A digital processing system for, a method, implemented on a digital processing system of, and a non transitory machine readable medium containing instructions that when executed implement the method of: automatically selecting one or more complementary images from a set of provided images for use with triangulation and determination of 3D properties of a user-selected point or geometric feature of interest, such as information on the slope (also called the pitch) and one or more dimensions of a roof of a building. The one or more complementary images are selected automatically by using an optimality criterion, also called a complementarity criterion.

An object identification method determines whether a first monitoring image and a second monitoring image captured by a monitoring camera apparatus have the same object. The object identification method includes acquiring the first monitoring image at a first point of time to analyze a first object inside a first angle of view of the first monitoring image, acquiring the second monitoring image at a second point of the time different from the first point of time to analyze a second object inside the first angle of view of the second monitoring image, estimating a first similarity between the first object inside the first angle of view of the first monitoring image and the second object inside the first angle of view of the second monitoring image; and determining whether the first object and the second object are the same according to comparison result of the first similarity with a threshold.

According to one embodiment, an inspection apparatus includes an image generation device which generates a second image corresponding to a first image and a defect detection device which detects a defect in the second image with respect to the first image. The defect detection device is configured to extract a first partial region in which an amount of change of a luminance of the first image and an amount of change of a luminance of the second image have a correlation, and correct, in the first partial region, the luminance of the first image with respect to the luminance of the second image.

Disclosed herein is a computing system for performing medical image analysis. A computing system for performing medical image analysis according to an embodiment of the present invention includes at least one processor. The at least one processor performs image processing on a first medical image, and segments at least one anatomical region in the first medical image. The at least one processor generates a first quantitative parameter for the at least one anatomical region based on quantitative measurement conditions that can be measured in the first medical image, and stores the first quantitative parameter in a database in association with the first medical image and the at least one anatomical region.

Provided are an image processing device and the like which implement personal privacy protection while suppressing a reduction in visibility for an image. The image processing device is provided with: a memory storing instructions; and one or more processors configured to execute the instructions to: detect a person region that is a region where a person appears in an image captured by a camera device; and perform, on the person region, privacy processing a strength of which differs according to a depth associated with coordinates of the person region or a predetermined index related to the depth.

The present invention is directed to implementing at least one of speed-up of detection processing and reduction of misdetection. An information processing apparatus includes an acquisition unit configured to acquire a captured image, a first setting unit configured to set a plurality of detection areas of an object for the captured image, a second setting unit configured to set a condition for detecting an object on a first detection area and a second detection area set by the first setting unit, wherein the condition includes a detection size in the captured image, and a detection unit configured to detect an object satisfying the detection size set by the second setting unit from the plurality of detection areas set by the first setting unit.

A method for detecting fallen cargo, the method may include receiving by a computerized system, sensed information related to driving sessions of multiple vehicles; applying a machine learning process on the sensed information to detect fallen cargo and to classify the fallen cargo to fallen cargo classes; estimating, from the sensed information, an impact of at least some of the fallen cargo classes on a behavior of at least some of the multiple vehicles; and determining, based on the impact, at least one suggested vehicle behavior as a response to a detection of at least some of the fallen cargo classes.

An information processing apparatus (100) determines, by referring to a storage unit that stores therein contour data of a plurality of objects, whether a plurality of pieces of contour data associated with a contour of a subject included in a captured image. The information processing apparatus (100) acquires, when a determination result is affirmative, by referring to the storage unit, a plurality of pieces of region data associated with the plurality of pieces of corresponding contour data associated with the contour of the subject and specifies, based on the plurality of pieces of acquired region data, an object associated with the subject from among the plurality of objects.

It is an object of the present invention to provide an object collating device and an object collating method that enable matching of images of a dividable medical article with desirable accuracy and easy confirmation of matching results. In the object collating device according to the first aspect, when the object is determined to be divided, the first image for matching is collated with the image for matching (the second matching image) for the objects in the undivided state, so that the region to be matched is not narrowed, and matching of the images of the dividable medical article is achieved with desirable accuracy. In addition, since the first and second display processing is performed on the images for display determined to contain the objects of the same type, matching results can easily be confirmed.

Embodiments of a device include on-board memory, an applications processor, a digital signal processor (DSP) cluster, a configurable accelerator framework (CAF), and at least one communication bus architecture. The communication bus communicatively couples the applications processor, the DSP cluster, and the CAF to the on-board memory. The CAF includes a reconfigurable stream switch and a data volume sculpting unit, which has an input and an output coupled to the reconfigurable stream switch. The data volume sculpting unit has a counter, a comparator, and a controller. The data volume sculpting unit is arranged to receive a stream of feature map data that forms a three-dimensional (3D) feature map. The 3D feature map is formed as a plurality of two-dimensional (2D) data planes. The data volume sculpting unit is also arranged to identify a 3D volume within the 3D feature map that is dimensionally smaller than the 3D feature map and isolate data from the 3D feature map that is within the 3D volume for processing in a deep learning algorithm.