An image processor includes an edge detection portion for scanning an image and detecting, as edges, an arrangement of pixels in which brightness value difference or color parameter difference between the pixels is equal to or greater than a threshold; a grouping portion for grouping the detected edge based on edge length, a distance between endpoints of the edges, and an angle between the edges; a determination portion for determining the grouped edges as a dashed line edge group when a pattern, in which the brightness value difference or the color parameter difference between the pixels is detected, matches a predetermined pattern; a correction portion for performing a linear approximation process on the dashed line edge group to correct a coordinate value of an endpoint of the dashed line edge; and a parking frame setting portion for setting a parking frame using the corrected dashed line edge.

A method, apparatus, and non-transitory computer readable medium for image processing are described. Embodiments of the method, apparatus, and non-transitory computer readable medium include identifying an original image including a plurality of semantic attributes, wherein each of the semantic attributes represents a complex set of features of the original image; identifying a target attribute value that indicates a change to a target attribute of the semantic attributes; computing a modified feature vector based on the target attribute value, wherein the modified feature vector incorporates the change to the target attribute while holding at least one preserved attribute of the semantic attributes substantially unchanged; and generating a modified image based on the modified feature vector, wherein the modified image includes the change to the target attribute and retains the at least one preserved attribute from the original image.

Example detecting methods and apparatus are described. One example method includes: acquiring a two-dimensional image; and constructing, for each of one or more objects under detection in the two-dimensional image, a structured polygon corresponding to the object under detection based on the acquired two-dimensional image, wherein for each object under detection, a structured polygon corresponding to the object represents projection of a three-dimensional bounding box corresponding to the object in the two-dimensional image; for each object under detection, calculating depth information of vertices in the structured polygon based on height information of the object and height information of vertical sides of the structured polygon corresponding to the object; and determining three-dimensional spatial information of the object under detection based on the depth information of the vertices in the structured polygon and two-dimensional coordinate information of the vertices of the structured polygon in the two-dimensional image.

Embodiments of the present invention disclose a vehicle feature acquisition method and a vehicle feature acquisition device. The vehicle feature acquisition method comprises: acquiring an image to be processed, the image to be processed comprising a vehicle; performing recognition on said image to obtain feature elements on the vehicle, wherein the feature elements can be vehicle side elements of a vehicle and elements of a vehicle end, and the vehicle end can be a vehicle front end or a vehicle rear end; determining, according to positions of the vehicle side elements of the vehicle and the elements of the vehicle end in said image, a side region of the vehicle and an end region of the vehicle, and accordingly acquiring regional features of the vehicle side according to the side region and acquiring regional features of the vehicle end according to the end region. In embodiments of the present application, the vehicle in the image to be processed is first divided into regions, and then regional features are obtained in the divided regions, such that the invention is able to obtain more comprehensive vehicle features when compared to the prior art in which only planar regions of the vehicle in the image to be processed can be determined.

A method, system, and device for audio-based identification interfaces for selecting objects from video generates and stores frequency-based audio identifiers associated with segments of an audio stream that is integrated with a video stream. The generation of the frequency-based audio identifiers may be performed by a hashing function applied to audio frequencies within audio segments. The video stream comprises identified objects that may be identified by application of a trained neural network. An audio segment is received from a user and a corresponding frequency-based audio identifier is generated and matched against stored frequency-based audio identifiers. The matching determines an audio segment and a temporally corresponding identified object, which is then embodied within an interactive user interface.

Provided are an image recognition apparatus, an image recognition method, and a program for enabling recognition of many kinds of objects with high precision. An overall recognition unit executes, for at least one given object, a process of recognizing the position of the object in an image. A partial image extraction unit extracts, from the image, a partial image which is a part of the image associated with the recognized position. A partial recognition unit executes a process of recognizing what is one or more objects represented by the partial image, the one or more objects including an object other than the given object the position of which is recognized.

A method includes receiving data indicating a plurality of dimensions of a manufactured device. The method further includes providing the data to a trained machine learning model. The method further includes receiving, from the trained machine learning model, a synthetic microscopy image associated with the manufactured device, wherein the synthetic microscopy image is generated in view of the first data. The method further includes performing at least one of (i) outputting the synthetic microscopy image to a display or (ii) performing one or more operations on the synthetic microscopy image.

A method, system, and device for audio-based identification interfaces for selecting objects from video generates and stores frequency-based audio identifiers associated with segments of an audio stream that is integrated with a video stream. The generation of the frequency-based audio identifiers may be performed by a hashing function applied to audio frequencies within audio segments. The video stream comprises identified objects that may be identified by application of a trained neural network. An audio segment is received from a user and a corresponding frequency-based audio identifier is generated and matched against stored frequency-based audio identifiers. The matching determines an audio segment and a temporally corresponding identified object, which is then embodied within an interactive user interface.

A method and system for incentivized neural network training and assurance processes provides incentives to object miners to identify objects in video streams for the purposes of enhancing the training of computer-implemented neural networks on the identified objects and/or augmenting the results of automatic object identification by trained neural networks. An object mining user interface and process is provided to object miners that provides incentives for identifying objects in video streams and technical capabilities for designating identified objects within multiple multi-dimensional regions of pixels. Incentives may be token-based and in accordance with end user interactions within a visual user interface with representations of the miner-identified objects within a video stream.

A multi-view interactive digital media representation (MVIDMR) of an object can be generated from live images of an object captured from a camera. Selectable tags can be placed at locations on the object in the MVIDMR. When the selectable tags are selected, media content can be output which shows details of the object at location where the selectable tag is placed. A machine learning algorithm can be used to automatically recognize landmarks on the object in the frames of the MVIDMR and a structure from motion calculation can be used to determine 3-D positions associated with the landmarks. A 3-D skeleton associated with the object can be assembled from the 3-D positions and projected into the frames associated with the MVIDMR. The 3-D skeleton can be used to determine the selectable tag locations in the frames of the MVIDMR of the object.