Abstract: Various systems are provided for non-uniform thickness and/or sampling of slabs of the breast to present DBT acquisitions. A method for generating a patient image as a set of slabs representing an imaged object, the method comprising acquiring a tomosynthesis projection, reconstructing a series of slab images, each slab representing a portion of a breast, and a plurality of slabs of non-uniform thickness and/or non-uniform sampling in a 3D reconstructed domain defined by x-, y-, and z-axes.

Abstract: A computer-implemented method for ascertaining an output signal, which characterizes an object detection of an object of an image. The method includes: ascertaining a plurality of object detections with respect to the image; ascertaining a graph based on the plurality of object detections, object detections of the plurality of object detections being characterized by nodes of the graph and overlaps between two object detections each being characterized by edges of the graph; ascertaining a cluster of the graph based on the nodes and on the edges of the graph with the aid of a density-based clustering method; ascertaining an object detection based on the cluster and providing the object detection in the output signal.

Abstract: A checking mechanism for workshop security applying a method for workshop security comprises a transmission device, a recording device, a storage subassembly and a control subassembly. The transmission device comprises a first end (115) and a second end (116). The recording device records personnel and objects in their possession through a security check process, to establish a correspondence. The storage subassembly stores the information comprising personnel and their objects. The control subassembly is used for controlling the recording device to record and store the information to the storage subassembly. The control subassembly can retrieve the information recorded by the recording device to detect a certain person's correspondence to the certain object.

Abstract: A method for recognizing different object-categories within images based on texture classification of the different categories, which is implemented in an electronic device, includes extracting texture features from block images segmented from original images according to at least one Gabor filter; determining a grayscale level co-occurrence matrix of each block image according to the texture features; calculating texture feature statistics of each block image according to the grayscale level co-occurrence matrix; training and generating an object recognition model using the texture features and the texture feature statistics; and recognizing and classifying at least one object in original image according to the object recognition model.

Abstract: The present disclosure discloses an apparatus and a method for detecting a facial pose, an image processing system, and a storage medium. The apparatus comprises: an obtaining unit to obtain at least three keypoints of at least one face from an input image based on a pre-generated neural network, wherein coordinates of the keypoints obtained via a layer in the neural network for obtaining coordinates are three-dimensional coordinates; and a determining unit to determine, for the at least one face, a pose of the face based on the obtained keypoints, wherein the determined facial pose includes at least an angle. According to the present disclosure, the accuracy of the three-dimensional coordinates of the facial keypoints can be improved, thus the detection precision of a facial pose can be improved.

Abstract: Systems and methods described herein relate, among other things, to unmixing more than three stains, while preserving the biological constraints of the biomarkers. Unlimited numbers of markers may be unmixed from a limited-channel image, such as an RGB image, without adding any mathematical complicity to the model. Known co-localization information of different biomarkers within the same tissue section enables defining fixed upper bounds for the number of stains at one pixel. A group sparsity model may be leveraged to explicitly model the fractions of stain contributions from the co-localized biomarkers into one group to yield a least squares solution within the group. A sparse solution may be obtained among the groups to ensure that only a small number of groups with a total number of stains being less than the upper bound are activated.

Abstract: The technology disclosed performs hand pose estimation on a so-called “joint-by-joint” basis. So, when a plurality of estimates for the 28 hand joints are received from a plurality of expert networks (and from master experts in some high-confidence scenarios), the estimates are analyzed at a joint level and a final location for each joint is calculated based on the plurality of estimates for a particular joint. This is a novel solution discovered by the technology disclosed because nothing in the field of art determines hand pose estimates at such granularity and precision. Regarding granularity and precision, because hand pose estimates are computed on a joint-by-joint basis, this allows the technology disclosed to detect in real time even the minutest and most subtle hand movements, such a bend/yaw/tilt/roll of a segment of a finger or a tilt an occluded finger, as demonstrated supra in the Experimental Results section of this application.

Abstract: Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.

Abstract: Various implementations disclosed herein include devices, systems, and methods for per-pixel filtering. In some implementations, a method includes obtaining an image data frame. In some implementations, the image data frame includes a plurality of pixels. In some implementations, the method includes generating a respective pixel characterization vector for each of the plurality of pixels. In some implementations, each pixel characterization vector includes an object label indicating an object type that the corresponding pixel of the plurality of pixels represents. In some implementations, the method includes modifying corresponding pixel data of the plurality of pixels having a first object label. In some implementations, the method includes synthesizing a first modified image data frame that includes modified pixel data for the plurality of pixels having the first object label and unmodified pixel data for the plurality of pixels not having the first object label.

Abstract: Systems and techniques are described herein for processing frames. The systems and techniques can be implemented by various types of systems, such as by an extended reality (XR) system or device. In some cases, a process can include obtaining feature information associated with a feature in a current frame, wherein the feature information is based on one or more previous frames; determining an estimated pose of the apparatus associated with the current frame; obtaining a distance associated with the feature in the current frame; and determining an estimated scale of the feature in the current frame based on the feature information associated with the feature, the estimated pose, and the distance associated with the feature.

Abstract: Methods, systems, and devices for equipment reading in a factory or plant environment are described, including: capturing an image of an environment including a measurement device; detecting a target region included in the image, the target region including at least a portion of the measurement device; determining identification information associated with the measurement device based on detecting the target region; and extracting measurement information associated with the measurement device based on detecting the target region. In some aspects, detecting the target region may include: providing the image to a machine learning network; and receiving an output from the machine learning network in response to the machine learning network processing the image based on a detection model, the output including the target region.

