Optimizing the reading of electronic text using rapid serial visual presentation

The focus on communications technology in recent years has led to the question of how to best display electronic text onto small-screened devices. Past studies have shown that the compact method of rapid serial visual presentation (RSVP) is efficient but not well liked. Two experiments were conducted to explore ways of improving the preference for and feasibility of RSVP. In experiment 1, the effects of a completion meter, punctuation pauses, and variable word duration were studied. Although the sentence-by-sentence and normal page formats were still superior, post-experiment ratings indicated that punctuation pauses improved user preference for RSVP, and its preference increased in general with practice. In experiment 2, a modified RSVP condition included a completion meter, punctuation pauses, interruption pauses and pauses at clause boundaries. This condition was significantly preferred to a normal RSVP condition. The present enhancements may increase the feasibility of using RSVP with small displays.     

Optimizing the reading of electronic text using rapid serial visual presentation

The focus on communications technology in recent years has led to the question of how to best display electronic text onto small-screened devices. Past studies have shown that the compact method of rapid serial visual presentation (RSVP) is efficient but not well liked. Two experiments were conducted to explore ways of improving the preference for and feasibility of RSVP. In experiment 1, the effects of a completion meter, punctuation pauses, and variable word duration were studied. Although the sentence-by-sentence and normal page formats were still superior, post-experiment ratings indicated that punctuation pauses improved user preference for RSVP, and its preference increased in general with practice. In experiment 2, a modified RSVP condition included a completion meter, punctuation pauses, interruption pauses and pauses at clause boundaries. This condition was significantly preferred to a normal RSVP condition. The present enhancements may increase the feasibility of using RSVP with small displays.     

Towards an improved readability on mobile devices: evaluating adaptive rapid serial visual presentation

Can readability on small screens be improved by using adaptive Rapid Serial Visual Presentation (RSVP) that adapts the presentation speed to the characteristics of the text instead of keeping it fixed? In this paper we introduce Adaptive RSVP, describe the design of a prototype on a mobile device, and report findings from a usability evaluation where the ability to read long and short texts was assessed. In a latin-square balanced repeated-measurement experiment, employing 16 subjects, two variants of Adaptive RSVP were benchmarked against Fixed RSVP and traditional text presentation. For short texts, all RSVP formats increased reading speed by 33% with no significant differences in comprehension or task load. For long texts, no differences were found in reading speed or comprehension, but all RSVP formats increased task load significantly. Nevertheless, Adaptive RSVP decreased task load ratings for most factors compared to Fixed RSVP. Causes, implications, and effects of these findings are discussed.     

Estimating vigilance level by using EEG and EMG signals

We developed a new method for estimation of vigilance level by using both EEG and EMG signals recorded during transition from wakefulness to sleep. Previous studies used only EEG signals for estimating the vigilance levels. In this study, it was aimed to estimate vigilance level by using both EEG and EMG signals for increasing the accuracy of the estimation rate. In our work, EEG and EMG signals were obtained from 30 subjects. In data preparation stage, EEG signals were separated to its subbands using wavelet transform for efficient discrimination, and chin EMG was used to verify and eliminate the movement artifacts. The changes in EEG and EMG were diagnosed while transition from wakefulness to sleep by using developed artificial neural network (ANN). Training and testing data sets consist of the subbanded components of EEG and power density of EMG signals were applied to the ANN for training and testing the system which gives three situations for the vigilance level of the subject: awake, drowsy, and sleep. The accuracy of estimation was about 98–99% while the accuracy of the previous study, which uses only EEG, was 95–96%.     

3D object segmentation using B-Surface

3D object segmentation is important in computer vision such as target detection in biomedical image analysis. A new method, called B-Surface algorithm, is generated for 3D object segmentation. An improved 3D external force field combined with the normalized GVF is utilized. After the initialization of a surface model near the target, B-Surface starts to deform to locate the boundary of the object. First, it overcomes the difficulty that comes from analyzing 3D volume image slice by slice. And the speed of B-Surface deformation is enhanced since the internal forces are not needed to compute in every iteration deformation step. Next, the normal at every surface point can be calculated easily since B-Surface is a continuous deformable model. And it has the ability to achieve high compression ratio (ratio of data to parameters) by presenting the whole surface with only a relatively small number of control points. Experimental results and analysis are presented in this paper. We can see that the B-Surface algorithm can find the surface of the target efficiently.     

