Novel Approach for Detection and Removal of Moving Cast Shadows Based on RGB, HSV and YUV Color Spaces

Cast shadow affects computer vision tasks such as image segmentation, object detection and tracking since objects and shadows share the same visual motion characteristics. This unavoidable problem decreases video surveillance system performance. The basic idea of this paper is to exploit the evidence that shadows darken the surface which they are cast upon. For this reason, we propose a simple and accurate method for detection of moving cast shadows based on chromatic properties in RGB, HSV and YUV color spaces. The method requires no a priori assumptions regarding the scene or lighting source. Starting from a normalization step, we apply canny filter to detect the boundary between self-shadow and cast shadow. This treatment is devoted only for the first sequence. Then, we separate between background and moving objects using an improved version of Gaussian mixture model. In order to remove these unwanted shadows completely, we use three change estimators calculated according to the intensity ratio in HSV color space, chromaticity properties in RGB color space, and brightness ratio in YUV color space. Only pixels that satisfy threshold of the three estimators are labeled as shadow and will be removed. Experiments carried out on various video databases prove that the proposed system is robust and efficient and can precisely remove shadows for a wide class of environment and without any assumptions. Experimental results also show that our approach outperforms existing methods and can run in real-time systems

Mobile e-detection of Banyuwangi’s citrus fruit maturity using k-nearest neighbor

Banyuwangi is the largest oranges-producing city in East Java, and the orange produced is Siamese citrus fruit. Siamese is Banyuwangi local citrus fruit often found at the harvest time and has a sweet taste. To determine the citrus fruit level, people can detect it from the color and texture. In this modern era, people can use an application to determine the citrus fruits' maturity level. From the elements of color and texture, this research will add the citrus fruit's contours, namely the pore size of the citrus fruit and the distance between the curve of the tip of the orange. Taking pictures of citrus fruits will be following the application stages that will be used as the image of inputting the data. The detection is then conducted using the K-NN method based on several criteria based on the input image after the feature extraction process. The feature extraction stages are segmentation, normalization, thresholding, and thinning, which will be produced in several criteria: the maximum RGB value, the minimum RGB value, pore size, and the distance between the tip's curve of the orange. The research results that have been carried out are based on the research stages to get a similarity percentage following the inputted data. The E-Detection application can provide information to citrus farmers, especially beginner citrus farmers, to know the level of fruit maturity oranges to be harvested