Abstract: An inspection model adapted to an environment of a site is generated with a lighter learning workload. A plurality of first element models different from each other in a specific feature are prepared. A second model is generated by adjusting at least one of the plurality of first element models to adapt to adjustment data different from training data of the model. From a set of element models including the plurality of first element models and the second element model, a plurality of the element models including at least one of the second element models are selected. An integrated model is generated by integrating the plurality of the element models selected. The integrated model outputs any of classes into which input data is classified based on presence or absence of all of a plurality of the specific features related to the plurality of the element models selected.

Abstract: A video distribution system in one embodiment includes a storage that stores a first post message posted by a first user belonging to a first user group and a second post message posted by a second user belonging to a second user group, and one or more computer processors. In the embodiment, the one or more processors execute computer readable instructions to distribute a video that includes a first display area in which the first post message is displayed and a second display area in which the second post message is displayed but the first post message is undisplayed.

Abstract: A delivery server includes a processor including hardware, the processor being configured to: transmit arrival information of a product to a user terminal carried by a user who has ordered the product; transmit, in a case where unattended delivery is instructed by the user terminal, unattended delivery information to a home delivery terminal of a delivery person who delivers the product, and request a signature of the user; transmit the signature information to the home delivery terminal in response to receiving signature information regarding the signature of the user from the user terminal; and transmit delivery completion information of the product to the user terminal.

Abstract: A defect inspecting device including an acquisition unit, an image generating unit, an inspection unit, and a setting unit. The image generating unit generates one or more feature extraction images by applying to an inspection image a learned classifier. The inspection unit identifies a region corresponding to a defect based on one or more determination parameters and a binarized image generated based on the feature extraction image. The setting unit provisionally sets the determination parameters on premise of a post-adjustment from a user when a number of image data for learning corresponding to the features is less than a threshold value in a case where the region corresponding to the defect is identified based on the pre-learned features, and sets the determination parameters according to designation from the user in a case where the region corresponding to the defect is identified based on a feature other than the pre-learned features.

Abstract: This image processing device, by superimposing a defect image 52 on a background image 51, with the original image of an inspection subject as the background image 51, and performing image processing in which the display format of the defect image 52 or the display format of the background image 51 is altered, a plurality of composite images 50 having different ways of seeing the defect image 52 with respect to the background image 51 are generated (step 403), the advisability of detecting the defect image 52 from each of the plurality of composite images 50 is verified (step 404), the detectable range 60 of the defect image 52 is estimated on the basis of the detection advisability verification results (step 405), and the detectable range 60 is displayed (step 406).

Abstract: An inspection apparatus according to one or more embodiments extracts an attention area from a target image using a first estimation model, performs a computational process with a second estimation model using the extracted attention area, and determines whether a target product has a defect based on a computational result from the second estimation model. The first estimation model is generated based on multiple first training images of defect-free products in a target environment. The second estimation model is generated based on multiple second training images of defects. The computational process with the second estimation model includes generating multiple feature maps with different dimensions by projecting the target image into different spaces with lower dimensions. The extracted attention area is integrated into at least one of the multiple feature maps in the computational process with the second estimation model.

Abstract: The present disclosure relates to a method, device, system and computer-program product for setting a lighting condition when an object is checked and a storage medium. The method includes that: the object is lighted by light sources capable of changing lighting parameters, and the object is captured by an image sensor in such lighting parameters to obtain captured images, wherein the object has known label data; and a part of or all of the captured images and the corresponding label data of the object are applied to learning of a machine learning model, and the lighting condition and the check algorithm parameters of the machine learning model is set simultaneously by optimizing both the lighting parameters and the check algorithm parameters, on the basis of a comparison result between an estimation result of the machine learning model and the label data. Therefore, operations are simplified.

Abstract: A convolutional neural network performs: a first masking process of masking a pixel region not to be inspected, by computing pixel values of corresponding pixels of an inspection image and of a mask image; an intermediate process for extracting a feature image from the inspection image that has been subjected to the first masking process; and a second masking process of masking the pixel region not to be inspected, by computing the pixel values of corresponding pixels of the inspection image that has been subjected to the intermediate process and of the mask image that has been subjected to a process identical to the intermediate process.

Abstract: Provided in the present disclosure are a method, an apparatus and a system for determining feature data of image data, and a storage medium. Wherein, the method comprises: acquiring features of image data, the features comprising a first feature and a second feature, wherein, the first feature is extracted from the image data using a first model, the first model being trained in a machine learning manner, and the second feature is extracted from the image data using a second model, the second model being constructed based on a pre-configured data processing algorithm; and determining feature data based on the first feature and the second feature. The present disclosure solves the technical problem that features recognized by the AI may not be consistent with human recognized features.

Abstract: An image generating part generating feature extraction images by applying an identification part, which has completed learning, that has executed learning in advance to extract features using learning image data to an inspection image, an inspection part specifying an area corresponding to a defect on the basis of judgment parameters for judging presence/absence of a defect in the inspection target object and a binary image generated on the basis of the feature extraction images, and a setting part calculating an image score based on a density of a color of pixels of a setting image using the setting image that is the binary image in which an area corresponding to the defect is specified and updating the judgment parameters such that a difference between an image score of the inside of the area and an image score of the outside of the area becomes relatively large are included.

