Abstract: A method includes obtaining an input image that contains blur. The method also includes providing the input image to a trained machine learning model, where the trained machine learning model includes (i) a shallow feature extractor configured to extract one or more feature maps from the input image and (ii) a deep feature extractor configured to extract deep features from the one or more feature maps. The method further includes using the trained machine learning model to generate a sharpened output image. The trained machine learning model is trained using ground truth training images and input training images, where the input training images include versions of the ground truth training images with blur created using demosaic and noise filtering operations.

Abstract: In one aspect, an example method includes a processor (1) applying a feature map network to an image to create a feature map comprising a grid of vectors characterizing at least one feature in the image and (2) applying a probability map network to the feature map to create a probability map assigning a probability to the at least one feature in the image, where the assigned probability corresponds to a likelihood that the at least one feature is an overlay. The method further includes the processor determining that the probability exceeds a threshold, and responsive to the processor determining that the probability exceeds the threshold, performing a processing action associated with the at least one feature.

Abstract: Encoding a three-dimensional point cloud. A set of points are obtained within the three-dimensional point cloud, a point within the set of points having a co-ordinate in three-dimensions. The points are converted into a two-dimensional representation. For a point within the set of points, information describing the co-ordinate is represented as a location within the two-dimensional representation and a value at the location. The two-dimensional representation is encoded using a tier-based hierarchical coding format to output encoded data. The tier-based hierarchical coding format encodes the two-dimensional representation as layers, the layers representing echelons of data used to progressively reconstruct the signal at different levels of quality.

Abstract: A seed camera disposed a first location is manually calibrated. A second camera, disposed at a second location, detects a physical marker based on predefined characteristics of the physical marker. The physical marker is located within an overlapping field of view between the seed camera and the second camera. The second camera is calibrated based on a combination of the physical location of the physical marker, the first location of the seed camera, the second location of the second camera, a first image of the physical marker generated with the seed camera, and a second image of the physical marker generated with the second camera.

Abstract: A method of filtering a target pixel in an image forms, for a kernel of pixels comprising the target pixel and its neighbouring pixels, a data model to model pixel values within the kernel; calculates a weight for each pixel of the kernel comprising: (i) a geometric term dependent on a difference in position between that pixel and the target pixel; and (ii) a data term dependent on a difference between a pixel value of that pixel and its predicted pixel value according to the data model; and uses the calculated weights to form a filtered pixel value for the target pixel, e.g. by updating the data model with a weighted regression analysis technique using the calculated weights for the pixels of the kernel; and evaluating the updated data model at the target pixel position so as to form the filtered pixel value for the target pixel.

Abstract: An image fusion system provides a predicted alignment between images of different modalities and synchronization of the alignment, once acquired. A spatial tracker detects and tracks a position and orientation of an imaging device within an environment. A predicted pose of an anatomical feature can be determined, based on previously acquired image data, with respect to a desired position and orientation of the imaging device. When the imaging device is moved into the desired position and orientation, a relationship is established between the pose of the anatomical feature in the image data and the pose of the anatomical feature imaged by the imaging device. Based on tracking information provided by the spatial tracker, the relationship is maintained even when the imaging device moves to various positions during a procedure.

Abstract: Methods and systems for generation and use of an accelerated tomographic reconstruction preconditioner (ATRP) for accelerated iterative tomographic reconstruction are disclosed. An example method for generating an ATRP for accelerated iterative tomographic reconstruction includes accessing data for a tomography investigation of a sample and determining a trajectory of the tomography investigation of a sample. At least one toy model sample depicting a feature characteristic of the sample are accessed and at least one candidate preconditioner is selected. A first performance of each of the at least one candidate preconditioner on the one or more toy samples is determined, where the candidate preconditioners are then updated to create updated candidate preconditioners. A second performance of each of the updated candidate preconditioners on the one or more toy samples is determined determining. An ATRP is then generated based on at least the first performance and the second performance.

Abstract: A model dataset is generated based on first image data. The model dataset and second image data map at least a common part of an examination region at a second detail level. The model dataset and the second image data are pre-aligned at a first detail level below the second detail level based on first features that are mapped at the first detail level in the model dataset and the second image data and/or an acquisition geometry of the second image data. The model dataset and the second image data are registered at the second detail level based on second features that are mapped at the second detail level in the model dataset and the second image data. The second class of features is mappable at the second detail level or above. The registered second image data and/or the registered model dataset is provided.

Abstract: Various techniques pertain to a user interface that includes an instructional display portion comprising one or more textual or graphical instructions pertaining to preparing for a body scan for body care of a client and a scan initiation display portion that includes a scan initiation widget which, when interacted by the client, invokes execution of a scan process and an analysis process for a respective body area of the client. The user interface further includes a scan result display portion including interactable textual or graphical information pertaining to a result of the body scan for the body care and an adjustment display portion having a plurality of adjustment widgets that transform at least a color index or value into a transformed color index or value in a L*A*B* color space or a Lch color space.

