A novel contextual memory algorithm for edge detection

Pattern Analysis and Applications - Edge detection plays an important role in many computer vision systems. In this paper, we propose a novel application agnostic algorithm for prediction of...     

A compact line and edge orientation detection sensor

A new line and edge orientation detection sensor using four position-sensitive devices is presented. This sensor is composed of four oblong position-sensing devices configured in a rectangular fashion. Each device detects the center of gravity of a light stripe on the device. By combining these positions obtained from four devices, a line orientation can be calculated. The simulation result demonstrates the translation invariance as well as the linewidth invariance if the sensor dimension is chosen such that the width/length ratio is smaller than or equal to 0.1. A pn diode formed by the n-well and the p-substrate shows the best position-sensing capability among the structures tested due to its relatively high well resistance. The position-sensing capability is investigated with different biases and under different levels of background illumination. Two position-sensing devices are used to measure the line orientation, which agrees well with the actual orientation.     

A protocol for performance evaluation of line detection algorithms

Accurate and efficient vectorization of line drawings is essential for any higher level processing in document analysis and recognition systems. In spite of the prevalence of vectorization and line detection methods, no standard for their performance evaluation protocol exists. We propose a protocol for evaluating both straight and circular line extraction to help compare, select, improve, and even design line detection algorithms to be incorporated into line drawing recognition and understanding systems. The protocol involves both positive and negative sets of indices, at pixel and vector levels. Time efficiency is also included in the protocol. The protocol may be extended to handle lines of any shape as well as other classes of graphic objects.     

线激光带边缘检测的快速稳健方法 *

线激光带边缘检测是机器人视觉系统处理的重要过程。由于激光带亮度不均匀,边缘不规则,造成传统边缘检测困难。为此,本文提出一种检测线激光带边缘的新方法,首先用Canny边缘检测算法初步筛选边缘点,再利用统计跳变回归分析判断边缘点和它的边缘方向,最后利用格式塔理论拟合边缘直线。实验结果证明,这种方法能有效地检测到线激光带的边缘,提高了检测的精度和速度。     

A soft multi-core architecture for edge detection and data analysis of microarray images

As configurable processing advances, elements from the traditional approaches of both hardware and software development can be combined by incorporating customized, application-specific computational resources into the processor’s architecture, especially in the case of field-programmable-gate-array-based systems with soft-processors, so as to enhance the performance of embedded applications. This paper explores the use of several different microarchitectural alternatives to increase the performance of edge detection algorithms, which are of fundamental importance for the analysis of DNA microarray images. Optimized application-specific hardware modules are combined with efficient parallelized software in an embedded soft-core-based multi-processor. It is demonstrated that the performance of one common edge detection algorithm, namely Sobel, can be boosted remarkably. By exploiting the architectural extensions offered by the soft-processor, in conjunction with the execution of carefully selected application-specific instruction-set extensions on a custom-made accelerating co-processor connected to the processor core, we introduce a new approach that makes this methodology noticeably more efficient across various applications from the same domain, which are often similar in structure. With flexibility to update the processing algorithms, an improvement reaching one order of magnitude over all-software solutions could be obtained. In support of this flexibility, an effective adaptation of this approach is demonstrated which performs real-time analysis of extracted microarray data; the proposed reconfigurable multi-core prototype has been exploited with minor changes to achieve almost 5× speedup.     

边缘增强图像互相关模板匹配的并行架构

基于归一化互相关测度(NCC)的模板匹配已经在图像处理领域得到了广泛的应用。对图像进行边缘检测然后进行模板匹配,可充分利用图像的空间相关性,锐化模板匹配结果的相关峰,提高匹配的准确度,可以获得更高的定位精度。为了有效提高定位精度,考虑到导弹制导系统的算法实时性、体积以及为适应战场不同任务阶段采用不同匹配策略的灵活性要求,基于FPGA,通过结合Sobel边缘检测,进一步改进了提出的图像归一化互相关模板匹配高速并行实现架构。对边缘检测前后图像模板匹配的仿真比较结果表明,边缘检测处理可有效锐化相关峰;基于Altera的FPGA芯片EP2S90和开发软件Quartus Ⅱ 8.0的并行实现架构功能与时序仿真及在实际目标识别系统中的应用表明,这种方案可有效地提高系统的运算速度和定位精度,FPGA实现本身也进一步缩小了系统的体积。     

A cascading scheme for speeding up multiple classifier systems

Pattern Analysis and Applications - The accuracy of multi-class classification problems is improving at a good pace. However, improving the accuracy often leads to slowing down the processing...     

