Saliency model-based face segmentation and tracking in head-and-shoulder video sequences

In this paper, a novel face segmentation algorithm is proposed based on facial saliency map (FSM) for head-and-shoulder type video application. This method consists of three stages. The first stage is to generate the saliency map of input video image by our proposed facial attention model. In the second stage, a geometric model and an eye-map built from chrominance components are employed to localize the face region according to the saliency map. The third stage involves the adaptive boundary correction and the final face contour extraction. Based on the segmented result, an effective boundary saliency map (BSM) is then constructed, and applied for the tracking based segmentation of the successive frames. Experimental evaluation on test sequences shows that the proposed method is capable of segmenting the face area quite effectively.     

基于“三网融合”的可视化资源协同

网络的宽带化发展有效促进了可视化资源的使用,特别是"三网融合",推动了应用协作和资源共享的发展,在三网间共享资源,实现整个社会信息资源的重复利用和深度挖掘,必将给社会带来巨大的经济效益和社会效益.因此,探索在"三网"间进行可视化资源协同、信息共享是社会信息化发展的重要内容.     

Is the reliability of a visual analog scale higher than an ordinal scale? An experiment with the GRBAS scale for the perceptual evaluation of dysphonia

Perceptual evaluation of 14 pathological voices was performed by 29 listeners using the GRBAS scale. To evaluate scale effects on the judgments, 2 versions of the scale were presented: the original 4-point scale and a visual analog scale. Each listener used the same voice samples for both versions of the scale with an interval of 2 weeks. Agreement was found to be higher with the original 4-point scale than with the visual analog version for all scale items G, R, B, A and S. Although a visual analog scale seems to enable a finer judgment of voice quality, this study showed that, with increased freedom of judgment, the interrater agreement decreased considerably. Therefore, we recommend the use of the original 4-point version of the GRBAS scale.     

A simple visual method for DNA detection based on the formation of gold nanoparticles

A simple visual method for DNA detection during the formation of gold nanoparticles (AuNPs) was developed based on different electrostatic properties of single strand DNA (ssDNA) and double strand DNA (dsDNA). Since the ssDNA is easy to bind to AuNPs due to its exposed bases which could prevent salt-induced aggregation of AuNPs. The dsDNA always present negative charge because its negatively charged phosphate backbone is exposed. In this case, the dsDNA could disturb the adsorption between dsDNA and AuNPs and result in non-aggregation of AuNPs. After hybridization, chloroauric acid and ascorbic acid were added to the mixture solution, and the solution changed to red immediately and turned to purple in 10 min in the present of target DNA. TEM results confirmed that the change of color stemed from aggregation of AuNPs. In order to obtain accurate results by naked eye, the DNA detection assay should be conducted under pH 7.0.     

Affine hull based target representation for visual tracking

Handling appearance variations is a challenging issue in visual tracking. Existing appearance models are usually built upon a linear combination of templates. With such kind of representation, accurate visual tracking is not desirable when heavy appearance variations are in presence. Under the framework of particle filtering, we propose a novel target representation for tracking. Namely, the target candidates are represented by affine combinations of a template set, which leads to better capability in describing unseen target appearances. Additionally, in order to adapt this representation to dynamic contexts across a video sequence, a novel template update scheme is presented. Different from conventional approaches, the scheme considers both the importance of one template to a target candidate in the current frame and the recentness of the template that is kept in the template set. Comprehensive experiments show that the proposed algorithm achieves superior performances in comparison with state-of-the-art works.     

Salience computation in images based on perceptual distinctness

A salience computation approach based on perceptual distinctness is proposed in this paper that predicts human eye fixation locations in images. The approach uses a novel representation of pattern in a patch around a pixel such that discrimination between patterns is in compliance with distinctness between them in-terms of a few well-known perceptual cues. Image patches of different sizes (resolutions) are considered. The representations of the value at and pattern around a pixel in different features such as lightness, color and orientation are discriminated from all those in the rest of the image to compute salience at that pixel. The effectiveness of the proposed salience computation approach in generating salience maps closer to human eye fixation density maps is demonstrated by comparing it both qualitatively and quantitatively to other existing state-of-the-art salience computation approaches. Several synthetic images and psychological patterns, and real-life images from five well-known datasets are considered for this purpose.     

A depth perception and visual comfort guided computational model for stereoscopic 3D visual saliency

With the emerging development of three-dimensional (3D) related technologies, 3D visual saliency modeling is becoming particularly important and challenging. This paper presents a new depth perception and visual comfort guided saliency computational model for stereoscopic 3D images. The prominent advantage of the proposed model is that we incorporate the influence of depth perception and visual comfort on 3D visual saliency computation. The proposed saliency model is composed of three components: 2D image saliency, depth saliency and visual comfort based saliency. In the model, color saliency, texture saliency and spatial compactness are computed respectively and fused to derive 2D image saliency. Global disparity contrast is considered to compute depth saliency. Particularly, we train a visual comfort prediction function to distinguish stereoscopic image pair as high comfortable stereo viewing (HCSV) or low comfortable stereo viewing (LCSV), and devise different computational rules to generate a visual comfort based saliency map. The final 3D saliency map is obtained by using a linear combination and enhanced by a “saliency-center bias” model. Experimental results show that the proposed 3D saliency model outperforms the state-of-the-art models on predicting human eye fixations and visual comfort assessment.     

Robust visual tracking via CAMShift and structural local sparse appearance model

This paper addresses issues in visual tracking where videos contain object intersections, pose changes, occlusions, illumination changes, motion blur, and similar color distributed background. We apply the structural local sparse representation method to analyze the background region around the target. After that, we reduce the probability of prominent features in the background and add new information to the target model. In addition, a weighted search method is proposed to search the best candidate target region. To a certain extent, the weighted search method solves the local optimization problem. The proposed scheme, designed to track single human through complex scenarios from videos, has been tested on some video sequences. Several existing tracking methods are applied to the same videos and the corresponding results are compared. Experimental results show that the proposed tracking scheme demonstrates a very promising performance in terms of robustness to occlusions, appearance changes, and similar color distributed background.     

Distinguishing and quantification of the human visual pathways using high-spatial-resolution diffusion tensor tractography

Quantification of the living human visual system using MRI methods has been challenging, but several applications demand a reliable and time-efficient data acquisition protocol. In this study, we demonstrate the utility of high-spatial-resolution diffusion tensor fiber tractography (DTT) in reconstructing and quantifying the human visual pathways. Five healthy males, age range 24–37 years, were studied after approval of the institutional review board (IRB) at The University of Texas Health Science Center at Houston. We acquired diffusion tensor imaging (DTI) data with 1-mm slice thickness on a 3.0-Tesla clinical MRI scanner and analyzed the data using DTT with the fiber assignment by continuous tractography (FACT) algorithm. By utilizing the high-spatial-resolution DTI protocol with FACT algorithm, we were able to reconstruct and quantify bilateral optic pathways including the optic chiasm, optic tract, optic radiations free of contamination from neighboring white matter tracts.