Chinese chess character recognition using Direction Feature Extraction and backpropagation

This study examines the classification of Alzheimer's disease. Alzheimer's is a memory disorder in an older person caused by degeneration of the central nervous system, which results in memory impairment and can cause death. Early detection of Alzheimer's can also be done based on image processing using magnetic resonance imaging (MRI) type images. Therefore, in this research, we use a feature extraction process to extract the characteristics of Alzheimer's disease that appear on MRI images using Moment Invariance and use the Naïve Bayes classification method to classify classes from images based on normal images, very mild disturbances, mild disturbances, and disturbances. Medium from brain images in the classification of Alzheimer's disease. The classification stage consists of several stages such as image acquisition, preprocessing (grayscaling), segmentation (canny edge detection, threshold), feature extraction using Invariant Moment, and image classification using Naïve Bayes. Based on the testing of the method proposed in this research, the results obtained for the accuracy of classifying Alzheimer's disease in this study are 94%.

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“…Then lthresholding laims lto lget the threshold value of black or white by using lthresholding [12] .…”

Section: Preprocessing and Segmentationmentioning

confidence: 99%

“…The next stage is thresholding which is an image segmentation method that is useful for separating objects from the background of an image based on the difference in the threshold value of the brightness or darkness of an image pixel [12] . The thresholding calculation process can be seen in equation 4.…”

Section: Thresholdingmentioning

confidence: 99%

Classification of Alzheimer Disease from MRI Image Using Combination Naïve Bayes and Invariant Moment

Lubis¹,

Sibagariang²,

Ardi³

2023

Proceedings of the 5th International Conference on Applied Engineering, ICAE 2022, 5 October 2022, Batam, Indonesia

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“…Wen [21] proposed an input image and database feature comparison method that consists of the noise filter, object extraction, normalization, feature calculation of the distance between the contour of the character and the center of the chessman, and maximum energy slop algorithm, for the Chinese chessmen. Seniman et al [22] presented the backpropagation algorithm of a feed-forward neural network as well as direction feature extraction method by iterating and calculating the directions surrounding each pixel in the image to obtain the features and recognize Chinese chess characters. The proposed method had the ability to resist noise, brightness changes and rotation, and was tested by five different fonts.…”

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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The morphological classification of normal and abnormal red blood cell using Self Organizing Map

Rahmat

Wulandari

Faza

et al. 2018

IOP Conf. Ser.: Mater. Sci. Eng.

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Chinese chess character recognition using Direction Feature Extraction and backpropagation

Cited by 6 publications

References 9 publications

Classification of Alzheimer Disease from MRI Image Using Combination Naïve Bayes and Invariant Moment

Classification of Alzheimer Disease from MRI Image Using Combination Naïve Bayes and Invariant Moment

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

The morphological classification of normal and abnormal red blood cell using Self Organizing Map

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