A FPGA based implementation of Sobel edge detection

Nausheen, Nazma; Seal, Ayan; Khanna, Pritee; Halder, Sourav

doi:10.1016/j.micpro.2017.10.011

Cited by 96 publications

(35 citation statements)

References 3 publications

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“…Among them, the Sobel algorithm is not accurate in locating the edge points. The edge of the image is more than one pixel and tends to be multipixel wide (Li, 2018; Nausheen et al ., 2018). (i‐1), (i‐2) and (i‐3) indicate the processing results of the Sobel algorithm.…”

Section: Methodsmentioning

confidence: 99%

Investigation of cross‐sectional image analysis method to determine the blending ratio of polyester/cotton yarn

Xin

Deng

et al. 2020

Journal of Microscopy

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It has been considered as a great challenge to identify the blending ratio of polyester/cotton yarn in the field of textile industry. A new digital cross-sectional image processing method based on geometrical shape analysis is proposed to improve the measurement accuracy of polyester/cotton blend ratio. A selfdeveloped microscope image capturing system is established to digitalise the cross-sectional images of polyester/cotton blended yarn. One set of image preprocessing algorithm is developed to conduct greyscale inversion, median filtering denoising and binarisation. The specially designed edge detection algorithm is used to identify the continuous profile of fibres. Finally, the roundness value of the cross-sectional fibre is calculated based on the proposed roundness algorithm, it can be used to identify the polyester/cotton fibres and calculate the blending ratio of them. Our experimental results show that the new digital analysis method proposed in this paper is feasible for the measurement of polyester/cotton blended ratio; therefore, it has a good application prospect in the field of textile quality control, including the development of new equipment, methods and standards.

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Section: Methodsmentioning

confidence: 99%

Investigation of cross‐sectional image analysis method to determine the blending ratio of polyester/cotton yarn

Xin

Deng

et al. 2020

Journal of Microscopy

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“…• Mean: It provides average color value in the image, calculated using the formula 3: (3) Where, N is the total number of pixels in the image and Pi specifies its ith pixel…”

Section: Fig 2 Block Diagram Of Proposed Approach B Techniques Of mentioning

confidence: 99%

“…In general, hardware offers much greater speed than a software implementation, but one must consider the increase in development time inherent in creating a hardware design [1], [2]. The development of applications using different hardware platforms is very difficult, as each one of them requires the use of different development environments [3]. FPGAs represent reconfigurable computing technology, which is in some ways ideally more appropriate.…”

Section: Introductionmentioning

confidence: 99%

Withdrawn

2020

IJEAT

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Today, skin cancer is a serious health problem and the quest for a correct clinical diagnosis has been an ongoing concern for dermatologists and for many researchers an important field study. Field Programmable Gate Array (FPGA) technology has become a key part of the current movement towards innovation. It provides a viable target for applying image processing algorithms. It presents a viable target for the implementation of algorithms suited to image processing even under real-time requirements, because of their flexibility and great potential. FPGAs have shown very high performance in spite of their low operational frequency and high parallelism in applications. In this paper, an embedded Content-Based Image Retrieval (CBIR) implementation based on FPGA is proposed to manage the image data efficiently and fastly. We describe the implementation of CBIR in image processing applications using FPGA. The main aim behind this implementation is to reach minimum timing by the maximum utilization of resources. We are conducting a study on the advantages of this embedded approach and discussed its effectiveness for a set of benchmark images publically available dataset PH2. The FPGA-based implementation shows the feasibility of the proposed methodology. The functional implementation of all processes using the Verilog code of FPGA has been compiled on the ALTERA QUARTUS-II software tool.

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“…The Intel Compiler creates pipeline architecture according to the device code and aims to execute complex instructions in one clock cycle. Several studies have discussed the optimization process of the Sobel edge detection algorithm on FPGAs, GPUs, and multi-core systems (Abbasi & Abbasi, 2007;Chouchene et al, 2014;Dore, 2014;Halder et al, 2012;Hill et al, 2015b;Nausheen et al, 2018;Vanishree & Reddy, 2013;Yasri et al, 2008;You et al, 2017). One study used a GPU (NVIDIA GeForce 310) to get a significant speedup where a data-parallelism model utilizes a large number of available cores.…”

Section: Introductionmentioning

confidence: 99%

An OpenCL-based parallel acceleration of aSobel edge detection algorithm Using IntelFPGA technology

Niesler

Louw

2020

SACJ

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This paper examines the feasibility of using commercial out-of-the-box Reconfigurable Field Programmable Gate Array (FPGA) technology and the OpenCL framework to create efficient Sobel edge-detection implementation, which is considered a fundamental part in the field of image and video processing. The revised proposed approach was created at a high level of abstraction and executed on high commodity Intel FPGA platform. This was performed in a manner that was designed to allow the high-level compiler/synthesis tool to manipulate a task a parallelism model. The most promising FPGA and the naive implementations were compared to their single-core CPU software equivalents while manipulating local-memory, pipelining, loop unrolling, vectorization, internal channels mechanisms and memory coalescing to provide a much more effective hardware design. The run-time and the power consumption attributes were estimated for each implementation. The proposed FPGA based implementations were found to have significantly better runtime and power consumption with approximately up to 37 folds of improvement in the whole execution/transfer time, and up to 53 folds of improvement in energy consumption when compared to a specific single-core CPU based implementation.

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A FPGA based implementation of Sobel edge detection

Cited by 96 publications

References 3 publications

Investigation of cross‐sectional image analysis method to determine the blending ratio of polyester/cotton yarn

Investigation of cross‐sectional image analysis method to determine the blending ratio of polyester/cotton yarn

Withdrawn

An OpenCL-based parallel acceleration of aSobel edge detection algorithm Using IntelFPGA technology

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