A property vector representing extractable measurable properties, such as musical properties, of a file is mapped to semantic properties for the file. This is achieved by using artificial neural networks “ANNs” in which weights and biases are trained to align a distance dissimilarity measure in property space for pairwise comparative files back towards a corresponding semantic distance dissimilarity measure in semantic space for those same files. The result is that, once optimised, the ANNs can process any file, parsed with those properties, to identify other files sharing common traits reflective of emotional-perception, thereby rendering a more liable and true-to-life result of similarity/dissimilarity. This contrasts with simply training a neural network to consider extractable measurable properties that, in isolation, do not provide a reliable contextual relationship into the real-world.

Systems and methods for correlating access-system primitives generated by an access control system and semantic primitives generated by a sensor data comprehension system.

Disclosed is a text classification method and a text classification device. The text classification method includes: receiving text data (S1), the text data comprising one or more text semantic units; replacing the text semantic unit with a corresponding text keyword (S2), based on a correspondence between text semantic elements and text keywords; extracting, with a semantic model, a semantic feature of the text keyword (S3); and classifying, with a classification model, the text keyword at least based on the semantic feature, as a classification result of the text data (S4).

In one implementation, a method of generating a plane hypothesis is performed by a device including one or more processors, non-transitory memory, and a scene camera. The method includes obtaining an image of a scene including a plurality of pixels. The method includes obtaining a plurality of points of a point cloud based on the image of the scene. The method includes obtaining an object classification set based on the image of the scene. Each element of the object classification set includes a plurality of pixels respectively associated with a corresponding object in the scene. The method includes detecting a plane within the scene by identifying a subset of the plurality of points of the point cloud that correspond to a particular element of the object classification set.

Systems and methods for correlating access-system primitives generated by an access control system and semantic primitives generated by a sensor data comprehension system.

A method for combining a plurality of images into a synthesis image and an electronic device implementing the same are provided. The image synthesis method of the present disclosure includes acquiring coordinates of an object in a source image; extracting a target image from the source image based on the coordinates such that the target image contains the object; and displaying the target image in a section of a frame mapped to the source image.

Various embodiments of a method and system for capturing and utilizing item attributes are described. Various embodiments may include a mobile image capture apparatus, which may include a computer system configured to execute an image capture application. The image capture application may instruct an agent to capture an image of an item label. A data extraction component may be configured to process the images captured by the mobile image capture apparatus. For a given captured image, the data extraction component may in various embodiments be configured to perform OCR to determine one or more strings of characters from the image. The data extraction component may be further configured to determine whether one or more patterns match a determined string of characters. In response to the detection of a particular pattern matching a particular string of characters, the data extraction component may extract and store an attribute of the corresponding item.

Various methods, apparatuses/systems, and media for real-time automated analysis of project specifications are disclosed. A processor calls an API to invoke an OCR micro-service with the project specifications data as input data received from a plurality of applications each including a file corresponding to real-time project specifications data; determines whether the file corresponding to the project specification data is an image file; implements, based on determining, a neural network based image processing algorithm to extract data corresponding to the project specifications data from the input data; compares the extracted data corresponding to the project specifications data with predefined expected business results data; generates a similarity score, based on comparing, that identifies how similar the project specifications data is compared to the predefined expected business results data; and automatically generates a real-time analysis report on the project specifications in connection with the plurality of applications based on the similarity score.

The present application discloses a method for image text recognition, an apparatus, a device, and a storage medium, and relates to image processing technologies in the field of cloud computing. A specific implementation is: acquiring an image to be processed, where at least one text line exists in the image to be processed; processing each text line in the image to be processed to obtain a composite encoded vector corresponding to each word in each text line, where the composite encoded vector carries semantic information and position information; and determining a text recognition result of the image to be processed according to the semantic information and the position information carried in the composite encoded vector corresponding to each word in each text line. This technical solution can accurately distinguish adjacent fields with small pixel spacing in the image and improve the accuracy of text recognition in the image.

In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of devices. The processor is to: receive compressed data from a first device, wherein the compressed data is associated with visual data captured by sensor(s); perform a current stage of processing on the compressed data using a current CNN, wherein the current stage of processing corresponds to one of a plurality of processing stages associated with the visual data, and wherein the current CNN corresponds to one of a plurality of CNNs associated with the plurality of processing stages; obtain an output associated with the current stage of processing; determine, based on the output, whether processing associated with the visual data is complete; if the processing is complete, output a result associated with the visual data; if the processing is incomplete, transmit the compressed data to a second device.