An apparatus may include an ultrasonic sensor array, a light source system and a control system. Some implementations may include an ultrasonic transmitter. The control system may be operatively configured to control the light source system to emit light that induces acoustic wave emissions inside a target object. The control system may be operatively configured to select a first acquisition time delay for the reception of acoustic wave emissions primarily from a first depth inside the target object. The control system may be operatively configured to acquire first ultrasonic image data from the acoustic wave emissions received by the ultrasonic sensor array during a first acquisition time window. The first acquisition time window may be initiated at an end time of the first acquisition time delay.

An image processing apparatus comprises an image acquiring unit configured to acquire a first image, which is an image including an eye; an image generating unit configured to generate a second image, which is a binary image representing an edge of the first image; a classifying unit configured to classify, based on presence or absence of the edge, pixels included in the second image; and a pupil-region generating unit configured to generate a pupil region, which is a region corresponding to a pupil in the first image, based on a classification result, wherein when pixels having a same value are present separately in a plurality of blocks in the second image, the classifying unit performs the classification of the pixels further based on areas of the blocks or luminance values of pixels in the first image corresponding to the blocks.

A method for generating a zebra crossing includes: obtaining an original vertex sequence for generating a zebra crossing from an image, and determining a first vertex sequence by merging a plurality of vertices in the original vertex sequence; obtaining a plurality of lines by connecting a plurality of vertices in the first vertex sequence in order, and determining a first target edge and a second target edge approximate to the first target edge from the plurality of lines; obtaining a first vertex set corresponding to the first target edge and a second vertex set corresponding to the second target edge, and generating an area to be cut based on the first vertex set and the second vertex set; and cutting the area to be cut to obtain cutting elements, and generating the zebra crossing by filling alternately a part of cutting elements.

Techniques for binarization and extraction of information from image data are disclosed. The inventive concepts include independently binarizing portions of the image data on the basis of individual features, e.g. per connected component, and using multiple different binarization thresholds to obtain the best possible binarization result for each portion of the image data. Determining the quality of each binarization result may be based on attempted recognition and/or extraction of information therefrom. Independently binarized portions may be assembled into a contiguous result. In one embodiment, a method includes: identifying a region of interest within a digital image; generating a plurality of binarized images based on the region of interest using different binarization thresholds; and extracting data from some or all of the plurality of binarized images. The extracted data includes connected components that overlap and/or are obscured by unique background. Corresponding systems and computer program products are disclosed.

An object tracking system includes a first sensor, a second sensor, and a tracking system. The first sensor is configured to capture a first frame of a global plane for at least a first portion of a space. The second sensor is configured to capture a second frame of at least a second portion of the space. The tracking system is configured to determine the object is within an overlap region with the second sensor based on a first pixel location. The tracking system is further configured to determine a first coordinate in the global plane for the object, to determine a second pixel location in the second frame for the object based on the first coordinate, and to store the second pixel location with an object identifier a tracking list associated with the second sensor.

Techniques are described for automated operations involving capturing and analyzing information from an interior of a house, building or other structure, for use in generating and providing a representation of that interior. Such techniques may include using a user's mobile device to capture visual data from multiple viewing locations (e.g., video captured while the mobile device is rotated for some or all of a full 360 degree rotation at each viewing location) within multiple rooms, capturing data linking the multiple viewing locations, analyzing each viewing location's visual data to create a panorama image from that viewing location, analyzing the linking data to determine relative positions/directions between at least some viewing locations, creating inter-panorama links in the panoramas to each of one or more other panoramas based on such determined positions/directions, and providing information to display multiple linked panorama images to represent the interior.

A method for detecting the presence and the correct or incorrect placement of target objects in a container or carrier divides the container space into areas where the states of placement of the target objects in the container can be recognized, to generate N number of sub-areas, N is a positive integer. An artificial intelligence model is obtained by training the same according to training images of objects, the training images being images of the N sub-areas. The images are input into the artificial intelligence model and the states of placement are determined. The disclosure also provides an electronic device and a non-transitory storage medium.

The invention relates to a parking lot corner point correction method and system. The method includes: an obtaining step of obtaining a parking lot corner point of a parking lot; and a correction step of performing, based on a position relationship between parking lot corner points of the parking lot, rectangular correction on the parking lot corner points to obtain a corrected parking lot. The obtaining step includes: a first obtaining sub-step of obtaining a parking lot corner point of a parking lot; a determination sub-step of determining whether a corner point confidence level of the parking lot corner point meets a first threshold and whether a corner point offset thereof meets a second threshold, and jumping to the correction step if it is determined that the corner point confidence level meets the first threshold and the corner point offset meets the second threshold, otherwise, proceeding to a second obtaining sub-step; and the second obtaining sub-step of discarding the parking lot corner point of the parking lot that is detected in the first obtaining sub-step, and then using a parking lot corner point at a corresponding position that is predicted by an odometer, as the parking lot corner point of the parking lot. According to the invention, the parking accuracy may be improved.

This disclosure relates to method and system for visual inspection of rotating components. The method includes representing rotation cycles of a rotating component as spatial features based on video or image frames, ascertaining and/or evolving Hidden Markov Model (HMM) chains for the cycles, ascertaining a count of the rotating component in the frames and/or labelling the frames with ascertained states of the HMM chains.

Disclosed are systems, devices, and methods for detecting a trachea, an exemplary system comprising an imaging device configured to obtain image data and a computing device configured to generate a three-dimensional (3D) model, identify a potential connected component in a first slice image, identify a potential connected component in a second slice image, label the first slice image as a top slice image, label the connected component in the top slice image as an active object, associate each connected component in a current slice image with a corresponding connected component in a previous slice image based on a connectivity criterion, label each connected component in the current slice image associated with a connected component of the preceding slice image as the active object, and identify the active object as the trachea, based on a length of the active object.