The present disclosure provides a logo picture processing method, apparatus, device and medium, and relates to technical field of image processing, and specifically to the technical field of artificial intelligence such as deep learning and computer vision. The logo picture processing method includes: obtaining a logo picture including: a current logo graph and current text information; performing text recognition on the logo picture to obtain the current text information; searching for a picture that matches both the current logo graph and the current text information, to obtain a matched picture. The present disclosure may improve the accuracy of the matched picture of the logo picture and thereby improve the logo picture recognition accuracy.

A non-transitory computer readable medium includes instructions that, when executed by at least one processor, cause the at least one processor to perform artificial-intelligence-based technical support operations. The operations may include receiving over at least one network first audio signals including speech data associated with a technical support session and first image signals including image data associated with a product for which support is sought from a mobile communications device, analyzing the first audio signals and the first image signals using artificial intelligence, aggregating the analysis thereof, accessing at least one data structure to identify an image capture instruction, presenting the image capture instruction including a direction to alter and capture second image signals of a structure identified in the first image signals to the mobile communications device, receiving from the mobile communications device second image signals, analyzing the same using artificial intelligence, and determining a technical support resolution status.

To provide a control device and a program capable of improving convenience of character reading from a captured image.

A control device includes a control unit configured to perform a process of recognizing character groups in a captured image obtained by capturing an image of a periphery of a user, a process of identifying a character group of which a defined priority exceeds a threshold among the recognized character groups, and a process of reading the identified character group by a voice.

A method is described herein. The method includes the designation of a player as a profile player or a non-profile player in each camera view. In response to the player being a non-profile player, the method includes extracting features from the detected player within the bounding box and classifying the features according to a label. In response to the player being a non-profile player, the method also includes selecting a label with a highest number of votes according to a voting policy as a final label.

A method and device for recognizing an image, electronic equipment and a storage medium are provided. The method includes: acquiring an image to be recognized; determining a potential recognition region based on a target algorithm model; determining an up-sampled potential recognition region by up-sampling the potential recognition region; and determining a classification recognition result based on the up-sampled potential recognition region.

A method and system of recognizing object edges and a computer-readable storage medium are provided. The method includes: obtaining an input image, where edges of an object in the input image includes a plurality of object vertices; recognizing, through an object vertex recognition model, the input image and obtaining a relative position of each object vertex and a corresponding image vertex thereof; determining a reference position of each object vertex in the input image according to the relative position of each object vertex and the corresponding image vertex thereof; performing corner point detection in a predetermined area where the reference position of the object vertex is located for each object vertex; determining an actual position of each object vertex in the input image according to a result of the corner point detection; and sequentially connecting adjacent object vertices to form edge lines to obtain the edges of the object with edges in the input image according to the actual position of each object vertex in the input image. By applying the solution provided by the disclosure, the edges of the object with edges may be detected in the image.

A printed matter reviewed by a user is read, and a difference image is generated based on a first image obtained as a result of reading the printed matter and an electronic document that is a printing source from which the printed matter is generated. Based on the difference image, a process is performed to identify an instruction relating to a revision additionally written on the printed matter and a character string to be subjected to the revision in the electronic document. Thereafter, a particular process is executed on the electronic document based on the instruction related to the revision and the character string to which the revision is applied.

One embodiment of the present invention sets forth a technique for extracting data from an architectural drawing. The technique includes performing one or more operations via one or more machine learning models to extract a first image of a floorplan area from the architectural drawing. The technique also includes generating a boundary segmentation based on the first image of the floorplan area, wherein the boundary segmentation includes one or more boundary types for one or more portions of the floorplan area.

Systems, methods and computer program products for detecting objects using a multi-detector are disclosed, according to various embodiments. In one aspect, a computer-implemented method includes defining analysis profiles, where each analysis profile: corresponds to one of a plurality of detectors, and comprises: a unique set of analysis parameters and/or a unique detection algorithm. The method further includes analyzing image data in accordance with the analysis profiles; selecting an optimum analysis result based on confidence scores associated with different analysis results; and detecting objects within the optimum analysis result. According to additional aspects, the analysis parameters may define different subregions of a digital image to be analyzed; a composite analysis result may be generated based on analysis of the different subregions by different detectors; and the optimum analysis result may be based on the composite analysis result.

An object recognition method related to the field of artificial intelligence comprises: collecting an object to be subjected to recognition (S101); according to a target text detection model corresponding to the object to be subjected to recognition, carrying out screening and recognition on full text information corresponding to the object to be subjected to recognition, so as to obtain point-of-interest text information therefrom (S102); and carrying out recognition on the point-of-interest text information according to a preset text recognition model (S103). A target text detection model obtains point-of-interest text information by means of carrying out screening and recognition on full text information, such that the recognition of full text information in the prior art is avoided, thus saving recognition time, and improving the recognition efficiency.