An object detection device that detects a specific object included in an input image includes a first candidate region specifying unit that specifies a first candidate region in which an object candidate is included from a first input image obtained by imaging a subject in a first posture, a second candidate region specifying unit that specifies a second candidate region in which an object candidate is included from a second input image obtained by imaging the subject in a second posture different from the first posture, a deformation displacement field generation unit that generates a deformation displacement field between the first input image and the second input image, a coordinate transformation unit that transforms a coordinate of the second candidate region to a coordinate of the first posture based on the deformation displacement field, an association unit that associates the first candidate region with the transformed second candidate region that is close to the first candidate region, and a same object determination unit that determines that the object candidates included in the candidate regions associated with each other by the association unit are the same object and are the specific object.

A system and method for lateral vehicle detection is disclosed. A particular embodiment can be configured to: receive lateral image data from at least one laterally-facing camera associated with an autonomous vehicle; warp the lateral image data based on a line parallel to a side of the autonomous vehicle; perform object extraction on the warped lateral image data to identify extracted objects in the warped lateral image data; and apply bounding boxes around the extracted objects.

Techniques are described for computing devices to perform automated operations to determine the acquisition locations of images, such as within a building interior based on automatically determined shapes of rooms of the building, and for using the determined image acquisition location information in further automated manners. The image may be a panorama image or of another type (e.g., a rectilinear perspective image) and acquired at an acquisition location in a multi-room building's interior, and the determined acquisition location for such an image may be at least a location on the building's floor plan and optionally an orientation/direction for at least a part of the image—in addition, the automated image acquisition location determination may be further performed without having or using information from any depth sensors or other distance-measuring devices about distances from an image's acquisition location to walls or other objects in the surrounding building.

A pattern matching apparatus includes a computer system configured to execute pattern matching processing between first pattern data based on design data 104 and second pattern data representing a captured image 102 of an electron microscope. The computer system acquires a first edge candidate group including one or more first edge candidates, acquires a selection-required number (the number of second edge candidates to be selected based on the second pattern data), acquires a second edge candidate group including the second edge candidates of the selection-required number, acquires an association evaluation value for each of different association combinations between the first edge candidate group and the second edge candidate group, selects one of the combinations based on the association evaluation value, and calculates a matching shift amount based on the selected combination.

Computer-implemented systems and methods for making a custom-fit goggle are described.

The present disclosure provides an image processing apparatus and an image processing method. The method comprising: performing a target detection on a first image to generate a first to-be-processed image, the first to-be-processed image has a size smaller than the size of the first image; performing the target detection on a second image to generate a second to-be-processed image, the second to-be-processed image has a size smaller than the size of the second image, the first image and the second image constitute an image pair containing the target, and a disparity exists between the first image and second image; and calculating, based on the first to-be-processed image and the second to-be-processed image, a disparity value of the target in the first to-be-processed image and the second to-be-processed image.

Embodiments provide an image processing method, program, and apparatus, for using a database of data manifestations of objects to identify those objects in image data representing a domain or space containing objects to be identified. Embodiments leverage 3D vector field representations of both the domain or space, and of the objects, to perform the recognition. Embodiments annotate the image data with information relating to the identified objects, and/or replace portions of the image data with a data manifestation of the recognised object imported from the database.

A computer-implemented method for extracting information from a document, for example an official document, is disclosed. The method comprises acquiring an input image comprising a document portion; performing image segmentation on the input image to form a binary input image that distinguishes the document portion from the remaining portion of the input image; estimating a first image transform to align the binary input image to a binary template image, using the first image transform on the input image to form an intermediate image; estimating a second image transform to align the intermediate image to a template image; using the second image transform on the intermediate image to form an output image; and extracting a field from the output image using a predetermined field of the template image.

Techniques are described for computing devices to perform automated operations to determine the acquisition locations of images, such as within a building interior based on automatically determined shapes of rooms of the building, and for using the determined image acquisition location information in further automated manners. The image may be a panorama image or of another type (e.g., a rectilinear perspective image) and acquired at an acquisition location in a multi-room building's interior, and the determined acquisition location for such an image may be at least a location on the building's floor plan and optionally an orientation/direction for at least a part of the image—in addition, the automated image acquisition location determination may be further performed without having or using information from any depth sensors or other distance-measuring devices about distances from an image's acquisition location to walls or other objects in the surrounding building.

Techniques for controlling patch-usage in image synthesis are described. In implementations, a curve is fitted to a set of sorted matching errors that correspond to potential source-to-target patch assignments between a source image and a target image. Then, an error budget is determined using the curve. In an example, the error budget is usable to identify feasible patch assignments from the potential source-to-target patch assignments. Using the error budget along with uniform patch-usage enforcement, source patches from the source image are assigned to target patches in the target image. Then, at least one of the assigned source patches is assigned to an additional target patch based on the error budget. Subsequently, an image is synthesized based on the source patches assigned to the target patches.