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Patent Agency Ranking
公开(公告)号：US20240233344A9
公开(公告)日：2024-07-11
申请号：US17973177
申请日：2022-10-25
Inventor： Yuya SUGASAWA , Hisaji MURATA , Nway Nway AUNG , Ariel BECK , Zong Sheng TANG
IPC: G06V10/776
CPC classification number: G06V10/776
Abstract: According to an embodiment, a method for estimating robustness of a trained machine learning model is disclosed. The method comprises receiving a labelled dataset, a model of an object for which defect detection is required, and the trained machine learning model. Further, the method comprises determining one or more parameters associated with image capturing conditions in the environment. Furthermore, the method comprises performing an auto extraction of one or more defects using the model of the object and the labelled dataset based on image processing. Furthermore, the method comprises generating one or more images based on the one or more parameters and the one or more defects. Additionally, the method comprises testing the trained machine learning model using the generated images. Moreover, the method comprises estimating a robustness report for the machine learning model based on the testing of the machine learning model.
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公开(公告)号：US20220253995A1
公开(公告)日：2022-08-11
申请号：US17173822
申请日：2021-02-11
Inventor： Ariel BECK , Chandra Suwandi WIJAYA , Athul M. MATHEW , Nway Nway AUNG , Ramdas KRISHNAKUMAR , Zong Sheng TANG , Yao ZHOU , Pradeep RAJAGOPALAN , Yuya SUGASAWA
Abstract: A method and system for checking data gathering conditions or image capturing conditions associated with images during AI based visual-inspection process. The method comprises generating a first representative (FR1) image for a first group of images and a second representative image (FR2) for a second group of images. A difference image data is generated between FR1 image and the FR2 image based on calculating difference between luminance values of pixels with same coordinate values. Thereafter, one or more of a plurality of white pixels or intensity-values are determined within the difference image based on acquiring difference image data formed of luminance difference-values of pixels. An index representing difference of data-capturing conditions across the FR1 image and the FR2 image is determined, said index having been determined at least based on the plurality of white pixels or intensity-values, for example, based on application of a plurality of AI or ML techniques.
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公开(公告)号：US20250095134A1
公开(公告)日：2025-03-20
申请号：US18368443
申请日：2023-09-14
Inventor： Andre IVAN , Zong Sheng TANG , Ariel BECK
Abstract: The present disclosure discloses a method and system for visual inspection of a target product. The method includes a) receiving an image associated with the target product; generating a plurality of region of interests (ROIs) associated with the image; identifying, based on the plurality of non-terminal ROIs, a first set of features and a second set of features associated with the image. The first set of features and the second set of features are indicative of one of a presence of defect within the image or an absence of defect within the image. The method also includes determining, based on the first set of features and the second set of features, a result of the visual inspection of the target product associated with the image. The result is a success result or a failure result.
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公开(公告)号：US20240135689A1
公开(公告)日：2024-04-25
申请号：US17973177
申请日：2022-10-24
Inventor： Yuya SUGASAWA , Hisaji MURATA , Nway Nway AUNG , Ariel BECK , Zong Sheng TANG
IPC: G06V10/776
CPC classification number: G06V10/776
Abstract: According to an embodiment, a method for estimating robustness of a trained machine learning model is disclosed. The method comprises receiving a labelled dataset, a model of an object for which defect detection is required, and the trained machine learning model. Further, the method comprises determining one or more parameters associated with image capturing conditions in the environment. Furthermore, the method comprises performing an auto extraction of one or more defects using the model of the object and the labelled dataset based on image processing. Furthermore, the method comprises generating one or more images based on the one or more parameters and the one or more defects. Additionally, the method comprises testing the trained machine learning model using the generated images. Moreover, the method comprises estimating a robustness report for the machine learning model based on the testing of the machine learning model.
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公开(公告)号：US20240020944A1
公开(公告)日：2024-01-18
申请号：US17867173
申请日：2022-07-18
Inventor： Ramdas KRISHNAKUMAR , Ariel BECK , Zong Sheng TANG , Khai Jun KEK , Satyam SATYAM , Masahiro ISHII , Yuto KITAGAWA
IPC: G06V10/72 , G06V10/762 , G06V10/764 , G06V10/74 , G06V10/98
CPC classification number: G06V10/72 , G06V10/762 , G06V10/764 , G06V10/761 , G06V10/993
Abstract: A method and system for sampling and augmenting a dataset associated with a first class and a second class, respectively, to balance the dataset of images is described. The method includes receiving a required number of reduced set of dataset images associated with the first class, creating a plurality of clusters from a set of images associated with the first class, and selecting a representative image from each cluster to provide a reduced set of images. Further, a median image and a non-defect artifact mask is generated corresponding to the set of images associated with the first class. Additionally, a defect foreground is extracted based on the median image and each defect image of another set of images associated with the second class. Finally, the at least one non-defect artifact is removed from the defect foreground to provide a new synthetic defect image for each defect image for augmentation.
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