Sign language recognition has been the focus of research in recent years because it has enabled the use of sign languages, which are the main medium of communication for the hearing impaired, for human-computer interaction. In this work, we propose a method to recognize signs using Improved Dense Trajectory (IDT) features which were previously used in large-scale action  recognition. Fisher Vectors (FV) are used to represent sign samples in the proposed method. Seven different combinations of features were compared using a test set of 200 signs, using a Support  Vector Machine (SVM) classifier. The best combination yielded 80; 43% recognition performance when Histogram of Optical Flow (HOF) and Motion Boundary Histogram (MBH) components  were used together.

This paper proposes using the state of the art 2D facial landmark localization method, Supervised Descent Method (SDM), for facial landmark localization in 3D depth images. The proposed method was evaluated on frontal faces with no occlusion from the Bosphorus 3D Face Database. In the experiments, in which 2D features were used to train SDM, the proposed approach achieved state-of-the-art performance for several landmarks over the currently available 3D facial landmark localization methods.

Gesture recognition is becoming popular as an efficient input method for human computer interaction. However, challenges associated with data collection, data annotation, maintaining standardization, and the high variance of data obtained from different users in different environments make developing such systems a difficult task. The purpose of this study is to integrate domain adaptation methods for the problem of gesture recognition. To achieve this task, domain adaptation is performed from hand written digit trajectory data to hand trajectories obtained from depth cameras. The performance of the applied Feature Augmentation method is evaluated through analysis of recognition performance vs percentage of target class samples in training and through the analysis of the transferability of different gestures.

Last decade witnessed the rapid development of Wireless Sensor Networks. More recently, the availability of inexpensive hardware such as CMOS cameras and microphones that are able to ubiquitously capture multimedia content from the environment has fostered the development of Wireless Multimedia Sensor Networks (WMSNs). There is a wide range of applications that are using Wireless Multimedia Sensor Networks, including Indoor Surveillance Systems. Nearly all surveillance systems start with a motion detection algorithm. After detection of motion in an image, either the motion areas are sent to another algorithm for more processing or an alarm is sent to the base station informing that there is motion in the environment. In this paper, we proposed a new motion detection algorithm, which is specifically designed for scenarios with no constant movement in the background. Our tests using Goyette’s datasets show that, our proposed algorithm achieved a 97% accuracy with an average execution time of 48ms for QVGA images on ARM9 architecture, and thus outperformed the two currently available methods.

Last decade witnessed the rapid development of Wireless Sensor Networks (WSNs). More recently, the availability of inexpensive hardware such as CMOS cameras and microphones that are able to ubiquitously capture multimedia content from the environment has fostered the development of Wireless Multimedia Sensor Networks (WMSNs). Nodes in such networks require significant amount of processing power to interpret the collected sensor data. Most of the currently available wireless multimedia sensor nodes are equipped with ARM7 core microcontrollers. On the other hand, ARM9 and ARM11 cores are viable alternatives, which deliver deterministic high performance and flexibility for demanding and cost-sensitive embedded applications. Thus, we evaluated the performance of the ARM9 and the ARM11 cores in terms of processing power and energy consumption. Our test results showed that the ARM11 core performed 3 to 4 times faster than the ARM9 core.