1. Introduction and motivation2. Overview of Edge Detection algorithm3. Structure of Canny’s algorithm and its hardware implementationStructure of canny’s algorithmHardware implementation and Project purpose4. PLX subword parallel architecture: Overview5. Algorithm for hardware implementation6. Initial approach for hardware implementation7. Memory efficient hardware implementation8. Experimental resultsSimulation VerificationCalculation of performance speedup10. References:Optimized PLX codeSequential PLX codeECE 734 Final Project ReportAcceleration of motion estimation byedge detection algorithm using PLXsubword parallel ISAECE 734 Project Final ReportSubmitted by Sanghamitra Roy and Dongkeun Oh1ECE 734 Final Project Report2ECE 734 Final Project ReportTable of contents1 Introduction and motivation--------------------------- 32 Overview of edge detection algorithm--------------- 43 Structure of Canny’s algorithm and its hardware implementation------------------------------------------- 74 PLX subword parallel architecture: Overview------- 115 Algorithm for hardware implementation-------------- 116 Initial approach for hardware implementation-------- 127 Memory efficient hardware implementation --------- 158 Experimental results ------------------------------------- 179 Conclusion and future work ---------------------------- 1910 References------------------------------------------------- 2111 Appendix-------------------------------------------------- 223ECE 734 Final Project Report1. Introduction and motivation With rapid increase in the amount of multimedia information over the internet, there has been a remarkable rise in the demand of video-driven applications such as teleconference, videophone, and image-based multimedia services. Thus, the amount of video information to be transmitted in the network has increased, although the transmission rate in the network has not increased at the same rate. Hence, low bit-rate video coding techniques have become necessary to ease these bottlenecks. The low bit-rate video coding algorithms can be divided into two categories. The first category consists of block-based algorithms such as H.261, H.263, MPEG-1, and MPEG-2. These algorithms are easy to implement and maintain a relatively good image quality at low bit rates. However, at very low bit rates, less than 28.8kbps, blocking and mosquito artifacts become visible and the reconstructed image quality becomes degraded.This is the reason why this strategy is not employed in MPEG-4. The second category is object or segmentation based coding. Many techniques for object based coding at very low bit rates have already been proposed. Object based coding achieves high compression rate by subdividing an image into a number of arbitrarily shaped objects andthe background, and by performing motion estimation of objects. The greatest advantageof this method is the ability to perform accurate motion estimation of moving objects andutilize the available bit rate efficiently, by focusing on moving objects. Therefore, the quality of images produced by this method varies dramatically depending on the quality of object segmentation. The object oriented approach supports high quality resolution for each individual object. The accurate motion representation of the object is the key to good motion compensation for coding purposes as well as for image format conversion. However, most of the object-based coding approaches are computationally expensive. Object segmentation and recognition is also a primary step of computer vision. Object recognition is used in many areas such as traffic monitoring and robot vision. While a single image provides a snapshot of a scene, different frames of a video taken over time represent the dynamics in the scene, making it possible to capture the motion in the 4ECE 734 Final Project Reportsequence. The recognition process of a moving object is processed in real time, which requires high performance image processors. Edge detection or object segmentation is the crucial part of object recognition. Edge features, which are recognized as an important aspect of human visual perception, are commonly used in shape analysis. Decomposition of images into two regions of low-frequency blocks and blocks containing visually important features such as edges or linesrequires analysis of visual continuity of the image. The objective of this project is to improve the computational power of an image processor by accelerating the edge detection algorithm. We propose to enhance the performance of the edge detection algorithm using sub-word parallelism, and implement this algorithm using PLX subword parallel ISA. 2. Overview of Edge Detection algorithm An edge in an image corresponds to a discontinuity in the intensity surface of the underlying scenes – a jump in intensity from one pixel to the next. Edge detecting significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. There are many ways to perform edge detection. However, the majority of different methods may be grouped into two categories, gradient and Laplacian. The gradient method detects the edges by lookingfor the maximum and minimum in the first derivative of the image. The Laplacian method searches for zero crossings in the second derivative of the image to find edges. An edge has the one-dimensional shape of a ramp. Calculating the derivative of the image can highlight its location. Suppose we have the following signal, with an edge shown by the jump in intensity below in figure 1: Figure 1: Intensity profile of pixels in 1D line5ECE 734 Final Project ReportIf we take the gradient of this signal (which, in one dimension, is just the first derivative with respect to t) we get the following as shown in figure 2: Figure 2: 1st Derivative of pixel intensityThe derivative shows a maximum located at the center of the edge in the original signal. This method of locating an edge is characteristic of gradient filter family of edge detection filters. A pixel location is declared an edge location if the value of the gradient exceeds some threshold. As mentioned before, pixels in edges will have higher intensity values than those surrounding it. So once a threshold is set, we can compare the gradient value to the threshold value and detect an edge whenever the threshold is exceeded. Furthermore, when the first derivative is at a
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