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This paper presents a novel robust people counting system based on fusing the depth and vision data. Conventional algorithms utilize the monoscopic or stereoscopic vision data to count people. However, these vision-based people counting methods often fail due to occasional illumination change and crowded environment. In the proposed algorithm, both the top-view vision and depth images are captured by a video-plus-depth camera mounted on the ceiling. The depth image is first processed by a morphological operator to alleviate depth artifacts such as the optical noise and lost data. Then the human object is extracted using a human model from the preprocessed depth image. Finally, the trajectory of the detected object is established by applying the bidirectional matching algorithm. Experimental results show that the proposed algorithm achieves over 98% accuracy in various testing environments. [ABSTRACT FROM PUBLISHER]

This paper presents a new image stitching algorithm for mobile multimedia devices. In general, the stitched image obtained by optimal seam finding can avoid the ghost effects but sometimes experience the unconformity of the image structure and color differences near the transition region between images. To solve these problems, the proposed algorithm adopts the seam carving/inserting operator and exploits an adaptive color blending algorithm. Experimental results show that the proposed algorithm can produce a stitched image without visible artifacts including the ghost effects, the unconformity of the structure, and the visible color differences. With respect to the visual quality of the stitched images, the proposed algorithm significantly outperforms simple conventional stitching algorithms and is comparable to the computationally demanding Poisson image editing algorithm1. [ABSTRACT FROM AUTHOR]