Eye movement can be regarded as a pivotal real-time input medium for human-computer communication, which is especially important for people with physical disability. In order to improve the reliability, mobility, and usability of eye tracking technique in user-computer dialogue, a novel eye control system with integrating both mouse and keyboard functions is proposed in this paper. The proposed system focuses on providing a simple and convenient interactive mode by only using user’s eye. The usage flow of the proposed system is designed to perfectly follow human natural habits. Additionally, a magnifier module is proposed to allow the accurate operation. In the experiment, two interactive tasks with different difficulty (searching article and browsing multimedia web) were done to compare the proposed eye control tool with an existing system. The Technology Acceptance Model (TAM) measures are used to evaluate the perceived effectiveness of our system. It is demonstrated that the proposed system is very effective with regard to usability and interface design.

An eye tracking technology provides a faster and more intuitive interface to explore the learner psychology. In this paper, with the aim of assisting researcher to expand the knowledge of video lectures, we present an efficient system named VLEYE based on eye tracking. The system allows gathering the dynamic areas of interest (AOIs) and combining them with eye movement data. There are mainly three modules in the proposed system, including “eye movement recorder,”“dynamic AOI module,”and “video analyzer.”The “eye movement recorder”records the eye movement data by eye tracking device, where mouse cursor control function is applied to support the use of low-cost devices. The AOIs could be drawn manually or detected and tracked by the provided algorithm automatically in the “dynamic AOI module.”The “video analyzer”provides various visualization modes to view and compare the eye movement information for single or multiple subject, as well as statistical charts and detail fixation information. In addition to performance testing, a case study was conducted to verify the applicability of the proposed system. We demonstrate that this system works well and is an essential step forward in efficiently helping studies to better understand learner cognitive processes for dynamic stimuli within video.

With the rapid development of network technology, concerns pertaining to the enhancement of security and protection against violations of digital images have become critical over the past decade. In this paper, an image copy detection scheme based on the Inception convolutional neural network (CNN) model in deep learning is proposed. The image dataset is transferred by a number of image processing manipulations and the feature values in images are automatically extracted for learning and detecting the suspected unauthorized digital images. The experimental results show that the proposed scheme takes on an extraordinary role in the process of detecting duplicated images with rotation, scaling, and other content manipulations. Moreover, the mechanism of detecting duplicate images via a convolutional neural network model with different combinations of original images and manipulated images can improve the accuracy and efficiency of image copy detection compared with existing schemes.

Seamlessly integrating image compression technique and secret data hiding technique into a single procedure for security and efficient data transmission is a novel research issue in modern, decentralized digital communication environments. The first joint data hiding and compression (JDHC) scheme on block truncation coding (BTC) compression domain is presented in this paper. In the compressing and embedding procedure, for the complex blocks, modified block truncation coding (MBTC) is utilized to embed secret data and compress blocks simultaneously, while further controlling the visual distortion that is caused during data embedding. For the smooth blocks, according to the current embedding bit, either image inpainting or block search order coding (BSOC) is used to embed secret data and compress blocks simultaneously with maintaining acceptable compression performance. According to the image compression codes that are provided as output, image decompression and secret bits extraction procedures can be conducted simultaneously. Experimental results indicate that outstanding compression bit rate can be achieved by the proposed JDHC scheme with satisfactory visual quality and hiding capacity.