A methodology for optimal voxel size computation in collision detection algorithms for virtual reality

Real-time Virtual Reality applications require accuracy but are also time dependent; therefore, in these environments, the time consumption is particularly important. For that reason, when facing the problem of Collision Detection for a Virtual Reality application, we firstly focus our attention on optimizing time performance for collisions among objects. Spatial Partitioning algorithms have been broadly used in Collision Detection. In particular, voxel-based methods are simple and quick, but finding the optimum voxel size is not trivial. We propose a methodology to easily determine the optimal voxel size for Collision Detection algorithms. Using an algorithm which represents volumetric objects with tetrahedra as an example, a performance cost function is defined in order to analytically bound the voxel size that gives the best computation times. This is made by inferring and estimating all the parameters involved. Thus, the cost function is delimited to depend only on geometric data. By doing so, it is possible to determine the optimal voxelization for any algorithm and scenario. Several solutions have been researched and compared. Experimental results with theoretical and real 3D models have validated the methodology. The reliability of our research has also been compared with traditional experimental solutions given by previous works.     

A low-cost and high-performance architecture for robust human detection using histogram of edge oriented gradients

Human detection on emerging intelligent transportation systems is a challenging task in hardware implementation. The histogram of oriented gradients (HOG) based human detection is the most successful algorithm due to its superior performance. Unfortunately, more intensive computations and poor performance at a multi-scale and low-contrast makes human detection more difficult and unreliable. To address the above-stated problems, an efficient histogram of edge oriented gradients (HEOG) based human detection is proposed for preserving the edge gradients at low-contrast and to support the multi-scale detection. The proposed algorithm uses approximation methods and adopts pipelined structure that utilizes low-cost and high-speed respectively. Experiments conducted on various challenging human datasets, shows that the outcome of the proposed method provides efficient detection. This algorithm has been synthesized on Xilinx Virtex-5 FPGA board and achieves better hardware utilization compared to other state-of-the-art approaches.     

A VLSI architecture for real-time edge linking

A real-time algorithm and its VLSI implementation for edge linking is presented. The linking process is based on the break points' directions and the weak level points. The proposed VLSI architecture is capable of outputting one pixel of the linked edge map per clock cycle with a latency of 11n+12 clock cycles, where n is the number of pixel columns in the image     

A method for speeding up beam-tracing simulation using thread-level parallelization

In recent years, the computational power of modern processors has been increasing mainly because of the increase in the number of processor cores. Computationally intensive applications can gain from this trend only if they employ parallelism, such as thread-level parallelization. Geometric simulations can employ thread-level parallelization because the main part of a geometric simulation can be divided into a subset of mutually independent tasks. This approach is especially interesting for acoustic beam tracing because it is an intensive computing task. This paper presents the parallelization of an existing beam-tracing simulation composed of three algorithms. Two of them are iterative algorithms, and they are parallelized with an already known technique. The most novel method is the parallelization of the third algorithm, the recursive octree generation. To check the performance of the multi-threaded parallelization, several tests are performed using three different computer platforms. On all of the platforms, the multi-threaded octree generation algorithm shows a significant speedup, which is linear when all of the threads are executed on the same processor.     

基于深度学习的图像边缘检测算法综述

边缘检测是将图像中的突变的重要信息提取出来的过程,是计算机视觉领域研究热点,也是图像分割、目标检测与识别等多种中高层视觉任务的基础。近几年来,针对边缘轮廓线过粗以及检测精度不高等问题,业内提出了谱聚类、多尺度融合、跨层融合等基于深度学习的边缘检测算法。为了使更多研究者了解边缘检测的研究现状,首先,介绍了传统边缘检测的实现理论及方法;然后,总结了近年来基于深度学习的主要边缘检测方法,并依据实现技术对这些方法进行了分类,对其涉及的关键技术进行分析,发现对多尺度多层次融合与损失函数的选择是重要的研究方向。通过评价指标对各类方法进行了比较,可知边缘检测算法在伯克利大学数据集（BSDS500）上的最优数据集规模（ODS）经过多年研究从0.598提高到了0.828,接近人类视觉水平。最后,展示了边缘检测算法研究的发展方向。     

