Abstract: The present invention discloses system and method for replacing a texture of a background region in a multimedia. The system is an AI-based multi-media processing system for replacing the original texture of the background of the multimedia with a texture template. The system applies a foreground mask to hide and protect the foreground region and multiple textures of the background. The system uses deep learning to segment specific texture from images or video sequences. The system replaces the texture of the original input image with a texture template to form a processed image.

Abstract: The present invention discloses system and method for replacing a texture of a background region in a multimedia. The system is an AI-based multi-media processing system for replacing the original texture of the background of the multimedia with a texture template. The system applies a foreground mask to hide and protect the foreground region and multiple textures of the background. The system uses deep learning to segment specific texture from images or video sequences. The system replaces the texture of the original input image with a texture template to form a processed image.

Abstract: The present subject matter refers a method for training image-alignment procedures in a computing environment. The method comprises communicating one or more images of an object to a user and receiving a plurality of user-selected zones within said one or more through a user-interface. An augmented data-set is generated based on said one or more images comprising the user-selected zones, wherein such augmented data set comprises a plurality of additional images defining variants of said one or more communicated images. Thereafter, a machine-learning based image alignment is trained based on at-least one of the augmented data set and the communicated images.

Abstract: The present subject matter refers a method for training image-alignment procedures in a computing environment. The method comprises communicating one or more images of an object to a user and receiving a plurality of user-selected zones within said one or more through a user-interface. An augmented data-set is generated based on said one or more images comprising the user-selected zones, wherein such augmented data set comprises a plurality of additional images defining variants of said one or more communicated images. Thereafter, a machine-learning based image alignment is trained based on at-least one of the augmented data set and the communicated images.

Abstract: The present subject matter refers an image-processing method comprises receiving a first-image of an object captured by a range-imaging device at a first viewing location. The transforming the first-image into a second image of the object, said second image corresponding to an image captured based on range-imaging at a second viewing location with respect to the object. The gaps in the second-image are identified based on comparison with the first image, such that the identified gaps within the second image are complemented to result in a complemented second image.