A framework for sign language recognition using support vector machines and active learning for skin segmentation and boosted temporal sub-units

This dissertation describes new techniques that can be used in a sign language recognition (SLR) system, and more generally in human gesture systems. Any SLR system consists of three main components: Skin detector, Tracker, and Recognizer. The skin detector is responsible for segmenting skin objects like the face and hands from video frames. The tracker keeps track of the hand location (more specifically the bounding box) and detects any occlusions that might happen between any skin objects. Finally, the recognizer tries to classify the performed sign into one of the sign classes in our vocabulary using the set of features and information provided by the tracker. In this work, we propose a new technique for skin segmentation using SVM (support vector machine) active learning combined with region segmentation information. Having segmented the face and hands, we need to track them across the frames. So, we have developed a unified framework for segmenting and tracking skin objects and detecting occlusions, where both components of segmentation and tracking help each other. Good tracking helps to reduce the search space for skin objects, and accurate segmentation increases the overall tracker accuracy. Instead of dealing with the whole sign for recognition, the sign can be broken down into elementary subunits, which are far less in number than the total number of signs in the vocabulary. This motivated us to propose a novel algorithm to model and segment these subunits, then try to learn the informative combinations of subunits/features using a boosting framework. Our results reached above 90% recognition rate using very few training samples

Automatic skin segmentation for gesture recognition combining region and support vector machine active learning

Skin segmentation is the cornerstone of many applications such as gesture recognition, face detection, and objectionable image filtering. In this paper, we attempt to address the skin segmentation problem for gesture recognition. Initially, given a gesture video sequence, a generic skin model is applied to the first couple of frames to automatically collect the training data. Then, an SVM classifier based on active learning is used to identify the skin pixels. Finally, the results are improved by incorporating region segmentation. The proposed algorithm is fully automatic and adaptive to different signers. We have tested our approach on the ECHO database. Comparing with other existing algorithms, our method could achieve better performance

Creating a web-scale video collection for research

This paper begins by considering a number of important design questions for a web-scale, widely available, multimedia test collection intended to support long-term scientific evaluation and comparison of content-based video analysis and exploitation systems. Such exploitation systems would include the kinds of functionality already explored within the annual TRECVid benchmarking activity such as search, semantic concept detection, and automatic summarisation. We then report on our progress in creating such a multimedia collection which we believe to be web scale and which will support a next generation of benchmarking activities for content-based video operations, and we report on our plans for how we intend to put this collection, the IACC.1 collection, to use

TRECVID 2008 - goals, tasks, data, evaluation mechanisms and metrics

The TREC Video Retrieval Evaluation (TRECVID) 2008 is a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in content-based exploitation of digital video via open, metrics-based evaluation. Over the last 7 years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. In 2008, 77 teams (see Table 1) from various research organizations --- 24 from Asia, 39 from Europe, 13 from North America, and 1 from Australia --- participated in one or more of five tasks: high-level feature extraction, search (fully automatic, manually assisted, or interactive), pre-production video (rushes) summarization, copy detection, or surveillance event detection. The copy detection and surveillance event detection tasks are being run for the first time in TRECVID. This paper presents an overview of TRECVid in 2008

TRECVID 2009 - goals, tasks, data, evaluation mechanisms and metrics

The TREC Video Retrieval Evaluation (TRECVID) 2009 was a TREC-style video analysis and retrieval evaluation, the goal of which was to promote progress in content-based exploitation of digital video via open, metrics-based evaluation. Over the last 9 years TRECVID has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. 63 teams from various research organizations — 28 from Europe, 24 from Asia, 10 from North America, and 1 from Africa — completed one or more of four tasks: high-level feature extraction, search (fully automatic, manually assisted, or interactive), copy detection, or surveillance event detection. This paper gives an overview of the tasks, data used, evaluation mechanisms and performanc

A Hexagonal Cell Automaton Model to Imitate Physarum Polycephalum Competitive Behaviour

Abubakr Awad research is supported by Elphinstone PhD Scholarship (University of Aberdeen). Wei Pang, George Coghill, and David Lusseau are supported by the Royal Society International Exchange program (Grant Ref IE160806).Publisher PD

Chlorpromazine for schizophrenia: a Cochrane systematic review of 50 years of randomised controlled trials

BACKGROUND: Chlorpromazine (CPZ) remains one of the most common drugs used for people with schizophrenia worldwide, and a benchmark against which other treatments can be evaluated. Quantitative reviews are rare; this one evaluates the effects of chlorpromazine in the treatment of schizophrenia in comparison with placebo. METHODS: We sought all relevant randomised controlled trials (RCT) comparing chlorpromazine to placebo by electronic and reference searching, and by contacting trial authors and the pharmaceutical industry. Data were extracted from selected trials and, where possible, synthesised and random effects relative risk (RR), the number needed to treat (NNT) and their 95% confidence intervals (CI) calculated. RESULTS: Fifty RCTs from 1955–2000 were included with 5276 people randomised to CPZ or placebo. They constitute 2008 person-years spent in trials. Meta-analysis of these trials showed that chlorpromazine promotes a global improvement (n = 1121, 13 RCTs, RR 0.76 CI 0.7 to 0.9, NNT 7 CI 5 to 10), although a considerable placebo response is also seen. People allocated to chlorpromazine tended not to leave trials early in both the short (n = 945, 16 RCTs, RR 0.74 CI 0.5 to 1.1) and medium term (n = 1861, 25 RCTs, RR 0.79 CI 0.6 to 1.1). There were, however, many adverse effects. Chlorpromazine is sedating (n = 1242, 18 RCTs, RR 2.3 CI 1.7 to 3.1, NNH 6 CI 5 to 8), increases a person's chances of experiencing acute movement disorders, Parkinsonism and causes low blood pressure with dizziness and dry mouth. CONCLUSION: It is understandable why the World Health Organization (WHO) have endorsed and included chlorpromazine in their list of essential drugs for use in schizophrenia. Low- and middle-income countries may have more complete evidence upon which to base their practice compared with richer nations using recent innovations