基于深度学习的图像边缘检测算法综述

边缘检测是将图像中的突变的重要信息提取出来的过程，是计算机视觉领域研究热点，也是图像分割、目标检测与识别等多种中高层视觉任务的基础。近几年来，针对边缘轮廓线过粗以及检测精度不高等问题，业内提出了谱聚类、多尺度融合、跨层融合等基于深度学习的边缘检测算法。为了使更多研究者了解边缘检测的研究现状，首先，介绍了传统边缘检测的实现理论及方法；然后，总结了近年来基于深度学习的主要边缘检测方法，并依据实现技术对这些方法进行了分类，对其涉及的关键技术进行分析，发现对多尺度多层次融合与损失函数的选择是重要的研究方向。通过评价指标对各类方法进行了比较，可知边缘检测算法在伯克利大学数据集（BSDS500）上的最优数据集规模（ODS）经过多年研究从0.598提高到了0.828，接近人类视觉水平。最后，展示了边缘检测算法研究的发展方向。     

Replicated shared object model for parallel edge detection algorithm based on spiral architecture

Edge detection in computer vision and image processing is a process which detects one kind of significant feature in an image that appears as discontinuities in intensities. A parallel edge detection algorithm based on spiral architecture is designed in this paper using a replicated shared object model (RSOM). We use this approach to meet the requirements of replicated object management, update propagation, underlying communication and consistency maintenance among replicated objects. Multi-scale image theory plays an important role in this research.     

Two pyramid machine algorithms for edge detection in noisy binary images

This paper presents a pair of fast algorithms which detect edges in noisy binary images. The algorithms run on a parallel hierarchical cellular array processor called a pyramid machine. Example results are illustrated and the time required by each algorithm is discussed.     

面向DRAM和NVM异构混合内存架构的排序连接算法优化

随着计算机技术的高速发展,数据的应用规模也在不断扩大,各行各业对于数据存取速度的要求也越来越高.为了满足这种需求,内存数据库的思想被提出,然而传统的内存存储器DRAM由于密度和能耗的限制无法大规模集成和扩展.与此同时,非易失内存(NVM)以其性能高、密度高、能耗低的优势弥补了DRAM的不足.DRAM和NVM结合在一起组...     

A design methodology for synthesizing parallel algorithms and architectures

In this paper, a design methodology for synthesizing efficient parallel algorithms and VLSI architectures is presented. A design process starts with a problem definition specified in the parallel programming language Crystal and is followed by a series of program transformations in Crystal, each aiming at optimizing the target design for a specific purpose. To illustrate the design methodology, a set of design methods for deriving systolic algorithms and architectures is given and the use of these methods in the design of a dynamic programming solver is described. The design methodology, together with this particular set of design methods, can be viewed as a general theory of systolic designs (or multidimensional pipelines). The fact that Crystal is a general purpose language for parallel programming allows new design methods and synthesis techniques, properties and theorems about problems in specific application domains, and new insights into any given problem to be integrated readily within the existing design framework.     

A general methodology for direction-based irregular routing algorithms

This paper presents a general methodology for generating deadlock-free routing algorithms for irregular networks. Constructing a spanning tree on the given network, assigning directions to the network channels, creating deadlock-free zones, and specifying a logical sequence of the produced deadlock-free zones are the four fundamental steps that the proposed methodology takes to generate deadlock-free and connected routing algorithms. By applying the proposed methodology with two known labeling methods we have generated six irregular routing algorithms: three of them are novel routing algorithms and three of them (the Up/Down, Left/Right, and L-turn routing algorithms) have already been proposed in the literature. Extensive simulation experiments have been performed considering various network topologies, different network sizes (considering different network nodes and network channels), various message lengths, a variety of spanning tree roots, and a wide range of message (traffic) generation rates. Simulation results show that the six routing algorithms can be divided into three pairs. Routing members of each pair show similar behavior in terms of message latencies and saturation generation rates. However, it is worth noting that for a given topology the performance of the six routing algorithms may be totally different and it mainly depends on the network topology.     

A methodology for constructing fuzzy algorithms for learning vectorquantization

This paper presents a general methodology for the development of fuzzy algorithms for learning vector quantization (FALVQ). The design of specific FALVQ algorithms according to existing approaches reduces to the selection of the membership function assigned to the weight vectors of an LVQ competitive neural network, which represent the prototypes. The development of a broad variety of FALVQ algorithms can be accomplished by selecting the form of the interference function that determines the effect of the nonwinning prototypes on the attraction between the winning prototype and the input of the network. The proposed methodology provides the basis for extending the existing FALVQ 1, FALVQ 2, and FALVQ 3 families of algorithms. This paper also introduces two quantitative measures which establish a relationship between the formulation that led to FALVQ algorithms and the competition between the prototypes during the learning process. The proposed algorithms and competition measures are tested and evaluated using the IRIS data set. The significance of the proposed competition measure is illustrated using FALVQ algorithms to perform segmentation of magnetic resonance images of the brain.