Intermediate-level feature extraction in novel parallel environments

Abstract. Parallel systems provide an approach to robust computing. The motivation for this work arises from using modern parallel environments in intermediate-level feature extraction. This study presents parallel algorithms for the Hough transform (HT) and the randomized Hough transform (RHT). The algorithms are analyzed in two parallel environments: multiprocessor computers and workstation networks. The results suggest that both environments are suitable for the parallelization of HT. Because scalability of the parallel RHT is weaker than with HT, only the multiprocessor environment is suitable. The limited scalability forces us to use adaptive techniques to obtain good results regardless of the number of processors. Despite the fact that the speedups with HT are greater than with RHT, in terms of total computation time, the new parallel RHT algorithm outperforms the parallel HT. Received: 8 December 2001 / Accepted: 5 June 2002 Correspondence to: V. Kyrki     

基于FPGA的实时Hough变换

为解决Hough变换实时性差的问题,提出一种基于现场可编程门阵列的实时Hough变换方法。采用分角度映射方法使Hough变换在空间上并行运算。设计一种基于流水线的计算结构,实现Hough空间参数计算与存储的时间并行性。通过双口RAM,在单个时钟内完成参数空间的一次投票操作,提出一种适合在FPGA中实现的快速寻找极值的二次搜索算法。与DSP进行效率对比实验,结果表明该方法效率较高,能满足实时性处理要求。     

On the magic of SLIDE

Subspace-based line detection (SLIDE) is a novel approach for straight line fitting that has recently been suggested by Aghajan and Kailath. It is based on an analogy made between a straight line in an image and a planar propagating wavefront impinging on an array of sensors. Efficient sensor array processing algorithms are used to detect the parameters of the line. SLIDE is computationally cheaper than the Hough transform, but it has not been clear whether or not this is a magical free bonus. In particular, it has not been known how the breakpoints of SLIDE relate to those of the Hough transform. We compare the failure modes and limitations of the two algorithms and demonstrate that SLIDE is significantly less robust than the Hough transform.     

A parallel algorithm for real-time object recognition

The aim of this paper is to present a new generalized Hough transform-based hardware algorithm in order to detect non-analytic objects in a two-dimensional (2D) image space. Our main idea consists to use, during voting process into the 5D parameter space, only meaningful set of edge points that belong to the boundary of the target object and that feature a similar geometric property. In this paper, a same line support property has been used. This has the merit to reduce the size of the 5D parameter space, while increasing the detection accuracy. The whole algorithm was implemented into a highly parallel architecture supported by a single PC board. It is composed of a mixture of digital signal processing and field programmable gate array technologies and uses the content addressable memory as a main processing unit. Complexity evaluation of the whole system indicated that a set of 46 different images of 256×256 pixels each can be classified in real-time (e.g. under frame rate).     

A Real-Time Image Segmentation on a Massively Parallel Architecture

The method described in this paper enables the two end points of a straight line to be obtained by a Modified Double Hough Transform (MDHT). It consists respectively of line detection, followed by segment extraction. The significance of this work is that the hardware implementation is based on the Content Addressable Memory (CAM) concept. Hence, during the first HT, voting is achieved for the every scan line of image, not every edge pixel. Therefore, all the steps which form the first HT: voting, thresholding and local maximum are achieved in a low constant time. The two end points of the line are extracted through the second HT. Here, a local neighbor parallel search is also achieved at the end of each scan line of the image not at every edge pixel. Therefore, the execution time is low since the neighboring range does not exceed a few lines. Experimental results are given to show the accuracy of our approach for use in high performance pattern recognition systems.     

基于图形处理器的实时直线段检测

提出了一种基于图形处理硬件的递进式直线段检测方法,该方法利用图形处理硬件实现了边缘提取、霍夫变换、端点检测等步骤。其中的GPU并行霍夫变换采用θ参数空间及图像空间双重划分的方法,并使用线程块内的共享存储器较好地解决了同一θ空间的各线程同步问题。实验表明,该方法既有递进式检测方法的准确性,又充分利用图形处理器强大的计算能力获得了较高的实时性。     

Real-time line detection through an improved Hough transform voting scheme

The Hough transform (HT) is a popular tool for line detection due to its robustness to noise and missing data. However, the computational cost associated to its voting scheme has prevented software implementations to achieve real-time performance, except for very small images. Many dedicated hardware designs have been proposed, but such architectures restrict the image sizes they can handle. We present an improved voting scheme for the HT that allows a software implementation to achieve real-time performance even on relatively large images. Our approach operates on clusters of approximately collinear pixels. For each cluster, votes are cast using an oriented elliptical-Gaussian kernel that models the uncertainty associated with the best-fitting line with respect to the corresponding cluster. The proposed approach not only significantly improves the performance of the voting scheme, but also produces a much cleaner voting map and makes the transform more robust to the detection of spurious lines.     

