Visual Image Processing of Humanoid Go Game Robot Based on OPENCV

Gui, Yongjie; Wu, Yanyan; Wang, Yajie; Yao, Chunpeng

doi:10.1109/ccdc49329.2020.9164541

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…Chang [3] proposed a Go-image-segmentation algorithm based on OpenCV, but it did not perform well on a board with a complex background. Gui [4] used image-processing technology to detect, locate, and segment chess pieces in a chess game involving board information extraction of the Go robot with an accuracy rate of 93.3%, but the problem of illumination influence was not solved. On this basis, Zhao [5] used an MLP model to overcome the influence of uneven illumination, and the recognition accuracy of the Go robot was more than 90%.…”

Section: Related Workmentioning

confidence: 99%

Go-Game Image Recognition Based on Improved Pix2pix

Zheng,

Qian

2023

J. Imaging

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Go is a game that can be won or lost based on the number of intersections surrounded by black or white pieces. The traditional method is a manual counting method, which is time-consuming and error-prone. In addition, the generalization of the current Go-image-recognition methods is poor, and accuracy needs to be further improved. To solve these problems, a Go-game image recognition based on an improved pix2pix was proposed. Firstly, a channel-coordinate mixed-attention (CCMA) mechanism was designed by combining channel attention and coordinate attention effectively; therefore, the model could learn the target feature information. Secondly, in order to obtain the long-distance contextual information, a deep dilated-convolution (DDC) module was proposed, which densely linked the dilated convolution with different dilated rates. The experimental results showed that compared with other existing Go-image-recognition methods, such as DenseNet, VGG-16, and Yolo v5, the proposed method could effectively improve the generalization ability and accuracy of a Go-image-recognition model, and the average accuracy rate was over 99.99%.

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Section: Related Workmentioning

confidence: 99%

Go-Game Image Recognition Based on Improved Pix2pix

Zheng,

Qian

2023

J. Imaging

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“…Checkerboard detection is a fundamental tool in computer vision applications such as camera calibration [1][2][3][4][5][6], projector-camera systems [7,8], simultaneous localisation and mapping (SLAM) [9], and robotics in general [10][11][12]. This topic is of such high importance that it has received a large amount of attention from the community over the past decades and a large variety of detection methods have been developed.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Checkerboard Detection Using Gaussian Processes

Hillen,

De Boi,

De Kerf

et al. 2023

Mathematics

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Accurate checkerboard detection is of vital importance for computer vision applications, and a variety of checkerboard detectors have been developed in the past decades. While some detectors are able to handle partially occluded checkerboards, they fail when a large occlusion completely divides the checkerboard. We propose a new checkerboard detection pipeline for occluded checkerboards that has a robust performance under varying levels of noise, blurring, and distortion, and for a variety of imaging modalities. This pipeline consists of a checkerboard detector and checkerboard enhancement with Gaussian processes (GP). By learning a mapping from local board coordinates to image pixel coordinates via a Gaussian process, we can fill in occluded corners, expand the board beyond the image borders, allocate detected corners that do not fit an initial grid, and remove noise on the detected corner locations. We show that our method can improve the performance of other publicly available state-of-the-art checkerboard detectors, both in terms of accuracy and the number of corners detected. Our code and datasets are made publicly available. The checkerboard detector pipeline is contained within our Python checkerboard detection library, called PyCBD. The pipeline itself is modular and easy to adapt to different use cases.

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“…In 2020, Gui applied traditional transformations such as binarization, color space transformation, threshold segmentation, high-pass filtering, Huff transform, etc. to detect, locate, and segment chess pieces [23], achieving an accuracy rate of 93.3%. In addition to taking photos, some studies also tried to apply video content for Go game images recognition.…”

Section: Introductionmentioning

confidence: 99%

Reliable Go Game Images Recognition Under Strong Light Attack

et al. 2021

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Go is a popular global game whose win or loss is only determined by the number of intersection points surrounded by black or white pieces. Among all the counting methods, the traditional manual counting method is time-consuming. Additionally, the current Go game images recognition technology cannot endure light reflection attacks or extreme image capture angles effectively. In this paper, a reliable Go game images recognition method is proposed which not only can resist light reflection attacks but also can endure various image capture angles. To obtain this goal, we propose a detection method based on the optimized CNNs (Convolutional Neural Network) framework. Experiments on recognizing 3220 images show that the average accuracy with our proposed method is over 99.99%, which is 22 times better than the accuracy of the stateof-the-art approach on Go game images recognition. Besides, our study provides potential references for the recognition of interfered small objects in groups that have few features. It provides a reference in similar application scenarios such as the detection of animal crowds, industrial parts, physiological tissues, and micro-particles.INDEX TERMS Go board detection, Go pieces recognition, CNN, model ensemble

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Visual Image Processing of Humanoid Go Game Robot Based on OPENCV

Cited by 7 publications

References 7 publications

Go-Game Image Recognition Based on Improved Pix2pix

Go-Game Image Recognition Based on Improved Pix2pix

Enhanced Checkerboard Detection Using Gaussian Processes

Reliable Go Game Images Recognition Under Strong Light Attack

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