A signal adaptive diffusion filter for video coding: Mathematical framework and complexity reductions

In this paper we combine video compression and modern image processing methods. We construct novel iterative filter methods for prediction signals based on Partial Differential Equation (PDE) based methods. The mathematical framework of the employed diffusion filter class is given and some desirable properties are stated. In particular, two types of diffusion filters are constructed: a uniform diffusion filter using a fixed filter mask and a signal adaptive diffusion filter that incorporates the structures of the underlying prediction signal. The latter has the advantage of not attenuating existing edges while the uniform filter is less complex. The filters are embedded into a software based on HEVC with additional QTBT (Quadtree plus Binary Tree) and MTT (Multi-Type-Tree) block structure. In this setting, several measures to reduce the coding complexity of the tool are introduced, discussed and tested thoroughly. The coding complexity is reduced by up to 70% while maintaining over 80% of the gain. Overall, the diffusion filter method achieves average bitrate savings of 2.27% for Random Access having an average encoder runtime complexity of 119% and 117% decoder runtime complexity. For individual test sequences, results of 7.36% for Random Access are accomplished.     

Estimates of pollutant temporal and spatial variability in commercial buildings from the joint urban 2003 field experiments

In this paper, we report on the indoor concentrations from a suite of full-scale outdoor tracer-gas point releases conducted in the downtown area of Oklahoma City in 2003. A point release experiment consisted of releases of sulfur hexafluoride (SF₆) in multiple buildings and from different outdoor locations. From the measurements, we are able to estimate the concentration variations indoors for a building operating under “typical” operating conditions. The mean indoor spatial coefficients of variation are 30% to 45% from a daytime outdoor release are around 80% during an outdoor evening release. Having estimates of the spatial coefficient of variation provides stakeholders, including first responders, with the likely range of concentrations in the building when little is known about the building characteristics and operating behavior, such as developing urban-scale hazard and consequence analyses. We show differences in indoor measurements at different distances to the release points, floors of the building, and heating, ventilation, and air conditioning system (HVAC) operation. We also show estimates at different time resolutions. The statistics show that in the studied medium to large commercial buildings, spatial differences would result in peak indoor concentrations in certain parts of the buildings that may be substantially higher than the building average. To our knowledge, very few tracer gas measurements have been conducted in buildings of this scope, particularly with measurements on multiple floors and within a floor. The resulting estimates of spatial variability provide a unique opportunity for hazard assessment, and comparison to multi-zone models.     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction is a challenging task due to the complicated nature of the retinal vessel structure, which also needs strong skill set and training. In this paper, a supervised technique for blood vessel extraction in retinal images using Modified Adaboost Extreme Learning Machine (MAD-ELM) is proposed. Firstly, the fundus image preprocessing is done for contrast enhancement and in-homogeneity correction. Then, a set of core features is extracted, and the best features are selected using “minimal Redundancy-maximum Relevance (mRmR).” Later, using MAD-ELM method vessels and non vessels are classified. DRIVE and DR-HAGIS datasets are used for the evaluation of the proposed method. The algorithm’s performance is assessed based on accuracy, sensitivity and specificity. The proposed technique attains accuracy of 0.9619 on the DRIVE database and 0.9519 on DR-HAGIS database, which contains pathological images. Our results show that, in addition to healthy retinal images, the proposed method performs well in extracting blood vessels from pathological images and is therefore comparable with state of the art methods.     

ECISER: Efficient Clip-art Image SEgmentation by Re-rasterization

Clip-art image segmentation is widely used as an essential step to solve many vision problems such as colorization and vectorization. Many of these applications not only demand accurate segmentation results, but also have little tolerance for time cost, which leads to the main challenge of this kind of segmentation. However, most existing segmentation techniques are found not sufficient for this purpose due to either their high computation cost or low accuracy. To address such issues, we propose a novel segmentation approach, ECISER, which is well-suited in this context. The basic idea of ECISER is to take advantage of the particular nature of cartoon images and connect image segmentation with aliased rasterization. Based on such relationship, a clip-art image can be quickly segmented into regions by re-rasterization of the original image and several other computationally efficient techniques developed in this paper. Experimental results show that our method achieves dramatic computational speedups over the current state-of-the-art approaches, while preserving almost the same quality of results.     

Differences between oculomotor and perceptual artifacts for temporally limited head mounted displays

We used perceptual and oculomotor measures to understand the negative impacts of low (phantom array) and high (motion blur) duty cycles with a high‐speed, AR‐likehead‐mounted display prototype. We observed large intersubject variability for the detection of phantom array artifacts but a highly consistent and systematic effect on saccadic eye movement targeting during low duty cycle presentations. This adverse effect on saccade endpoints was also related to an increased error rate in a perceptual discrimination task, showing a direct effect of display duty cycle on the perceptual quality. For high duty cycles, the probability of detecting motion blur increased during head movements, and this effect was elevated at lower refresh rates. We did not find an impact of the temporal display characteristics on compensatory eye movements during head motion (e.g., VOR). Together, our results allow us to quantify the tradeoff of different negative spatiotemporal impacts of user movements and make subsequent recommendations for optimized temporal HMD parameters.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

一种改进的新三步搜索算法

在对三步法工作原理进行研究的基础上提出了新三步搜索算法（NITSS）。该方法充分利用视频序列运动矢量概率分布上的中心偏置特性．在三步搜索算法的基础上引入了十字型分布的4个点构成搜索点群。实验结果表明。新三步搜索算法解决了三步法的小运动估计效果较差问题．提高了搜索精度，保持了三步法的高效率。     

小型机载数字视频记录系统的设计与实现

采用嵌入式控制模块SOM2386和专用视频处理芯片SZ1510,设计和实现了一种基于电子硬盘存储的嵌入式小型机载数字视频记录系统。给出了系统总体方案,阐述了视音频解码编码、接口、控制及存储单元的硬件设计,介绍了系统的软件设计,并给出了系统软件的整体工作流程。该系统体积小、重量轻、抗震性好、使用维护方便,已在某型飞机上得到应用。     

Allocentric Versus Egocentric Spatial Memory After Unilateral Temporal Lobectomy in Humans.

Thirty patients who had undergone either a right or left unilateral temporal lobectomy (14 RTL; 16 LTL) and 16 control participants were tested on a computerized human analogue of the Morris Water Maze. The procedure was designed to compare allocentric and egocentric spatial memory. In the allocentric condition, participants searched for a target location on the screen, guided by object cues. Between trials, participants had to walk around the screen, which disrupted egocentric memory representation. In the egocentric condition, participants remained in the same position, but the object cues were shifted between searches to prevent them from using allocentric memory. Only the RTL group was impaired on the allocentric condition, and neither the LTL nor RTL group was impaired on additional tests of spatial working memory or spatial manipulation. The results support the notion that the right anterior temporal lobe stores long-term allocentric spatial memories. (PsycINFO Database Record (c) 2010 APA, all rights reserved)