A scanning camera for capturing a set of images along a curved scan path within an area of interest, the 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 a camera optical axis; the scanning mirror is tilted relative to the camera optical axis and 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 by sampling the imaging beam at a corresponding spin angle.

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 computing device of a first vehicle may receive a first image and a second image of a second vehicle having flashing light signals. The computing device may determine, in the first image and the second image, an image region that bounds the second vehicle such that the image region substantially encompasses the second vehicle. The computing device may determine a polar grid that partitions the image region in the first image and the second image into polar bins, and identify portions of image data exhibiting a change in color and a change in brightness between the first image and the second image. The computing device may determine a type of the flashing light signals and a type of the second vehicle; and accordingly provide instructions to control the first vehicle.

Provided is a method for automatically generating a search heuristic that is optimal for a test subject program and a method of concolic testing that uses a parameterized search heuristic to yield a consistent test performance for any program.

An image recognition apparatus (100) includes: an object specifying unit (102) that specifies a position, in a captured image, of a detection target object which is set in a predetermined arrangement according to a processing target object in an imaging target and has a feature depending on the processing target object, by image recognition; and a processing unit (104) that specifies, based on object position data indicating a relative position between the detection target object in the imaging target and the processing target object which is set in a predetermined arrangement according to the imaging target and has a feature depending on the imaging target, the processing target object in the captured image which is present at the relative position from the position, in the captured image, of the detection target object specified by the object specifying unit (102), and executes a process allocated to the specified processing target object.

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 image processing apparatus includes a first memory used for first rearrangement processing on a group of pixels in an input image, and a second memory used for second rearrangement processing on a group of pixels in an image obtained by the first rearrangement processing, and performs correction processing that includes the first rearrangement processing and the second rearrangement processing on the input image. One of the first and second memories is capable of higher-speed random access than the other memory and has a smaller memory capacity than the other memory. One of the first rearrangement processing and the second rearrangement processing is processing for rearranging a group of pixels in each of a plurality of block images generated from the input image, and the other rearrangement processing is processing for rearranging pixel rows among the block images. The one rearrangement processing involves random access to the one memory.

Method of detecting defects in an additively manufactured metal part is disclosed. In some embodiments, methods of detecting defects in an additively manufactured metal part include: additively manufacturing each metal layer of a metal body, capturing one or more images of each metal layer, and processing the images to detect potential defect areas in each metal layer.

Provided is a segment-block-based handwritten signature authentication system and a method thereof, and more particularly, to a handwritten signature authentication system and a method thereof that enrolls a handwritten signature including handwritten signature characteristics information based on segment blocks disjointed by a user, acquires segment-block-based handwritten signature characteristics information from the handwritten signature upon request for handwritten signature authentication, and performs handwritten signature authentication by comparing the pre-enrolled handwritten signature characteristics information based on segments and the acquired handwritten signature characteristics information.