Chinese chess recognition algorithm based on computer vision

Gui, Weihua; Tao, Jun

doi:10.1109/ccdc.2014.6852759

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…After down sampling, the outputs are inputted to the fully connected layer for classification [ 37 , 38 ]. The feature map is flattened in the fully connected layer, and the weights are updated in the neural network through backpropagation.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

CNN-Based QR Code Reading of Package for Unmanned Aerial Vehicle

Yang

Jan

Chen

et al. 2023

Sensors

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This paper plans to establish a warehouse management system based on an unmanned aerial vehicle (UAV) to scan the QR codes printed on packages. This UAV consists of a positive cross quadcopter drone and a variety of sensors and components, such as flight controllers, single-board computers, optical flow sensors, ultrasonic sensors and cameras, etc. The UAV stabilizes itself by proportional-integral-derivative (PID) control and takes pictures of the package as it reaches ahead of the shelf. Through convolutional neural networks (CNNs), the placement angle of the package can be accurately identified. Some optimization functions are applied to compare system performance. When the angle is 90°, that is, the package is placed normally and correctly, the QR code will be read directly. Otherwise, image processing techniques that include Sobel edge computing, minimum circumscribed rectangle, perspective transformation, and image enhancement is required to assist in reading the QR code. The experimental results showed that the proposed algorithm provided good performance of a recognition rate of 94% for the stochastic gradient descent (SGD) and 95% for Adadelta optimization functions. After that, successful QR code reading was presented.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

CNN-Based QR Code Reading of Package for Unmanned Aerial Vehicle

Yang

Jan

Chen

et al. 2023

Sensors

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show abstract

“…It is a type of downsampling, because the size of the data will be reduced, so the number of parameters and calculations are reduced, which speeds up the system operation, reduces the possibility of overfitting, and has the effect of anti-interference. After sampling, the outputs are inputted to the fully connected layer [23,24]. The fully connected layer is a general neural network for classification.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…The proposed method had the ability to resist noise, brightness changes and rotation, and was tested by five different fonts. The image preprocessing and advanced Hough transformation [23] was used to segment the image and calculate the location of the center of the chessman and the circle edge of the chessman, respectively. Fang [24] designed a machine vision system for Chinese chess-playing robots with two color cameras taking two images from different angles simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Development of a Chinese Chess Robotic System for the Elderly Using Convolutional Neural Networks

et al. 2020

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According to the data from Alzheimer’s Disease International (ADI) in 2018, it is estimated that 10 million new dementia patients will be added worldwide, and the global dementia population is estimated to be 50 million. Due to a decline in the birth rate and the development and great progress of medical technology, the proportion of elderly people has risen annually in Taiwan. In fact, Taiwan has become one of the fastest-growing aged countries in the world. Consequently, problems related to aging societies will emerge. Dementia is one of most prevailing aging-related diseases, with a great influence on daily life and a great economic burden. Dementia is not a single disease, but a combination of symptoms. There is currently no medicine that can cure dementia. Finding preventive measures for dementia has become a public concern. Older people should actively increase brain-protective factors and reduce risk factors in their lives to reduce the risk of dementia and even prevent the occurrence of dementia. Studies have shown that engaging in mental or creative activities that stimulate brain function has a relative risk reduction of nearly 50%. Elderly people should develop the habit of life-long learning to strengthen effective neural bonds between brain cells and preserve brain cognitive functions. Playing chess is one of the suggested activities. This paper aimed to develop a Chinese robotic chess system for the elderly. It mainly uses a camera to capture the contour of the Chinese chessman, recognizes the character and location of the chessman, and then transmits this information to the robotic arm, which will grab and place the chessman in the appropriate position on the chessboard. The camera image is transmitted to MATLAB for image recognition. The character of the chessman is recognized by convolutional neural networks (CNNs). Forward and inverse kinematics are used to manipulate the robotic arm. Even if the chessmen are arbitrarily placed, the experiment showed that their coordinates can be found through the camera as long as they are located within the working scope of the camera and the robotic arm. For black chessmen, no matter how many degrees they are rotated, they can be recognized correctly, while the red ones can be recognized 100% of the time within 90° of rotation and 98.7% with more than a 90° rotation.

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Computer Games Based on Artificial Intelligence

2021

Lecture Notes on Data Engineering and Communications Technologies

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Chinese chess recognition algorithm based on computer vision

Cited by 4 publications

References 9 publications

CNN-Based QR Code Reading of Package for Unmanned Aerial Vehicle

CNN-Based QR Code Reading of Package for Unmanned Aerial Vehicle

Development of a Chinese Chess Robotic System for the Elderly Using Convolutional Neural Networks

Computer Games Based on Artificial Intelligence

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