Manolya Kavakli-Thorne

Hamed Alqahtani, Charles Z. Liu, Manolya Kavakli-Thorne et al.

This paper presents a design of agent-based intelligent HCI (iHCI) system using collaborative information for MR to improve user experience and information security based on context-aware computing. In order to implement target awareness system, we propose the use of non-parameter stochastic adaptive learning and a kernel learning strategy for improving the adaptivity of the recognition. The proposed design involves the use of a context-aware computing strategy to recognize patterns for simulating human awareness and processing of stereo pattern analysis. It provides a flexible customization method for scene creation and manipulation. It also enables several types of awareness related to the interactive target, user-experience, system performance, confidentiality, and agent identification by applying several strategies, such as context pattern analysis, scalable learning, data-aware confidential computing.

Hamed Alqahtani, Manolya Kavakli-Thorne, Charles Z. Liu

Person re-identification is a basic subject in the field of computer vision. The traditional methods have several limitations in solving the problems of person illumination like occlusion, pose variation and feature variation under complex background. Fortunately, deep learning paradigm opens new ways of the person re-identification research and becomes a hot spot in this field. Generative Adversarial Nets (GANs) in the past few years attracted lots of attention in solving these problems. This paper reviews the GAN based methods for person re-identification focuses on the related papers about different GAN based frameworks and discusses their advantages and disadvantages. Finally, it proposes the direction of future research, especially the prospect of person re-identification methods based on GANs.