A new fast corner detection method based on template matching

Zhang, Yanling; Xixi, Cheng; Lu, Qiao; Cui, Pengfei

doi:10.1088/1757-899x/563/5/052093

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…Traditional template matching algorithms may not be effectively suited for industrial production. Therefore, additional detection and verification of template images and the images to be tested are necessary [25][26][27]. The flow of the multi-level detection and verification algorithm based on the Otsu method-template-four quadrants, as designed in this paper, is depicted in Figure 2.…”

Section: Three-stage Detection Verification Based On Otsu Method-temp...mentioning

confidence: 99%

Enhancing Vehicle-Machine Interface Icon Detection in Automated Testing Through a Three-Stage Machine Vision Verification Algorithm

Ren,

Xu,

Fei

et al. 2024

IEEE Access

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In the context of automated testing in the automotive industry, the accurate and effective detection of target icons on vehicle-machine interfaces is crucial for ensuring the efficiency and precision of automated testing processes. This paper addresses the challenge of effectively detecting vehicle-machine interface icons by proposing a three-stage verification detection algorithm based on machine vision. This paper employs an enhanced template matching technique to achieve precise positioning and initial verification of car and machine icons. Subsequently, a two-stage verification process is implemented, which focuses on detecting local changes using pixel blocks. The three-stage verification is accomplished through a three-channel separation detection of characteristic pixel points based on RGB values. Additionally, this paper utilizes the collaborative capabilities of LabVIEW and OpenCV to overcome integration challenges. This joint programming approach bridges the gap between LabVIEW and OpenCV. Experimental results unequivocally underscore the paramount significance of the three-stage verification detection algorithm, rooted in the realm of machine vision. The algorithm's novel three-stage approach, intricately designed to combat background interference, not only elevates the precision of icon detection but also introduces an ingenious mechanism for classifying detection results. In this regard, it successfully overcomes the limitations that have historically hindered traditional machine vision algorithms, ultimately demonstrating its paramount importance in the field of computer vision.INDEX TERMS Vehicle-machine interface, machine vision, three-stage verification, pixel block.

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Section: Three-stage Detection Verification Based On Otsu Method-temp...mentioning

confidence: 99%

Enhancing Vehicle-Machine Interface Icon Detection in Automated Testing Through a Three-Stage Machine Vision Verification Algorithm

Ren,

Xu,

Fei

et al. 2024

IEEE Access

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“…The ORB (Oriented FAST and Rotated BRIEF) algorithm is divided into two parts: feature point extraction and description. Feature point extraction is based on the improved oFAST [31] detection operator of FAST to detect feature points. In contrast, feature point description is calculated 5 International Journal of Aerospace Engineering using the extremely fast rBRIEF [32] binary descriptor algorithm to improve the speed of image feature extraction.…”

Section: Orb Algorithmmentioning

confidence: 99%

Fast Matching Method of UAV Aerial Photography Enhanced Low Illumination Image

Liu

et al. 2022

International Journal of Aerospace Engineering

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Aiming at the problems of insufficient image contrast in three-dimensional reconstruction of UAV in low illumination environment and the unstable iteration times of the RANSAC algorithm in the feature matching process, real-time matching method of UAV aerial image is proposed. First, a new image enhancement algorithm is applied to the image to enhance its quality and visibility. Second, the enhanced fast algorithm in ORB extracts the feature points from the preprocessed image, and cross-matching performs rough matching. Finally, the PROSAC algorithm solves the homography matrix by selecting the highest quality interior points from the extracted feature points. To improve the matching accuracy, some exterior points that do not conform to the geometric characteristics of the image are removed based on the homography matrix and the set mismatch threshold. The results show that the improved ORB algorithm is applied to the low illumination environment of UAV aerial photography, the image matching accuracy in 3D reconstruction is improved, and the correct matching rate tends to 97.24~99.39%. The relevant research findings and conclusions provide a fast and effective method for UAV image matching in different low illumination environments.

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“…Natural Future Tracking Natural Feature Tracking (NFT) adalah teknik pendeteksi gambar yang dapat melacak dan mendeteksi fitur khusus yang secara alami terdapat pada gambar yang ingin dideteksi. Fitur tersebut bisa saja garis, sudut, gumpalan dan lain -lain[8].Ada beberapa perbedaan pendekatan terhadap NFT yaitu seperti SURF, SIFT dan Ferns. Perbedaannya terdapat pada fitur gambar yang digabungkan diantara gambar pada video dan representasi terhadap sebuah objek atau lingkungan yang akan dilacak.…”

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Algoritma FCD dan NFT Pada Sistem Pencernaan Berbasis AR Menggunakan Single Marker

Mohajerani¹

2021

JATISI

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Manusia diciptakan dengan berbagai sistem yang memiliki kemampuan yang berbeda-beda, mulai dari sistem pernapasan, sistem pencernaan, sistem peredaran darah dan juga sistem ekskresi. Dari beban kerangka ini ada berbagai organ yang juga memiliki tugas yang berbeda. Namun, tidak sedikit orang yang tidak tahu apa-apa tentang kerangka dan organ apa yang ada di dalam tubuh, misalnya apa saja organ dan fungsi yang terkait dengan sistem pencernaan. Dalam penelitian ini, akan membidik pada organ-organ dalam kerangka terkait pencernaan manusia. Agar lebih menarik untuk dipelajari, pencipta akan memperkenalkan organ-organ ini sebagai aplikasi augmented reality. Eksplorasi ini diarahkan untuk memberikan pemahaman tentang organ dan fungsi apa yang ada dalam kerangka terkait pencernaan manusia. Dalam penelitian ini menggunakan strategi Single Marker, dengan desain aplikasi sistem, yaitu model waterfall yang terdiri dari analisis, desain, implementas, pengujian dan pemeliharaan dan menggunakan Fats Corner Detection (FCD) dan Natural Future Trackingx (NFT). Hasil pengujian dengan tiga device, jarak terdekat untuk menampilkan objek adalah ±12 cm sedangkan jarak terjauh adalah 80-100 cm. Terlebih lagi, hasil tes yang bergantung pada waktu respon untuk memunculkan artikel 3d pada tiga device membutuhkan waktu di bawah 2 detik.

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A new fast corner detection method based on template matching

Cited by 4 publications

References 2 publications

Enhancing Vehicle-Machine Interface Icon Detection in Automated Testing Through a Three-Stage Machine Vision Verification Algorithm

Enhancing Vehicle-Machine Interface Icon Detection in Automated Testing Through a Three-Stage Machine Vision Verification Algorithm

Fast Matching Method of UAV Aerial Photography Enhanced Low Illumination Image

Algoritma FCD dan NFT Pada Sistem Pencernaan Berbasis AR Menggunakan Single Marker

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