Optimization-based automatic segmentation of organic objects of similar types

For the segmentation of multiple objects on unknown background in images, some approaches for specific objects exist. However, no approach is general enough to segment an arbitrary group of organic objects of similar type, like wood logs, apples, or tomatoes. Each approach contains restrictions in the object shape, texture, color or in the image background. Many methods are based on probabilistic inference on Markov Random Fields - summarized in this work as optimization based segmentation. In this paper, we address the automatic segmentation of organic objects of similar types by using optimization based methods. Based on the result of object detection, a fore- and background model is created enabling an automatic segmentation of images. Our novel and more general approach for organic objects is a first and important step in a measuring or inspection system. We evaluate and compare our approaches on images with different organic objects on very different backgrounds, which vary in color and texture. We show that the results are very accurate.