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As applied in the field of computer vision, graph cut optimization can be employed to efficiently solve a wide variety of low-level computer vision problems (early vision [1]), such as image smoothing, the stereo correspondence problem, image segmentation, object co-segmentation, and many other computer vision problems that can be formulated in terms of energy minimization.
Cropping is the removal of unwanted outer areas from a photographic or illustrated image. The process usually consists of the removal of some of the peripheral areas of an image to remove extraneous visual data from the picture, improve its framing, change the aspect ratio, or accentuate or isolate the subject matter from its background.
GrabCut is an image segmentation method based on graph cuts. Starting with a user-specified bounding box around the object to be segmented, the algorithm estimates the color distribution of the target object and that of the background using a Gaussian mixture model.
OBJ CUT [7] is an efficient method that automatically segments an object. The OBJ CUT method is a generic method, and therefore it is applicable to any object category model. Given an image D containing an instance of a known object category, e.g. cows, the OBJ CUT algorithm computes a segmentation of the object, that is, it infers a set of ...
By copying the selected image sequences without transcoding or re-rendering, it achieves very fast creation of the target file [10] in comparison to tools that re-encode frames. [11] Completely lossless copying is achieved when the source file is cut at the reference frames of a group of pictures only. This is being visualised when operating ...
The cut is the most basic and common type of transition. In a cut, one image on the screen is instantly replaced by another, often with a change in camera angle. Shot A ends abruptly and shot B begins. [4] Cuts can be classified into different types, [5] as detailed below.