Various embodiments relate to an apparatus, method and a non-transitory computer readable medium for detecting radio-frequency interference (RFI) and recovering image data in the RFI using Hilbert-Huang Transform (HHT2-RFI) configured to apply Empirical Mode Decomposition (EMD2) to decompose image data into a plurality of bi-dimensional intrinsic mode functions (BIMFs), determine a RFI by entropic interpretation, augment the RFI by applying Hilbert Spectral Analysis (HSA2) to the RFI resulting in RFI data points and perform science data recovery by subtracting the amplitudes of the RFI and the RFI data points from the image data.

A computing device is described which has a memory holding at least two input images depicting different parts of a panoramic scene, the images having been captured by a user moving the camera by hand to capture the panorama. The computing device has an image stitching component configured to identify, at a processor, a region of overlap between the at least two images and to calculate a displacement vector for each of a plurality of warp points in the region of overlap. The image stitching component is arranged to warp a second one of the at least two images using the warp points; and to join the warped second image to the first image.

A vision system of a vehicle includes a camera configured to be disposed at a vehicle so as to have a field of view exterior of the vehicle. An image processor is operable to process image data captured by the camera. The image processor is operable to determine lane markers on a road on which the vehicle is traveling. The image processor processes intensity gradient information of captured image data to determine lane markers, and, responsive to processing of captured image data, the image processor is operable to detect straight or curved lane markers. The image processor is operable to adapt the processing of lane marker image data in subsequent frames of captured image data responsive to image processing of lane marker image data in previous frames of captured image data.

This invention provides a system and method for finding features in images that exhibit saddle point-like structures using relatively computationally low-intensive processes, illustratively consisting of an anti-correlation process, and associated anti-correlation kernel, which operates upon a plurality of pixel neighborhoods within the image. This process enables an entire image to be quickly analyzed for any features that exhibit such saddle point-like structures by determining whether the anti-correlation kernel generates a weak or strong response in various positions within the image. The anti-correlation kernel is designed to generate a strong response regardless of the orientation of a saddle point-like structure. The anti-correlation process examines a plurality of pixel neighborhoods in the image, thereby locating any saddle point-like structures regardless of orientation, as it is angle-independent. The structures are then grouped and refined (for example in a grid) in an effort to locate and decode ID topologies within the image.

Data analytics engines and methods of incident scene focus area determination. The method includes receiving a plurality of directional inputs from a plurality of sources. The method also includes assigning weighting factors to the plurality of directional inputs. The method further includes generating weighted position vectors for each of the plurality of sources based on the plurality of directional inputs and the weighting factors. The method also includes determining when the weighted position vectors for at least two sources of the plurality of sources intersect. The method further includes determining an intersection location and a confidence level based on the weighted position vectors of the at least two sources. The method also includes identifying an incident scene focus area based on the intersection location and the confidence level.

Image description and image recognizable method, it contain (a) It obtain an image which possess plural pixels. (b) It determines a starting position in the image. (c) In the image, From the starting point along the trajectory of the former spiral aggregation makes a pixel sampling, and the pixel on the trajectory rank to the former spiral aggregation. (d) the angle increases with the increase of the variance, it forming a the angle of the latter spiral aggregation. From the starting point along a trajectory of the former spiral aggregation makes the pixel sample, and the pixel on the trajectory rank to the former spiral aggregation. (e) It decides how many frequencies the angle variation increase, and repeatedly performs the step (d). After obtaining a plurality of the latter spiral aggregation, the pixel corresponds to the value. (f) It ranks the former spiral aggregation and the latter spiral aggregation. Then, spiral aggregation map will be formed and recorded the every value of the pixel.

An image processing device includes a feature point detecting section which detects a feature point from an image and which includes a ternarizing section and a determining section. The ternarizing section sets each pixel of the image as a target pixel and inputs pixel values of the target pixel and surrounding pixels. The ternarizing section changes each of the pixel values of the surrounding pixels to one of three signal values which represent that brightness of a surrounding pixel is brighter or darker than or similar to brightness of the target pixel. When the number of consecutive surrounding pixels having the same signal value is equal to or greater than a threshold, the determining section determines the target pixel as the feature point.

Data analytics engines and methods of incident scene focus area determination. The method includes receiving a plurality of directional inputs from a plurality of sources. The method also includes assigning weighting factors to the plurality of directional inputs. The method further includes generating weighted position vectors for each of the plurality of sources based on the plurality of directional inputs and the weighting factors. The method also includes determining when the weighted position vectors for at least two sources of the plurality of sources intersect. The method further includes determining an intersection location and a confidence level based on the weighted position vectors of the at least two sources. The method also includes identifying an incident scene focus area based on the intersection location and the confidence level.

Image description and image recognizable method, it contain (a) It obtain an image which possess plural pixels. (b) It determines a starting position in the image. (c) In the image, From the starting point along the trajectory of the former spiral aggregation makes a pixel sampling, and the pixel on the trajectory rank to the former spiral aggregation.(d) the angle increases with the increase of the variance, it forming a the angle of the latter spiral aggregation. From the starting point along a trajectory of the former spiral aggregation makes the pixel sample, and the pixel on the trajectory rank to the former spiral aggregation. (e) It decides how many frequencies the angle variation increase, and repeatedly performs the step (d). After obtaining a plurality of the latter spiral aggregation, the pixel corresponds to the value. (f) It ranks the former spiral aggregation and the latter spiral aggregation. Then, spiral aggregation map will be formed and recorded the every value of the pixel.

A control system includes a projection section that projects predetermined patterned light on a target object, a first imaging section that captures an image of the target object on which the predetermined patterned light is projected by the projection section, a second imaging section that is disposed in a position different from a position where the first imaging section is disposed and captures an image of the target object on which the predetermined patterned light is projected by the projection section, and a calculation section that calculates a three-dimensional shape of the target object based on a first point in a first captured image captured by the first imaging section and a second point in a second captured image captured by the second imaging section.