Abstract: The disclosure relates to a method including: (S1) Determining the jaw arch form as a model-based prior knowledge about the anatomy of a patient based on one or more previous panoramic images or 3D images or one or more optical 3D scans of the patient; (S2) Determining a patient-specific x-ray device imaging trajectory for the panoramic image of the patient; (S3) Performing the imaging on the basis of the determined patient-specific x-ray device imaging trajectory to acquire imaging data; (S4) Adjusting reconstruction parameters according to the determined patient-specific x-ray device imaging trajectory; (S5) Determining the patient-specific focal curve for the panoramic image of the patient to be produced by using the model-based prior knowledge; (S6) Reconstructing the panoramic image using the acquired imaging data, the adjusted reconstruction parameters including the determined patient-specific focal curve, and a layer to be imaged which overlaps with the determined jaw arch

Abstract: An image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry obtains, on the basis of a magnetic resonance image, a plurality of shift images resulting from shifting the position of the pixel sampling grid of the magnetic resonance image by a plurality of mutually-different shift amounts. The processing circuitry generates a plurality of ringing-corrected images, by determining, with respect to each of the pixels included in each of the plurality of shift images, a shift amount from the position of the pixel to a position where ringing artifacts will be reduced and further performing a ringing correction to correct the ringing artifacts occurring in the shift images on the basis of the determined shift amounts. The processing circuitry combines, while interleaving, pixels included in each of the plurality of ringing-corrected images.

Abstract: Disclosed in the present invention are a particle detection method, an electronic device, and a storage medium. The particle detection method includes obtaining a detection image; identifying an imaging of a particle in the detection image and obtaining a shadow region of the particle; determining a maximum influence degree of the particle based on an influence degree of each pixel point in the shadow region; and obtaining size information of the particle based on the maximum influence degree of the particle. The present disclosure can distinguish the particles of different sizes, thereby accurately calculating the size of the particle and improving the accuracy of the particle detection.

Abstract: An action analysis device includes an acquisition unit that acquires behavior data indicating a behavior of an object during an operation process, which has been recognized by a model preliminarily learned for recognizing the object; and a determination unit that determines a required time for a process corresponding to the behavior data based on the behavior data acquired by the acquisition unit. For example, based on time information set as the required time for the process, the determination unit determines a required time for a process corresponding to the behavior data.

Abstract: A method for training a super-resolution model, a super-resolution method, and a system are provided, and the super-resolution method and the system are implemented through an AI super-resolution model that is trained by the method. In the method, an input image is provided, and a magnification ratio and an image quality threshold are set. Pixel values of the input image are retrieved, and image features of the input image are extracted. Multiple channel images are obtained through a super-resolution model based on the image features and the magnification ratio. Phase information can be obtained according to the magnification ratio and positions of output pixels, and the phase information is used to obtain masks mapping to the channel images. Therefore, an output image can be reshuffled. After a comparison with the image quality threshold, model parameters of the output image can be assessed for training the AI super-resolution model.

Abstract: This disclosure relates to computer vision and generative artificial intelligence (AI) techniques for generating a target image set based on content included in a source image. The target image set comprises a plurality of target images, each of which is compliant with or more target display specifications for an electronic platform. The target images can be generated by an image adaptation network that comprises various AI models, including one or more saliency model, one or more generative models, one or more scene detection models, and/or one or more segmentation models.

Abstract: A non-transitory computer-readable storage medium storing an information processing program that causes at least one computer to execute a process, the process includes acquiring video data that has target objects that include a person and an object; specifying each relationship of each of the target objects in the acquired video data by using graph data that indicates a relationship of each of target object stored in a storage unit; specifying a behavior of the person in the acquired video data by using a feature of the person; and predicting a future behavior of the person or a state of the person by comparing the specified behavior of the person and the specified relationship with a behavior prediction rule, the behavior prediction rule being a rule associated with a future behavior of a person for each combination of a behavior of the person and a relationship.

Abstract: Techniques are disclosed for determining the presence of a particular person based on facial characteristics. For example, a device may include a first image in a reference set of images based on determining that a face shown in the first image is not covered by a face covering. A trained model of the device may determine a first set of characteristics from the first image, whereby the trained model is trained utilizing simulated face coverings to match a partially covered face of a particular person with a non-covered face of the particular person. The device may also determine a second set of characteristics associated with a face of a second person based on a second image. The trained model may then determine a score corresponding to a level of similarity between both sets of characteristics, and then determine whether the first person is the second person based on the score.

Abstract: Systems, methods, and computer-readable media for providing a localization processing service for enabling localization of a navigation network-restricted subsystem are provided.

Abstract: A method for detecting a road-boundary based on radar information and visual information, the method may include (a) estimating, based on the visual information obtained by a visual sensor, locations of road-boundary points up to a first distance from the visual sensor; wherein a distance ambiguity of visual based location determination of road-boundary points with the first distance range does not exceed a predefined ambiguity threshold; wherein a distance ambiguity of visual based location determination of road-boundary points outside the first distance exceeds a predefined ambiguity threshold; (b) estimating, based at least on (i) the radar information, and (ii) angular-constrains regarding angular relationships between adjacent road-boundary points, locations of road-boundary points outside the first distance range; and (c) determining the shape and position of the road-boundary based on the locations of the road-boundary points within the first range distance.