Fast prototyping of parallel-vision applications using functional skeletons

We present a design methodology for real-time vision applications aiming at significantly reducing the design-implement-validate cycle time on dedicated parallel platforms. This methodology is based upon the concept of algorithmic skeletons, i.e., higher order program constructs encapsulating recurring forms of parallel computations and hiding their low-level implementation details. Parallel programs are built by simply selecting and composing instances of skeletons chosen in a predefined basis. A complete parallel programming environment was built to support the presented methodology. It comprises a library of vision-specific skeletons and a chain of tools capable of turning an architecture-independent skeletal specification of an application into an optimized, deadlock-free distributive executive for a wide range of parallel platforms. This skeleton basis was defined after a careful analysis of a large corpus of existing parallel vision applications. The source program is a purely functional specification of the algorithm in which the structure of a parallel application is expressed only as combination of a limited number of skeletons. This specification is compiled down to a parametric process graph, which is subsequently mapped onto the actual physical topology using a third-party CAD software. It can also be executed on any sequential platform to check the correctness of the parallel algorithm. The applicability of the proposed methodology and associated tools has been demonstrated by parallelizing several realistic real-time vision applications both on a multi-processor platform and a network of workstations. It is here illustrated with a complete road-tracking algorithm based upon white-line detection. This experiment showed a dramatic reduction in development times (hence the term fast prototyping), while keeping performances on par with those obtained with the handcrafted parallel version. Received: 22 July 1999 / Accepted: 9 November 2000     

A fast digital Radon transform—An efficient means for evaluating the Hough transform

A fast digital Radon transform based on recursively defined digital straight lines is described, which has the sequential complexity of N² log N additions for an N × N image. This transform can be used to evaluate the Hough transform to detect straight lines in a digital image. Whilst a parallel implementation of the Hough transform algorithm is difficult because of global memory access requirements, the fast digital Radon transform is vectorizable and therefore well suited for parallel computation. The structure of the fast algorithm is shown to be quite similar to the FFT algorithm for decimation in frequency. It is demonstrated that even for sequential computation the fast Radon transform is an attractive alternative to the classical Hough transform algorithm.     

Proposal of a method to extract straight line and circle using one-dimensional histogram

A new method for fast extraction of straight line and circle is proposed in this study. The method utilizes the Polytope method which is one of minimization algorithms. For the extraction of figures, one-dimensional histogram is used. Basically, main algorithm of the extraction of straight line is the same as those of circle and ellipse. Only the definition of histogram and the evaluation function are changed according as figures. By the comparison with Hough transform, it is understood that the using of memory space is very small and processing time is very short. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Straight lines have to be straight

Most algorithms in 3D computer vision rely on the pinhole camera model because of its simplicity, whereas video optics, especially low-cost wide-angle or fish-eye lenses, generate a lot of non-linear distortion which can be critical. To find the distortion parameters of a camera, we use the following fundamental property: a camera follows the pinhole model if and only if the projection of every line in space onto the camera is a line. Consequently, if we find the transformation on the video image so that every line in space is viewed in the transformed image as a line, then we know how to remove the distortion from the image. The algorithm consists of first doing edge extraction on a possibly distorted video sequence, then doing polygonal approximation with a large tolerance on these edges to extract possible lines from the sequence, and then finding the parameters of our distortion model that best transform these edges to segments. Results are presented on real video images, compared with distortion calibration obtained by a full camera calibration method which uses a calibration grid. Received: 27 December 1999 / Accepted: 8 November 2000     

AGV视觉导向Hough变换算法的FPGA硬件实现

目前,自动导引小车的应用越来越普遍,视觉导向作为一种灵活的自动导引小车的导向方式有广阔的应用前景。为了实现地面标示线的自动识别跟踪,提出了一种基于FPGA硬件实现Hough变换算法的方法,应用在自动导引小车上可以满足实时的图像处理与路径跟踪需求。     

用于线段特征提取的改进Hough变换

线段是符号的一个具有旋转、平移和尺度不变的稳定性特征,正确提取符号的线段特征对于提高符号识别系统的识别率有很重要的意义。针对已有的基于Hough变换的线段提取算法的缺点,该文提出了一种用于线段特征提取的改进Hough变换算法。通过采用“多对一”映射;将Hough变换的投票过程和线段参数的检测过程融为一体;动态管理算法所需的临时存储空间等手段,使该算法具有较好的计算复杂度和空间复杂度。针对数字图像的量化特点,精心设计了用于检测在直线上点的条形区域,从而大大地降低了噪声对线段参数检测的影响,使该算法具有较好的检测性能和鲁棒性。实验表明,该文算法能正确提取出线段的端点坐标及其长度。     

