A method and system for generating a three-dimensional (3D) model of a garment. The method includes: providing an image of the garment; generating a 3D garment representation based on the image; registering the 3D garment representation to a 3D model to obtain a preliminary 3D garment model; projecting the preliminary 3D garment model dressed on the 3D model to an 2D projected image; and comparing the 2D projected image with the image of the garment, so as to refine the preliminary 3D garment model to obtain the final 3D model of the garment.

Provided is a deep learning-based panoptic segmentation accelerated processing technique using a complexity-based RPN skip method. An image segmentation system includes: a first processing unit configured to extract dynamic objects in an instance segmentation method by using an extracted feature; a calculation unit configured to control to skip some areas of the feature extracted at the network by the first processing unit, on the basis of complexity of the input image; a second processing unit configured to extract static objects in a semantic segmentation method by using the feature extracted at the network; and a fusion unit configured to fuse a result of extracting by the first processing unit and a result of extracting by the second processing unit. Accordingly, the panoptic segmentation method can be easily performed even in an embedded environment by reducing complexity for panoptic segmentation processing by reducing a calculation burden.

A method for processing an input frame for an on-device AI model is provided. The method may include obtaining an input frame. The method may include building at least one kernel independent of the scale of the input frame by passing input variables to the at least one kernel using preprocessor directives independent of the scale of the input frame. The method may include inputting the input frame to the on-device AI model including the at least one kernel independent of the scale of the input frame. The method may include processing the input frame in the on-device AI model.

This disclosure relates generally to estimating age of a leaf using morphological features extracted from segmented leaves. Traditionally, leaf age estimation requires a single leaf to be plucked from the plant and its image to be captured in a controlled environment. The method and system of the present disclosure obviates these needs and enables obtaining one or more full leaves from images captured in an uncontrolled environment. The method comprises segmenting the image to identify veins of the leaves that further enable obtaining the full leaves. The obtained leaves further enable identifying an associated plant species. The method also discloses some morphological features which are fed to a pre-trained multivariable linear regression model to estimate age of every leaf. The estimated leaf age finds application in estimation of multiple plant characteristics like photosynthetic rate, transpiration, nitrogen content and health of the plants.

Disclosed herein are systems and methods that relate to wireless communication mesh network design and operation. In one aspect, the disclosed process may involve (1) obtaining potential-customer information related to a set of potential customers for a service to be provided through a wireless communication mesh network in an AOI, where the potential-customer information comprises both (i) information related to potential customers that are identified during a pre-marketing phase and (ii) information related to potential customers that are identified during a door-to-door marketing phase, and where the set of potential customers have a corresponding set of customer locations in the AOI, (2) evaluating the obtained potential-customer information and thereby identifying a subset of customer locations at which to deploy the wireless communication mesh network, and (3) generating and outputting information that facilitates deployment of the wireless communication mesh network at the identified subset of customer locations in the AOI.

A handwritten text processing system processes a digitized document including handwritten text input to generate an output version of the digitized document that allows users to execute text processing functions on the textual content of the digitized document. Each word of the digitized data is extracted by converting the digitized document into images, binarizing the images, and segmenting the images into binary image patches. Each binary image patch is further processed to identify if the word is machine-generated or if the word is handwritten. The output version is generated by combining underlying images of the pages of the digitized document with words from the pages superimposed in a transparent font at positions that coincide with the positions of the words in the underlying images.

Systems and methods for autofocus using artificial intelligence include (i) capturing a plurality of monochrome images over a nominal focus range, (ii) identifying one or more connected components within each monochrome image, (iii) sorting the identified connected components based on a number of pixels associated with each connected component, (iv) generating a focus quality estimate of at least a portion of the sorted connected components using a machine learning module, and (iv) calculating a target focus position based on the focus quality estimate of the evaluated connected components. The calculated target focus position can be used to perform cell counting using artificial intelligence, such as by (i) generating a seed likelihood image and a whole cell likelihood image based on output—a convolutional neural network and (ii) generating a mask indicative quantity and/or pixel locations of objects based on the seed likelihood image.

The embodiments of the application disclose a method for detecting a game prop in a game region, a device, and a storage medium. The method includes that: an image frame sequence collected from a game region at a game prop operating stage is acquired, the image frame sequence including a first preset frame number of game images and the first preset frame number being more than or equal to 2; target detection is performed on each frame of game image in the image frame sequence to obtain a first set of recognition results belonging to the same game prop, each recognition result at least including a confidence of the game prop; and reliability of the first set of recognition results of the game prop is determined based on all confidences in the first set of recognition results of the game prop and a confidence threshold.

Provided are a target detection method and apparatus, an electronic device, and a computer storage medium. The method includes that: a first detection result of a game platform image is determined, the game platform image being obtained by performing resolution reducing processing on the original game platform image, and the first detection result being used for characterizing a region where a target object is located; the region where the target object is located is expanded outward in the original game platform image to obtain the clipping region, and the original game platform image is clipped to obtain the clipped image according to the clipping region; and the first detection result is optimized to obtain the second detection result according to the clipped image.

A gesture recognition apparatus according to the present invention includes at least one memory and at least one processor which function as: a first detection unit configured to detect from a captured image a first portion making a gesture; a second detection unit configured to detect from the captured image a second portion making the gesture in the first portion detected by the first detection unit; and a recognition unit configured to recognize the gesture on a basis of motion of the first portion detected by the first detection unit and motion of the second portion detected by the second detection unit, wherein in a case where a detection result satisfying a predetermined condition is not obtained by the second detection unit, the recognition unit recognizes the gesture using a detection result satisfying the predetermined condition obtained in a past by the second detection unit.