A 'Human-in-the-Loop' Mobile Image Recognition Application for Rapid Scanning of Water Quality Test Results

This paper describes an interactive system for drinking water quality testing in small community supplies, particularly in the developing world. The system combines a lowcost field test (the Aquatest field kit), a mobile phone for data processing and communications, and a human operator who is able to react immediately to a test result. Once a water sample has been collected and incubated, the mobile phone camera is used to 'scan' the test and obtain the result, which is displayed to the user along with information about the health implications of the water quality. Initial prototypes, while not yet sufficiently robust for real-world use, demonstrate that the system is technically feasible. This opens up interesting possibilities for wider use of 'human-in-the-loop' sensor systems in environmental monitoring

Vision Based Extraction of Nutrition Information from Skewed Nutrition Labels

An important component of a healthy diet is the comprehension and retention of nutritional information and understanding of how different food items and nutritional constituents affect our bodies. In the U.S. and many other countries, nutritional information is primarily conveyed to consumers through nutrition labels (NLs) which can be found in all packaged food products. However, sometimes it becomes really challenging to utilize all this information available in these NLs even for consumers who are health conscious as they might not be familiar with nutritional terms or find it difficult to integrate nutritional data collection into their daily activities due to lack of time, motivation, or training. So it is essential to automate this data collection and interpretation process by integrating Computer Vision based algorithms to extract nutritional information from NLs because it improves the user’s ability to engage in continuous nutritional data collection and analysis. To make nutritional data collection more manageable and enjoyable for the users, we present a Proactive NUTrition Management System (PNUTS). PNUTS seeks to shift current research and clinical practices in nutrition management toward persuasion, automated nutritional information processing, and context-sensitive nutrition decision support. PNUTS consists of two modules, firstly a barcode scanning module which runs on smart phones and is capable of vision-based localization of One Dimensional (1D) Universal Product Code (UPC) and International Article Number (EAN) barcodes with relaxed pitch, roll, and yaw camera alignment constraints. The algorithm localizes barcodes in images by computing Dominant Orientations of Gradients (DOGs) of image segments and grouping smaller segments with similar DOGs into larger connected components. Connected components that pass given morphological criteria are marked as potential barcodes. The algorithm is implemented in a distributed, cloud-based system. The system’s front end is a smartphone application that runs on Android smartphones with Android 4.2 or higher. The system’s back end is deployed on a five node Linux cluster where images are processed. The algorithm was evaluated on a corpus of 7,545 images extracted from 506 videos of bags, bottles, boxes, and cans in a supermarket. The DOG algorithm was coupled to our in-place scanner for 1D UPC and EAN barcodes. The scanner receives from the DOG algorithm the rectangular planar dimensions of a connected component and the component’s dominant gradient orientation angle referred to as the skew angle. The scanner draws several scan lines at that skew angle within the component to recognize the barcode in place without any rotations. The scanner coupled to the localizer was tested on the same corpus of 7,545 images. Laboratory experiments indicate that the system can localize and scan barcodes of any orientation in the yaw plane, of up to 73.28 degrees in the pitch plane, and of up to 55.5 degrees in the roll plane. The videos have been made public for all interested research communities to replicate our findings or to use them in their own research. The front end Android application is available for free download at Google Play under the title of NutriGlass. This module is also coupled to a comprehensive NL database from which nutritional information can be retrieved on demand. Currently our NL database consists of more than 230,000 products. The second module of PNUTS is an algorithm whose objective is to determine the text skew angle of an NL image without constraining the angle’s magnitude. The horizontal, vertical, and diagonal matrices of the (Two Dimensional) 2D Haar Wavelet Transform are used to identify 2D points with significant intensity changes. The set of points is bounded with a minimum area rectangle whose rotation angle is the text’s skew. The algorithm’s performance is compared with the performance of five text skew detection algorithms on 1001 U.S. nutrition label images and 2200 single- and multi-column document images in multiple languages. To ensure the reproducibility of the reported results, the source code of the algorithm and the image data have been made publicly available. If the skew angle is estimated correctly, optical character recognition (OCR) techniques can be used to extract nutrition information

Using natural user interfaces to support synchronous distributed collaborative work

Synchronous Distributed Collaborative Work (SDCW) occurs when group members work together at the same time from different places together to achieve a common goal. Effective SDCW requires good communication, continuous coordination and shared information among group members. SDCW is possible because of groupware, a class of computer software systems that supports group work. Shared-workspace groupware systems are systems that provide a common workspace that aims to replicate aspects of a physical workspace that is shared among group members in a co-located environment. Shared-workspace groupware systems have failed to provide the same degree of coordination and awareness among distributed group members that exists in co-located groups owing to unintuitive interaction techniques that these systems have incorporated. Natural User Interfaces (NUIs) focus on reusing natural human abilities such as touch, speech, gestures and proximity awareness to allow intuitive human-computer interaction. These interaction techniques could provide solutions to the existing issues of groupware systems by breaking down the barrier between people and technology created by the interaction techniques currently utilised. The aim of this research was to investigate how NUI interaction techniques could be used to effectively support SDCW. An architecture for such a shared-workspace groupware system was proposed and a prototype, called GroupAware, was designed and developed based on this architecture. GroupAware allows multiple users from distributed locations to simultaneously view and annotate text documents, and create graphic designs in a shared workspace. Documents are represented as visual objects that can be manipulated through touch gestures. Group coordination and awareness is maintained through document updates via immediate workspace synchronization, user action tracking via user labels and user availability identification via basic proxemic interaction. Members can effectively communicate via audio and video conferencing. A user study was conducted to evaluate GroupAware and determine whether NUI interaction techniques effectively supported SDCW. Ten groups of three members each participated in the study. High levels of performance, user satisfaction and collaboration demonstrated that GroupAware was an effective groupware system that was easy to learn and use, and effectively supported group work in terms of communication, coordination and information sharing. Participants gave highly positive comments about the system that further supported the results. The successful implementation of GroupAware and the positive results obtained from the user evaluation provides evidence that NUI interaction techniques can effectively support SDCW