A method and a system for processing an image and transform it into a high resolution and high-definition image using a computationally efficient image transformation procedure is provided. The transformation of the image comprises receiving an intensity image and generating an application programming interface (API) call for transforming the received intensity image. The API call is then transmitted to an image processing server for transforming the intensity image into a layered distance field (DF) image. Further, a response is received from the image processing server, wherein the response comprises one or more functions for obtaining the layered DF image.

A method and apparatus for providing a rotational invariant neural network is herein disclosed. According to one embodiment, a method includes receiving a first input of an image in a first orientation and training a kernel to be symmetric such that an output corresponding to the first input is the same as an output corresponding to a second input of the image in a second orientation.

According to an embodiment of the disclosure, an electronic device may include: a display, a memory, and a processor operatively connected to the display and the memory. According to an embodiment, the memory may store instructions that, when executed, cause the processor to: obtain a first image of a first shape, obtain linear information indicating a morphological characteristic of an object in the first image of the first shape, determine a conversion method for converting the first image of the first shape into an image of a second shape based on the obtained linear information, convert the first image of the first shape into a second image of the second shape based on the determined conversion method, and control the display to display the converted second image of the second shape on the display.

According to an embodiment of the disclosure, an electronic device may include: a display, a memory, and a processor operatively connected to the display and the memory. According to an embodiment, the memory may store instructions that, when executed, cause the processor to: obtain a first image of a first shape, obtain linear information indicating a morphological characteristic of an object in the first image of the first shape, determine a conversion method for converting the first image of the first shape into an image of a second shape based on the obtained linear information, convert the first image of the first shape into a second image of the second shape based on the determined conversion method, and control the display to display the converted second image of the second shape on the display.

A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.

In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.

Saliency regions are identified in a global scene depicted by volumetric video. Saliency video streams that track the saliency regions are generated. Each saliency video stream tracks a respective saliency region. A saliency stream based representation of the volumetric video is generated to include the saliency video streams. The saliency stream based representation of the volumetric video is transmitted to a video streaming client.

Methods, systems, and articles of manufacture to improve image recognition searching are disclosed. In some embodiments, a first document image of a known object is used to generate one or more other document images of the same object by applying one or more techniques for synthetically generating images. The synthetically generated images correspond to different variations in conditions under which a potential query image might be captured. Extracted features from an initial image of a known object and features extracted from the one or more synthetically generated images are stored, along with their locations, as part of a common model of the known object. In other embodiments, image recognition search effectiveness is improved by transforming the location of features of multiple images of a same known object into a common coordinate system. This can enhance the accuracy of certain aspects of existing image search/recognition techniques including, for example, geometric verification.

An artificial intelligence based method and apparatus for processing information. A specific embodiment of the method includes: acquiring search click information recorded within a predetermined time period; generating a candidate entry set by selecting, from the search click information, entries having click volumes exceeding a click volume threshold within a preset unit time period; forming, for each candidate entry in the candidate entry set, a click volume sequence according to a chronological order of each of the click volumes corresponding to the candidate entry in the predetermined time period; determining, based on click volume sequences, categories of the candidate entries respectively corresponding to click volume sequences; and determining candidate entries having the categories being a preset category as points of interest to generate a set of points of interest.

Provided is a method and processing unit for computer-implemented analysis of a classification model which is adapted to map, as a prediction, a number of input instances, each of them having a number n of features, into a number of probabilities of output classes, as a classification decision, according to a predetermined function, and which is adapted to determine a relevance value for each feature resulting in a saliency map. The disclosure includes the step of identifying an effect of each feature on the prediction of the instance by determining, for each feature, a relevance information representing a contextual information for all features of the instance omitting the considered feature. Then, the relevance value for each feature is determined. Finally, the plurality of relevance values for the features of the instance is evaluated to identify the effect of each feature on the prediction of the instance.