A YCbCr Model Based Shadow Detection and Removal Approach On Camouflaged Images

A shadow in an image can disturb the actual outcome in computer vision and pattern recognition applications. The reason is that the shadow will act as an individual object resulting in the false interpretation and performance degradation of subsequent computer vision tasks. Here we propose a process to detect and remove shadows from an image using the YCbCr colour model. A small portion of the image is identified as a shadow area. The features at the pixel level and along the boundaries in the shadow area are learned. A method based on the locations of the border of the shadow is applied to remove the shadow. Experiments have been conducted on the benchmark camouflaged image dataset and the non-camouflaged image dataset to evaluate the approach. The methodology achieves promising performance in detecting and removing shadows from an image.