Abstract: Aspects of the present disclosure relate to a method of processing an input image of a culture device for a condition. The method can include receiving the input image and classifying the input image with a trained machine learning model that is configured to be trained on a training set of images having the condition. The method can include determining that the condition exists in the input image based on the classification and performing at least one action in response to the determination that the condition exists.

Abstract: Systems, methods, apparatuses and non-transitory, computer-readable storage mediums are disclosed for generating AR self-portraits or “AR selfies.” In an embodiment, a method comprises: capturing, by a first camera of a mobile device, live image data, the live image data including an image of a subject in a physical, real-world environment; receiving, by a depth sensor of the mobile device, depth data indicating a distance of the subject from the camera in the physical, real-world environment; receiving, by one or more motion sensors of the mobile device, motion data indicating at least an orientation of the first camera in the physical, real-world environment; generating a virtual camera transform based on the motion data, the camera transform for determining an orientation of a virtual camera in a virtual environment; and generating a composite image data, using the image data, a matte and virtual background content selected based on the virtual camera orientation.

Abstract: Iterative content-adaptive online training for end-to-end (E2E) neural image compression (NIC) using a neural network performed by at least one processor, is provided, including receiving an input image, to an E2E NIC framework, fine-tuning the E2E NIC framework, based on the input image, computing parameter updates using a first neural network of the fine-tuned E2E NIC framework, enhancing the fine-tuned E2E NIC framework based on a second neural network, the second neural network being a post-enhancement network, and generating an updated E2E NIC framework, based on the enhanced E2E NIC framework and the parameter updates.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for data processing. A method for data processing includes acquiring an image input and text input pair, the image input and text input pair comprising image input and text input, obtaining an image feature codebook corresponding to the image input and a text feature codebook corresponding to the text input, and training a text-image association model by using the image feature codebook and the text feature codebook, the text-image association model implementing the data processing based on the association between the image input and the text input. For example, a model may be adopted to learn an association between an image feature codebook corresponding to image input and a text feature codebook corresponding to text input, and data processing, such as data compression or data retrieval, is implemented using the learned association.

Abstract: In an embodiment a computer system includes at least one master module configured to process data having a format of N bits, a framebuffer configured to store pixel color component values of an image, the framebuffer having a resolution of N bits, each pixel being coded on P bits in the framebuffer and the pixels being stored one after another in the framebuffer and a memory management unit configured to control memory accesses of the at least one master module to the framebuffer, wherein the memory management unit is further configured to receive read memory access requests from the at least one master module, read at least one pixel in the framebuffer saved on P bits, and modify the format of the at least one read pixel by adding Q additional bits equal to a difference between N and P so as to format the at least one pixel on N bits before transmitting the at least one pixel to the at least one master module.

Abstract: A method for wafer image equalization includes obtaining a wafer image, converting the wafer image into bitmap data, generating a grayscale distribution of RGB pixels according to the bitmap data, generating a grayscale cumulative probability distribution of the RGB pixels according to the grayscale distribution, generating a mapping function according to the grayscale cumulative probability distribution of the RGB pixels, converting the grayscale distribution of the RGB pixels by the mapping function into an equalized grayscale distribution of the RGB pixels, and generating an equalized wafer image according to the equalized grayscale distribution of the RGB pixels.

Abstract: In one embodiment, a device receives a request for a machine learning model to make an inference about input data included in the request. The device retrieves metadata regarding training data used to train the machine learning model from a ledger associated with the machine learning model. The device assesses bias of the machine learning model by comparing the input data in the request to the metadata from the ledger. The device provides an indication of the bias of the machine learning model for display.

Abstract: Methods of training a machine learning model for image processing are described, as well as methods for image processing to determine a predicted rotation value for an image based on the trained machine learning model. A method of training includes utilising as a learning objective a reduction or minimisation of a classification loss. Training image pairs may be associated with classifications corresponding to rotation values for the purpose of determining the classification loss. An image may be divided into a plurality of images for determining an input to the machine learning model, for training or for image processing.

Abstract: A method of generating a plurality of spatially co-registered data elements, each spatially co-registered data element being associated with and generated from a pair of co-registered tissue sections obtained from adjacent positions of a core taken from a tissue sample and including an image data section and a genomic data section. The method includes, for each pair of co-registered tissue sections: (i) obtaining and storing as part of a data element a plurality of multi to hyperplexed images from the imaging data section of the co-registered tissue section, (ii) generating and storing as part of the data element image data from the plurality of multi to hyperplexed images, and (iii) generating and storing as part of the data element genomic data from the genomic data section of the associated co-registered tissue section.

Abstract: A computer-implemented method includes receiving an image of an article of interest to be evaluated relative to one or more features of interest, identifying a reference feature of a known size in the received image, identifying two or more extremities of the reference feature in the received image and a number of pixels between the two or more extremities of the reference feature, calculating a pixel size for the selected image based on the reference feature size and the number of pixels between the two or more extremities of the reference feature, annotating the received image to include one or more tolerance lines for the one or more features of interest, and determining whether the one or more features of interest in the image comply with the one or more tolerance lines. A computer program product and computer system corresponding to the method are also disclosed.