A method of determining macrotexture of an object is disclosed which includes obtaining a plurality of stereo images from an object by an imaging device, generating a coordinate system for each image of the plurality of stereo images, detecting one or more keypoints each having a coordinate in each image of the plurality of stereo images, wherein the coordinate system is based on a plurality of ground control points (GCPs) with apriori position knowledge of each of the plurality of GCPs, generating a sparse point cloud based on the one or more keypoints, reconstructing a 3D dense point cloud of the object based on the generated sparse point cloud and based on neighboring pixels of each of the one or more keypoints and calculating the coordinates of each pixel of the 3D dense point cloud, and obtaining the macrotexture based on the reconstructed 3D dense point cloud of the object.

There is provided an image capturing apparatus that captures a plurality of images, calculates a three-dimensional position from the plurality of images, and outputs the plurality of images and information about the three-dimensional position. The image capturing apparatus includes an image capturing unit, a camera parameter storage unit, a position calculation unit, a position selection unit, and an image complementing unit. The image capturing unit outputs the plurality of images using at least three cameras. The camera parameter storage unit stores in advance camera parameters including occlusion information. The position calculation unit calculates three dimensional positions of a plurality of points. The position selection unit selects a piece of position information relating to a subject area that does not have an occlusion, and outputs selected position information. The image complementing unit generates a complementary image, and outputs the complementary image and the selected position information.

An electronic device estimates a pose of one or more subjects in an environment based on estimating a correspondence between a data volume containing a data mesh based on a current frame captured by a depth camera and a reference volume containing a plurality of fused prior data frames based on spectral embedding and performing bidirectional non-rigid matching between the reference volume and the current data frame to refine the correspondence so as to support location-based functionality. The electronic device predicts correspondences between the data volume and the reference volume based on spectral embedding. The correspondences provide constraints that accelerate the convergence between the data volume and the reference volume. By tracking changes between the current data mesh frame and the reference volume, the electronic device avoids tracking failures that can occur when relying solely on a previous data mesh frame.

Various embodiments are disclosed for detecting, tracking and counting objects of interest in video. In an embodiment, a method of detecting and tracking objects of interest comprises: obtaining, by a computing device, multiple frames of images from an image capturing device; detecting, by the computing device, objects of interest in each frame; accumulating, by the computing device, multiple frames of object detections; creating, by the computing device, object tracks based on a batch of object detections over multiple frames; and associating, by the computing device, the object tracks over consecutive batches.

Systems and methods in accordance with embodiments of the invention are configured to render images using light field image files containing an image synthesized from light field image data and metadata describing the image that includes a depth map. One embodiment of the invention includes a processor and memory containing a rendering application and a light field image file including an encoded image, a set of low resolution images, and metadata describing the encoded image, where the metadata comprises a depth map that specifies depths from the reference viewpoint for pixels in the encoded image. In addition, the rendering application configures the processor to: locate the encoded image within the light field image file; decode the encoded image; locate the metadata within the light field image file; and post process the decoded image by modifying the pixels based on the depths indicated within the depth map and the set of low resolution images to create a rendered image.

A 3D imaging system is proposed in which an object is successively illuminated in at least three directions and at least three images of the object are captured by one or more energy sensors. A set of images is produced computationally showing the object from multiple viewpoints, and illuminated in the at least three directions simultaneously. This set of images is used stereoscopically to form an initial 3D model of the object. Variations in the brightness of the object provides features useful in the stereoscopy. The initial model is refined using photometric data obtained from images in which the object is illuminated in the at least three directions successively.

Systems and methods in accordance with embodiments of the invention estimate depth from projected texture using camera arrays that includes at least two two-dimensional arrays of cameras each several cameras; an illumination system configured to illuminate a scene with a projected texture; a processor; and memory containing an image processing pipeline application and an illumination system controller application. In addition, the illumination system controller application directs the processor to control the illumination system to illuminate a scene with a projected texture. Furthermore, the image processing pipeline application directs the processor to: utilize the illumination system controller application to control the illumination system to illuminate a scene with a projected texture capture a set of images of the scene illuminated with the projected texture; determining depth estimates for pixel locations in an image from a reference viewpoint using at least a subset of the set of images.

According to an embodiment, a processing device includes a generating unit, a detecting unit, and a determining unit. The generating unit is configured to generate two-dimensional distribution information of an object, the two-dimensional distribution information associating between at least a lateral direction distance and a depth direction distance of the object. The detecting unit is configured to detect a continuous area having continuity in a depth direction in the two-dimensional distribution information. The determining unit is configured to determine whether the continuous area represents a detection target.

Methods for extracting a shape feature of an object and security inspection methods and apparatuses. Use is made of CT's capability of obtaining a 3D structure. The shape of an object in an inspected luggage is used as a feature of a suspicious object in combination with a material property of the object. For example, a false alarm rate in detection of suspicious explosives may be reduced.

A depth map generation device for merging multiple depth maps includes two image capturers, a depth map generator, and a mixer. The two image capturers are used for generating two first images. The depth map generator is coupled to the two image capturers, wherein the depth map generator generates a first depth map and a second depth map according to the two first images. The mixer is coupled to the depth map generator for merging the first depth map and the second depth map to generate a final depth map, wherein the first depth map and the second depth map have different characteristics.