Abstract: Techniques are provided for image segmentation based on image differencing, using recursive neural networks. A methodology implementing the techniques according to an embodiment includes quantizing pixels of a first image frame, performing a rigid translation of the quantized first image frame to generate a second image frame, and performing a differencing operation between the quantized first image frame and the second image frame to generate a sparse image frame. A neural network can then be applied to the sparse image frame to generate a segmented image. In still another embodiment, the methodology is applied to a sequence or set of image frames, for example from a video or still camera, and pixels from a first and second image frame of the sequence/set are quantized. The sparse image frame is generated from a difference between quantized image frames. The method further includes training the neural network on sparse training image frames.

Abstract: Techniques are provided for image segmentation based on image differencing, using recursive neural networks. A methodology implementing the techniques according to an embodiment includes quantizing pixels of a first image frame, performing a rigid translation of the quantized first image frame to generate a second image frame, and performing a differencing operation between the quantized first image frame and the second image frame to generate a sparse image frame. A neural network can then be applied to the sparse image frame to generate a segmented image. In still another embodiment, the methodology is applied to a sequence or set of image frames, for example from a video or still camera, and pixels from a first and second image frame of the sequence/set are quantized. The sparse image frame is generated from a difference between quantized image frames. The method further includes training the neural network on sparse training image frames.