Abstract: An image processing device that processes a color image in which each pixel has gradation values of three primary colors of RGB includes an image acquiring section to acquire an image; a difference image generating section to use a first primary color image extracted from the color image and a second primary color image in which a gradation value of a second primary color except the first primary color is extracted from the color image, to generate a difference image; a recognition image generating section configured to generate a recognition image having a gradation value obtained by subtracting a gradation value of the difference image from a gradation value of an image in which any one of the three primary colors of RGB is extracted from the color image; and a recognition processing section configured to perform recognition processing of the recognition target using the recognition image.

Abstract: The ambient illuminance sensor system disclosed herein includes a light sensor to generate a series of ambient illuminance values from an area surrounding a computing device, a human presence sensor configured to detect presence of a human in vicinity of the computing device, and computer implemented instructions for determining ambient illuminance level (AIL) based on at least in part on the series of ambient illuminance values, determining presence of a human in vicinity of the light sensor, and based on at least in part on determining that the AIL is below a threshold and that a human is present, applying a hysteresis filter to process the series of ambient illuminance values.

Abstract: Reducing data used during capture in a physical capture volume by selectively activating image capture devices from a virtual view, including: setting up a virtual camera to receive information about and visualize the physical capture volume and a plurality of image capture devices in the virtual view; providing, to the virtual camera, the virtual view of the physical capture volume with a capability to move around the physical capture volume and activate or deactivate each of the plurality of image capture devices; calculating a view frustum, wherein the view frustum is a region of 3-D space within the physical capture volume that would appear on a view screen of the virtual camera; and defining the view frustum of the virtual camera which intersects with the plurality of image capture devices defined in the virtual view.

Abstract: In implementations of systems for generating and applying editing presets, a computing device implements a preset system to detect objects depicted in a digital image that is displayed in a user interface of an application for editing digital content. Input data is received describing an edited region of the digital image and properties of an editing operation performed in the edited region. The preset system identifies a particular detected object of the detected objects based on a bounding box of the particular detected object and an area of the edited region. An additional digital image is edited by applying the properties of the editing operation to a detected object that is depicted in the additional digital image based on a classification of the detected object and a classification of the particular detected object.

Abstract: A system for tracking a target object across a plurality of image frames. The system comprises a logic machine and a storage machine. The storage machine holds instructions executable by the logic machine to calculate a trajectory for the target object over one or more previous frames occurring before a target frame. Responsive to assessing no detection of the target object in the target frame, the instructions are executable to predict an estimated region for the target object based on the trajectory, predict an occlusion center based on a set of candidate occluding locations for a set of other objects within a threshold distance of the estimated region, each location of the set of candidate occluding locations overlapping with the estimated region, and automatically estimate a bounding box for the target object in the target frame based on the occlusion center.

Abstract: An embodiment in accordance with the present invention provides a method for accurate and objective quantification of stone fragment size. The method includes intraoperative measurement of objects during URS. The method analyzes URS procedures for ureteral and renal stones during basket extraction of fragments. An instrument is passed through an instrument channel of the ureteroscope and advanced until it is adjacent to the stone fragment to be measured. The measurement of stone fragment size is based on the known distance of a tip of the instrument in the ureteroscope's visual field.

Abstract: Examples are disclosed relating to performing signal processing on time-of-flight sensor data using pixelwise temporal metrics. One example provides a computing system comprising a logic machine, and a storage machine holding instructions executable by the logic machine to obtain temporal phase data for a plurality of pixels as acquired by a time-of-flight image sensor, the temporal phase data comprising phase data for a plurality of light modulation frequencies, determine temporal active brightness data for the pixel, and, for each pixel of the plurality of pixels, determine a statistical metric for the temporal active brightness data. The instructions are further executable to perform phase unwrapping on the temporal phase data for the plurality of pixels to obtain a depth image, based on the statistical metric for the temporal active brightness data, perform a denoising operation on at least some pixels of the depth image, and output the depth image.

Abstract: A method of analyzing a multidimensional image tensor containing a plurality of images comprises: performing imaging scans of a subject imaging data; generating the multidimensional image tensor from the imaging data; determining a spatial basis tensor containing basis images based on the multidimensional image tensor; determining a temporal basis tensor containing basis functions for a temporal dimension based on the multidimensional image tensor; determining a core tensor that relates the spatial basis tensor to the temporal basis tensor; pre-multiplying the core tensor and the temporal basis tensor to produce a modified temporal basis tensor; storing the spatial basis tensor and the modified temporal basis tensor; and generating an image by multiplying at least (i) at least a portion of the spatial basis tensor and (ii) at least a portion of the modified temporal basis tensor.

Abstract: The technology disclosed performs hand pose estimation on a so-called “joint-by-joint” basis. So, when a plurality of estimates for the 28 hand joints are received from a plurality of expert networks (and from master experts in some high-confidence scenarios), the estimates are analyzed at a joint level and a final location for each joint is calculated based on the plurality of estimates for a particular joint. This is a novel solution discovered by the technology disclosed because nothing in the field of art determines hand pose estimates at such granularity and precision. Regarding granularity and precision, because hand pose estimates are computed on a joint-by-joint basis, this allows the technology disclosed to detect in real time even the minutest and most subtle hand movements, such a bend/yaw/tilt/roll of a segment of a finger or a tilt an occluded finger, as demonstrated supra in the Experimental Results section of this application.