Fuzzy granulation and constraint neighbourhood granulation structure for object classification in unevenly illuminated images

Abstract

Classification of objects and background in a complex scene is a challenging early vision problem. Specifically, the problem is compounded under poor illumination conditions. In this paper, the problem of object and background classification has been addressed under unevenly illuminated conditions. The challenge is to extract the actual boundary under poorly illuminated portion. This has been addressed using the notion of rough sets and granular computing. In order to take care of differently illuminated portions over the image, adaptive window growing approach has been employed to partition the image into different windows and optimum threshold for classification over a given window has been determined by the four proposed granular computing based schemes. Over a particular window, illumination varies significantly posing a challenge for classification. In order to deal with these issues, we have proposed four schemes based on heterogeneous and non-homogeneous granulation. They are; (i) Heterogeneous Granulation based Window Growing (HGWG), (ii) Empirical Non-homogeneous Granulation based Window Growing (ENHWG), (iii) Fuzzy Gradient Non-homogeneous based Window Growing (FNHWG), (iv) Fuzzy Gradient Non-homogeneous Constrained Neighbourhood based Window Growing (FNHCNGWG). The proposed schemes have been tested with images from Berkeley image database, specifically with unevenly illuminated images having single and multiple objects. The performance of the proposed schemes has been evaluated based on four metrics. The performance of the FNHWG and FNHCNGWG schemes has been compared with Otsu, K-means, FCM, PCM, Pal’s method, HGWG, ENHWG and Fuzzy Non-homogeneous Neighbourhood based Window growing (FNHNGWG) schemes and found to be superior to the existing ones.