Dynamic Hand Gesture Recognition of Arabic Sign Language using Hand Motion Trajectory Features

In this paper we propose a system for dynamic hand gesture recognition of Arabic Sign Language The proposed system takes the dynamic gesture video stream as input extracts hand area and computes hand motion features then uses these features to recognize the gesture The system identifies the hand blob using YCbCr color space to detect skin color of hand The system classifies the input pattern based on correlation coefficients matching technique The significance of the system is its simplicity and ability to recognize the gestures independent of skin color and physical structure of the performers The experiment results show that the gesture recognition rate of 20 different signs performed by 8 different signers is 85 6

Autocalibration with the Minimum Number of Cameras with Known Pixel Shape

In 3D reconstruction, the recovery of the calibration parameters of the cameras is paramount since it provides metric information about the observed scene, e.g., measures of angles and ratios of distances. Autocalibration enables the estimation of the camera parameters without using a calibration device, but by enforcing simple constraints on the camera parameters. In the absence of information about the internal camera parameters such as the focal length and the principal point, the knowledge of the camera pixel shape is usually the only available constraint. Given a projective reconstruction of a rigid scene, we address the problem of the autocalibration of a minimal set of cameras with known pixel shape and otherwise arbitrarily varying intrinsic and extrinsic parameters. We propose an algorithm that only requires 5 cameras (the theoretical minimum), thus halving the number of cameras required by previous algorithms based on the same constraint. To this purpose, we introduce as our basic geometric tool the six-line conic variety (SLCV), consisting in the set of planes intersecting six given lines of 3D space in points of a conic. We show that the set of solutions of the Euclidean upgrading problem for three cameras with known pixel shape can be parameterized in a computationally efficient way. This parameterization is then used to solve autocalibration from five or more cameras, reducing the three-dimensional search space to a two-dimensional one. We provide experiments with real images showing the good performance of the technique.Comment: 19 pages, 14 figures, 7 tables, J. Math. Imaging Vi

Surface Area Distribution Descriptor for object matching

Matching 3D objects by their similarity is a fundamental problem in computer vision, computer graphics and many other fields. The main challenge in object matching is to find a suitable shape representation that can be used to accurately and quickly discriminate between similar and dissimilar shapes. In this paper we present a new volumetric descriptor to represent 3D objects. The proposed descriptor is used to match objects under rigid transformations including uniform scaling. The descriptor represents the object by dividing it into shells, acquiring the area distribution of the object through those shells. The computed areas are normalised to make the descriptor scale-invariant in addition to rotation and translation invariant. The effectiveness and stability of the proposed descriptor to noise and variant sampling density as well as the effectiveness of the similarity measures are analysed and demonstrated through experimental results

Human action recognition using trajectory-based representation

AbstractRecognizing human actions in video sequences has been a challenging problem in the last few years due to its real-world applications. A lot of action representation approaches have been proposed to improve the action recognition performance. Despite the popularity of local features-based approaches together with “Bag-of-Words” model for action representation, it fails to capture adequate spatial or temporal relationships. In an attempt to overcome this problem, a trajectory-based local representation approaches have been proposed to capture the temporal information. This paper introduces an improvement of trajectory-based human action recognition approaches to capture discriminative temporal relationships. In our approach, we extract trajectories by tracking the detected spatio-temporal interest points named “cuboid features” with matching its SIFT descriptors over the consecutive frames. We, also, propose a linking and exploring method to obtain efficient trajectories for motion representation in realistic conditions. Then the volumes around the trajectories’ points are described to represent human actions based on the Bag-of-Words (BOW) model. Finally, a support vector machine is used to classify human actions. The effectiveness of the proposed approach was evaluated on three popular datasets (KTH, Weizmann and UCF sports). Experimental results showed that the proposed approach yields considerable performance improvement over the state-of-the-art approaches

A Generic OCR Using Deep Siamese Convolution Neural Networks

This paper presents a generic optical character recognition (OCR) system based on deep Siamese convolution neural networks (CNNs) and support vector machines (SVM). Supervised deep CNNs achieve high level of accuracy in classification tasks. However, fine-tuning a trained model for a new set of classes requires large amount of data to overcome the problem of dataset bias. The classification accuracy of deep neural networks (DNNs) degrades when the available dataset is insufficient. Moreover, using a trained deep neural network in classifying a new class requires tuning the network architecture and retraining the model. All these limitations are handled by our proposed system. The deep Siamese CNN is trained for extracting discriminative features. The training is performed once using a group of classes. The OCR system is then used for recognizing different classes without retraining or fine-tuning the deep Siamese CNN model. Only few samples are needed from any target class for classification. The proposed OCR system is evaluated on different domains: Arabic letters, Eastern-Arabic numerals, Hindu-Arabic numerals, and Farsi numerals using test sets that contain printed and handwritten letters and numerals. The proposed system achieves a very promising recognition accuracy close to the results achieved by CNNs trained for specific target classes and recognition systems without the need for retraining. The system outperforms the state of the art method that uses Siamese CNN in one-shot classification task by around 12%.</p