Jianmin Jiang

TL;DR: This work solves the problem of salient object detection by investigating how to expand the role of pooling in convolutional neural networks by building a global guidance module (GGM) and designing a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down path- way.

TL;DR: A focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing is provided to increase awareness of how neural networks can be applied to these areas.

TL;DR: Experimental results demonstrate that the proposed approach is able to select feature subsets with better classification performance than the SFFS method and the integrated feature pre-selection mechanism helps to solve the over-fitting problems and reduces the chances of getting a local optimal solution.

TL;DR: This paper presents an extensive survey on the development of neural networks for image compression which covers three categories: direct image compression by neural networks; neural network implementation of existing techniques, and neural network based technology which provide improvement over traditional algorithms.

TL;DR: A comprehensive survey of authentication protocols for Internet of Things (IoT) is presented in this article, where more than forty authentication protocols developed for or applied in the context of the IoT are selected and examined in detail.