Abstract: A learned model generation method includes: using a teacher image including a defect map that is an image indicating a distribution of a defect portion of a surface of steel and having an equal image size, and presence/absence of periodic defects assigned in advance to the defect map; and generating a learned model by machine learning, the learned model for which: an input value is a defect map that is an image indicating a distribution of a defect portion of a surface of steel and having an image size of the equal image size; and an output value is a value concerning presence/absence of periodic defects in the defect map.

Abstract: An image processing method is performed by an electronic device, the method including: obtaining, according to a quantity of pixels whose color feature values fall within color feature intervals in an original image, a first histogram corresponding to the original image; obtaining, according to a quantity of pixels whose color feature values fall within the color feature intervals in an upsampled image of the original image, a second histogram corresponding to the upsampled image; respectively determining, according to the first histogram and the second histogram, target color feature intervals matching the color feature intervals in the second histogram; and processing, according to the target color feature intervals matching the color feature intervals in the second histogram, the color feature values of the pixels falling within the color feature intervals in the upsampled image, to obtain a target image.

Abstract: An automated method for detecting glass-ceramic materials in cullet, includes detecting the glass-ceramic material in cullet, during which fragments thought to be glass-ceramic material are identified in the cullet, obtaining a digital image resulting from the detecting of the glass-ceramic material, the image including at least one group of pixels corresponding to a fragment thought to be glass-ceramic material, colorimetrically processing the image, during which at least the group of pixels of the image corresponding to fragments thought to be glass-ceramic material is processed by a colorimetric image processing module according to an RGB model.

Abstract: A system and computer-implemented method for facilitating a user of a mobile device obtaining image data of damage to a vehicle for damage assessment includes capturing image data of a vehicle with the mobile device. The mobile device may include an orientation model for capturing the image data. The captured image data is analyzed, and a determination is made of the orientations of the images of the captured image data. In addition, a determination is made as to whether the captured image data can be used for the damage assessment. The captured image data may then be transmitted to a damage estimator computing device for estimating an amount of damage to the vehicle.

Abstract: A method for welding a workpiece with a vision guided welding platform. The welding platform comprises a welding tool, and a camera for guiding the movement of the welding tool from a start point to an end point. The method includes the steps of adjusting a focal length of the camera such that a focal plane of the camera is located on a surface of the workpiece and obtaining a surface image of the workpiece. The method further includes the steps of determining a current focal length of the camera, determining a corrected pixel length of a pixel in the surface image and determining the number of pixels between the start point and the end point of each movement of the welding tool. Using the corrected pixel length, a distance between the start and end points is determined and the welding tool is guided to move therebetween.

Abstract: A method of generating a defect image for deep learning and a system therefor are provided. The method and the system are intended to be used in generating training data for an artificial intelligence algorithm. More specifically, the training data are defect images required to train an algorithm that identifies a defect from a product.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing highlight recovery. One of the methods includes receiving raw image data of an image, the raw image data comprising, for each of a plurality of highlight regions in the image, original channel values for one or more channels of the highlight region; processing the raw image data to generate updated image data, wherein the updated image data comprises updated channel values for each highlight region in the image, and wherein, for each of one or more highlight regions of the image, one or more updated channel values of the highlight region exceed a maximum channel value according to a predetermined data precision; and performing a hue correction process on the updated channel values of the one or more highlight regions of the image to generate final channel values that satisfy the predetermined data precision.

Abstract: In some embodiments, a method receives query content that includes first visual data and select reference content that includes second visual data. A first visual feature mapping for the query content is generated using a model that is trained to generate visual feature mappings using content pairs where alterations in one instance of the content pair are used in the training. A second visual feature mapping is determined for the reference content. The second visual feature mapping is determined using the model. The method compares the first visual feature mapping and the second visual feature mapping to generate a score that rates a similarity between the query content and the reference content. The score is based on recognizing an alteration in one instance of the query content and the reference content. The score is output.

Abstract: A system and method for assessing categorized pixels of satellite images associated with agriculture land parcel using an artificial intelligence (AI) model are provided.

Abstract: An imaging system is for recording images of a raceway of a bearing ring, the ring having two opposing axial ends, a central axis extending between the axial ends, and inner and outer circumferential surfaces. The raceway is formed on the inner circumferential surface or the outer circumferential surface of the ring and has first and second ends spaced along the central axis. The imaging system includes a frame movably coupleable with the bearing ring and having a centerline. A drive is mounted to the frame and is configured to angularly displace the frame about the bearing central axis. An optical imager has a lens and an adjustable positioner is mounted on the frame and is configured to position the optical imager such that the lens is disposed axially between the raceway first and second ends and faces generally perpendicular to the bearing raceway.

Abstract: An edge inspection apparatus includes a guide bracket and a camera mount. The guide bracket includes at least one contact surface and an abradant retainer. The contact surface makes contact with a corresponding surface of a target object and aids in positioning the guide bracket relative to an edge of the target object. The abradant retainer secures an abradant to the guide bracket and secures the abradant in a position relative to the edge of the target object. The camera mount secures a camera in a fixed position relative to the guide bracket and orients the camera lens toward the edge of the target object.

