Abstract: Devices, systems, computer program products and computer implemented methods are provided for dynamically assessing a moving object from a sequence of consecutive volumetric image frames of such object, which images are timely separated by a certain time interval, by: identifying in at least one image of the sequence the object of interest; segmenting the object to identify object contour; propagating the object contour as identified to other images of the sequence; and performing dynamic analysis of the object based on the object contour as propagated.

Abstract: This wire shape inspecting apparatus comprises a camera which captures an image of a wire from above, a light which illuminates the wire from above, and a control unit, wherein the control unit performs: an inspection image acquiring process of acquiring a plurality of inspection images by causing the camera to capture images of the wire a plurality of times while changing a focal distance; and a first shape detecting process of identifying, in each inspection image, a light emitting portion, which is an image part including reflected light comprising light from the light that has been reflected by the wire, and identifying an actual light emitting portion position, which is the actual position of the light emitting portion, on the basis of the position of the light emitting portion in the inspection image and the focal distance when the inspection image was acquired.

Abstract: A corresponding region movement amount calculation unit calculates the amount of movement of each of a plurality of corresponding characteristic regions between a reference image and a base image. A clustering processing unit groups one or more characteristic regions exhibiting a substantially identical tendency in the calculated amounts of movement as belonging to a plane group located on the same plane, and classifies the plurality of characteristic regions in one or more plane groups. A projection transform matrix calculation unit calculates one or more projection transform matrices, by using the amounts of movement of the characteristic regions and the result of the grouping performed by the clustering processing unit.

Abstract: The present disclosure provides a method for obtaining image tracking points. The method can be applied to an image tracking point obtaining device, the method includes obtaining, when a current video frame comprises a first image of a target object, a second image of the target object and determining a position of a second feature point on the second image; obtaining, on the first image, a first feature point corresponding to the second feature point; obtaining a first area to which the first feature point belongs in the first image, and obtaining a second area to which the second feature point belongs in the second image; and determining the first feature point as a tracking point of the current video frame when an image similarity between the first area and the second area meets a screening condition.

Abstract: The present disclosure relates to an image processing apparatus and a method that allow suppression of a reduction in the subjective image quality. An inverse filter of a filter configured to transform an input image to be projected by a plurality of projection sections into a projection image projected by the plurality of projection sections is generated on the basis of an individual-blur amount and a superimposition-blur amount. The individual-blur amount indicates a magnitude of optical blur generated in an individual projection image projected by each of the plurality of projection sections. The superimposition-blur amount indicates a magnitude of optical blur generated from superimposition of a plurality of the projection images. The input image is transformed using the generated inverse filter to generate a phase image.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for image artifact removal. The method, computer program product and computer system may include computing device which may receive a primary image and analyze the primary image for global artifacts and local artifacts. The computing device may, in response to identifying a global artifact in the primary image, generate a secondary image with the global artifact removed utilizing a first generative adversarial network. The computing device may, in response to identifying a local artifact in the primary image, generate a patch with the local artifact removed utilizing a second generative adversarial network. The computing device may generate a hybrid image containing a reduction of global artifacts and a reduction of local artifacts by combining the secondary image and the patch utilizing a hybrid generative adversarial network.

Abstract: A method for detecting image defects is described, which includes obtaining an image to be detected, down-sampling the image to be detected to obtain a down-sampled image, de-cluttering the down-sampled image to obtain a de-cluttered image, restoring the de-cluttered image into a restored image having the same resolution as the image to be detected so as to be used as a background image, and comparing the image to be detected with the background image to determine defects in the image to be detected. An apparatus for detecting image defects, a computing device and a storage medium are also described.

Abstract: A method and a device for processing an image saturation based on depth of field, an electronic device and a computer readable storage medium are provided, for processing scene data collected by an imaging device. The scene data includes a first scene image. The method includes the following. The scene data is processed to determine whether a portrait exists in a scene. In response to determining that the portrait exists, the scene data is processed to identify a portrait region. The first scene image is processed to reduce a saturation of the portrait region.

Abstract: Devices, methods, and computer-readable media describing an adaptive approach to reference image selection are disclosed herein, e.g., to generate fused images with reduced motion distortion. More particularly, an incoming image stream may be obtained from an image capture device, which image stream may comprise a variety of different image captures, e.g., including “image frame pairs” (IFPs) that are captured consecutively, wherein the images in a given IFP are captured with differing exposure settings. When a capture request is received at the image capture device, the image capture device may select two or more images from the incoming image stream for fusion, e.g., including at least one IFP. In some embodiments, one of the images from the at least one IFP will be designated as the reference image for a fusion operation, e.g., based on a robust motion detection analysis process performed on the images of the at least one IFP.

Abstract: A method for generating a predicted segmentation map for potential objects in a future scene depicted in a future image is described. The method includes receiving input images that depict a same scene; processing a current input image to generate a segmentation map for potential objects in the current input image and a respective depth map; generating a point cloud for the current input image; processing the input images to generate, for each pair of two input images in the sequence, a respective ego-motion output that characterizes motion of the camera between the two input images; processing the ego-motion outputs to generate a future ego-motion output; processing the point cloud of the current input image and the future ego-motion output to generate a future point cloud; and processing the future point cloud to generate the predicted segmentation map for potential objects in the future scene depicted in the future image.

Abstract: A method for detecting dimension of box based on depth map includes: receiving a depth map generated by a camera, the depth map corresponds to pixels of an image including a box; performing a coordinate transformation to transform the depth map into camera coordinates of each of the pixels; dividing some of the pixels into plural blocks, each of blocks includes a number of the pixels adjacent to each other; statistically analyzing an average normal vector of each of the blocks according to the camera coordinates of the pixels of each of the blocks; classifying the blocks into plural clusters according to the average normal vector of each of the blocks; performing a plane extraction to obtain edge vectors according to plane formulas of the clusters; obtaining vertexes of the box according to the edge vectors; and obtaining a dimension of the box according the vertexes.

Abstract: Hand segmentation on wearable devices is a challenging computer vision problem with a complex background because of varying illumination conditions, computational capacity of device(s), different skin tone of users from varied race, and presence of skin color background. The present application provides systems and methods for performing, in real time, hand segmentation by pre-processing an input image to improve contrast and removing noise/artifacts. Multi Orientation Matched Filter (MOMF) is implemented and applied on the pre-processed image by rotating the MOMF at various orientations to form an edge image which comprises strong edges and weak edges. Weak edges are further removed using morphological operation. The edge image is then added to the input image (or pre-processed image) to separate different texture region in image. Largest skin-color blob is then extracted which is considered to be correct segmented hand.