Images degraded by light scattering and absorption, such as hazy, sandstorm, and underwater images, often suffer color distortion and low contrast because of light traveling through turbid media. In order to enhance and restore such images, we first estimate ambient light using the depth-dependent color change. Then, via calculating the difference between the observed intensity and the ambient light, which we call the scene ambient light differential, scene transmission can be estimated. Additionally, adaptive color correction is incorporated into the image formation model (IFM) for removing color casts while restoring contrast. Experimental results on various degraded images demonstrate the new method outperforms other IFM-based methods subjectively and objectively. Our approach can be interpreted as a generalization of the common dark channel prior (DCP) approach to image restoration, and our method reduces to several DCP variants for different special cases of ambient lighting and turbid medium conditions.

Abstract. In this paper we present a system for human body model acquisition and tracking of its parameters from voxel data. 3D voxel reconstruction of the body in each frame is computed from silhouettes extracted from multiple cameras. The system performs automatic model acquisition using a template based initialization procedure and a Bayesian network for refinement of body part size estimates. The twist-based human body model leads to a simple formulation of the extended Kalman filter that performs the tracking and with joint angle limits guarantees physically valid posture estimates. Evaluation of the approach was performed on several sequences with different types of motion captured with six cameras.

We present a framework for articulated body

Traditional image features are not able to effectively represent railway fasteners under varied illumination and conditions. We propose the line local binary pattern encoding method that considers the relationship between the center point and its upper and lower neighborhoods. The method can effectively represent the key components of fasteners. In comparison with several stateof-the-art methods, the proposed method has good performance on detecting the completely missing and partly missing fasteners on real data sets, especially when the illumination and background are not ideal.

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