An image processing apparatus includes a subject area detector and a subject area determinator. The subject area detector is configured to perform subject detection processing to detect a subject area from an input image. The subject area determinator is configured to determine a final subject area by majority decision processing that is based on the subject areas detected in the subject detection processing performed a plurality of times.

A vending machine recognition apparatus includes: a receiving unit configured to receive a captured image obtained by capturing an image of a vending machine; and a recognition unit configured to recognize merchandise from the captured image and recognize a price of the merchandise from a peripheral image region of an image region of the recognized merchandise.

Image processing apparatus includes a local amount generation unit, a correspondence point calculation unit, a relative correspondence point information calculation unit, a correspondence point selecting unit and a decision unit. The local feature value generation unit calculates, for a first image, a set of information about first local feature values including a first feature point(s). The correspondence point calculation unit calculates, for a second image, as information about correspondence point(s), a correspondence relation between the first and second feature point(s) contained in a set of information about second local feature values calculated from the second image. The relative correspondence point information calculation unit calculates relationships of scales of feature points, as the information about the relative scale sizes of correspondence points, based on the set of information about the first local feature values and the set of information about the second local feature values on the information about correspondence point(s).

In one embodiment, the disclosure relates to a method for determining a degree of homogeneity in one or more inspection images of cargo in one or more containers, comprising: determining whether a zone of interest in one or more processed inspection images comprises one or more patterns, wherein the one or more processed inspection images are processed from one or more inspection images generated by an inspection system configured to inspect the one or more containers; and in the event that one or more patterns is determined and that a variation in the determined one or more patterns is identified, classifying the one or more inspection images as having a degree of homogeneity below a predetermined homogeneity threshold.

Systems and method are provided for controlling an autonomous vehicle. A camera configured to capture an image, and a controller can execute an autonomous driving system (ADS) that classify that image. The ADS comprises a classification system for classifying objects in an environment within a driveable area of the autonomous vehicle. The classification system comprises a processor configured to execute a region proposal generator module and an image classification module. The region proposal generator module generates a set of bounding box region proposals for the image. The bounding box region proposals are selected areas of the image that include objects to be classified. The image classification module classifies, via a neural network executed by the processor, the objects from the image that are within one of the bounding box region proposals

Determining a label from an image is disclosed, including: obtaining an image; determining a first portion of the image associated with a special mark; determining a second portion of the image associated with a label based at least in part on the first portion of the image associated with the special mark; and applying character recognition to the second portion of the image associated with the label to determine a value associated with the label.

A computer-implemented method for finding objects in a collection of images or video, the method comprising: assessing a similarity between two subgraphs; detecting the objects in the collection; assessing a relationship between the objects; finding attributes of the objects; constructing a collection graph for the collection where each of the objects is the vertices and nodes of the collection graph and their relationships with other objects and attributes are edges of the collection graphs; recursively identifying coherent subgraphs and turning the coherent subgraphs into new meta-nodes of the collection graph; identification of the stable graph reducing the collection graph into zero or more distinct stories; assigning a similarity score for each pair of the stories based on a similarity between the corresponding stories; linking the stories into inlier stories based on the similarity score being greater than a first pre-determined threshold level.

The apparatus includes an image data obtainer, a candidate region extractor, a candidate line extractor, an overlap degree determiner, and a clip image region extractor. The candidate region extractor extracts, as a candidate region, a region containing an object detectable from the image data. The candidate line extractor extracts, as a candidate line, a line that is at least either a line segment or an arc included in the image data. The overlap degree determiner determines whether the degree of overlap between a closed line forming the outline of the candidate region extracted and the candidate line extracted is greater than or equal to a preset predetermined first percentage value. If the overlap degree determiner determines that the degree of overlap is greater than or equal to the first percentage value, the clip image region extractor 19 extracts the candidate region as a clip image.

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.

Provided is a multispectral imaging device for providing precise tracking and verification. The imaging device may configure a first filter for a sensor, and may determine first spectral properties of a target object based on a first image of the target object generated from visible light passing through the first filter onto the sensor. The imaging device may configure a different second filter for the sensor, and may determine second spectral properties of the target object based on a second image of the target object generated from the non-visible light passing through the second filter onto the sensor. The imaging device may align the second spectral properties of the second image with the first spectral properties of the first image, and may present the first spectral properties with the second spectral properties in a single composite image of the target object.