Analogue CMOS prototype vision chip with prewitt edge processing

Garcia-Lamont, Jair

doi:10.1007/s10470-011-9694-6

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…A single-instruction multiple-data architecture enabled programmable mask-based image processing computation on each pixel. Garcia-Lamont [ 45 ] described an analog SIS to compute the Prewitt edge detection algorithm using a simple arithmetic analog circuit integrated with each photodetector in the imaging array. Suárez et al.…”

Section: Related Workmentioning

confidence: 99%

A CMOS Image Readout Circuit with On-Chip Defective Pixel Detection and Correction

López-Portilla

Valenzuela

Zarkesh-Ha

et al. 2023

Sensors

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Images produced by CMOS sensors may contain defective pixels due to noise, manufacturing errors, or device malfunction, which must be detected and corrected at early processing stages in order to produce images that are useful to human users and image-processing or machine-vision algorithms. This paper proposes a defective pixel detection and correction algorithm and its implementation using CMOS analog circuits, which are integrated with the image sensor at the pixel and column levels. During photocurrent integration, the circuit detects defective values in parallel at each pixel using simple arithmetic operations within a neighborhood. At the image-column level, the circuit replaces the defective pixels with the median value of their neighborhood. To validate our approach, we designed a 128×128-pixel imager in a 0.35μm CMOS process, which integrates our defective-pixel detection/correction circuits and processes images at 694 frames per second, according to post-layout simulations. Operating at that frame rate, our proposed algorithm and its CMOS implementation produce better results than current state-of-the-art algorithms: it achieves a Peak Signal to Noise Ratio (PSNR) and Image Enhancement Factor (IEF) of 45 dB and 198.4, respectively, in images with 0.5% random defective pixels, and a PSNR of 44.4 dB and IEF of 194.2, respectively, in images with 1.0% random defective pixels.

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Section: Related Workmentioning

confidence: 99%

A CMOS Image Readout Circuit with On-Chip Defective Pixel Detection and Correction

López-Portilla

Valenzuela

Zarkesh-Ha

et al. 2023

Sensors

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“…In recent years, some researchers gradually used digital image processing and machine learning methods to analyze and identify fibers, the key of which is the processing of fiber images and the extraction of texture features. For the processing of fiber images, the traditional method is mainly using the Sobel [4], Laplacian [5], Prewitt [6], Log [7] or other operators to directly detect and extract the edge or texture of the original images [8]. Moreover, different algorithms also can be combined to get the hopeful results.…”

Section: Introductionmentioning

confidence: 99%

Identification of Extremely Similar Animal Fibers Based on Matched Filter and HOG-SVM

et al. 2019

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Identification of extremely similar animal fibers has always been one of the challenging research topics in the textile field. In this paper, an improved image-based recognition method was proposed for the identification of extremely similar animal fibers including cashmere and wool. A total of 100 groups of wool and 100 groups of cashmere fiber images were collected using the self-developed microscope image analysis system. The contrast of original fiber micro images usually was not high enough and some impurities always existed during the slicing process, so the matched filters were first applied for these images to extract the enhanced fiber binary texture, which only needs to set reasonable segment length and the threshold for different fiber images to eliminate impurities and background. Then, the high-dimensional texture features of the original color images, gray images, and the images processed by matched filter were extracted by calculating and analyzing the histogram of oriented gradient (HOG). The 200 sets of original color images, gray images, and processed images were divided into the training set and testing set according to different proportions, and the recognition expert system based on the support vector machine (SVM) could be trained and validated accordingly. The experimental results show that the recognition accuracy of the fiber images processed by matched filter was obviously improved compared with that of the other two data sets, and the recognition rate reaches the highest with 92.5%. It also proves that the proposed algorithm in this paper can classify and identify extremely similar wool and cashmere fibers more quickly and effectively compared with other texture feature extraction and identification algorithms. INDEX TERMS Extremely similar fibers, image identification, matched filter, HOG, SVM.

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Edge Detection Based on Primary Visual Pathway

Zhang

et al. 2017

Lecture Notes in Computer Science

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Analogue CMOS prototype vision chip with prewitt edge processing

Cited by 5 publications

References 10 publications

A CMOS Image Readout Circuit with On-Chip Defective Pixel Detection and Correction

A CMOS Image Readout Circuit with On-Chip Defective Pixel Detection and Correction

Identification of Extremely Similar Animal Fibers Based on Matched Filter and HOG-SVM

Edge Detection Based on Primary Visual Pathway

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