereo vision are also used prosperously in robotics. Although, biological systems seem to compute disparities effortless, artificial methods suffer from high energy demands and latency. The crucial part is the correspondence problem; finding the matching points of two images. The development of event-based cameras, inspired by the retina, enables the exploitation of an additional physical constraint—time. Due to their asynchronous course of operation, considering the precise occurrence of spikes, Spiking Neural Networks take advantage of this constraint. In this work, we investigate sensors and algorithms for event-based stereo vision leading to more biologically plausible robots. Hereby, we focus mainly on binocular stereo vision

Stereoscopic hand-detection system based on FPGA

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major de Telecomunicações). Faculdade de Engenharia. Universidade do Porto. 200

Towards Highly-Integrated Stereovideoscopy for \u3ci\u3ein vivo\u3c/i\u3e Surgical Robots

When compared to traditional surgery, laparoscopic procedures result in better patient outcomes: shorter recovery, reduced post-operative pain, and less trauma to incisioned tissue. Unfortunately, laparoscopic procedures require specialized training for surgeons, as these minimally-invasive procedures provide an operating environment that has limited dexterity and limited vision. Advanced surgical robotics platforms can make minimally-invasive techniques safer and easier for the surgeon to complete successfully. The most common type of surgical robotics platforms -- the laparoscopic robots -- accomplish this with multi-degree-of-freedom manipulators that are capable of a diversified set of movements when compared to traditional laparoscopic instruments. Also, these laparoscopic robots allow for advanced kinematic translation techniques that allow the surgeon to focus on the surgical site, while the robot calculates the best possible joint positions to complete any surgical motion. An important component of these systems is the endoscopic system used to transmit a live view of the surgical environment to the surgeon. Coupled with 3D high-definition endoscopic cameras, the entirety of the platform, in effect, eliminates the peculiarities associated with laparoscopic procedures, which allows less-skilled surgeons to complete minimally-invasive surgical procedures quickly and accurately. A much newer approach to performing minimally-invasive surgery is the idea of using in-vivo surgical robots -- small robots that are inserted directly into the patient through a single, small incision; once inside, an in-vivo robot can perform surgery at arbitrary positions, with a much wider range of motion. While laparoscopic robots can harness traditional endoscopic video solutions, these in-vivo robots require a fundamentally different video solution that is as flexible as possible and free of bulky cables or fiber optics. This requires a miniaturized videoscopy system that incorporates an image sensor with a transceiver; because of severe size constraints, this system should be deeply embedded into the robotics platform. Here, early results are presented from the integration of a miniature stereoscopic camera into an in-vivo surgical robotics platform. A 26mm X 24mm stereo camera was designed and manufactured. The proposed device features USB connectivity and 1280 X 720 resolution at 30 fps. Resolution testing indicates the device performs much better than similarly-priced analog cameras. Suitability of the platform for 3D computer vision tasks -- including stereo reconstruction -- is examined. The platform was also tested in a living porcine model at the University of Nebraska Medical Center. Results from this experiment suggest that while the platform performs well in controlled, static environments, further work is required to obtain usable results in true surgeries. Concluding, several ideas for improvement are presented, along with a discussion of core challenges associated with the platform. Adviser: Lance C. Pérez [Document = 28 Mb

A Scalable Multiple Description Scheme for 3D Video Coding Based on the Interlayer Prediction Structure

The most recent literature indicates multiple description coding (MDC) as a promising coding approach to handle the problem of video transmission over unreliable networks with different quality and bandwidth constraints. Furthermore, following recent commercial availability of autostereoscopic 3D displays that allow 3D visual data to be viewed without the use of special headgear or glasses, it is anticipated that the applications of 3D video will increase rapidly in the near future. Moving from the concept of spatial MDC, in this paper we introduce some efficient algorithms to obtain 3D substreams that also exploit some form of scalability. These algorithms are then applied to both coded stereo sequences and to depth image-based rendering (DIBR). In these algorithms, we first generate four 3D subsequences by subsampling, and then two of these subsequences are jointly used to form each of the two descriptions. For each description, one of the original subsequences is predicted from the other one via some scalable algorithms, focusing on the inter layer prediction scheme. The proposed algorithms can be implemented as pre- and postprocessing of the standard H.264/SVC coder that remains fully compatible with any standard coder. The experimental results presented show that these algorithms provide excellent results