The Experts below are selected from a list of 135 Experts worldwide ranked by ideXlab platform

Danijel Skočaj - One of the best experts on this subject based on the ideXlab platform.

  • Segmentation-based deep-learning approach for Surface-defect detection
    Journal of Intelligent Manufacturing, 2019
    Co-Authors: Domen Tabernik, Samo Šela, Jure Skvarč, Danijel Skočaj
    Abstract:

    Automated Surface-Anomaly detection using machine learning has become an interesting and promising area of research, with a very high and direct impact on the application domain of visual inspection. Deep-learning methods have become the most suitable approaches for this task. They allow the inspection system to learn to detect the Surface Anomaly by simply showing it a number of exemplar images. This paper presents a segmentation-based deep-learning architecture that is designed for the detection and segmentation of Surface anomalies and is demonstrated on a specific domain of Surface-crack detection. The design of the architecture enables the model to be trained using a small number of samples, which is an important requirement for practical applications. The proposed model is compared with the related deep-learning methods, including the state-of-the-art commercial software, showing that the proposed approach outperforms the related methods on the specific domain of Surface-crack detection. The large number of experiments also shed light on the required precision of the annotation, the number of required training samples and on the required computational cost. Experiments are performed on a newly created dataset based on a real-world quality control case and demonstrates that the proposed approach is able to learn on a small number of defected Surfaces, using only approximately 25–30 defective training samples, instead of hundreds or thousands, which is usually the case in deep-learning applications. This makes the deep-learning method practical for use in industry where the number of available defective samples is limited. The dataset is also made publicly available to encourage the development and evaluation of new methods for Surface-defect detection.

  • a compact convolutional neural network for textured Surface Anomaly detection
    Workshop on Applications of Computer Vision, 2018
    Co-Authors: Domen Racki, Dejan Tomazevic, Danijel Skočaj
    Abstract:

    Convolutional neural methods have proven to outperform other approaches in various computer vision tasks. In this paper we apply the deep learning technique to the domain of automated visual Surface inspection. We design a unified CNN-based framework for segmentation and detection of Surface anomalies. We investigate whether a compact CNN architecture, which exhibit fewer parameters that need to be learned, can be used, while retaining high classification accuracy. We propose and evaluate a compact CNN architecture on a dataset consisting of diverse textured Surfaces with variously-shaped weakly-labeled anomalies. The proposed approach achieves state-of-the-art results in terms of Anomaly segmentation as well as classification.

  • WACV - A Compact Convolutional Neural Network for Textured Surface Anomaly Detection
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Domen Racki, Dejan Tomazevic, Danijel Skočaj
    Abstract:

    Convolutional neural methods have proven to outperform other approaches in various computer vision tasks. In this paper we apply the deep learning technique to the domain of automated visual Surface inspection. We design a unified CNN-based framework for segmentation and detection of Surface anomalies. We investigate whether a compact CNN architecture, which exhibit fewer parameters that need to be learned, can be used, while retaining high classification accuracy. We propose and evaluate a compact CNN architecture on a dataset consisting of diverse textured Surfaces with variously-shaped weakly-labeled anomalies. The proposed approach achieves state-of-the-art results in terms of Anomaly segmentation as well as classification.

Shen-shyang Ho - One of the best experts on this subject based on the ideXlab platform.

  • A fast sparse reconstruction approach for high resolution image-based object Surface Anomaly detection
    2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), 2017
    Co-Authors: Woon Huei Chai, Chi-keong Goh, Liang-tien Chia, Shen-shyang Ho, Hiok Chai Quek
    Abstract:

    We propose an approach to resolve two issues in a recent proposed sparse reconstruction based, Anomaly detection approach as a part of automated visual inspection (AVI). The original approach needs large computation and memory for high resolution problem. To solve it, we proposed a two-step sparse reconstruction, 1) the first sparse representation of input image is estimated in a sparse reconstruction with low resolution downsampled images and 2) the high resolution residual values is generated in another sparse reconstruction with the sparse representation. The first step provides the flexibility of freely adjusting the computation and the demand of memory storage with small trade-off of detection accuracy. Moreover, an illumination adaptive threshold with morphological operators is used in the Anomaly classification. Empirical results show that the proposed approach can effectively replace the original approach with better results.