Abstract: The present disclosure includes systems and methods for handwriting recognition. Handwriting data is received. Geometric data of text in handwriting data is determined. Sub-characters of the text are determined. Sub-characters of text are matched to a model. Most probable characters of the text is determined based on the matching.

Abstract: The second multi-dimensional feature vectors 92a of sample image data 34a having instruction signals that are converted by a feature converter 27 are read in (Step S10), two-dimensional graph data for model 36a is generated based on the read second multi-dimensional feature vectors 92a to be stored (Step S12), two-dimensional model graphs Og and Ng are generated based on the generated two-dimensional graph data for model 36a, to be displayed on the window 62 (Step S14). The second multi-dimensional feature vectors 92a are indicators appropriate for visualization of the trained state (individuality) of a trained model 35. Thus, it is possible to visually check and evaluate whether the trained model 35 is in an appropriately trained state (individuality) or not.

Abstract: System, device, and method for improved encoding and enhanced compression of images, videos, and media content. A method includes: (a) receiving a source image, and analyzing its content on a pixel-by-pixel basis, and classifying each pixel as either (I) a pixel associated with Photographic content, or (II) a pixel associated with Non-Photographic content; (c) generating a pixel-clusters map that indicates (i) clusters of pixels that were classified as Photographic content, and (ii) clusters of pixels that were classified as Non-Photographic content; (d) generating a composed image, by: (d1) applying a first encoding technique, particularly lossy encoding, to encode pixel-clusters that were classified as Photographic content; (d2) applying a second, different, encoding technique, particularly lossless encoding, to encode pixel-clusters that were classified as Non-Photographic content.

Abstract: Method are provided that exhibit increased quality and compression factor for compressing images. The methods can include generating a set of coefficients indicative of image contents of a block of image pixels at a plurality of spatial frequencies. The set of coefficients is scaled to generate a first set of scaled coefficients. An assessment is performed for a plurality of quantization levels, which includes quantizing a subset of the first set of scaled coefficients according to respective quantization levels to generate a quantized subset of the first set of scaled coefficients and determining a post-quantization energy of the quantized subset of the first set of scaled coefficients. Based on the assessment of the plurality of quantization levels, a scaled and quantized version of the set of coefficients is generated. An encoded version of the image based on the scaled and quantized version of the set of coefficients is generated.

Abstract: One example method includes gathering, by a domain adversarial neural network model deployed in an autonomous vehicle operating in a domain, a dataset that comprises unsegmented and unlabeled image data about the domain, sampling the dataset to create an adapted domain dataset, detaching a domain classifier from the domain adversarial neural network, using the domain classifier as a domain change detector model to predict a class of the unsegmented and unlabeled image data in the adapted domain dataset, and based on the class, either: determining that the domain is changed or is unknown; or, determining that the domain has not changed.

Abstract: An image capturing method has: providing an image capturing area on a display screen of a user device; providing an indication area in the image capturing area; marking a license plate after identifying the license plate based on at least one license plate feature in the image capturing area; determining whether the marked license plate is located in the indication area and presented in a predetermined ratio; and capturing an image including the license plate in the image capturing area after the marked license plate is located in the indication area and presented in a predetermined ratio.

Abstract: One embodiment provides a method, the method including: receiving, at a shock analysis system, image data corresponding to at least one image capture device and associated with transportation of a package; determining, at the shock analysis system and by analyzing the image data, the package has undergone a shock of a magnitude exceeding a predetermined threshold; and performing, at the shock analysis system and responsive to determining the package has undergone the shock exceeding the predetermined threshold, at least one predetermined action, wherein the at least one predetermined action comprises notifying a user of the shock.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an encoder neural network configured to receive a data item and to process the data item to output a compressed representation of the data item. In one aspect, a method includes, for each training data item: processing the data item using the encoder neural network to generate a latent representation of the training data item; processing the latent representation using a hyper-encoder neural network to determine a conditional entropy model; generating a compressed representation of the training data item; processing the compressed representation using a decoder neural network to generate a reconstruction of the training data item; processing the reconstruction of the training data item using a discriminator neural network to generate a discriminator network output; evaluating a first loss function; and determining an update to the current values of the encoder network parameters.

Abstract: An information processing apparatus includes: an acquisition unit that acquires height information relating to a height of a capturing position of an imaging device that captured an image in which a face of a user was detected; and a determination unit that determines, based on the height information, whether or not the user is a candidate for person requiring assistance, who may be a person requiring assistance.

Abstract: A method for increasing the confidence of a match between a test image and an image stored in a library database in which features are extracted from the test image and compared to features stored in the image database. If a match is determined, one or more transformations are performed on the test image to generate pose-altered images from which features are extracted and matched with pose-altered images in the database. The scores for the subsequent matchings can be aggregated to determine an overall probability of a match between the test image in an image in the library database.

Abstract: Apparatuses, systems, and techniques are presented to generate ultrasound images. In at least one embodiment, use one or more neural networks are used to generate one or more ultrasound images of one or more objects based, at least in part, upon one or more acoustic properties of the one or more objects.