Computational Analysis between Software and Hardware Implementation of Sobel Edge Detection Algorithm

Megalingam, Rajesh Kannan; Karath, Malavika; Prajitha, P; Pocklassery, Goutham

doi:10.1109/iccsp.2019.8697929

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…In order to prove accurately, the selected sub-matrix is multiplied by the PRIWITT matrix. If the value obtained is greater than zero, it is confirmed that there exists an edge over the pivot pixel [22][23] [24]. Figure 7, illustrates how an edge is detected from an image.…”

Section: Proposed Edge Detection Methodsmentioning

confidence: 99%

A versatile hybrid image fusion model to fuse multispectral image data

Ignatious

El-Sayed

Khan

et al. 2022

Preprint

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Higher-level autonomous driving necessitates the best possible execution of important moves under all conditions. Most of the accidents in recent years caused by the AVs launched by leading automobile manufacturers are due to inadequate decision-making caused by their poor perceivance of environmental information. In todays, technology-bound scenarios, versatile sensors are used by AVs to collect environmental information. Due to various technical and natural calamities, the environmental information acquired by the sensors may not be complete and clear, due to which the AVs may misinterpret the information in a different context leading to inadequate decision making causing fatal accidents. To overcome this drawback effective preprocessing of raw sensory data is a mandatory task. Pre-processing the sensory data involves two vital tasks namely data cleaning and data fusion. Since the raw sensory data is complex and exhibits multimodal characteristics, more emphasis is given to data preprocessing. Since more innovative models have been proposed for data cleaning, this study focuses on data fusion. In particular, we propose a generic data fusion engine, which classifies different formats of sensory data and fuses them accordingly to improve accuracy. This study proposes a generic framework to fuse the text, image, and audio data. In the first stage of this research, an innovative hybrid model is proposed to fuse multispectral image and video data. Simple and efficient models to extract the salient image features are also proposed. The proposed models are evaluated using various standard metrics with existing popular models. The proposed image fusion model performed better than the other models.

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Section: Proposed Edge Detection Methodsmentioning

confidence: 99%

A versatile hybrid image fusion model to fuse multispectral image data

Ignatious

El-Sayed

Khan

et al. 2022

Preprint

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“…As a result, it tends to lose edge information. Additionally, threshold selection relies entirely on subjective experience, lacking adaptability and robustness [12][13][14] .…”

Section: Improved Sobel Edge Detection Algorithmmentioning

confidence: 99%

P‐6.16: Design of Hardware Accelerator for Real‐Time Edge Detection System Based on FPGA

Yuan,

Hu,

et al. 2024

Symp Digest of Tech Papers

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Edge detection is an important research field in image processing and computer vision, especially in feature extraction. The detection efficiency of the traditional Sobel edge detection algorithm is reduced due to serial instructions, and its recognition ability for complex image textures is weak. The threshold needs to be set artificially. In this context, we present an improved Sobel algorithm, which enlarges the traditional operator convolution factor size to 5×5, extends the direction to 16 directions, and introduces the median as a local adaptive threshold for judgment. The improved algorithm is implemented using FPGA and MATLAB respectively, and processes image data with a resolution of 1280×720. The experimental results show that: When the clock frequency of the image processing module is 84 MHz, the FPGA processes a 1280×720 image in about 0.0109 s, while the MATLAB processes the same image in an average time of 0.915 s. The Sobel edge detection algorithm proposed in this work effectively denoises the image while also detecting the edge information of the image efficiently and accurately. The algorithm processing speed is improved by about 84 times, which can well meet the real‐time requirements of video image processing systems.

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“…In [6], a ZynqMP UltraScale MPSoC is used, focusing on the advantages of the xfOpenCV acceleration library to develop a faster Sobel Edge Detection method. In [7], the Vivado HLS v2015.3 tool is used and a comparison between software and hardware implementations of the Sobel Algorithm is presented. Furthermore, in [8] the authors implemented a frame-level Canny algorithm on Xilinx Virtex-5 FPGA platform reducing the resources and achieving better performance.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Vitis Framework for Accelerating Sobel Algorithm

Mousouliotis

Zogas

Christakos

et al. 2021

2021 10th Mediterranean Conference on Embedded Computing (MECO)

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Edge detection is one of the most common operations needed in the image processing domain. In this work, alternative implementations of the Sobel algorithm are tested on a ZCU102 Xilinx embedded platform, demonstrating how different optimization techniques can be conveniently configured in Xilinx Vitis environment. We exploit (a) Xilinx Runtime library (XRT) that allows the reprogramming of the reconfigurable logic at real time and (b) the various high-level attributes offered by the OpenCL API for efficient resource allocation in the state-of-the-art Xilinx Ultrascale Multi-Processor System-on-Chips (MPSoC). Specifically, different implementations of the Sobel algorithm (varying the data transfer models and data packing modes) are demonstrated and analyzed. Our experimental results shows that starting from a CPU implementation with 656 ms latency, the frame processing time is reduced to a range between 17 ms and 22 ms depending on the allocated resources, leading to a solution that is up to 38 times faster.

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Computational Analysis between Software and Hardware Implementation of Sobel Edge Detection Algorithm

Cited by 7 publications

References 9 publications

A versatile hybrid image fusion model to fuse multispectral image data

A versatile hybrid image fusion model to fuse multispectral image data

P‐6.16: Design of Hardware Accelerator for Real‐Time Edge Detection System Based on FPGA

Exploiting Vitis Framework for Accelerating Sobel Algorithm

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