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Semantic segmentation is an approach detecting, for every pixel, the belonging class. [18] For example, in a figure with many people, all the pixels belonging to persons will have the same class id and the pixels in the background will be classified as background.
The random walker algorithm is an algorithm for image segmentation.In the first description of the algorithm, [1] a user interactively labels a small number of pixels with known labels (called seeds), e.g., "object" and "background".
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Text segmentation is the process of dividing written text into meaningful units, such as words, sentences, or topics. The term applies both to mental processes used by humans when reading text, and to artificial processes implemented in computers, which are the subject of natural language processing .
Sentence boundary disambiguation (SBD), also known as sentence breaking, sentence boundary detection, and sentence segmentation, is the problem in natural language processing of deciding where sentences begin and end.
This example shows how Euclidean distance will calculate the distance between objects to determine how similar the items are. Note that most text embeddings will be at least a few hundred dimensions instead of just two. Euclidean distance is a standard distance metric used to measure the dissimilarity between two points in a multi-dimensional ...
Example video frames and their object co-segmentation annotations (ground truth) in the Noisy-ViDiSeg [1] dataset. Object segments are depicted by the red edge. In computer vision, object co-segmentation is a special case of image segmentation, which is defined as jointly segmenting semantically similar objects in multiple images or video ...
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