Implementation of Sobel operator based image edge detection on FPGA

Ravivarma, G.; Gavaskar, K.; Malathi, D.; Asha, K.G.; Ashok, B.; Aarthi, S.

doi:10.1016/j.matpr.2020.10.825

Cited by 30 publications

(12 citation statements)

References 12 publications

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“…The speed of the image processing and the quality of the object classification are greatly improved when images are subjected to preprocessing steps. The image-to-image conversion is implemented using image filters [31][32][33][34], thresholding operations [29,30,[35][36][37], morphological operations [31,38], and artificial neural networks [39,40].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions

et al. 2022

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The sustainable functioning of the transport system requires solving the problems of identifying and classifying road users in order to predict the likelihood of accidents and prevent abnormal or emergency situations. The emergence of unmanned vehicles on urban highways significantly increases the risks of such events. To improve road safety, intelligent transport systems, embedded computer vision systems, video surveillance systems, and photo radar systems are used. The main problem is the recognition and classification of objects and critical events in difficult weather conditions. For example, water drops, snow, dust, and dirt on camera lenses make images less accurate in object identification, license plate recognition, vehicle trajectory detection, etc. Part of the image is overlapped, distorted, or blurred. The article proposes a way to improve the accuracy of object identification by using the Canny operator to exclude the damaged areas of the image from consideration by capturing the clear parts of objects and ignoring the blurry ones. Only those parts of the image where this operator has detected the boundaries of the objects are subjected to further processing. To classify images by the remaining whole parts, we propose using a combined approach that includes the histogram-oriented gradient (HOG) method, a bag-of-visual-words (BoVW), and a back propagation neural network (BPNN). For the binary classification of the images of the damaged objects, this method showed a significant advantage over the classical method of convolutional neural networks (CNNs) (79 and 65% accuracies, respectively). The article also presents the results of a multiclass classification of the recognition objects on the basis of the damaged images, with an accuracy spread of 71 to 86%.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions

et al. 2022

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“…It is based to detect the vertical and horizontal edges in the image. There are several platforms, programs and languages that can be used to implement the Sobel operator such as MATLAB Software [7], field programmable gate array (FPGA) [7], [8], and OpenCV based FPGA [9], [10]. In terms of FPGA, there are various FPGA studies have implemented the Sobel edge-detection using different algorithms.…”

Section: Introductionmentioning

confidence: 99%

Hardware implementation of Sobel edge detection system for blood cells images-based field programmable gate array

Younis

Jarjees

2022

IJEECS

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The microscopic-blood image has been used to diagnose various diseases according to the morphological specifications of red and white blood cells. <span lang="EN-US">However, the manual analysis and procedures are not accurate due to the human error. Therefore, several studies conducted to find new techniques to perform this analysis using computer algorithms. The complexity of these algorithms led to thinking in simpler ways or to the hardware solutions. On the other hand, edge detection is a mathematical procedure that play an essential role in the field of medical image processing. It is considered as one of the foundations' processes for other procedures, such as the segmentation and the classification of the image. The Sobel filter is one of the conventional methods that is used to perform the edge detection process. It is based on finding the local contrast for the level of intensity of the image. This paper presents a proposed and a new method for detecting the edges of cells in the microscopic blood images using Sobel filter and its hardware implementation on the field programmable gate array (FPGA) chip. Three different techniques are proposed: MATLAB, OpenCV standard code, and FPGA customize code which give the best visual results, minimum timing results than the others.</span>

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“…Edge detection is performed by identifying the discontinuities of the pixel intensities in an input image. Sobel [4,5], Prewitt [6,7], and Canny [8][9][10] are some common operators and filters which can effectively locate the boundaries of an object in the input image. To date, many researchers proposed various effective edge detection methods but with a tradeoff of high computation complexity which are not suitable for high-speed application.…”

Section: Introductionmentioning

confidence: 99%

A Simple, Precise, and High-Speed Die Edge Detection Framework for the Semiconductor Industry

Tan

Leow

Cheor

2021

Preprint

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This paper presents an automated high-speed die edge detection method that is meant to be implemented in non-destructive die-level defects inspection in the semiconductor industry. This paper specifically aims to achieve high accuracy (or yield) of greater than 99.95% with stable performance within a short computation time (i.e., less than 30 ms), provided the targeted unit-per-hour (UPH) is at 30,000. To demonstrate the applicability of the proposed method, the proposed method is validated using three production units (i.e., Production units A: 6000 units; Production unit B: 3500 units; Production unit C: 4000 units) and is benchmarked to two common edge detection methods that are widely implemented in the semiconductor industry nowadays (i.e., cross-correlation and normalized cross-correlation methods). The results obtained show that the proposed method is capable to perform die edge detection with promising accuracy and stable performance by achieving 100.0% yield in all three production units and outperformed the benchmarking methods. Also, the overall computation time (considering die edge detection and die rotation) of the proposed method is short, which is approximately 30 ms.

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Implementation of Sobel operator based image edge detection on FPGA

Cited by 30 publications

References 12 publications

Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions

Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions

Hardware implementation of Sobel edge detection system for blood cells images-based field programmable gate array

A Simple, Precise, and High-Speed Die Edge Detection Framework for the Semiconductor Industry

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