Abstract: A drive-through vehicle inspection system with a method for acquiring information from markings on tire sidewall surfaces of a moving vehicle. As the vehicle passes through the inspection system, sets of colored light sources, disposed at different relative orientations on opposite lateral sides of the vehicle, illuminate each passing wheel, enabling optical imaging systems associated with the opposite lateral sides of the inspection lane to acquire color images of the illuminated tire sidewall surfaces. Acquired color images are passed to a processing system and separated into individual red, green, and blue color channels for image processing. The processed output from each color channel is recombined by the processing system into a synthesized grayscale image highlighting and emphasizing markings present on the tire sidewall surfaces for evaluated by an OCR algorithm to retrieve tire identifying information.

Abstract: The present disclosure provides a method for in-situ monitoring of a full cycle of spatter formation, ejection, and falling in laser additive manufacturing. In a spatter formation stage, a paraxial multiocular high-speed photographing apparatus is used to provide a high temporal-spatial resolution, accurately collect spatial positions of spatters at each sampling time, and reconstruct a motion trajectory of the spatters in three-dimensional (3D) space. In an ejection stage, a coaxial monocular high-speed photographing apparatus is used to provide a long-term high temporal-spatial resolution and accurately collect and count an ejection velocity and angle and a motion trajectory of the spatters, and a photodiode monitors a radiation intensity and changes of the spatters. In a falling stage, a multiocular photographing apparatus is used to provide a high spatial resolution covering an entire powder bed and collect a distribution state of falling spatters.

Abstract: The present disclosure provides a method for locating an image region. The method comprises determining one or more regions in an image. Each of the regions corresponding to a respective candidate object in the image. The method comprises generating respective semantic information having a one-to-one correspondence with a corresponding candidate object in each of the regions. The method comprises obtaining respective enhanced semantic information corresponding to the respective semantic information using a graph convolutional network (GCN). A matching degree between a text feature set corresponding to a to-be-located text and each of the respective enhanced semantic information is obtained using an image region locating network model. The method further comprises determining a target image candidate region from the one or more regions according to the matching degree between the text feature set and each of the respective enhanced semantic information.

Abstract: A method for qualifying a specimen prepared on one or more hematoxylin and eosin (H&E) slides by assessing an expected yield of nucleic acids for tumor cells and providing associated unstained slides for subsequent nucleic acid analysis is provided.

Abstract: A system and method for unsupervised superpixel-driven instance segmentation of a remote sensing image are provided. The remote sensing image is divided into one or more image patches. The one or more image patches are processed to generate one or more superpixel aggregation patches based on a graph-based aggregation model, respectively. The graph-based aggregation model is configured to learn at least one of a spatial affinity or a feature affinity of a plurality of superpixels from each image patch and aggregate the plurality of superpixels based on the at least one of the spatial affinity or the feature affinity of the plurality of superpixels. The one or more superpixel aggregation patches are combined into an instance segmentation image.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for training an image analysis engine to identify surface features of the Earth consistent with subsurface hydrogen accumulation. An example method includes receiving, by communications circuitry, a training dataset of labeled images illustrating surface features consistent with subsurface hydrogen accumulation, the surface features consistent with subsurface hydrogen accumulation comprising ovoid surficial depressions. The example method further includes training, by a model generator and using the training dataset, an image classification model of the image analysis engine to identify whether new images contain surface features consistent with subsurface hydrogen accumulation. The example method further includes hosting the image classification model by the image analysis engine.

Abstract: An object detecting device includes a feature extracting circuit configured to extract first feature data from an input image; a feature transforming circuit configured to transform the first feature data into transformed feature data according to a transformation function; and a decoder circuit configured to decode the transformed feature data into a region map indicating a detected object.

Abstract: An image analysis method, including: obtaining influencing factors of t frames of images, where the influencing factors include self-owned features of h target subjects in each of the t frames of images and relational vector features between the h target subjects in each of the t frames of images, self-owned features of each target subject include a location feature, an attribute feature, and a posture feature, and t and h are natural numbers greater than 1; and obtaining a panoramic semantic description based on the influencing factors, where the panoramic semantic description includes a description of relationships between target subjects, relationships between actions of the target subjects and the target subjects, and relationships between the actions of the target subjects.

Abstract: The display controller displays a first tomographic image of a three-dimensional image consisting of a plurality of tomographic images on a display unit. A first correction unit corrects the boundary of a first region of interest extracted from the first tomographic image, by a correction instruction using a correction instruction region for the boundary of the first region of interest. The first instruction region setting unit sets a first instruction region on a second tomographic image of the plurality of tomographic images. The second correction unit corrects the boundary of a second region of interest extracted from the second tomographic image by setting a second instruction region on the second tomographic image.