Abstract: A method for operating a robotic system includes obtaining and processing first data representative of an object at a start location. An event may be detected while implementing an operation based on the image data. A gripper height that corresponds to the event may be determined. Accordingly, the method may include calculating an object height that represents a height estimate of the object.

Abstract: A method includes, identifying, in a three-dimensional (3D) anatomical image of a patient organ, multiple anatomical points corresponding to respective predefined locations on a skin of the patient organ in a first coordinate system. Multiple positions in a second coordinate system, measured by a position sensor of a position-tracking system at the respective predefined locations on the skin of the patient organ, are received. At each predefined location, a distance is calculated between a respective anatomical point and closest bone tissue of the patient organ. Weights are assigned to the predefined locations based on respective distances between the anatomical points and the closest bone tissue. The first and second coordinate systems are registered, by correlating between the positions and the respective anatomical points using the assigned weights.

Abstract: Disclosed is a medical image data processing method for determining a clinical target volume for a medical treatment, wherein the method comprises executing, on at least one processor (3) of at least one computer (2), steps of: a) acquiring (S1) first image data describing at least one image of an anatomical structure of a patient; b) acquiring (S2) second image data describing an indicator for a preferred spreading direction or probability distribution of at least one target cell; c) determining (S3) registration data describing a registration of the first image data to the second image data by performing a co-registration between the first image data and the second image data using a registration algorithm; d) determining (S4) gross target region data describing a target region in the at least one image of the anatomical structure based on the first image data; e) determining (S5) margin region data describing a margin around the target region based on the gross target region data; f) determining (S6) clini

Abstract: Systems and methods for use in identifying a brain condition of a subject are provided. In some aspects, a provided method includes constructing a classifier to identify a brain condition of a subject comprising steps of receiving image data obtained from a plurality of subjects, wherein the image data is acquired during an acquisition period following administration of at least one radioactive tracer. The method also includes defining a plurality of brain condition classes using the image data associated with one or more time frames during the acquisition period, and processing the image data to generate signatures corresponding to each of the plurality brain condition classes. The method further includes constructing the classifier using the signatures. The classifier can then be applied to determine a degree to which the subject expresses one or more disease states in order to determine a brain condition of the subject.

Abstract: Provided is a method for generating pose transformation data between first and second rigidly mounted digital cameras having non-coincident fields of view. The method includes obtaining a first plurality of images of a first calibration object, obtaining a second plurality of images of the first or a second calibration object, generating first and second object point data, generating first and second calibration data of the first and second digital cameras, determining first pose data between a first frame of reference of the first digital camera and the frame of reference of the first calibration object, determining second pose data between the frame of reference of the second digital camera and the frame of reference of the first or second calibration objects, and calculating the pose transformation data between the pose of the first digital camera and the pose of the second digital camera.

Abstract: Embodiments of the disclosure may be directed to an image processing system configured to receive a medical image of a region of a subject's body taken at a first time and to receive a surface image of an exterior portion of the region of the subject's body taken at the first time. The image processing may also be configured to receive a medical image of the region of the subject's body taken at a second time and to register the medical image taken at the first time, the surface image taken at the first time, and the medical image taken at the second time.

Abstract: Systems and methods are described for segmenting a medical image using a two-stage variational method in which fracture mechanics are used in the second stage to remove fine-scale contact bridges that connect object contours generated in the first stage. The image segmentation can be used to segment objects, such as bone objects, from surrounding tissues in a medical image, such as an image obtained with a computed tomography (“CT”) or other x-ray imaging system.

Abstract: Systems and methods for determining a breast region in a medical image are provided. The methods may include obtaining a first image relating to the breast region, determining a first region including a first plurality of pixels in the breast region, determining a second region including a second plurality of pixels relating to an edge of the breast region, and determining the breast region by combining the first region and the second region. The second plurality of pixels may include at least a portion of the first plurality of pixels.

Abstract: The present document describe monocular machine vision systems and methods for determining locations of target elements when the three dimensional orientation of the monocular machine vision system relative to the target system is unknown. The machine vision system described herein captures and uses information gleaned from the captured target elements to determine the locations of these captured target elements relative to the vision system, or reversely, the location and orientation of the vision system relative to the target elements.

Abstract: According to one embodiment, a medical information processing apparatus includes processing circuitry. The processing circuitry is configured to acquire information on an accuracy of a trained model that outputs information on a lesion included in data related to a medical image based on inputted data related to the medical image. Further, the processing circuitry is configured to determine a data collection condition for the medical image in a medical image diagnostic apparatus based on the information on the accuracy.

