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  1. Book ; Online: Weighted Intersection over Union (wIoU)

    Cho, Yeong-Jun

    A New Evaluation Metric for Image Segmentation

    2021  

    Abstract: In recent years, many semantic segmentation methods have been proposed to predict label of pixels in the scene. In general, we measure area prediction errors or boundary prediction errors for comparing methods. However, there is no intuitive evaluation ... ...

    Abstract In recent years, many semantic segmentation methods have been proposed to predict label of pixels in the scene. In general, we measure area prediction errors or boundary prediction errors for comparing methods. However, there is no intuitive evaluation metric that evaluates both aspects. In this work, we propose a new evaluation measure called weighted Intersection over Union (wIoU) for semantic segmentation. First, it build a weight map generated from a boundary distance map, allowing weighted evaluation for each pixel based on a boundary importance factor. The proposed wIoU can evaluate both contour and region by setting a boundary importance factor. We validated the effectiveness of wIoU on a dataset of 33 scenes and demonstrated its flexibility. Using the proposed metric, we expect more flexible and intuitive evaluation in semantic segmentation filed are possible.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2021-07-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Analysis of Training Deep Learning Models for PCB Defect Detection.

    Park, Joon-Hyung / Kim, Yeong-Seok / Seo, Hwi / Cho, Yeong-Jun

    Sensors (Basel, Switzerland)

    2023  Volume 23, Issue 5

    Abstract: Recently, many companies have introduced automated defect detection methods for defect-free PCB manufacturing. In particular, deep learning-based image understanding methods are very widely used. In this study, we present an analysis of training deep ... ...

    Abstract Recently, many companies have introduced automated defect detection methods for defect-free PCB manufacturing. In particular, deep learning-based image understanding methods are very widely used. In this study, we present an analysis of training deep learning models to perform PCB defect detection stably. To this end, we first summarize the characteristics of industrial images, such as PCB images. Then, the factors that can cause changes (contamination and quality degradation) to the image data in the industrial field are analyzed. Subsequently, we organize defect detection methods that can be applied according to the situation and purpose of PCB defect detection. In addition, we review the characteristics of each method in detail. Our experimental results demonstrated the impact of various degradation factors, such as defect detection methods, data quality, and image contamination. Based on our overview of PCB defect detection and experiment results, we present knowledge and guidelines for correct PCB defect detection.
    MeSH term(s) Commerce ; Data Accuracy ; Deep Learning ; Drug Contamination ; Industry
    Language English
    Publishing date 2023-03-02
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s23052766
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Product Inspection Methodology via Deep Learning: An Overview.

    Kim, Tae-Hyun / Kim, Hye-Rin / Cho, Yeong-Jun

    Sensors (Basel, Switzerland)

    2021  Volume 21, Issue 15

    Abstract: In this study, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. In addition, we explain the steps for building a ... ...

    Abstract In this study, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. In addition, we explain the steps for building a deep-learning-based inspection system in detail. Second, we address connection schemes that efficiently link deep learning models to product inspection systems. Finally, we propose an effective method that can maintain and enhance a product inspection system according to improvement goals of the existing product inspection systems. The proposed system is observed to possess good system maintenance and stability owing to the proposed methods. All the proposed methods are integrated into a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compare and analyze the performance of the methods in various test scenarios. We expect that our study will provide useful guidelines to readers who desire to implement deep-learning-based systems for product inspection.
    MeSH term(s) Consumer Product Safety ; Deep Learning
    Language English
    Publishing date 2021-07-25
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s21155039
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: PaMM: Pose-Aware Multi-Shot Matching for Improving Person Re-Identification.

    Cho, Yeong-Jun / Yoon, Kuk-Jin

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

    2018  Volume 27, Issue 8, Page(s) 3739–3752

    Abstract: Person re-identification is the problem of recognizing people across different images or videos with non-overlapping views. Although a significant progress has been made in person re-identification over the last decade, it remains a challenging task ... ...

    Abstract Person re-identification is the problem of recognizing people across different images or videos with non-overlapping views. Although a significant progress has been made in person re-identification over the last decade, it remains a challenging task because the appearances of people can seem extremely different across diverse camera viewpoints and person poses. In this paper, we propose a novel framework for person re-identification by analyzing camera viewpoints and person poses called pose-aware multi-shot matching. It robustly estimates individual poses and efficiently performs multi-shot matching based on the pose information. The experimental results obtained by using public person re-identification data sets show that the proposed methods outperform the current state-of-the-art methods, and are promising for accomplishing person re-identification under diverse viewpoints and pose variances.
    Language English
    Publishing date 2018-08
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0042
    ISSN (online) 1941-0042
    DOI 10.1109/TIP.2018.2815840
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Book ; Online: Product Inspection Methodology via Deep Learning

    Kim, Tae-Hyun / Kim, Hye-Rin / Cho, Yeong-Jun

    An Overview

    2021  

    Abstract: In this work, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. Also we explain entire steps for building a deep ... ...

    Abstract In this work, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. Also we explain entire steps for building a deep learning-based inspection system in great detail. Second, we address connection schemes that efficiently link the deep learning models to the product inspection systems. Finally, we propose an effective method that can maintain and enhance the deep learning models of the product inspection system. It has good system maintenance and stability due to the proposed methods. All the proposed methods are integrated in a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compared and analyzed the performance of methods in various test scenarios.
    Keywords Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Systems and Control
    Subject code 006
    Publishing date 2021-03-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  6. Book ; Online: Spatial-temporal Vehicle Re-identification

    Kim, Hye-Geun / Na, YouKyoung / Joe, Hae-Won / Moon, Yong-Hyuk / Cho, Yeong-Jun

    2023  

    Abstract: Vehicle re-identification (ReID) in a large-scale camera network is important in public safety, traffic control, and security. However, due to the appearance ambiguities of vehicle, the previous appearance-based ReID methods often fail to track vehicle ... ...

    Abstract Vehicle re-identification (ReID) in a large-scale camera network is important in public safety, traffic control, and security. However, due to the appearance ambiguities of vehicle, the previous appearance-based ReID methods often fail to track vehicle across multiple cameras. To overcome the challenge, we propose a spatial-temporal vehicle ReID framework that estimates reliable camera network topology based on the adaptive Parzen window method and optimally combines the appearance and spatial-temporal similarities through the fusion network. Based on the proposed methods, we performed superior performance on the public dataset (VeRi776) by 99.64% of rank-1 accuracy. The experimental results support that utilizing spatial and temporal information for ReID can leverage the accuracy of appearance-based methods and effectively deal with appearance ambiguities.

    Comment: 10 pages, 6 figures
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence
    Subject code 629
    Publishing date 2023-09-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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