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l image, they have suffered from at least one of the following three problems.(1)Needing some prior knowledge, such as human’s interaction.(2)Effective in specific application.(3)Failing on complex scenes. In contrast, the method proposed in this work is not designed for specific applications and can automatically extract shadows from single still images, even those with complex outdoor scenes.

 

Classification plays an important role in various fields such as pattern recognition, decision-making, data mining and modeling. There are three main types of fuzzy clustering - fuzzy clustering based on fuzzy relation, fuzzy clustering based on objective function, and fuzzy generalized k-nearest neighbor rule. The fuzzy clustering based on objective function is the most popular one, because it is quite facile, and allows the most precise formulation of the clustering criteria. However, these methods also have some weakness. For examples, the most known Fuzzy C-Means [17,18] (FCM) algorithm is generally less sensible with the local extremes due to its non-convex objective function, and the performance of them mostly rely on the initialization of parameters or the initial solution. Therefore many researchers introduce global optimization methods, such as Genetic algorithm (GA), and Tabu search (TS) to avoid the above problems.

 

Technically, shadow detection methods can be classified into property-based and physics-based. Physics-based techniques need some prior knowledge, such as light and geometry, camera calibration, or indoor scenes. However, it is extremely difficult to obtain the accurate model for an arbitrary scene because the environments are complex and the light sources vary from time to time and from place to place. Hence, most of physics-based techniques are designed for specific applications, such as moving cast shadow detection and shadow detection in aerial images [5]. Physics-based methods exploit some knowledge of the scene, which result in these methods being only used in specific applications they are designed for. When the application environments are different, the algorithms may fail. Property-based techniques identify shadows through shadow features. The most straightforward feature of a shadow is that it darkens the surface it cast on, and this feature is used by al-most all methods. Other features like edge, histograms, texture, geometry property, color ratios, and gradient are also widely adopted. Sometimes, only one feature is not enough. For example, shadows usually have lower pixel values, but pixels that have lower values may not be shadows. Computer vision cannot directly judge that a dark region is a shadow or is a black object. Therefore, most methods combine more than one feature. Property-based approaches are more flexible than physics-based ones. They can be applied to a wider class of scenes.

 

Fuzzy C-Means (FCM)[17,18] algorithm has good clustering efficiency, for which it is widely used in the field of image segmentation. The fuzzy c-means algorithm can speed up the segmentation process for gray-level images. The traditional fuzzy c-means (FCM) clustering algorit本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。