Abstract: A method of providing a sharpness measure for an image comprises detecting an object region within an image; obtaining meta-data for the image; and scaling the chosen object region to a fixed size. A gradient map is calculated for the scaled object region and compared against a threshold determined for the image to provide a filtered gradient map of values exceeding the threshold. The threshold for the image is a function of at least: a contrast level for the detected object region, a distance to the subject and an ISO/gain used for image acquisition. A sharpness measure for the object region is determined as a function of the filtered gradient map values, the sharpness measure being proportional to the filtered gradient map values.

Abstract: A tracker is described which comprises a processor configured to receive captured sensor data depicting an object. The processor is configured to access a rigged polygon mesh model of the object and to compute a plurality of approximate surface normals of a limit surface of the rigged polygon mesh. The processor is configured to compute values of pose parameters of the model by calculating an optimization to fit the model to the captured sensor data where the optimization uses an evaluation function based on the plurality of approximate surface normals.

Abstract: A video analyzer measures and outputs a visual indication of a dynamic range of a video signal. The video analyzer includes a video input to receive the video signal and a cumulative distribution function generator generates a cumulative distribution function curve from a component of the video signal. A feature detector generates one or more feature vectors from the cumulative distribution function curve and a video dynamic range generator produces a visual output indicating a luminance of one or more portions of the video signal.

Abstract: An apparatus includes a raw image pipeline comprising a first circuit and a second circuit. The first circuit of the raw image pipeline may be configured to distinguish between smooth picture noisy areas and non-smooth clean areas of an image by performing tone based non-smooth detection on data of the image to obtain an adjusted non-smoothness value for at least one area comprising a plurality of pixels of the image. The second circuit of the raw image pipeline may be configured to adjust one or more of noise reduction filtering or sharpening filtering performed on the at least one area of the image based on the adjusted non-smoothness value.

Abstract: Data structure for image-processing data is for creating image-processing data necessary for performing image processing on captured images of multiple workpieces when an articulated robot extracts a workable target workpiece from among multiple supplied workpieces. The data structure includes workpiece shape data for recognizing a target workpiece by pattern matching and tool data configured to check whether there is interference between a tool mounted on the articulated robot and a peripheral workpiece. The data structure is configured such that the combination of the workpiece shape data and the tool data can be rearranged for each workpiece or for each tool.

Abstract: An encoding device and methods for point cloud encoding are disclosed. The method for encoding includes generating, using a processor of an encoder, a first frame and a second frame that include patches representing a cluster of points of three-dimensional (3D) point cloud; identifying a patch to segment in the patches of the first frame and the second frame; determining, in response to identifying the patch, a path representing a boundary between segmented regions within the patch; segmenting the patch along the path into two patches for the first frame and the second frame; encoding the first frame and the second frame to generate a compressed bitstream; and transmitting, using a communication interface operably coupled to the processor, the compressed bitstream.

Abstract: An image processing method includes obtaining an original image; partitioning the original image into a first part and a second part such that distortion of at least a part of an image in the first part of the original image is smaller than a predetermined threshold, and distortion of at least a part of an image in the second part of the original image is greater than or equal to the predetermined threshold; correcting the second part of the original image so as to obtain a distortion-corrected image corresponding to the second part; and recognizing the first part of the original image and the distortion-corrected image so as to recognize an object in the original image.

Abstract: An image processing method and device, storage medium and computer device are provided. The method includes: generating an original gray scale image of an original image; performing a histogram equalization process on the original gray scale image to obtain an equalized gray scale image; generating decision factor distribution image, wherein the decision factor distribution image includes a first marked region including a region where pixels that are adjacent in position and have standard deviations smaller than set value in the original image are located, and second marked region; obtaining final gray scale image according to original gray scale image, equalized gray scale image and decision factor distribution image. Gray scale values of pixel corresponding to second marked region and first marked region in final gray scale image are respectively gray scale values of corresponding pixel in equalized gray scale image and original gray scale image; and restoring a processed image.

Abstract: A computer-implemented method for training a forward generator neural network G to translate a source image in a source domain X to a corresponding target image in a target domain Y is described.

Abstract: Techniques are disclosed for tracking objects in sensor data, such as multiple images or multiple LIDAR clouds. The techniques may include comparing segmentations of sensor data such as by, for example, determining a similarity of a first segmentation of first sensor data and a second segmentation of second sensor data. Comparing the similarity may comprise determining a first embedding associated with the first segmentation and a second embedding associated with the second segmentation and determining a distance between the first embedding and the second embedding. The techniques may improve the accuracy and/or safety of systems integrating the techniques discussed herein.