Corner detection using the point‐to‐centroid distance technique

Zhang, Shizheng; Huangfu, Luwen; Zhang, Zhifeng; Huang, Sheng; Li, Pu; Wang, Heng

doi:10.1049/iet-ipr.2020.0164

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…The proposed classifier is capable of discriminating between Y-type edges, higher order edges, and simple squares. W. Zhang et al [6] Demonstrate how corner detection is used in a variety of statistical image analysis and learning applications, including recognizing items and image comparison. According to our research, the accuracy with which current corner recognition algorithms are able to differentiate between fringes and corners results in inaccurate corner findings.…”

Section: Literature Surveymentioning

confidence: 99%

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Edge detection using fast pixel based matching and contours mapping algorithms

Arulananth¹,

Chinnasamy

Babu³

et al. 2023

PLoS ONE

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Current methods of edge identification were constrained by issues like lighting changes, position disparity, colour changes, and gesture variability, among others. The aforementioned modifications have a significant impact, especially on scaled factors like temporal delay, gradient data, effectiveness in noise, translation, and qualifying edge outlines. It is obvious that an image’s borders hold the majority of the shape data. Reducing the amount of time it takes for image identification, increase gradient knowledge of the image, improving efficiency in high noise environments, and pinpointing the precise location of an image are some potential obstacles in recognizing edges. the boundaries of an image stronger and more apparent locate those borders in the image initially, sharpening it by removing any extraneous detail with the use of the proper filters, followed by enhancing the edge-containing areas. The processes involved in recognizing edges are filtering, boosting, recognizing, and localizing. Numerous approaches have been suggested for the previously outlined identification of edges procedures. Edge detection using Fast pixel-based matching and contours mappingmethods are used to overcome the aforementioned restrictions for better picture recognition. In this article, we are introducing the Fast Pixel based matching and contours mapping algorithms to compare the edges in reference and targeted frames using mask-propagation and non-local techniques. Our system resists significant item visual fluctuation as well as copes with obstructions because we incorporate input from both the first and prior frames Improvement in performance in proposed system is discussed in result section, evidences are tabulated and sketched. Mainly detection probabilities and detection time is remarkably reinforced Effective identification of such things were widely useful in fingerprint comparison, medical diagnostics, Smart Cities, production, Cyber Physical Systems, incorporating Artificial Intelligence, and license plate recognition are conceivable applications of this suggested work.

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Section: Literature Surveymentioning

confidence: 99%

“…In an image processing environment time delay is take an important credential. Desirable to achieve the above-mentioned quality parameter, here proposed some approaches is known as fast pixel based matching contours mapping algorithms [6]. These methods are different from the traditional edge detection techniques.…”

Section: Introductionmentioning

confidence: 99%

Edge detection using fast pixel based matching and contours mapping algorithms

Arulananth¹,

Chinnasamy

Babu³

et al. 2023

PLoS ONE

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“…Bok et al (Bok et al, 2023) proposed to represent the asynchronous spatiotemporal pulse signal by the relevant nodes of the probabilistic graph model, and verified the spatiotemporal representation ability of this method on visual tasks such as pulse signal noise reduction and optical flow estimation. Shen et al (Shen et al, 2023) modeled asynchronous spatiotemporal pulse signals for the first time in a graph neural network, and achieved a significant performance improvement over frequency cumulative images and hand-designed features in recognition tasks such as gestures (Zhang et al, 2020), alphanumeric, and moving objects (Luo et al, 2022;Lin X. et al, 2023).…”

Section: End-to-end Deep Networkmentioning

confidence: 99%

Artificial intelligence-based spatio-temporal vision sensors: applications and prospects

Li,

Sun

2023

Front. Mater.

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Introduction: Advances in machine vision and mobile electronics will be accelerated by the creation of sophisticated optoelectronic vision sensors that allow for sophisticated picture recognition of visual information and data pre-processing. Several new types of vision sensors have been devised in the last decade to solve these drawbacks, one of which is neuromorphic vision sensors, which have exciting qualities such as high temporal resolution, broad dynamic range, and low energy consumption. Neuromorphic sensors are inspired by the working principles of biological sensory neurons and would be useful in telemedicine, health surveillance, security monitoring, automatic driving, intelligent robots, and other applications of the Internet of Things.Methods: This paper provides a comprehensive review of various state-of-the-art AI vision sensors and frameworks.Results: The fundamental signal processing techniques deployed and the associated challenges were discussed.Discussion: Finally, the role of vision sensors in computer vision is also discussed.

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