Shadows often introduce errors in the performance of computer vision algorithms, such as object detection and tracking. This paper proposes a method to remove shadows from real images based on a probability shadow map. The probability shadow map identifies how much light is impinging on a surface. The lightness of shadowed regions in an image is increased and then the color of that part of the surface is corrected so that it matches the lit part of the surface. The result is compared with two other shadow removal frameworks. The advantage of our method is that after removal, the texture and all the details in the shadowed regions remain intact.

Abstract. Recent progress in computational photography has shown that we can acquire physical information beyond visible (RGB) image representations. In particular, we can acquire near-infrared (NIR) cues with only slight modification to any standard digital camera. In this paper, we study whether this extra channel can improve semantic image segmentation. Based on a state-of-the-art segmentation framework and a novel manually segmented image database that contains 4-channel images (RGB+NIR), we study how to best incorporate the specific characteristics of the NIR response. We show that it leads to improved performances for 7 classes out of 10 in the proposed dataset and discuss the results with respect to the physical properties of the NIR response.

Standard digital cameras are sensitive to radiation in the near-infrared domain, but this additional cue is in general discarded. In this paper, we consider the scene categorisation problem in the context of images where both standard visible RGB channels and near infrared information are available. Using efficient local patch-based Fisher Vector image representations, we show based on thorough experimental studies the benefit of using this new type of data. We investigate which image descriptors are relevant, and how to best combine them. In particular, our experiments show that when combining texture and colour information, computed on visible and near-infrared channels, late fusion is the best performing strategy and outperforms the state-of-the-art categorisation methods on RGB-only data.

Abstract-We propose a compression framework for fourchannel images, composed of color (RGB) and near-infrared (NIR) channels, which exploits the correlation between the visible and the NIR information. The high-frequency components of both visible and NIR scene representations are strongly correlated. By encoding only the DCT components that differ above a chosen threshold, we significantly improve compression ratios for a given quality level. To evaluate our proposed method, we compare our results with standard JPEG compression, as well as PCA-based approaches that are often employed to compress multispectral images. Our experiments show that applying our proposed method yields the same quality at a lower bit-rate, compared to conventional JPEG and PCA-based algorithms.