Abstract: The present disclosure provides a method, device, system and computer-program product for setting a lighting condition when an object is checked and a storage medium. The method includes that: the object is lighted by light sources capable of changing lighting parameters specifying the lighting condition when the object is captured, and the object with corresponding label data is captured by an image sensor under multiple such lighting parameters to obtain multiple captured images; estimation images of the object are generated on the basis of image data sets obtained by associating the captured images and the corresponding lighting parameters; and the estimation images and the corresponding label data are applied to learning of the machine learning model, and the lighting condition is set on the basis of a comparison result between an estimation result of a machine learning model and the label data. Therefore, operations are simplified.

Abstract: A data generation apparatus includes: an acquisition unit configured to acquire an image of an object to be inspected including a defect, and a region of the image that includes the defect; a correction unit configured to correct the region acquired by the acquisition unit by expanding an outer edge of the region so that the number of pixels included in the region is increased by a predetermined amount; and a generation unit configured to generate learning data by associating the region corrected by the correction unit with the image.

Abstract: A convolutional neural network performs: a first masking process of masking a pixel region not to be inspected, by computing pixel values of corresponding pixels of an inspection image and of a mask image; an intermediate process for extracting a feature image from the inspection image that has been subjected to the first masking process; and a second masking process of masking the pixel region not to be inspected, by computing the pixel values of corresponding pixels of the inspection image that has been subjected to the intermediate process and of the mask image that has been subjected to a process identical to the intermediate process.

Abstract: This image processing device, by superimposing a defect image 52 on a background image 51, with the original image of an inspection subject as the background image 51, and performing image processing in which the display format of the defect image 52 or the display format of the background image 51 is altered, a plurality of composite images 50 having different ways of seeing the defect image 52 with respect to the background image 51 are generated (step 403), the advisability of detecting the defect image 52 from each of the plurality of composite images 50 is verified (step 404), the detectable range 60 of the defect image 52 is estimated on the basis of the detection advisability verification results (step 405), and the detectable range 60 is displayed (step 406).

Abstract: A defect inspecting device including an acquisition unit, an image generating unit, an inspection unit, and a setting unit. The image generating unit generates one or more feature extraction images by applying to an inspection image a learned classifier. The inspection unit identifies a region corresponding to a defect based on one or more determination parameters and a binarized image generated based on the feature extraction image. The setting unit provisionally sets the determination parameters on premise of a post-adjustment from a user when a number of image data for learning corresponding to the features is less than a threshold value in a case where the region corresponding to the defect is identified based on the pre-learned features, and sets the determination parameters according to designation from the user in a case where the region corresponding to the defect is identified based on a feature other than the pre-learned features.

Abstract: Provided in the present disclosure are a method, an apparatus and a system for determining feature data of image data, and a storage medium. Wherein, the method comprises: acquiring features of image data, the features comprising a first feature and a second feature, wherein, the first feature is extracted from the image data using a first model, the first model being trained in a machine learning manner, and the second feature is extracted from the image data using a second model, the second model being constructed based on a pre-configured data processing algorithm; and determining feature data based on the first feature and the second feature. The present disclosure solves the technical problem that features recognized by the AI may not be consistent with human recognized features.

Abstract: An inspection apparatus includes: an image capturing apparatus configured to capture an image of an object to be inspected: a determination unit configured to determine, based on the image, whether or not the object to be inspected includes a defect, using an identification device that has been trained using learning data: an input unit configured to accept an input indicating whether or not a determination result by the determination unit is correct; an extraction unit configured to extract a partial image of the image based on which the determination has been made; and a generation unit configured to generate new learning data based on the partial image, if a fact that the determination result by the determination unit is not correct has been input.

Abstract: The present disclosure relates to a method, device, system and computer-program product for setting a lighting condition when an object is checked and a storage medium. The method includes that: the object is lighted by light sources capable of changing lighting parameters, and the object is captured by an image sensor in such lighting parameters to obtain captured images, wherein the object has known label data; and a part of or all of the captured images and the corresponding label data of the object are applied to learning of a machine learning model, and the lighting condition and the check algorithm parameters of the machine learning model is set simultaneously by optimizing both the lighting parameters and the check algorithm parameters, on the basis of a comparison result between an estimation result of the machine learning model and the label data. Therefore, operations are simplified.

Abstract: The present disclosure relates to a method, device, system and computer-program product for setting a lighting condition when an object is checked and a storage medium. The method includes that: the object is lighted by light sources capable of changing lighting parameters specifying the lighting condition when the object is captured, and the object with corresponding label data is captured by an image sensor under multiple such lighting parameters to obtain multiple captured images; estimation images of the object are generated on the basis of image data sets obtained by associating the captured images and the corresponding lighting parameters; and the estimation images and the corresponding label data are applied to learning of the machine learning model, and the lighting condition is set on the basis of a comparison result between an estimation result of a machine learning model and the label data. Therefore, operations are simplified.

Abstract: A data generation apparatus includes: an acquisition unit configured to acquire a result determined by a determination unit, which uses an identification device trained using learning data, as to whether or not an object to be inspected includes a part to be detected based on an image of the object to be inspected; an evaluation unit configured to evaluate whether or not the determination result is correct; and a generation unit configured to generate, if the evaluation unit has evaluated that the determination result is not correct, new learning data by associating at least one of the image and a composite image generated based on the image with information in which the determination result is corrected.