  • exploiting sparsity for image based object Surface Anomaly detection
    International Conference on Acoustics Speech and Signal Processing, 2016
    Co-Authors: Woon Huei Chai, Shen-shyang Ho
    Abstract:

    The Anomaly detection task plays an important role in quality control in many industrial or manufacturing processes. However, in many such processes, Anomaly detection is done visually by human experts who have in-depth knowledge and vast experience on a product in order to perform well in the detection task. In this paper, we present an approach that (i) identifies anomalies in an image based on the sparse residuals (or errors) obtained during image reconstruction using sparse representation and (ii) learns the threshold to classify an image pixel based on its residual value. The intuitions for our proposed sparse approximation driven approach are, namely: (i) anomalies are infrequent and (ii) anomalies are unwanted portions of an image reconstruction. Empirical results on a real-world image dataset for an industrial Surface defect detection task are used to demonstrate the feasibility of our proposed approach.

  • ICASSP - Exploiting sparsity for image-based object Surface Anomaly detection
    2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016
    Co-Authors: Woon Huei Chai, Shen-shyang Ho
    Abstract:

    The Anomaly detection task plays an important role in quality control in many industrial or manufacturing processes. However, in many such processes, Anomaly detection is done visually by human experts who have in-depth knowledge and vast experience on a product in order to perform well in the detection task. In this paper, we present an approach that (i) identifies anomalies in an image based on the sparse residuals (or errors) obtained during image reconstruction using sparse representation and (ii) learns the threshold to classify an image pixel based on its residual value. The intuitions for our proposed sparse approximation driven approach are, namely: (i) anomalies are infrequent and (ii) anomalies are unwanted portions of an image reconstruction. Empirical results on a real-world image dataset for an industrial Surface defect detection task are used to demonstrate the feasibility of our proposed approach.

Woon Huei Chai - One of the best experts on this subject based on the ideXlab platform.

  • A fast sparse reconstruction approach for high resolution image-based object Surface Anomaly detection
    2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), 2017
    Co-Authors: Woon Huei Chai, Chi-keong Goh, Liang-tien Chia, Shen-shyang Ho, Hiok Chai Quek
    Abstract:

    We propose an approach to resolve two issues in a recent proposed sparse reconstruction based, Anomaly detection approach as a part of automated visual inspection (AVI). The original approach needs large computation and memory for high resolution problem. To solve it, we proposed a two-step sparse reconstruction, 1) the first sparse representation of input image is estimated in a sparse reconstruction with low resolution downsampled images and 2) the high resolution residual values is generated in another sparse reconstruction with the sparse representation. The first step provides the flexibility of freely adjusting the computation and the demand of memory storage with small trade-off of detection accuracy. Moreover, an illumination adaptive threshold with morphological operators is used in the Anomaly classification. Empirical results show that the proposed approach can effectively replace the original approach with better results.

  • exploiting sparsity for image based object Surface Anomaly detection
    International Conference on Acoustics Speech and Signal Processing, 2016
    Co-Authors: Woon Huei Chai, Shen-shyang Ho
    Abstract:

    The Anomaly detection task plays an important role in quality control in many industrial or manufacturing processes. However, in many such processes, Anomaly detection is done visually by human experts who have in-depth knowledge and vast experience on a product in order to perform well in the detection task. In this paper, we present an approach that (i) identifies anomalies in an image based on the sparse residuals (or errors) obtained during image reconstruction using sparse representation and (ii) learns the threshold to classify an image pixel based on its residual value. The intuitions for our proposed sparse approximation driven approach are, namely: (i) anomalies are infrequent and (ii) anomalies are unwanted portions of an image reconstruction. Empirical results on a real-world image dataset for an industrial Surface defect detection task are used to demonstrate the feasibility of our proposed approach.

