Citation Abstract The pervasiveness of mobile cameras has resulted in a dramatic increase in food photos, which are pictures reflecting what people eat. In this paper, we study how taking pictures of what we eat in restaurants can be used for the purpose of automating food journaling. We propose to leverage the context of where […]
Paper Abstract A fully automated framework for video-based surgical skill assessment is presented that incorporates the sequential and qualitative aspects of surgical motion in a data-driven manner. The Objective Structured Assessment of Technical Skills (OSATS) assessments is replicated, which provides both an overall and in-detail evaluation of basic suturing skills required for surgeons. Video analysis […]
Title: Automatic recognition of American Sign Language Classifiers Zahoor ZafrullaSchool of Interactive ComputingCollege of ComputingGeorgia Institute of Technologyhttp://www.cc.gatech.edu/grads/z/zahoor/ Committee: Dr. Thad Starner (Advisor, School of Interactive Computing, Georgia Tech) Dr. Irfan Essa (Co-Advisor, School of Interactive Computing, Georgia Tech) Dr. Jim Rehg (School of Interactive Computing, Georgia Tech) Dr. Harley Hamilton (School of Interactive Computing, […]
Abstract The assessment of surgical skills is an essential part of medical training. The prevalent manual evaluations by expert surgeons are time-consuming and often their outcomes vary substantially from one observer to another. We present a video-based framework for automated evaluation of surgical skills based on the Objective Structured Assessment of Technical Skills (OSATS) criteria. […]
Abstract First-person point-of-view (FPPOV) images taken by wearable cameras can be used to better understand people’s eating habits. Human computation is a way to provide effective analysis of FPPOV images in cases where algorithmic approaches currently fail. However, privacy is a serious concern. We provide a framework, the privacy-saliency matrix, for understanding the balance between […]
Abstract This paper reports on methods and results of an applied research project by a team consisting of SAIC and four universities to develop, integrate, and evaluate new approaches to detect the weak signals characteristic of insider threats on organizations’ information systems. Our system combines structural and semantic information from a real corporate database of […]
Abstract We introduce a new problem domain for activity recognition: the analysis of children’s social and communicative behaviors based on video and audio data. We specifically target interactions between children aged 1-2 years and an adult. Such interactions arise naturally in the diagnosis and treatment of developmental disorders such as autism. We introduce a new […]
Abstract We present data-driven techniques to augment Bag of Words (BoW) models, which allow for more robust modeling and recognition of complex long-term activities, especially when the structure and topology of the activities are not known a priori. Our approach specifically addresses the limitations of standard BoW approaches, which fail to represent the underlying temporal […]
Citation Abstract We propose to learn pixel-level segmentations of objects from weakly labeled (tagged) internet videos. Espe cially, given a large collection of raw YouTube content, along with potentially noisy tags, our goal is to automatically generate spatiotemporal masks for each object, such as dog”, without employing any pre-trained object detectors. We formulate this problem as […]