图像分割中字符号码类目标的快速定位方法

针对字符号码类目标,提出一种无需全图阈值分割和先验特征匹配计算的方法。先利用方向梯度块精确定位目标,再对目标区域作局部阈值分割,从而快速地得到高质量的目标信息。     

融合分割先验的多图像目标语义分割

目的 在序列图像或多视角图像的目标分割中，传统的协同分割算法对复杂的多图像分割鲁棒性不强，而现有的深度学习算法在前景和背景存在较大歧义时容易导致目标分割错误和分割不一致。为此，提出一种基于深度特征的融合分割先验的多图像分割算法。方法 首先，为了使模型更好地学习复杂场景下多视角图像的细节特征，通过融合浅层网络高分辨率的细节特征来改进PSPNet-50网络模型，减小随着网络的加深导致空间信息的丢失对分割边缘细节的影响。然后通过交互分割算法获取一至两幅图像的分割先验，将少量分割先验融合到新的模型中，通过网络的再学习来解决前景/背景的分割歧义以及多图像的分割一致性。最后通过构建全连接条件随机场模型，将深度卷积神经网络的识别能力和全连接条件随机场优化的定位精度耦合在一起，更好地处理边界定位问题。结果 本文采用公共数据集的多图像集进行了分割测试。实验结果表明本文算法不但可以更好地分割出经过大量数据预训练过的目标类，而且对于没有预训练过的目标类，也能有效避免歧义的区域分割。本文算法不论是对前景与背景区别明显的较简单图像集，还是对前景与背景颜色相似的较复杂图像集，平均像素准确度（PA）和交并比（IOU）均大于95%。结论 本文算法对各种场景的多图像分割都具有较强的鲁棒性，同时通过融入少量先验，使模型更有效地区分目标与背景，获得了分割目标的一致性。     

3-D object segmentation using ant colonies

3-D object segmentation is an important and challenging topic in computer vision that could be tackled with artificial life models.A Channeler Ant Model (CAM), based on the natural ant capabilities of dealing with 3-D environments through self-organization and emergent behaviours, is proposed.Ant colonies, defined in terms of moving, pheromone laying, reproduction, death and deviating behaviours rules, is able to segment artificially generated objects of different shape, intensity, background.The model depends on few parameters and provides an elegant solution for the segmentation of 3-D structures in noisy environments with unknown range of image intensities: even when there is a partial overlap between the intensity and noise range, it provides a complete segmentation with negligible contamination (i.e., fraction of segmented voxels that do not belong to the object). The CAM is already in use for the automated detection of nodules in lung Computed Tomographies.     

Color-based object segmentation method using artificial neural network

This paper presents a color-based technique for object segmentation in colored digital images. Principally, we make use of some color spaces to segment pixels as either objects of interest or non-objects using artificial neural networks (ANN). This study clearly shows how a novel method for fusion of the existing color spaces produces better results in practice than individual color spaces. The segmented objects include lips, faces, hands, fingers and tree leaves. Using several databases to represent these problems, the ANN was trained on the color of the pixel and its surrounding 8 neighbors to be an object or non-object; in the test mode the trained set was used to segment the 9 pixels in the test image into object or non-object. The feature vector was used for training and testing results from the fusion of different types of color information that came from different color models of the targeted pixel. Several experiments were conducted on different databases and objects to evaluate the proposed method; significant results were recorded, showing the power of expressiveness of color and some texture information to deal with the object segmentation problem.     

Emotion recognition from EEG signals by using multivariate empirical mode decomposition

This paper explores the advanced properties of empirical mode decomposition (EMD) and its multivariate extension (MEMD) for emotion recognition. Since emotion recognition using EEG is a challenging study due to nonstationary behavior of the signals caused by complicated neuronal activity in the brain, sophisticated signal processing methods are required to extract the hidden patterns in the EEG. In addition, multichannel analysis is another issue to be considered when dealing with EEG signals. EMD is a recently proposed iterative method to analyze nonlinear and nonstationary time series. It decomposes a signal into a set of oscillations called intrinsic mode functions (IMFs) without requiring a set of basis functions. In this study, a MEMD-based feature extraction method is proposed to process multichannel EEG signals for emotion recognition. The multichannel IMFs extracted by MEMD are analyzed using various time and frequency domain techniques such as power ratio, power spectral density, entropy, Hjorth parameters and correlation as features of valance and arousal scales of the participants. The proposed method is applied to the DEAP emotional EEG data set, and the results are compared with similar previous studies for benchmarking.     

Leukocyte image segmentation using simulated visual attention

Computer-aided automatic analysis of microscopic leukocyte is a powerful diagnostic tool in biomedical fields which could reduce the effects of human error, improve the diagnosis accuracy, save manpower and time. However, it is a challenging to segment entire leukocyte populations due to the changing features extracted in the leukocyte image, and this task remains an unsolved issue in blood cell image segmentation. This paper presents an efficient strategy to construct a segmentation model for any leukocyte image using simulated visual attention via learning by on-line sampling. In the sampling stage, two types of visual attention, “bottom-up” and “top-down” together with the movement of the human eye are simulated. We focus on a few regions of interesting and sample high gradient pixels to group training sets. While in the learning stage, the SVM (support vector machine) model is trained in real-time to simulate the visual neuronal system and then classifies pixels and extracts leukocytes from the image. Experimental results show that the proposed method has better performance compared to the marker controlled watershed algorithms with manual intervention and thresholding-based methods.     

