85,016 research outputs found

    American Sign Language alphabet recognition using Microsoft Kinect

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    American Sign Language (ASL) fingerspelling recognition using marker-less vision sensors is a challenging task due to the complexity of ASL signs, self-occlusion of the hand, and limited resolution of the sensors. This thesis describes a new method for ASL fingerspelling recognition using a low-cost vision camera, which is Microsoft\u27s Kinect. A segmented hand configuration is first obtained by using a depth contrast feature based per-pixel classification algorithm. Then, a hierarchical mode-finding method is developed and implemented to localize hand joint positions under kinematic constraints. Finally, a Random Decision Forest (RDF) classifier is built to recognize ASL signs according to the joint angles. To validate the performance of this method, a dataset containing 75,000 samples of 24 static ASL alphabet signs is used. The system is able to achieve a mean accuracy of 92%. We have also used a publicly available dataset from Surrey University to evaluate our method. The results have shown that our method can achieve higher accuracy in recognizing ASL alphabet signs in comparison to the previous benchmarks. --Abstract, page iii

    Beyond writing: The development of literacy in the Ancient Near East

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    Previous discussions of the origins of writing in the Ancient Near East have not incorporated the neuroscience of literacy, which suggests that when southern Mesopotamians wrote marks on clay in the late-fourth millennium, they inadvertently reorganized their neural activity, a factor in manipulating the writing system to reflect language, yielding literacy through a combination of neurofunctional change and increased script fidelity to language. Such a development appears to take place only with a sufficient demand for writing and reading, such as that posed by a state-level bureaucracy; the use of a material with suitable characteristics; and the production of marks that are conventionalized, handwritten, simple, and non-numerical. From the perspective of Material Engagement Theory, writing and reading represent the interactivity of bodies, materiality, and brains: movements of hands, arms, and eyes; clay and the implements used to mark it and form characters; and vision, motor planning, object recognition, and language. Literacy is a cognitive change that emerges from and depends upon the nexus of interactivity of the components

    Automatic recognition of fingerspelled words in British Sign Language

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    We investigate the problem of recognizing words from video, fingerspelled using the British Sign Language (BSL) fingerspelling alphabet. This is a challenging task since the BSL alphabet involves both hands occluding each other, and contains signs which are ambiguous from the observer’s viewpoint. The main contributions of our work include: (i) recognition based on hand shape alone, not requiring motion cues; (ii) robust visual features for hand shape recognition; (iii) scalability to large lexicon recognition with no re-training. We report results on a dataset of 1,000 low quality webcam videos of 100 words. The proposed method achieves a word recognition accuracy of 98.9%

    Kecelaruan personaliti antisosial di kalangan pelajar politeknik : satu kajian awal

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    Kajian ini adalah bertujuan untuk mengenalpasti kecelaruan personalis antisosial (KPA) yang berlaku di kalangan remaja atau muda-mudi terutama di Politeknik Malaysia yang mungkin mengakibatkan berlakunya masalah sosial di kalangan mereka. Kajian ini berbentuk kuantitatif. Sampel kajian telah dipilih di empat buah politeknik. Politeknik yang terlibat adalah politeknik zon selatan. Responden kajian ini terdiri daripada 340 orang pelajar pengambilan bam semester satu yang memasuki institusi berkenaan. Responden juga terdiri daripada pelajar peringkat sijil dan diploma daripada pelbagai pengkhususan. Instrumen yang digunakan adalah borang soal selidik. Data yang telah dikumpulkan dianalisis menggunakan Statistical Package for Social Science (SPSS). Statistik yang digunakan adalah statistik deskriptif. Dapatan kajian menunjukkan di antara 10 jenis kecelaruan, kecelaruan avoidant mencatatkan skor min tertinggi iaitu dengan skor min 3.24 (a = 1.055). Selain itu, pengkaji mendapati personaliti antisosial yang berlaku di kalangan pelajar politeknik adalah pada tahap yang sederhana iaitu skor min 2.35 (a =0.933). Hasil daripada kajian juga mendapati faktor sosial mencatatkan skor min tertinggi iaitu 2.07 (a = 0.851). Faktor keluarga pula hanya mencatatkan skor min 2.03 (g = 0.887). Pengkaji juga mendapati responden lebih gemar kepada konsep keagamaan berbanding konsep-konsep yang lain sekiranya mereka menghadapi masalah. Oleh itu diharapkan kajian ini dapat memberikan penjelasan sedikit sebanyak mengenai kecelaruan personaliti antisosial yang berlaku di kalangan pelajar politeknik di masa kini

    A fast algorithm for vision-based hand gesture recognition for robot control

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    We propose a fast algorithm for automatically recognizing a limited set of gestures from hand images for a robot control application. Hand gesture recognition is a challenging problem in its general form. We consider a fixed set of manual commands and a reasonably structured environment, and develop a simple, yet effective, procedure for gesture recognition. Our approach contains steps for segmenting the hand region, locating the fingers, and finally classifying the gesture. The algorithm is invariant to translation, rotation, and scale of the hand. We demonstrate the effectiveness of the technique on real imagery

    Classifying types of gesture and inferring intent

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    In order to infer intent from gesture, a rudimentary classification of types of gestures into five main classes is introduced. The classification is intended as a basis for incorporating the understanding of gesture into human-robot interaction (HRI). Some requirements for the operational classification of gesture by a robot interacting with humans are also suggested
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