A weighted full-reference image quality assessment based on visual saliency

In full reference image quality assessment (IQA), the images without distortion are usually employed as reference, while the structures in both reference images and distorted images are ignored and all pixels are equally treated. In addition, the role of human visual system (HVS) is not taken account into subjective IQA metric. In this paper, a weighted full-reference image quality metric is proposed, where a weight imposed on each pixel indicates its importance in IQA. Furthermore, the weights can be estimated via visual saliency computation, which can approximate the subjective IQA via exploiting the HVS. In the experiments, the proposed metric is compared with several objective IQA metrics on LIVE release 2 and TID 2008 database. The results demonstrate that SROCC and PLCC of the proposed metric are 0.9647 and 0.9721, respectively,which are higher than other methods and it only takes 427.5 s, which is lower than that of most other methods.     

Learning of perceptual grouping for object segmentation on RGB-D data

Object segmentation of unknown objects with arbitrary shape in cluttered scenes is an ambitious goal in computer vision and became a great impulse with the introduction of cheap and powerful RGB-D sensors. We introduce a framework for segmenting RGB-D images where data is processed in a hierarchical fashion. After pre-clustering on pixel level parametric surface patches are estimated. Different relations between patch-pairs are calculated, which we derive from perceptual grouping principles, and support vector machine classification is employed to learn Perceptual Grouping. Finally, we show that object hypotheses generation with Graph-Cut finds a globally optimal solution and prevents wrong grouping. Our framework is able to segment objects, even if they are stacked or jumbled in cluttered scenes. We also tackle the problem of segmenting objects when they are partially occluded. The work is evaluated on publicly available object segmentation databases and also compared with state-of-the-art work of object segmentation.     

基于眼动感知信息的彩色图像感兴趣区域提取算法

提出了一种由眼动感知信息引导区域生长的彩色图像感兴趣区域提取算法.在对被试眼动感知数据分析的基础上,算法首先提取感兴趣位置点,然后以这些点的邻域信息作为种子点,在特定的颜色空间中采用颜色同质准则提取出一系列初始感兴趣区域,最后将这些区域按照被试的"关注度"进行排序得到最终的感兴趣区域.算法在RGB、HSV、L*a*b等...     

高分辨率遥感图像感兴趣目标的提取算法

在遥感图像的判读和解译过程中,快速准确地提取感兴趣的目标可以极大地改善整个处理系统的负担.借鉴人类的注意机制,提出了一种基于视觉注意的感兴趣目标提取算法.该方法首先通过分析图像的谱残差求取图像显著图,再利用全局竞争机制和返回抑制策略控制注意焦点快速搜索显著图,实现无先验知识的显著区域提取;处于焦点的大部分显著区域利用感...     

Image saliency estimation via random walk guided by informativeness and latent signal correlations

Visual saliency is an effective tool for perceptual image processing. In the past decades, many saliency models have been proposed by primarily considering visual cues such as local contrast and global rarity. However, such explicit cues derived only from input stimuli are often insufficient to separate targets from distractors, leading to noisy saliency maps. In fact, the latent cues, especially the latent signal correlations that link visually distinct stimuli (e.g., various parts of a salient target), may also play an important role in saliency estimation. In this paper, we propose a graph-based approach for image saliency estimation by incorporating both explicit visual cues and latent signal correlations. In our approach, the latent correlations between various image patches are first derived according to the statistical prior obtained from 10 million reference images. After that, the informativeness of image patches and their latent correlations are jointly considered to construct a directed graph, on which a random walking process is performed to generate saliency maps that pop-out only the most salient locations. Experimental results show that our approach achieves impressive performances on three public image benchmarks.     

Spatiotemporal saliency detection and salient region determination for H.264 videos

In this study, a spatiotemporal saliency detection and salient region determination approach for H.264 videos is proposed. After Gaussian filtering in Lab color space, the phase spectrum of Fourier transform is used to generate the spatial saliency map of each video frame. On the other hand, the motion vector fields from each H.264 compressed video bitstream are backward accumulated. After normalization and global motion compensation, the phase spectrum of Fourier transform for the moving parts is used to generate the temporal saliency map of each video frame. Then, the spatial and temporal saliency maps of each video frame are combined to obtain its spatiotemporal saliency map using adaptive fusion. Finally, a modified salient region determination scheme is used to determine salient regions (SRs) of each video frame. Based on the experimental results obtained in this study, the performance of the proposed approach is better than those of two comparison approaches.     

一种室内弱纹理环境下的视觉SLAM算法

针对室内机器人在弱纹理环境下视觉SLAM(同步定位与地图构建)不鲁棒、不精确的问题, 提出一种视觉惯性SLAM优化算法。首先, 利用基于注意力机制的深度学习模块直接对相邻两帧图像进行特征匹配, 结合传统特征检测方法, 解决无法提取足够特征点的问题; 其次, 建立相机深度置信模型, 在计算相机帧间位姿变换关系前, 引入空间点的深度置信概率来减小远距离特征的漂移误差; 最后, 将参与后端优化的所有匹配特征点的深度置信度作为动态鲁棒核函数的分段阈值, 优化传统光束平差法, 协调整体运动轨迹。实际场景实验表明, 该算法在弱纹理环境下有明显的鲁棒性, 相较于VINS-RGBD算法, 该算法的绝对轨迹误差降低了50.38%, 相对轨迹误差降低了85.75%。     

基于指尖触控的互动投影系统

随着人机交互技术的不断发展,互动投影技术的应用逐渐广泛.该技术通过摄像机等传感器技术感知周围场景动态并结合投影的显示功能实现交互的方式,如增强现实、结构光编码、三维重建等.基于触控的互动投影技术在未来的嵌入式人机交互系统中具有广阔的前景.文章基于单目摄像机与投影仪系统实现能够在任意平面徒手触摸的交互.系统的功能主要通过前景区域分割、指尖点的定位以及触摸检测三个步骤来实现.与目前相关领域的研究文献进行的对比实验证明,所提算法在稳定性、准确性以及实时性等方面具有一定优势.