Abstract: An embodiment of an image processing apparatus may comprise one or more processors, memory coupled to the one or more processor to store image and mask data, and logic coupled to the one or more processors and the memory, the logic to capture a volumetric broadcast video signal in real-time and generate a sequence of frame images from the captured real-time volumetric broadcast video signal, segment an input image, which corresponds to a single frame of the sequence of frame images, to generate a mask image associated with the input image, and determine a mask quality score based on the input image and the associated mask image in real-time. Other embodiments are disclosed and claimed.

Abstract: An embodiment of an image processing apparatus may comprise one or more processors, memory coupled to the one or more processor to store image and mask data, and logic coupled to the one or more processors and the memory, the logic to capture a volumetric broadcast video signal in real-time and generate a sequence of frame images from the captured real-time volumetric broadcast video signal, segment an input image, which corresponds to a single frame of the sequence of frame images, to generate a mask image associated with the input image, and determine a mask quality score based on the input image and the associated mask image in real-time. Other embodiments are disclosed and claimed.

Abstract: An embodiment of mask quality predication technology may include a memory to store a set of input images and a reference mask image associated with each input image of the set of input images, a processor communicatively coupled to the memory, and logic communicatively coupled to the processor and the memory to generate a set of two or more masks of different quality associated with each input image of the set of input images, and determine a quality score for each generated mask. Other embodiments are disclosed and claimed.

Abstract: A method for unsupervised image segmentation based on a background likelihood estimation is described herein. The method includes calculating a distance for each pixel in an input image and transforming each distance into a probability of the distance of belonging to a background of an input image or a foreground of the input image. The method also includes transforming each probability into a log likelihood value to form a unary term and minimizing an energy function including the unary term to obtain a set of foreground labels and a set of background labels.

Abstract: Techniques related to video background estimation inclusive of generating a final background picture absent foreground objects based on input video are discussed. Such techniques include generating first and second estimated background pictures using temporal and spatial background picture modeling, respectively, and fusing the first and second estimated background pictures based on first and second confidence maps corresponding to the first and second estimated background pictures to generate the final estimated background picture.

Abstract: A method for unsupervised image segmentation based on a background likelihood estimation is described herein. The method includes calculating a distance for each pixel in an input image and transforming each distance into a probability of the distance of belonging to a background of an input image or a foreground of the input image. The method also includes transforming each probability into a log likelihood value to form a unary term and minimizing an energy function including the unary term to obtain a set of foreground labels and a set of background labels.

Abstract: An embodiment of mask quality predication technology may include a memory to store a set of input images and a reference mask image associated with each input image of the set of input images, a processor communicatively coupled to the memory, and logic communicatively coupled to the processor and the memory to generate a set of two or more masks of different quality associated with each input image of the set of input images, and determine a quality score for each generated mask. Other embodiments are disclosed and claimed.