LAUSR

Image de-fencing is often used by digital photographers to remove regular or near-regular fence-like patterns from an image. The goal of image de-fencing is to remove a fence object from an image in such a seamless way that it appears as if the fence never existed in the image. This task is mainly challenging due to a wide range intra-class variation of fence, complexity of background, and common occlusions. We present a novel image de-fencing technique to automatically detect fences of regular and irregular patterns in an image. We use a data-driven approach that detects a fence using encoded images as feature descriptors. We use a variant of the histograms of oriented gradients (HOG) descriptor for feature representation. We modify the conventional HOG descriptor to represent each pixel rather than representing a full patch. We evaluated our algorithm on 41 different images obtained from various sources on the Internet based on a well-defined selection criteria. Our evaluation shows that the proposed algorithm is capable of detecting a fence object in a given image with more than 98% accuracy and 87% precision.