In this paper, we propose an extension to our previous work on food portion size estimation using a single image and a multi-view volume estimation method. The single-view technique estimates food volume by using prior information (segmentation and food labels) generated from food identification methods we described earlier. For multi-view volume estimation, we use“Shape from Silhouettes”to estimate the food portion size. The experimental results of our volume estimation methods demonstrate our results with respect to accuracy and reliability.

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Tattoos can provide useful information related to criminal gang activity. Law enforcement can use the information embedded in tattoos to identify and track the criminal history of a suspect. For matching processes, tattoo images are difficult to use due to problems such as deformations and weak edge structures. In this paper we describe a tattoo image retrieval and matching system based on a combination of local and global image matching methods to improve matching accuracy. The proposed local shape context combined with SIFT descriptors are used for local features of a tattoo object and global shape is used for overall shape of a tattoo object. The contributions of this paper include the introduction of a multiple different sizedbin polar histograms based local shape context (MHLC) and a global shape descriptor combining the multiple different sized-bin polar histogram and 2D Fourier Transform for robustness of translation, scale, rotation and shape distortions. We also describe robust similarity for local descriptors and a weighted matching method based on local and global descriptors. Our experimental results show that our proposed method performs better than previously published tattoo image retrieval systems.

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