Journal of Real-Time Image Processing manuscript No. (will be inserted by the editor) Evaluation of real-time LBP computing in multiple architectures

Abstract Local Binary Pattern (LBP) is a texture operator that is used in several different computer vision applications requiring, in many cases, real-time operation in multiple computing platforms. The irruption of new video standards has increased the typical resolutions and frame rates, which need considerable computational performance. Since LBP is essentially a pixel operator that scales with image size, typical straightforward implementations are usually insufficient to meet these requirements. To identify the solutions that maximize the performance of the real-time LBP extraction, we compare a series different implementations in terms of computational performance and energy efficiency while analyzing the different optimizations that can be made to reach real-time performance on multiple platforms and their different available computing resources. Our contribution addresses the extensive survey of LBP implementations in different platforms that can be found in the literature. To provide for a more complete evaluation, we have implemented the LBP algorithms in several platforms such as Graphics Processing Units, mobile processors and a hybrid programming model image coprocessor. We have extended the evaluation of some of the solutions that can be found in previous work. In addition, we publish the source code of our implementations

Advances in Stereo Vision

Stereopsis is a vision process whose geometrical foundation has been known for a long time, ever since the experiments by Wheatstone, in the 19th century. Nevertheless, its inner workings in biological organisms, as well as its emulation by computer systems, have proven elusive, and stereo vision remains a very active and challenging area of research nowadays. In this volume we have attempted to present a limited but relevant sample of the work being carried out in stereo vision, covering significant aspects both from the applied and from the theoretical standpoints

Architecture and applications of the FingerMouse: a smart stereo camera for wearable computing HCI

In this paper we present a visual input HCI system for wearable computers, the FingerMouse. It is a fully integrated stereo camera and vision processing system, with a specifically designed ASIC performing stereo block matching at 5Mpixel/s (e.g. QVGA 320×240at 30fps) and a disparity range of 47, consuming 187mW (78mW in the ASIC). It is button-sized (43mm×18mm) and can be worn on the body, capturing the user's hand and processing in real-time its coordinates as well as a 1-bit image of the hand segmented from the background. Alternatively, the system serves as a smart depth camera, delivering foreground segmentation and tracking, depth maps and standard images, with a processing latency smaller than 1ms. This paper describes the FingerMouse functionality and its applications, and how the specific architecture outperforms other systems in size, latency and power consumptio

Optimization techniques for computationally expensive rendering algorithms

Realistic rendering in computer graphics simulates the interactions of light and surfaces. While many accurate models for surface reflection and lighting, including solid surfaces and participating media have been described; most of them rely on intensive computation. Common practices such as adding constraints and assumptions can increase performance. However, they may compromise the quality of the resulting images or the variety of phenomena that can be accurately represented. In this thesis, we will focus on rendering methods that require high amounts of computational resources. Our intention is to consider several conceptually different approaches capable of reducing these requirements with only limited implications in the quality of the results. The first part of this work will study rendering of time-­¿varying participating media. Examples of this type of matter are smoke, optically thick gases and any material that, unlike the vacuum, scatters and absorbs the light that travels through it. We will focus on a subset of algorithms that approximate realistic illumination using images of real world scenes. Starting from the traditional ray marching algorithm, we will suggest and implement different optimizations that will allow performing the computation at interactive frame rates. This thesis will also analyze two different aspects of the generation of anti-­¿aliased images. One targeted to the rendering of screen-­¿space anti-­¿aliased images and the reduction of the artifacts generated in rasterized lines and edges. We expect to describe an implementation that, working as a post process, it is efficient enough to be added to existing rendering pipelines with reduced performance impact. A third method will take advantage of the limitations of the human visual system (HVS) to reduce the resources required to render temporally antialiased images. While film and digital cameras naturally produce motion blur, rendering pipelines need to explicitly simulate it. This process is known to be one of the most important burdens for every rendering pipeline. Motivated by this, we plan to run a series of psychophysical experiments targeted at identifying groups of motion-­¿blurred images that are perceptually equivalent. A possible outcome is the proposal of criteria that may lead to reductions of the rendering budgets

Parallel computation in low-level vision

This thesis is concerned with problems of using computers to interpret scenes from television camera pictures. In particular, it tackles the problem of interpreting the picture in terms of lines and curves, rather like an artist's line drawing. This is very time consuming if done by a single, serial processor. However, if many processors were used simultaneously it could be done much more rapidly. In this thesis the task of line and curve extraction is expressed in terms of constraints, in a form that is susceptible to parallel computation. Iterative algorithms to perform this task have been designed and tested. They are proved to be convergent and to achieve the computation specified. Some previous work on the design of properly convergent, parallel algorithms has drawn on the mathematics of optimisation by relaxation. This thesis develops the use of these techniques for applying "continuity constraints" in line and curve description. First, the constraints are imposed "almost everywhere" on the grey-tone picture data, in two dimensions. Some "discontinuities" - places where the constraints are not satisfied - remain, and they form the lines and curves required for picture interpretation Secondly, a similar process is applied along each line or curve to segment it. Discontinuities in the angle of the tangent along the line or curve mark the positions of vertices. In each case the process is executed in parallel throughout the picture. It is shown that the specification of such a process as an optimisation problem is non-convex and this means that an optimal solution cannot necessarily be found in a reasonable time A method is developed for efficiently achieving a good sub-optimal solution. A parallel array processor is a large array of processor cells which can act simultaneously, throughout a picture. A software emulator of such a processor array was coded in C and a POP-2 based high level language, PARAPIC, to drive it was written and used to validate the parallel algorithms developed in the thesis It is argued that the scope, in a vision system, of parallel methods such as those exploited in this work is extensive. The implications for the design of hardware to perform low-level vision are discussed and it is suggested that a machine consisting of fewer, more powerful cells than in a parallel array processor would execute the parallel algorithms more efficiently

Frame-rate stereopsis using non-parametric transforms and programmable logic

A frame-rate stereo vision system, based on non-parametric matching metrics, is described. Traditional metrics, such as normalized cross-correlation, are expensive in terms of logic. Non-parametric measures require only simple, parallelizable, functions such as comparators, counters and exclusive-or, and are thus very well suited to implementation in reprogrammable logic