Browsing by Author "Dadgostar, Farhad"

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    A color hand gesture database for evaluating and improving algorithms on hand gesture and posture recognition
    (Massey University, 2005) Dadgostar, Farhad; Barczak, Andre L.C.; Sarrafzadeh, Abdolhossein
    With the increase of research activities in vision-based hand posture and gesture recognition, new methods and algorithms are being developed. Although less attention is being paid to developing a standard platform for this purpose. Developing a database of hand gesture images is a necessary first step for standardizing the research on hand gesture recognition. For this purpose, we have developed an image database of hand posture and gesture images. The database contains hand images in different lighting conditions and collected using a digital camera. Details of the automatic segmentation and clipping of the hands are also discussed in this paper.
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    Real-time hand tracking using a set of cooperative classifiers based on Haar-like features
    (Massey University, 2005) Barczak, Andre L.C.; Dadgostar, Farhad
    In this paper we discuss the importance of the choice of features in digital image object recognition. The features can be classified as invariants or noninvariants. Invariant features are robust against one or more modifications such as rotations, translations, scaling and different light (illumination) conditions. Non-invariant features are usually very sensitive to any of these modifiers. On the other hand, non-invariant features can be used even in the event of translation, scaling and rotation, but the feature choice is in some cases more important than the training method. If the feature space is adequate then the training process can be straightforward and good classifiers can be obtained. In the last few years good algorithms have been developed relying on noninvariant features. In this article, we show how non-invariant features can cope with changes even though this requires additional computation at the detection phase. We also show preliminary results for a hand detector based on a set of cooperative Haar-like feature detectors. The results show the good potential of the method as well as the challenges to achieve real-time detection.
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    Real-time vision-based hand and face tracking and recognition of gesture : a PhD dissertation submitted in partial fulfillment of the requirement for the degree of Doctor of Philosophy (Ph.D.) in Computer Science
    (Massey University, 2006) Dadgostar, Farhad
    In this dissertation, we present the research pathway to the design and implementation of a real-time vision-based gesture recognition system. This system was built based on three components, representing three layers of abstraction: i) detection of skin and localization of hand and face, ii) tracking multiple skin blobs in video sequences and finally iii) recognition of gesture movement trajectories. The adaptive skin detection, the first component, was implemented based on our novel adaptive skin detection algorithm for video sequences. This algorithm has two main sub-components: i) the static skin detector, which is a skin detection method based on the hue factor of the skin color, and ii) the adaptive skin detector which retrains itself based on new data gathered from movement of the user. The results of our experiments show that the algorithm improves the quality of skin detection within the video sequences. For tracking, a new approach for boundary detection in blob tracking based on the Mean-shift algorithm was proposed. Our approach is based on continuous sampling of the boundaries of the kernel and changing the size of the kernel using our novel Fuzzy-based algorithm. We compared our approach to the kernel density-based approach, which is known as the CAM-Shift algorithm, in a set of different noise levels and conditions. The results show that the proposed approach is superior in stability against white noise, and also provides correct boundary detection for arbitrary hand postures, which is not achievable by the CAM-Shift algorithm. Finally we presented a novel approach for gesture recognition. This approach includes two main parts: i) gesture modeling, and ii) gesture recognition. The gesture modeling technique is based on sampling the gradient of the gesture movement trajectory and presenting the gesture trajectory as a sequence of numbers. This technique has some important features for gesture recognition including robustness against slight rotation, a small number of required samples, invariance to the start position and device independence. For gesture recognition, we used a multi-layer feed-forward neural-network. The results of our experiments show that this approach provides 98.71% accuracy for gesture recognition, and provides a higher accuracy rate than other methods introduced in the literature. These components form the required framework for vision-based real-time gesture recognition and hand and face tracking. The components, individually or as a framework, can be applied in scientific and commercial extensions of either vision-based or hybrid gesture recognition systems.