We present a system and a method to segment the head-shoulder image of participants in a video call using mobile devices such as a smartphone or a tablet. Participants can choose to send only the segmented head-shoulder foreground image and overlay it on top of a static background image or a background video on the receiver side of the video call, as well as to replace the background of the caller himself/herself during the video call. Our proposed method extracts the head-shoulder area of each video frame based on detected face region, superpixel clustering, and efficient label propagation.
Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. We
are developing a system, known as the mobile device food record (mdFR), to automatically identify and quantify foods
and beverages consumed based on analyzing meal images captured with a mobile device. The mdFR makes use of a
fiducial marker and other contextual information to calibrate the imaging system so that accurate amounts of food can be
estimated from the scene. Food identification is a difficult problem since foods can dramatically vary in appearance. Such
variations may arise not only from non-rigid deformations and intra-class variability in shape, texture, color and other
visual properties, but also from changes in illumination and viewpoint. To address the color consistency problem, this
paper describes illumination quality assessment methods implemented on a mobile device and three post color correction
methods.
As obesity concerns mount, dietary assessment methods for prevention and intervention are being developed. These
methods include recording, cataloging and analyzing daily dietary records to monitor energy and nutrient intakes. Given
the ubiquity of mobile devices with built-in cameras, one possible means of improving dietary assessment is through
photographing foods and inputting these images into a system that can determine the nutrient content of foods in the images.
One of the critical issues in such the image-based dietary assessment tool is the accurate and consistent estimation of food
portion sizes. The objective of our study is to automatically estimate food volumes through the use of food specific shape
templates. In our system, users capture food images using a mobile phone camera. Based on information (i.e., food name
and code) determined through food segmentation and classification of the food images, our system choose a particular food
template shape corresponding to each segmented food. Finally, our system reconstructs the three-dimensional properties
of the food shape from a single image by extracting feature points in order to size the food shape template. By employing
this template-based approach, our system automatically estimates food portion size, providing a consistent method for
estimation food volume.
Accurate methods and tools to assess food and nutrient intake are essential for the association between diet
and health. Preliminary studies have indicated that the use of a mobile device with a built-in camera to obtain
images of the food consumed may provide a less burdensome and more accurate method for dietary assessment.
We are developing methods to identify food items using a single image acquired from the mobile device. Our
goal is to automatically determine the regions in an image where a particular food is located (segmentation)
and correctly identify the food type based on its features (classification or food labeling). Images of foods are
segmented using Normalized Cuts based on intensity and color. Color and texture features are extracted from
each segmented food region. Classification decisions for each segmented region are made using support vector
machine methods. The segmentation of each food region is refined based on feedback from the output of classifier
to provide more accurate estimation of the quantity of food consumed.
Dietary intake provides valuable insights for mounting intervention programs for prevention of disease. With
growing concern for adolescent obesity, the need to accurately measure diet becomes imperative. Assessment
among adolescents is problematic as this group has irregular eating patterns and have less enthusiasm for recording
food intake. Preliminary studies among adolescents suggest that innovative use of technology may improve
the accuracy of diet information from young people. In this paper we describe further development of a novel
dietary assessment system using mobile devices. This system will generate an accurate account of daily food and
nutrient intake among adolescents. The mobile computing device provides a unique vehicle for collecting dietary
information that reduces burden on records that are obtained using more classical approaches. Images before
and after foods are eaten can be used to estimate the amount of food consumed.
Dietary intake provides valuable insights for mounting intervention programs for prevention of disease. With
growing concern for adolescent obesity, the need to accurately measure diet becomes imperative. Assessment
among adolescents is problematic as this group has irregular eating patterns and have less enthusiasm for recording
food intake. Preliminary studies among adolescents suggest that innovative use of technology may improve
the accuracy of diet information from young people. In this paper, we propose a novel food record method
using a mobile device that will provide an accurate account of daily food and nutrient intake among adolescents.
Our approach includes the use of image analysis tools for identification and quantification of food consumption.
Images obtained before and after food is consumed can be used to estimate the diet of an individual. In this
paper we describe our initial results and indicate the potential of the proposed system.
In this paper, we investigate spatial and temporal models for texture analysis and synthesis. The goal is to use
these models to increase the coding efficiency for video sequences containing textures. The models are used to
segment texture regions in a frame at the encoder and synthesize the textures at the decoder. These methods
can be incorporated into a conventional video coder (e.g. H.264) where the regions to be modeled by the textures
are not coded in a usual manner but texture model parameters are sent to the decoder as side information. We
showed that this approach can reduce the data rate by as much as 15%.
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