Guo et al. [5] summarized four primary problems about texture analysis which are respectively image classification based on texture content, image segmentation of homogeneous texture regions, texture synthesis for graphics applications, and shape information acquisition from texture cue.
Mena et al. proposed an approach for automatic road extraction in rural and semi-urban areas which includes four modules: data preprocessing, binary segmentation based on texture progressive analysis, vectorization of the binary image by means of skeletal extraction and morphological operations, and finally evaluation of the proposed system by comparing with manually drawn road map [20].
A two stage statistical model based on texture and shape for fully automatic choroidal segmentation of normal and pathologic eyes obtained by a 1060 nm optical coherence tomography (OCT) system is developed.
Various image processing approaches based on textures, shapes, and an object boundary, as well as some segmentation methods like, edge detection and color segmentations, are highlighted.
The SLIC algorithm automatically adjusts the shape of each superpixel based on the segmentation scale and texture information of the image, which is sensitive to the boundary of the object.
Due to its complexity, multi-slice evaluation based on 3D segmentation of the lesion and 3D texture analysis was not envisaged in first instance.
Figure10 shows a case of data segmentation based on the random segmentation approach.
In order to classify one pixel as a member of a class "road," there are a number of possible segmentation methods based on color, texture descriptors based on statistic parameters, structure, or frequency spectrum, etc.
Second, design a weighting criteria based on proposed extended segmentation based fractal texture analysis (ESFTA), boundary connections, number of connected labels and distance from the center.
This new scan-matching procedure is named Polar Scan Matching based on Kalman Segmentation (PSM-KS).
