Perceptual visual quality assessment using deeply-learned gaze shifting kernel

Image quality assessment (IQA) is a useful technique in computer vision and machine intelligence. It is widely applied in image retrieval, image clustering and image recognition. IQA algorithms generally rely on human visual system (HVS), which can reflect how human perceive salient regions in the image. In this paper, we leverage both low-level features and high-level semantic features to select salient regions, which will be concatenated to form GSPs by the designed saliency-constraint algorithm to mimic human visual system. We design an enhanced IQA index based on the GSPs to calculate the simialrity between reference image and test image to achieve image quality assessment. Experiments demonstrate that our IQA method can achieve satisfactory performance.     

视觉信号质量评价的挑战与新进展

While quality assessment is essential for testing, optimizing, benchmarking, monitoring, and inspecting related systems and services, it also plays an essential role in the design of virtually all visual signal processing and communication algorithms, as well as various related decision-making processes. In this pa-per, we first provide an overview of recently derived quality assessment approaches for traditional visual signals (i.e., 2D im-ages/videos), with highlights for new trends (such as machine learning approaches). On the other hand, with the ongoing development of devices and multimedia services, newly emerged visual signals (e.g., mobile/3D videos) are becoming more and more popular. This work focuses on recent progresses of quality metrics, which have been reviewed for the newly emerged forms of visual signals, which include scalable and mobile videos, High Dynamic Range (HDR) images, image segmentation results, 3D images/videos, and retargeted images.     

Adaptive reconstruction technique for the lost information of the rectangular image area

The adaptive reconstruction for the lost information of the rectangular image area is very important for the robust transmission and restoration of the image. In this paper, a new reconstruction method based on the Discrete Cosine Transform (DCT) domain has been put forward. According to the low pass character of the human visual system and the energy distribution of the DCT coefficients on the rectangular boundary, the DCT coefficients of the rectangular image area are adaptively selected and recovered. After the Inverse Discrete Cosine Transform (IDCT), the lost information of the rectangular image area can be reconstructed. The experiments have demonstrated that the subjective and objective qualities of the reconstructed images are enhanced greatly than before.     

一种结合人脸检测的小波图像编码方法

本文基于人脸检测的基础上，提出了一种结合矢量量化（ＶＱ）的小波图像编码方法。该方法充分利用人眼的视觉特性，高压缩比时恢复图像仍能保持较好的主观质量。     

信息科技下北海职业学院人力资源开发与管理的研究

本文对北海职业学院的教师人力资源管理状况进行了深入的分析,并指出其管理中存在的问题和不足,并提出针对性的改进措施,以期能提高学院教师的人力资源管理水平,充分调动教师的教学、科研、社会实践的积极性,不断提升学院的办学质量,促进学院健康可持续发展.     

基于Matlab的图像自动标注

图像自动标注在检索大量数字图像时起到关键作用,它能将图像的视觉特征转化为图像的标注字信息,为用户的使用及检索带来极大的方便。研究了图像自动语义标注方法,设计并实现了基于Matlab图像自动标注系统,能够提取图像颜色特征和纹理特征,与已标注图像进行相似性度量并标注出图像语义关键词     

用于人工因素—目视系统参量研究的折—衍混杂系统

基于Erfle目镜设计了一个折－衍混杂目镜，该目镜不仅在像差特性有很大改善，而且在结构上更趋向轻、小，其焦距、视场、出瞳距离及出瞳直径均满足头戴显示系统（HMDs)的要求，镜头直径亦满足双目系统筒间距的要求；同时选取Cooke3片镜作为中继物镜，对254mm液晶显示屏成像，构成了用于人因素－目视系统参量研究的折－衍混杂系统，并且满足系统参量可调的要求。     

A human visual system-driven image segmentation algorithm

This paper presents a novel image segmentation algorithm driven by human visual system (HVS) properties. Segmentation quality metrics, based on perceptual properties of HVS with respect to segmentation, are integrated into an energy function. The energy function encodes the HVS properties from both region-based and boundary-based perspectives, where the just-noticeable difference (JND) model is employed when calculating the difference between the image contents. Extensive experiments are carried out to compare the performances of three variations of the presented algorithm and several representative segmentation and clustering algorithms available in the literature. The results show superior performance of our approach.     

Computer Vision Method in Human Motion Detection

Human motion detection based on computer vision is a frontier research topic and is causing an increasing attention in the field of computer vision research. The wavelet transform is used to sharpen the ambiguous edges in human motion image. The shadow＇s effect to the image processing is also removed. The edge extraction can be successfully realized. This is an effective method for the research of human motion analysis system.