Textural discrimination in unconstrained environment

Object region segmentation for object detection and identi cation in images captured in a complex background environment is one of the most challenging tasks in image processing and computer vision areas especially for objects that have non-homogeneous body textures. This paper presents an object segmentation technique in an unconstrained environment based on textural descriptors to extract the object region of interest from other surrounding objects and backgrounds in order to get an accurate identi cation of the segmented area precisely. The proposed segmentation method is developed on a textural based analysis and employs Seeded Region Growing (SRG) segmentation algorithm to accomplish the process. In our application of obtaining the region of a chosen object for further manipulation through data mining, human input is used to choose the object of interest through which seed points are identi ed and employed. User selection of the object of interest could be achieved in di erent ways, one of which is using mouse based point and click procedure. Therefore, the proposed system provides the user with the choice to select the object of interest that will be segmented out from other background regions and objects. It is important to note that texture information gives better description of objects and plays an important role for the characterization of regions. In region growing segmentation, three key factors are satis ed such as choice of similarity criteria, selection of seed points, and stopping rule. The choice of similarity criteria is accomplished through texture descriptors and connectivity properties. The selection of seed points is determined interactively by the user when they choose the object of interest. The de nition of a stopping rule is achieved using a test for homogeneity and connectivity measures, therefore, a region would stop growing when there are no further pixels that satisfy the homogeneity and connectivity criteria. The segmentation region is iteratively grown by comparing all unallocated neighbouring pixels to that region. The di erence between seed pixels' mean intensity value and the region's textural descriptors is used as a measure of similarity of pixels. The pixel with the smallest di erence measured would be allocated to the particular segmentation region. Seeded region growing factors would change interactively according to the intensity levels of the chosen object of interest. The algorithm automatically computes segmentation thresholds based on local feature analysis. The system starts by measuring the intensity level of the selected object and accordingly adapts growing and stopping rules of the segmented region. The proposed segmentation method has been tested on a relatively large variety of databases with di erent objects of varying textures. The experimental results show that this simple framework is capable of achieving high quality performance and that this method can better handle the problem of segmenting objects of non-homogeneous textural bodies and correctly separate those objects from other objects and complex backgrounds. This framework can also be easily adapted to di erent applications by substituting suitable image feature de nitions.