Benchmarking Hough Transform Architectures for Real-Time

This paper reviews the Hough transform hardware implementations, with a specific analysis of the architectures that explicitly address the “real-time” issue. The work presents an introduction for a critical assessment of the notion of “real-time”, especially for what concerns modern multimedia applications. The main contribution of this work is the proposal of a new metric for measuring the performance of Hough transform architectures against a given definition of “real-time”. The basic idea is that there is no single set of constraints that define “real-time” for every application domain, and that even the simplest case of Hough transform for line detection, must be properly characterized within a specific application domain. The architectures are classified and evaluated, after a proper characterization of the Hough transform complexity, in terms of dimensions of parameter space and time complexity.     

Edge-preserving texture filtering for real-time rendering

Texture filtering is essential in enhancing the visual quality of real-time rendering. Conventional schemes do not consider the characteristics of texture content, thus the sharpness of edges in texture images cannot be retained. This paper proposes a novel texture-filtering algorithm, which consists of edge-preserving interpolation and edge-preserving MIP-map prefiltering. The memory bandwidth requirement is kept the same as in conventional schemes by dynamically adjusting the interpolation kernel. Hardware implementation is also provided to show the real-time processing capability. Published online: 28 January 2003     

用于相贯线焊缝检测机器人的图像处理算法

针对管道相贯线焊缝难以检测的问题,提出了一种用于管道相贯线焊缝自动检测机器人轨道识别和定位的图像处理算法.在颜色空间转换和彩色边缘检测基础上,引入图像金字塔方法的Hough变换直线检测算法,快速识别磁性色码条并实现机器人自身姿态和运动轨迹的实时控制,将多阈值处理引入区域生长算法分割人工标识点,对人工标识点进行计数从而实现定位.实验结果验证了提出的图像处理算法的有效性.     

基于FPGA的数字水印提取系统的设计

针对传统软件实现数字水印系统难以满足实时性的问题,提出了基于现场可编程门阵列(FPGA)的硬件实现方案。通过对数字水印提取系统进行深入研究,设计了易于FPGA实现的数字水印算法和适用于5/3小波变换的算法结构,并进一步设计出与算法相对应的新的水印提取结构。该结构体现了流水线和高度并行性,计算效率高,具有体积小、功耗低、实时性强等特点。经仿真验证证实了所设计系统的正确性,算法结构具有广泛的适用性。     

利用Hough变换的匹配对提纯

目的 针对传统的匹配对提纯算法存在容错性差、效率低等问题,提出了一种利用Hough变换的匹配对提纯算法。方法 假设正确的匹配对一致性地服从一个变换模型。首先,为两幅图像的变换关系选择一个合适的数学模型,利用Hough变换确定模型方程参数的解。然后检验原始匹配对,保留符合模型方程的匹配对,从而达到提纯的目的。结果 与传统的RANSAC(random sample consensus)等算法相比,本文算法具有更高的容错率、召回率与更优的运行效率,且是稳定的。实验结果表明,在误配率低于85%时算法能完全剔除误匹配,且误配率高达95%时依然有50%的可能性成功剔除误匹配。结论 把Hough变换引入到匹配对提纯的应用中,该算法在所选模型准确或近似准确的情况下能鲁棒地提纯匹配对。由于模型方程参数个数决定参数空间维数,维数高导致投票及搜索最大值点的时间、空间复杂度大,因此该算法适用于模型参数较少(不大于4)的情况。     

Accurate and Real-time Image Processing on a New PC-compatible Board

The aim of this paper is to present a parallel hardware architecture well dedicated for complex two-dimensional (2D) and three-dimensional (3D) video processing. It is composed of a mixture of Digital Signal Processing (DSP) and Field Programmable Gate Array (FPGA) technologies and uses the Content Addressable Memory (CAM) as a main processing unit. Some applications ranging from accurate 2D segment extraction to 3D segment reconstruction have been successfully implemented. Their common characteristic is to use the Hough Transform (HT) as the basic concept, augmented with some modifications in order to make them well adapted to the hardware, and in addition to resolve some of their classical problems. This may include quantization errors, huge memory-processing requirements of the parameter space, and occlusion. Experimental results indicate that a small amount of hardware, mounted on a PC board, can deliver real-time performance and high accuracy. This is an improvement over previous systems, where execution time of the second-order using a greater amount of hardware has been proposed.