  • ICASSP - Exploiting sparsity for image-based object Surface Anomaly detection
    2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016
    Co-Authors: Woon Huei Chai, Shen-shyang Ho
    Abstract:

    The Anomaly detection task plays an important role in quality control in many industrial or manufacturing processes. However, in many such processes, Anomaly detection is done visually by human experts who have in-depth knowledge and vast experience on a product in order to perform well in the detection task. In this paper, we present an approach that (i) identifies anomalies in an image based on the sparse residuals (or errors) obtained during image reconstruction using sparse representation and (ii) learns the threshold to classify an image pixel based on its residual value. The intuitions for our proposed sparse approximation driven approach are, namely: (i) anomalies are infrequent and (ii) anomalies are unwanted portions of an image reconstruction. Empirical results on a real-world image dataset for an industrial Surface defect detection task are used to demonstrate the feasibility of our proposed approach.

Domen Racki - One of the best experts on this subject based on the ideXlab platform.

  • a compact convolutional neural network for textured Surface Anomaly detection
    Workshop on Applications of Computer Vision, 2018
    Co-Authors: Domen Racki, Dejan Tomazevic, Danijel Skočaj
    Abstract:

    Convolutional neural methods have proven to outperform other approaches in various computer vision tasks. In this paper we apply the deep learning technique to the domain of automated visual Surface inspection. We design a unified CNN-based framework for segmentation and detection of Surface anomalies. We investigate whether a compact CNN architecture, which exhibit fewer parameters that need to be learned, can be used, while retaining high classification accuracy. We propose and evaluate a compact CNN architecture on a dataset consisting of diverse textured Surfaces with variously-shaped weakly-labeled anomalies. The proposed approach achieves state-of-the-art results in terms of Anomaly segmentation as well as classification.

  • WACV - A Compact Convolutional Neural Network for Textured Surface Anomaly Detection
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Domen Racki, Dejan Tomazevic, Danijel Skočaj
    Abstract:

    Convolutional neural methods have proven to outperform other approaches in various computer vision tasks. In this paper we apply the deep learning technique to the domain of automated visual Surface inspection. We design a unified CNN-based framework for segmentation and detection of Surface anomalies. We investigate whether a compact CNN architecture, which exhibit fewer parameters that need to be learned, can be used, while retaining high classification accuracy. We propose and evaluate a compact CNN architecture on a dataset consisting of diverse textured Surfaces with variously-shaped weakly-labeled anomalies. The proposed approach achieves state-of-the-art results in terms of Anomaly segmentation as well as classification.

Dejan Tomazevic - One of the best experts on this subject based on the ideXlab platform.

  • a compact convolutional neural network for textured Surface Anomaly detection
    Workshop on Applications of Computer Vision, 2018
    Co-Authors: Domen Racki, Dejan Tomazevic, Danijel Skočaj
    Abstract:

    Convolutional neural methods have proven to outperform other approaches in various computer vision tasks. In this paper we apply the deep learning technique to the domain of automated visual Surface inspection. We design a unified CNN-based framework for segmentation and detection of Surface anomalies. We investigate whether a compact CNN architecture, which exhibit fewer parameters that need to be learned, can be used, while retaining high classification accuracy. We propose and evaluate a compact CNN architecture on a dataset consisting of diverse textured Surfaces with variously-shaped weakly-labeled anomalies. The proposed approach achieves state-of-the-art results in terms of Anomaly segmentation as well as classification.

  • WACV - A Compact Convolutional Neural Network for Textured Surface Anomaly Detection
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Domen Racki, Dejan Tomazevic, Danijel Skočaj
    Abstract:

    Convolutional neural methods have proven to outperform other approaches in various computer vision tasks. In this paper we apply the deep learning technique to the domain of automated visual Surface inspection. We design a unified CNN-based framework for segmentation and detection of Surface anomalies. We investigate whether a compact CNN architecture, which exhibit fewer parameters that need to be learned, can be used, while retaining high classification accuracy. We propose and evaluate a compact CNN architecture on a dataset consisting of diverse textured Surfaces with variously-shaped weakly-labeled anomalies. The proposed approach achieves state-of-the-art results in terms of Anomaly segmentation as well as classification.