International Journal of Computer Applications (0975 – 8887) Volume 119 – No.20, June 2015 5 Shadow Detection and Compensation in Aerial Images using MATLAB Sachin Tiwari M.Tech. Scholar in Deptt. of ECE SAGAR Institute of Research and Technology Bhopal M.P. India Krishna Chauhan Assistant Professor in Deptt. of ECE SAGAR Institute Research and of Technology Bhopal M.P. India Yashwant Kurmi Research scholar in Deptt. of ECE Maulana Azad National of Technology Bhopal M.P. India ABSTRACT Recently, Kuo-Liang Chung presented an efficient algorithm uses the ratio of hue over intensity as the parameter to determine the coarse shadow map and then the local thresholding method (STS) in which it needs the different threshold levels which is comparatively more time consuming (and if the threshold is not proper then the resultant image is not the desired image, because threshold determination is typical task in itself) for fine shadow determination in color aerial images. In our proposed method we again modify the ratio with some empirical relation that give better result taking less time. Under four testing images, experimental results show that, for all four images that are low and medium intensity images have better shadow detection but the shadow compensation algorithm is gives the good result in all the testing images. We also have the comparison between three algorithms Tsai's, STS and our proposed algorithms with result of shadow detected images [15]. Keywords Shadow compensation, modified hue intensity ratio, shadow detection. 1. INTRODUCTION In this modern era everything is being watched out by cameras (all time watchman) in the absence of paid watchman. This camera provides the security at every moment. At present high resolution cameras have ability to detect the things very accurately. In the images that have shadow it is difficult to identify the object or thing. Shadows can either aid or confound scene interpretation, depending on whether we model the shadows or ignore them. In addition, shadows are also responsible to degrade the image quality. Shadows in images are typically affected by several phenomena in the scene, including physical phenomena such as lighting conditions, type and behaviour of shadowed surfaces, occluding objects; etc .Human vision system is very immune to shadows. We do not find any difficulty in recognizing, tracking objects even with shadows. But in the case of computer vision, shadows create problems and reduce the reliability of the system. In addition, shadows are also responsible to degrade the image quality. Therefore, shadow compensation is an important pre-processing step for computer vision and image enhancement algorithm [1], [2], [3].Shadow compensation from image can be used for object detection, such as cancer detection, military object detection etc., as sometimes images are covered by shadows. After compensating these shadows, objects in the images will appear more obviously so that they are recognized correctly. Therefore, shadow detection and compensation is an important pre-processing step for computer vision and image enhancement. Shadow compensation from respective image can also be used for object detection, in medical field image analysis, in military object detection, in satellite image analysis etc. Many times all type of images are covered by shadows. After compensating these shadows, objects in the images will appear more clearly so that they are recognized correctly. 1.1Shadow Identification V.J.D.Tsai. Presented an efficient algorithm to detect shadows for color aerial RGB [4], [13], [16] images. The input image can be first transformed into the HSI; Hue, saturation and value (HSV); luma, blue-difference chroma and red-difference chroma ), hue, chroma and value (HCV) or luminance, hue and saturation (YIQ) color models. Tsai first calculated the ratio of the hue over intensity for each pixel to construct the ratio map and then a global threshold of a constructed ratio map is determined to identify shadows [7], [14]. To improve the performance we have done some modification in that ratio using some empirical relations. 1.2 Shadow removal: The shadow compensation problem can be broken down into two main constituent parts: shadow identification and shadow compensation. Shadow identification is a well-defined concept, in which the presence of shadows upon different material image surfaces is detected and their exact position upon those surfaces is located. We primarily focus on shadow compensation upon any one of these material surfaces. We assume that such an identification method has recognized a surface as having a shadow on it and passed it to our algorithm. Our shadow compensation approach is based on a simple shadow model where lighting consists of directed light and environment light [8]. 2. PROPOSED WORK First take the Image as input then apply the median filter to remove the noise components. And the following equation is given to transform the RGB color model into the HSI color model.                                        (1)      +       (2)   =              (3) Where   and   have been defined in (1) and value of   and   are bounded in range [0, 255].