Hand shape classification using DTW and LCSS as similarity measures for vision-based gesture recognition system

Kuzmanić, Ana; Zanchi, Vlasta

doi:10.1109/eurcon.2007.4400350

Cited by 43 publications

(21 citation statements)

References 12 publications

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“…DTW is a standard method for comparing signals that can stretch and compress in time (e.g., in speech and motion recognition - Kuzmanic & Zanchi, 2007;Sakoe & Chiba, 1978;space telemetry -Senin, 2008;signal processing -Müller, Mattes, & Kurth, 2006; protein sequence alignment and chemical engineering - Aach & Church, 2001;Vial et al, 2009) because, unlike linear methods of comparison, it is insensitive to small temporal differences between otherwise similar signals (Berndt & Clifford, 1994). DTW shows better performance at clustering similar signals when they are offset in time and equivalent performance when signals are temporally aligned (Berndt & Clifford, 1994;Keogh & Ratanamahatana, 2004).…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

Pupil dilation patterns reflect the contents of consciousness

Kang

Wheatley

2015

Consciousness and Cognition

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Section: Dynamic Time Warpingmentioning

confidence: 99%

Pupil dilation patterns reflect the contents of consciousness

Kang

Wheatley

2015

Consciousness and Cognition

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“…After the linearization, the transition matrix r A can be computed as (18) where are calculated by using the following equations…”

Section: B Kalman Filter Designmentioning

confidence: 99%

Real-time hand-writing tracking and recognition by integrated micro motion and vision sensors platform

Zhou

Zhang

Chung

et al. 2012

2012 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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A real-time 2D gesture tracking and recognition system based on micro motion and vision sensors fusion is presented in this paper. The 100 Hz inertial data from MEMS sensors and 5 Hz visual data from a CMOS camera (a small webcam) are fused by using extended Kalman filter (EKF). To synchronize data from the two sensing components (i.e., inertial sensor and vision sensor) and filter out high frequency noise, the inertial data are processed by passing it through a moving average filter in the data preprocessing stage. Moreover, features of a checkerboard image are extracted by using the Harris corner extraction algorithm, and the pose the webcam relative to the world coordinate could be thus estimated by using direct linear transform (DLT). In order to avoid the gimbal lock problem, rotation quaternion is preferred to represent the orientation of the overall system. For trajectory feature extraction, the obtained trajectories are first normalized to a 0.5×0.5 area through a linear normalization, and then the features are extracted by using a linear Discrete Cosine Transform (DCT). The extracted data are subsequently sent to a dynamic time warping (DTW) algorithm for recognition. The experimental results show that 92.3% accuracy can be achieved for recognizing hand written numbers from 0 to 9, where ~100ms is required for recognition if hand writing speed is ~137 mm/s.

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“…It offers advantage to scene illumination changes and even light condition changes. The edges of the sequences of images would be still same [18]. All the Indian sign language gestures have been captured in different lighting conditions.…”

Section: Preprocessing and Feature Extractionmentioning

confidence: 99%

Recognizing and Interpreting Sign Language Gesture for Human Robot Interaction

Singh¹,

Jain²,

Kumar³

2012

IJCA

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Visual interpretation of sign language gesture can be useful in accomplishing natural human robot interaction. This paper describes a sign language gesture based recognition, interpreting and imitation learning system using Indian Sign Language for performing Human Robot Interaction in real time. It permits us to construct a convenient sign language gesture based communication with humanoid robot. The classification, recognition, learning, interpretation process is carried out by extracting the features from Indian sign language (ISL) gestures. Chain code and fisher score is considered as a feature vector for classification and recognition process. It is to be done by the two statistical approaches namely known as Hidden Markov Model (HMM) technique and feed forward back propagation neural network (FNN) in order to achieve satisfactory recognition accuracy. The sensitivity, specificity and accuracy were found to be equal 98.60%, 97.64% and 97.52% respectively. It can be concluded that FNN gives fast and accurate recognition and it works as promising tool for recognition and interpretation of sign language gesture for human computer interaction. The overall accuracy of recognition and interpretation of the proposed system is 95.34%. Thus, this approach is suitable for automated real time human computer interaction tool.

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Hand shape classification using DTW and LCSS as similarity measures for vision-based gesture recognition system

Cited by 43 publications

References 12 publications

Pupil dilation patterns reflect the contents of consciousness

Pupil dilation patterns reflect the contents of consciousness

Real-time hand-writing tracking and recognition by integrated micro motion and vision sensors platform

Recognizing and Interpreting Sign Language Gesture for Human Robot Interaction

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