Provided is an image processing device with which it is possible to automate the setting of a detection parameter that is suitable for obtaining a desired detection result. An image processing device is provided with: an object detection unit for detecting the image of an object from input image data using a detection parameter; a detection rate calculation unit for comparing the result of detection by the object detection unit with information that represents a desired detection result so as to calculate at least one of a non-detection rate and a false detection rate in object detection by the objection detection unit; an objective function value calculation unit for calculating the value of an objective function, the input variable of which is at least one of the non-detection rate and the false detection rate; and a detection parameter search unit for performing a search of the detection parameter by changing the value of the detection parameter and repeating object detection, calculation of at least one of the non-detection rate and the false detection rate, and calculation of the value of the objective function until the value of the objective function satisfies a prescribed condition or the number of times a search of the detection parameter is performed reaches a prescribed count.

A geometric pattern matching method and a device for performing the method includes determining, by a geometric pattern matching device, information on reference geometric pattern contour points for a learning object on a learning image. The method can further include determining, by the geometric pattern matching device, information on detection object contour points for a detection object on a detection image, and performing, by the geometric pattern matching device, geometric pattern matching between the learning object and the detection object on the basis of the information on the reference geometric pattern contour points and the information on the detection object contour points.

The present disclosure provides for a system for facilitating a completely automated process that may directly fetch an imagery of a given location and area from any mapping module and extract a plurality of objects in the given imagery. Further, a deep learning-based object segmentation such as but not limited to a cascaded reverse mask RCNN framework method may generate a set of predefined vectors associated with the image. The system may be configured to automate the generation of the predefined vectors based on the image received from the image sensing assembly.

A method for tracking an object performed by an object tracking system includes encoding locations of visible objects in an environment captured in a current frame of a sequence of frames. The method also includes generating a representation of a current state of the environment based on an aggregation of the encoded locations and an encoded location of each object visible in one or more frames of the sequence of frames occurring prior to the current frame. The method further includes predicting a location of an object occluded in the current frame based on a comparison of object centers decoded from the representation of the current state to object centers saved from each prior representation associated with a different respective frame of the sequence of frames occurring prior to the current frame. The method still further includes adjusting a behavior of an autonomous agent in response to identifying the location of the occluded object.

The current document is directed to automated methods and systems, controlled by various constraints and parameters, that identify contours in digital images, including curved contours. Certain of these parameters constrain contour identification to those contours in which the local curvature of a contour does not exceed a threshold local curvature and to those contours orthogonal to intensity gradients of at least threshold magnitudes. The currently described methods and systems identify seed points within a digital image, extend line segments from the seed points as an initial contour coincident with the seed point, and then iteratively extend the initial contour by adding line segments to one or both ends of the contour. The identified contours are selectively combined and filtered in order to identify a set of relevant contours for use in subsequent image-processing tasks.

Enhanced vectorization of raster images is described. An image vectorization module converts a raster image with bitmapped data to a vector image with vector elements based on mathematical formulas. In some embodiments, spatially-localized control of a vectorization operation is provided to a user. First, the user can adjust an intensity of a denoising operation differently at different areas of the raster image. Second, the user can adjust an automated segmentation by causing one segment to be split into two segments along a zone marked with an indicator tool, such as a brush. Third, the user can adjust an automated segmentation by causing two segments to be merged into a combined segment. The computation of the vector elements is based on the adjusted segmentation. In other embodiments, semantic information gleaned from the raster image is incorporated into the vector image to facilitate manipulation, such as joint selection of multiple vector elements.

Computer-implemented methods for detecting and characterizing surface depressions in a topographical landscape based on processing of high resolution digital elevation model data according to a local tree contour algorithm applied to an elevation contour representation of the landscape, and characterizing the detected surface depressions according to morphometric threshold values derived from data relevant to surface depressions of the topographical area. Non-transitory computer readable media comprising computer-executable instructions for carrying out the methods are also provided.

Methods and image processing systems are provided for determining a dominant gradient orientation for a target region within an image. A plurality of gradient samples are determined for the target region, wherein each of the gradient samples represents a variation in pixel values within the target region. The gradient samples are converted into double-angle gradient vectors, and the double-angle gradient vectors are combined so as to determine a dominant gradient orientation for the target region.

A method for tracking vehicles includes: extracting a target image at a current moment from a video stream obtained during traveling of vehicles; performing instance segmentation on the target image to obtain detection boxes corresponding to individual vehicles in the target image; extracting, from the detection box for each vehicle, a set of pixel points corresponding to each vehicle; processing image features of each pixel point in the set of pixel points corresponding to each vehicle to determine features of each vehicle in the target image; and determining, according to the features of each vehicle in the target image and the degree of matching between the features of each vehicle in past images, movement trajectory of each vehicle in the target image, wherein the past images are n images adjacent to and before the target image in the video stream, and n is a positive integer.

An image can be input to a deep neural network to determine a point in the image based on a center of a Gaussian heatmap corresponding to an object included in the image. The deep neural network can determine an object descriptor corresponding to the object and include the object descriptor in an object vector attached to the point. The deep neural network can determine object parameters including a three-dimensional location of the object in global coordinates and predicted pixel offsets of the object. The object parameters can be included in the object vector, and the deep neural network can predict a future location of the object in global coordinates based on the point and the object vector.