• Advanced Institute of Engineering and Technology (AVITECH)

  • Seminars

    November 03, 2020: Dr. Dinh Tran Hiep (AVITECH), Summit Navigator automatic thresholding for image binarization with application to crack detection

    Machine vision has found many industrial applications. For surface inspection, a straightforward and effective segmentation algorithm is of vital importance for a successful extraction of crack pixels from the image background. This work presents a novel binarization method using Summit Navigator for local maxima extraction and contrast-based region merging without any prior knowledge about the test image under segmentation. Based on the detected peaks of the image histogram, an initial segmentation is generated, and a contrast-based region merging technique is proposed to effectively assign different regions into object and background. Then, a bagging technique using the decision trees method is employed to train a classifier for automatic parameter selection. Experimental results on some benchmark data sets demonstrate the advantages of the proposed method over existing techniques in terms of accuracy and consistency. To illustrate its validity, the approach is then applied to detect surface cracks in a commercial building by using images captured by an unmanned aerial vehicle.

    Speaker: Dr. Dinh Tran Hiep, AVITECH

    Time: 15:30, Tuesday, November 03, 2020

    Venue: G2-315, 144 Xuan Thuy, Cau Giay, Hanoi


    Tran Hiep Dinh received his M.Sc. degree in mechatronics from the Leibniz University Hanover, Germany, and Ph.D. degree in engineering from the University of Technology Sydney, Australia, in 2010 and 2020, respectively. He is currently with the Faculty of Engineering Mechanics and Automation, VNU University of Engineering and Technology. His research interests include image processing, robotics, and machine learning.


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    While Machine Learning (ML) has rapidly transformed several domains and applications with incredible successes, there are also important areas where the progress is significantly slower. Specifically, there exists a widened complexity gap between the methods currently investigated in research and those used in practice in these areas. One reason is that many algorithms, despite achieving […]

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    Multimodal Emotion Recognition in Conversation (Multimodal ERC) is a critical area of research for interpreting human communication in diverse applications. Nevertheless, the persistent issue of uncertain missing modalities poses a major hurdle, hampering the development of robust Multimodal ERC models. Existing approaches face limitations in effectively leveraging a fusion of diverse data modalities encompassing audio, […]