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[IEEE 2018 International Conference on Cyberworlds (CW) - Singapore, Singapore (2018.10.3-2018.10.5)] 2018 International Conference on Cyberworlds (CW) - Towards Automatic Optical Inspection of Soldering Defects
摘要： This paper proposes a method for automatic image-based classification of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs). Machine learning-based approaches are frequently used for image-based inspection. However, a main challenge is to manually create sufficiently large labeled training databases to allow for high accuracy of defect detection. Creating such large training databases is time-consuming, expensive, and often unfeasible in industrial production settings. In order to address this problem, an active learning framework is proposed which starts with only a small labeled subset of training data. The labeled dataset is then enlarged step-by-step by combining K-means clustering with active user input to provide representative samples for the training of an SVM classifier. Evaluations on two databases with insufficient and shifting solder joints samples have shown that the proposed method achieved high accuracy while requiring only minimal user input. The results also demonstrated that the proposed method outperforms random and representative sampling by ~ 3.2% and ~ 2.7%, respectively, and it outperforms the uncertainty sampling method by ~ 0.5%.
关键词： Classification of solder joint defects,active learning,Automatic Optical Inspection (AOI),SVM classifier,K-means
更新于2025-09-23 15:23:52
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Coarse-to-Fine Extraction of Small-Scale Lunar Impact Craters From the CCD Images of the Chang'E Lunar Orbiters
摘要： Lunar impact craters form the basis for lunar geological stratigraphy, and small-scale craters further enrich the basic statistical data for the estimation of local geological ages. Thus, the extraction of lunar impact craters is an important branch of modern planetary studies. However, few studies have reported on the extraction of small-scale craters. Therefore, this paper proposes a coarse-to-fine resolution method to automatically extract small-scale impact craters from charge-coupled device (CCD) images using histogram of oriented gradient (HOG) features and a support vector machine (SVM) classifier. First, large-scale craters are extracted as samples from the Chang'E-1 images with spatial resolutions of 120 m. The SVM classifier is then employed to establish the criteria for classifying craters and noncraters from the HOG features of the extracted samples. The criteria are then used to extract small-scale craters from higher resolution Chang'E-2 CCD images with spatial resolutions of 1.4, 7, and 50 m. The sample database is updated with the newly extracted small-scale craters for the purpose of the progressive optimization of the extraction. The proposed method is tested on both simulated images and multiple resolutions of real CCD images acquired by the Chang'E orbiters and provides high accuracy results in the extraction of the small-scale impact craters, the smallest of which is 20 m.
关键词： small-scale impact craters,Chang'E satellites,charge-coupled device (CCD) images,support vector machine (SVM) classifier,histogram of oriented gradient (HOG) feature
更新于2025-09-11 14:15:04
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[IEEE 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Bangalore (2018.2.9-2018.2.10)] 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Determination of Absolute Heart Beat from Photoplethysmographic Signals in the Presence of Motion Artifacts
摘要： In Wireless Body Area Networks (WBANs), accurate monitoring of heart rate (HR) using Photoplethysmography (PPG) signals is always a difficult task, especially when the subjects are under radical exercises. This is due to the signals corrupted by severely strong Motion Artifacts (MA) caused by the subject’s body movements. In this work, a novel approach has been proposed consisting of signal decomposition for denoising using principal component analysis (PCA), spare signal reconstruction (SSR), peak detection and tracking and support vector machine (SVM) classifier for accurate estimation of HR, based on the wrist type PPG signals. With this approach, we are able to achieve high accuracy and also, it is strong enough to remove MA. Experiments were conducted on 12 subjects and their datasets are obtained from 2015 IEEE Signal Processing CUP, running on a threadmill with varying speeds ranging from 0 to a maximum speed of 15 km/hour. From the results, it is observed that the average absolute error of heart rate estimation is 1.66 beats per minute (BPM).
关键词： SVM classifier,PCA,HR,Wireless Body Area Networks (BAN),SSR,Accelerometer,PPG
更新于2025-09-04 15:30:14