From the ancient age, gesture was the first mode of communication, after the evolution of human civilization they developed the verbal communication, but still non-verbal communication is equally significant. Such non-verbal communication is not only used for physically challenged person but also it can be efficiently used for various applications such as 3D gaming, aviation, surveying, etc. This is the best method to interact with computer without any peripheral devices. Many Researchers are still developing robust and efficient new hand gesture recognition techniques. The major steps associated while designing the system are: data acquisition, segmentation and tracking, feature extraction and gesture recognition. There are different methodologies associated with several substeps present at each step. A various segmentation and Tracking, feature extraction and recognition techniques are studied and analyzed. This paper reviews the comparative study of various hand gesture recognition techniques which are presented up-till now.

No abstract

No abstract

One of the main open challenges in visualisation applications such as cathode ray tube (CRT) monitor, liquid‐crystal display (LCD), and organic light‐emitting diode (OLED) display is the robustness for high dynamic range (HDR) environs. This is due to the imperfections in the sensor and the incapability to track interest points successfully because of the brightness constancy in visualisation applications. To address this problem, different tone mapping operators are required for visualising HDR images on standard displays. However, these standard displays have different dynamic ranges. Thus, there is a need for a new model to find the best quality tone mapped image for specific kinds of visualisation applications. The authors propose a hybrid deep emperor penguin classifier to accurately classify the tone mapped images for different visualisation applications. Here, a selective deep neural network is trained to predict the quality of a tone‐mapped image. Based on this quality, a decision is made as to the suitability of the image for CRT monitor, LCD display or OLED display. Also, they evaluate the proposed model on the TMIQD database and the simulation results prove that the proposed model outperforms the state‐of‐the‐art image quality assessment methods.

No abstract