Abstract: Processors, systems, and techniques to identify one or more regions of an image, using at least one neural network based at least in part on a description of the region(s) of the image. In at least one embodiment, a natural language expression including a description is input into at least one neural network, and the at least one neural network identifies a portion of pixels in an image corresponding to the description. In at least one embodiment, one or more neural networks use semantic information obtained from language data describing at least one region of interest in an image to generate classifications classifying a plurality of locations in the image as being inside or outside the at least one region, and use the plurality of locations to generate a segmentation mask. In at least one embodiment, the segmentation mask is used to cause a machine to perform task(s).

Abstract: This invention provides a method for image enhancement based on histogram modification and specification, which comprises the following steps, calculating a first cumulative distribution function for a gray levels of a first image; calculating a first transforming function by the first distribution function and a second distribution function; providing a second transforming function by a first equation or a second equation; providing the second transforming function, the second transforming function is close to the first transforming function; providing the second transforming function, the second transforming function is smooth low-pass filtering result of the first transforming function; and outputting a second image from the second transforming function.