A method of image processing in a structured light imaging system is provided that includes receiving a captured image of a scene, wherein the captured image is captured by a camera of a projector-camera pair, and wherein the captured image includes a binary pattern projected into the scene by the projector, applying a filter to the rectified captured image to generate a local threshold image, wherein the local threshold image includes a local threshold value for each pixel in the rectified captured image, and extracting a binary image from the rectified captured image wherein a value of each location in the binary image is determined based on a comparison of a value of a pixel in a corresponding location in the rectified captured image to a local threshold value in a corresponding location in the local threshold image.

Multivariant feature extraction is used for training volumes or 2D images, (real or synthetic) coupled to process (effective) values probably obtained from direct simulation. These features are coupled with machine learning/regression algorithms to make a predictive model for the effective property. This model can then be used on a real geometry of a sample for effective parameter prediction.

Systems and methods for cloud-based scalable segmentation model training solutions including a computing interface by which a client/user/customer can upload and store training data in a storage device of a cloud-based network, provide access to the training data stored in the storage device, initiate a request for training a segmentation model, monitor the training of the segmentation model, and download the trained segmentation model, and a computing system operatively coupled with a client device through the computing interface and configured to pre-process the training data using a first set of computing resources of the cloud-based network, store the processed training data in a storage device of the cloud-based network, deploy, upon a training request from the client device, a training application on a second set of computing resources of the cloud-based network to train the segmentation model based on the processed training data, provide access to the client device to monitor the training, and provide access to the trained segmentation model.

Apparatus for diagnosing breast cancer, the apparatus comprising a controller having a set of instructions executable to: acquire a contrast enhanced region of interest (CE-ROI) in an X-ray image of a patient's breast, the X-ray image comprising X-ray pixels that indicate intensity of X-rays that passed through the breast to generate the image; determine a texture neighborhood for each of a plurality of X-ray pixels in the CE-ROI, the texture neighborhood for a given X-ray pixel of the plurality of X-ray pixels extending to a bounding pixel radius of BPR pixels from the given pixel; generate a texture feature vector (TF) having components based on the indications of intensity provided by a plurality of X-ray pixels in the CE-ROI that are located within the texture neighborhood; and use a classifier to classify the texture feature vector TF to determine whether the CE-ROI is malignant

Systems and methods for processing spatial structure data are provided. The system accesses spatial structure data, which describes object structure, and which has depth information indicative of a plurality of layers for the object structure. The system further extracts, from the spatial structure data, a portion of the spatial structure data representative of one layer of the plurality of layers. The system identifies, from the portion of the spatial structure data, a plurality of vertices that describe a contour of the layer. Additionally, the system identifies convex corners of the layer based on the plurality of vertices and performs object recognition according to the convex corners.

An aerial camera for capturing images along two or more curved scan paths, the aerial camera comprising a scanning camera associated with each scan path, each scanning camera comprising an image sensor, a lens, a scanning mirror, and a drive coupled to the scanning mirror; wherein the drive is operative to rotate the scanning mirror about a spin axis according to a spin angle, the spin axis is tilted relative to the camera optical axis, the scanning mirror is tilted relative to both the camera optical axis and the spin axis and is positioned to reflect an imaging beam into the lens, the lens is positioned to focus the imaging beam onto the image sensor, and the image sensor is operative to capture each image along the scan path by sampling the imaging beam at a corresponding spin angle.

The present disclosure relates to a method and apparatus for recognizing a sequence in an image, an electronic device, and a storage medium. The method includes: performing feature extraction on a to-be-processed image to obtain a first feature map of the to-be-processed image, where the to-be-processed image includes a sequence formed by stacking at least one object along a stacking direction; determining a region feature of each segmented region in the to-be-processed image based on the first feature map, where all segmented regions are obtained by dividing the to-be-processed image into k regions along the stacking direction, k is a set number of objects stacked along the stacking direction, and k is an integer greater than 1; and determining a category of each object in the sequence based on the region feature of each segmented region. Embodiments of the present disclosure may implement recognition of stacked objects in a sequence.

A specific area detection device that detects a specific area in an imaging area based on a captured image includes: an estimation unit configured to estimate a plurality of points and a direction of a straight line connecting two predetermined points among the plurality of points from a captured image using a learning model created by learning about a specific area defined by a predetermined number of the points in an imaging area using a captured image for learning; and a detection unit configured to detect the specific area by classifying the plurality of points for each specific area based on the direction.

Apparatus for diagnosing breast cancer, the apparatus comprising a controller having a set of instructions executable to: acquire a contrast enhanced region of interest (CE-ROI) in an X-ray image of a patient's breast, the X-ray image comprising X-ray pixels that indicate intensity of X-rays that passed through the breast to generate the image; determine a texture neighborhood for each of a plurality of X-ray pixels in the CE-ROI, the texture neighborhood for a given X-ray pixel of the plurality of X-ray pixels extending to a bounding pixel radius of BPR pixels from the given pixel; generate a texture feature vector (TF) having components based on the indications of intensity provided by a plurality of X-ray pixels in the CE-ROI that are located within the texture neighborhood; and use a classifier to classify the texture feature vector TF to determine whether the CE-ROI is malignant.

A system that compares the images submitted with a preprocessed database containing pictures, drawings, and patent drawings, among other media. The images are interrelated by comparing the content of the patent images, the narrative in the patents with the other visual media which may or may not be pre-tagged.