Video object segmentation using color-component-selectable learning for self-organizing maps

In this article, self-organizing-map-based video object segmentation is proposed, assuming that either Y-quantification or HSV-quantification can be systematically selected. Given a video sequence, the value of the probability density function for each component value is calculated according to a kernel estimation at the first frame. Some areas randomly chosen from the background are then examined, using each component value, to judge whether or not they include the target object. The quantification is determined so that the frequency of occurrence of false extractions can be reduced. The data presented to the maps are generated based on the selected quantification. Experimental results show that the proposed method recognizes the target object well.     

基于窄带图割的交互式快速目标提取

针对传统算法易陷入局部极值、提取效率不高的不足,运用图割理论,提出一种将目标提取问题转化为能量最小化的组合优化问题的BandCut算法。BandCut通过人机交互获取一个将目标边界包围在内的环状窄带区域,对该区域生成距离图,构造s-t网络,进行最小代价切割获取目标。实验表明,BandCut能获取最优解,提取效率是GrabCut的5倍。     

Computer diagnosis from EEG signals using bootstrap identification

This paper considers a new parametric model for analysis of EEG signals. Three different bootstarp type algorithms are attempted to fit the model to observed data. Model parameters are described in terms of spectral properties of the KEG. One normal and two pathological records are studied. Results of ease studies are interesting and indicate the usefulness of model and algorithms in the computer processing of EEG.     

Color object segmentation and tracking using flexible statistical model and level-set

Multimedia Tools and Applications - This study presents an unsupervised novel algorithm for color image segmentation, object detection and tracking based on unsupervised learning step followed with...     

帧差累积和减背景相结合的运动对象分割方法

针对低比特率的多媒体视频序列,提出了一种综合利用帧差累积和背景减法来进行运动对象分割的方法。由一种改进的帧差累积方法得到初步的运动对象区域,通过背景减法得到运动对象区域,把由两种方法得到的运动对象区域相结合取得完整准确的结果,二值化后再经过形态学处理和二次扫描填充即可得到运动对象掩模,用原图像的灰度值填充该区域。实验表明,该方法快速,准确,并有一定的应用价值。     

Classification of EEG signals using neural network and logistic regression

Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of EEG signals using wavelet transform and classification using artificial neural network (ANN) and logistic regression (LR). Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities and repeated patterns where other signal processing approaches fail or are not as effective. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. In epileptic seizure classification we used lifting-based discrete wavelet transform (LBDWT) as a preprocessing method to increase the computational speed. The proposed algorithm reduces the computational load of those algorithms that were based on classical wavelet transform (CWT). In this study, we introduce two fundamentally different approaches for designing classification models (classifiers) the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on ANN. Logistic regression as well as multilayer perceptron neural network (MLPNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used LBDWT coefficients of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or non-epileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying LBDWT in connection with MLPNN, we obtained novel and reliable classifier architecture. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN based classifier was more accurate than the LR based classifier.     

Classification of BMD and ADHD patients using their EEG signals

Bipolar Mood Disorder (BMD) and Attention Deficit Hyperactivity Disorder (ADHD) patients mostly share clinical signs and symptoms in children; therefore, accurate distinction of these two mental disorders is a challenging issue among the psychiatric society. In this study, 43 subjects are participated including 21 patients with ADHD and 22 subjects with BMD. Their electroencephalogram (EEG) signals are recorded by 22 electrodes in two eyes-open and eyes-closed resting conditions. After a preprocessing step, several features such as band power, fractal dimension, AR model coefficients and wavelet coefficients are extracted from recorded signals. This paper is aimed to achieve a high classification rate between ADHD and BMD patients using a suitable classifier to their EEG features. In this way, we consider a piece wise linear classifier which is designed based on XCSF. Experimental results of XCSF-LDA showed a significant improvement (86.44% accuracy) compare to that of standard XCSF (78.55%). To have a fair comparison, the other state-of-art classifiers such as LDA, Direct LDA, boosted JD-LDA (BJDLDA), and XCSF are assessed with the same feature set that finally the proposed method provided a better results in comparison with the other rival classifiers. To show the robustness of our method, additive white noise with different amplitude is added to the raw signals but the results achieved by the proposed classifier empirically confirmed a higher robustness against noise compare to the other classifiers. Consequently, the proposed classifier can be considered as an effective method to classify EEG features of BMD and ADHD patients.