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A Survey on Instance Segmentation State of the art

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Document pages: 28 pages

Abstract: Object detection or localization is an incremental step in progression fromcoarse to fine digital image inference. It not only provides the classes of theimage objects, but also provides the location of the image objects which havebeen classified. The location is given in the form of bounding boxes orcentroids. Semantic segmentation gives fine inference by predicting labels forevery pixel in the input image. Each pixel is labelled according to the objectclass within which it is enclosed. Furthering this evolution, instancesegmentation gives different labels for separate instances of objects belongingto the same class. Hence, instance segmentation may be defined as the techniqueof simultaneously solving the problem of object detection as well as that ofsemantic segmentation. In this survey paper on instance segmentation -- itsbackground, issues, techniques, evolution, popular datasets, related work up tothe state of the art and future scope have been discussed. The paper providesvaluable information for those who want to do research in the field of instancesegmentation.

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