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This paper proposes an automatic method that handles hand gesture spotting and recognition simultaneously. To spot meaningful gestures of numbers (0-9) accurately, a stochastic method for designing a non-gesture model with Hidden Markov Models (HMMs) versus Conditional Random Fields (CRFs) is proposed without training data. The non-gesture model provides a confidence measure that is used as an adaptive...
Temporal invariant shape moments intuitively seem to provide an important visual cue to human activity recognition in video sequences. In this paper, an SVM based method for human activity recognition is introduced. With this method, the feature extraction is carried out based on a small number of computationally-cheap invariant shape moments. When tested on the popular KTH action dataset, the obtained...
In this paper, we study the discriminative models like CRFs, HCRFs and LDCRFs to recognize alphabet characters (A-Z) and numbers (0-9) in real-time from stereo color image sequences. To handle isolated gesture, CRFs, HCRFs and LDCRFs with different number of window size are applied on 3D combined features of location, orientation and velocity. The gesture recognition rate is improved initially as...
In this paper, we propose a system to recognize alphabet characters (A-Z) and numbers (0-9) in real-time from stereo color image sequences using Hidden Markov Models (HMMs). Additionally, a robust method for hand tracking in a complex environment using Mean-shift analysis in conjunction with 3D depth map is introduced. The depth information solve the overlapping problem between hands and face, which...
In this paper, we propose an automatic system that executes hand gesture spotting and recognition simultaneously without any time delay based on Hidden Markov Models (HMM). Our system is based on three main stages; preprocessing, feature extraction and classification. In preprocessing stage, color and 3D depth map are used to detect hands. The hand trajectory will take place in further steps using...
This paper proposes a system to recognize isolated American Sign Language and Arabic numbers in real-time from stereo color image sequences using Hidden Markov Models (HMMs). Our system is based on three main stages; preprocessing, feature extraction and classification. In preprocessing stage, color and 3D depth map are used to detect and track the hand. The second stage, 3D combined features of location,...
In this paper, we propose an automatic system that recognizes both isolated and continuous gestures for Arabic numbers (0-9) in real-time based on hidden Markov model (HMM). To handle isolated gestures, HMM using ergodic, left-right (LR) and left-right banded (LRB) topologies with different number of states ranging from 3 to 10 is applied. Orientation dynamic features are obtained from spatio-temporal...
This paper describes a method to recognize the alphabets from a single hand motion using Hidden Markov Models (HMM). In our method, gesture recognition for alphabets is based on three main stages; preprocessing, feature extraction and classification. In preprocessing stage, color and depth information are used to detect both hands and face in connection with morphological operation. After the detection...
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