Computer Motherboard Component Recognition Using Texture and Shape Features

Sabri, Nurbaity; Mukim, Mahfuzah; İbrahim, Zarina Bibi; Hasan, Noraini; Ibrahim, Shafaf

doi:10.1109/icsgrc.2018.8657579

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Segmentation is required in texture feature extraction utilizing GLCM [20]. Furthermore, the extracted GLCM features are then classified using machine learning techniques, such as support vector machine-based classification [26]. In the study [17,27], a pre-trained machine learning model incorporating GLCM-based texture feature extraction achieved remarkable results.…”

Section: Related Workmentioning

confidence: 99%

Enhancing GLCM-Based Feature Extraction for THT Solder Joint Inspection through Log Polar Transformation

2024

IJIES

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Quality inspection of solder connections in electronic circuit manufacturing is commonly performed using automatic optical inspection (AOI) technology. The utilization of deep learning in AOI has demonstrated high accuracy and fast computation, yet it requires expensive graphics processing unit (GPU) equipped computers. To enable broader and more cost-effective utilization, there is a need for an AOI model that can be embedded in simple central processing unit (CPU) based computers. To pursue this objective, several research efforts have been undertaken to develop AOI models based on classical machine learning techniques. However, the accuracy and speed achieved by these models have not yet matched deep learning-based AOI models. This study aims to enhance the computational efficiency of classic machine learning by processing only pixels containing textural information. The effectiveness enhancement is achieved through the application of log-polar transformation in the extraction of texture features using the gray level co-occurrence matrix (GLCM) to detect defects in trough hole technology (THT) solder joint connections. By transforming cartesian coordinates into polar coordinates, the textural areas to be detected assume a square shape, facilitating efficient texture feature extraction. To ensure a significant improvement in performance, a comparative performance evaluation is conducted on classic machine learning-based AOI models with and without the log-polar transformation. The texture features extracted from both models are classified using the support vector machine (SVM) method. Model testing and evaluation reveals that the proposed enhancement effort is capable of increasing accuracy levels to 95% and reach computation time by 120 milliseconds.

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Section: Related Workmentioning

confidence: 99%

Enhancing GLCM-Based Feature Extraction for THT Solder Joint Inspection through Log Polar Transformation

2024

IJIES

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“…Deep learning is a learning approach based on neural networks. One of the benefits of deep learning is that it can automatically extract characteristics from photos [8] [9]. Deep learning enables computational models made up of numerous processing layers to learn data representations with varying degrees of abstraction.…”

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confidence: 99%

Automatic plant recognition using convolutional neural network on malaysian medicinal herbs: the value of data augmentation

Roslan

Diah

İbrahim

et al. 2023

Int. J. Adv. Intell. Informatics

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Herbs are an important nutritional source for humans since they provide a variety of nutrients. Indigenous people have employed herbs, in particular, as traditional medicines since ancient times. Malaysia has hundreds of plant species; herb detection may be difficult due to the variety of herb species and their shape and color similarities. Furthermore, there is a scarcity of support datasets for detecting these plants. The main objective of this paper is to investigate the performance of convolutional neural network (CNN) on Malaysian medicinal herbs datasets, real data and augmented data. Malaysian medical herbs data were obtained from Taman Herba Pulau Pinang, Malaysia, and ten kinds of native herbs were chosen. Both datasets were evaluated using the CNN model developed throughout the research. Overall, herbs real data obtained an average accuracy of 75%, whereas herbs augmented data achieved an average accuracy of 88%. Based on these findings, herbs augmented data surpassed herbs actual data in terms of accuracy after undergoing the augmentation technique.

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Computer Motherboard Component Recognition Using Texture and Shape Features

Cited by 2 publications

References 11 publications

Enhancing GLCM-Based Feature Extraction for THT Solder Joint Inspection through Log Polar Transformation

Enhancing GLCM-Based Feature Extraction for THT Solder Joint Inspection through Log Polar Transformation

Automatic plant recognition using convolutional neural network on malaysian medicinal herbs: the value of data augmentation

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