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3D video applications are easily accessible to consumers in this era. In general, high quality depth maps are crucial for interpolating virtual view images that are vital in 3D video applications. Many applications adopt time-of-flight (ToF) cameras due to their real-time distance capturing. Yet, the raw ToF image exhibit limitations such as inaccurate distance values in object boundaries and areas that can absorb the infrared ray. This paper presents a method that handles this issue. The existing methods do not focus on this problem. The proposed method is tested on a camera system that consists of a color camera and a ToF camera. Assuming immersive applications such as teleconferencing or virtual broadcasting, the object of interest is captured. The errors existing in the ToF images are modified as a preprocessing step. Afterward, the low-resolution ToF image is warped to the viewpoint of the color camera; empty areas are filled considering the neighbor information. The depth map results show improvements over the state-of-the-art method. The proposed method can increase the overall quality of the 3D video system, ultimately making 3D video consumer applications more marketable. [ABSTRACT FROM PUBLISHER]

To efficiently locate identical objects in heterogeneous cameras and possibly propagate reliable information between cameras and refine detection, many techniques were used to recognize vehicles. In this paper, we investigate several key problems and present a novel approach for automatic vehicle recognition (AVR) in multiple cameras for video surveillance application. We propose a level-based region comparison algorithm to AVR in multiple cameras. For improving the recognition accuracy, new license plate recognition method is also proposed. Experimental results show that the proposed algorithm is simple and efficient, and the quality of the composed image can be comparable with the results of the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

We identify and study two types of 'accidental' images that can be formed in scenes. The first is an accidental pinhole camera image. The second class of accidental images are 'inverse' pinhole camera images, formed by subtracting an image with a small occluder present from a reference image without the occluder. Both types of accidental cameras happen in a variety of different situations. For example, an indoor scene illuminated by natural light, a street with a person walking under the shadow of a building, etc. The images produced by accidental cameras are often mistaken for shadows or interreflections. However, accidental images can reveal information about the scene outside the image, the lighting conditions, or the aperture by which light enters the scene. [ABSTRACT FROM AUTHOR]