Previous studies have shown that eye activities, including blinks, can indicate the psychological state of an individual. However, almost all previous studies analyzing blinks merely concentrated on traditional descriptive statistics, which are unable to reflect their dynamic processes. Furthermore, the states of non-blink (opening the eyes) and blink alternate with each other, forming a physiological cycle. If we only investigate blinks alone, it may be inadequate to describe how blinking works. Therefore, we attempted to recognize the affective state (“relaxation” vs. “stress”) of an individual through the dynamics of blink and non-blink cyclicity (BNBC), as one example, to illustrate this method. First, the “Stroop Test” was employed for emotion elicitation. Then, features were extracted from a categorical time series (0: non-blink; 1: blink), which was recorded by the eye-tracking system. Finally, the areas under the receiver operating characteristic curve (AUC) values were obtained via eight commonly used classifiers. The results show that, compared with the traditional approaches for blink analysis, BNBC exhibits more compelling proficiency to detect stress. In summation, BNBC can be considered a new type of psychophysiological measure, which could be widely applied in psychology, medicine, and engineering.

This study presents a comprehensive survey on mixed impulse and Gaussian denoising filters which are applied to an image in order to gauge the effects of this type of noise combination and to then determine optimal ways that can overcome such effects. The random noise model considered in this survey is the combined effect of impulse (salt and pepper) and Gaussian noise. After describing the noise models, the denoising filters which are applied to the images are classified and explained according to their design structure, the type of filters they use, the noise level they could overcome, and the limitations they face. This survey covers all related denoising methods and provides an assessment of the strengths and practical limitations of the different classes of denoising filters.

This study explored interactions between users and a 3D virtual world using a multi-touch display and sought to identify the advantages of this form of interaction. An experiment was conducted with 28 subjects, who were classified into two groups: experienced gamers and casual gamers. The experiment involved a pseudo-universe, where users were required to find five objects using the multi-touch display, and five objects using a game controller (GamePad). Overall, multi-touch was slightly faster than the GamePad in the search for objects. Experienced users performed significantly better when using the GamePad, compared to casual gamers. When casual gamers were analyzed separately, their performance was better (at a statistically significant level) with the multi-touch display, compared to the GamePad. Finally, users had a significantly higher error rate when switching from the GamePad to the keyboard, than when switching from the multi-touch to the keyboard.

Recent years have seen an accelerated development of Virtual Reality (VR) and Augmented Reality (AR) systems. Through these systems, computers immerse users in a completely artificial environment or in one that is partially based on the user's physical surroundings, but partially created artificially. One primary objective of these systems is to cause the perception of actually “being there” (in the environment created by the computer). However, this perception of full immersion may be weakened by the need to use “unnatural” devices (e.g., game controller) to execute actions within the VR or AR environment. Accordingly, increased interest has emerged in developing mechanisms by which the user could interact with VR and AR environments using natural actions, such as the movements of his/her hands.

We present a whole-body gesture elicitation study using Head Mounted Displays, including a legacy bias reduction technique. The motivation for this study was to understand the type of gesture agreement rates for selection and manipulation interactions and to improve the user experience for whole-body interactions. We found that regardless of the production technique used to remove legacy bias, legacy bias was still found in some of the produced gestures.

We present a whole-body gesture elicitation study using Head Mounted Displays, including a legacy bias reduction. The motivation for this study was to understand the type of gesture agreement rates for selection and manipulation interactions and to improve the user experience for whole-body interactions. We looked at 23 participants and 20 distinct referents (with multiple gestures per referent). We found that regardless of the production technique used to remove legacy bias, legacy bias was still found in some of the produced gestures. In some instances, gestures were derived from previous interactions but were still appropriate for the environment presented. This study provides a rich set of information and useful recommendations for future designers and/or developers.

Understanding gaming expertise is important in user studies. We present a study comprised of 60 participants playing a First Person Shooter Game (Counter-Strike: Global Offensive). This study provides results related to a keyboard model used to determine an objective measurement of gamers' skill. We also show that there is no correlation between frequency questionnaires and user skill.

This study introduces an image denoising method in the presence of combined speckle and Gaussian noise. The dual‐tree complex wavelet transform is applied to the image in order to obtain specific coefficients characterising these types of noise. Then, these extracted coefficients are removed by thresholding and an inverse wavelet transform is applied in order to obtain the reconstructed image. A comparison between the dual‐tree and standard wavelet‐based denoising filters is provided on the basis of different structural metrics. Finally, in order to remove any remaining noise, a spatial denoising filter is applied to the image. The results obtained on medical ultrasound images corrupted by this noise combination support the authors’ assertion on the method's resilience to the combined effects of speckle and Gaussian noise, and are compared to well‐known and most effective speckle–Gaussian denoising filters.

Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). Using multimodal biomarkers for this high-dimensional classification problem, the widely used algorithms include Support Vector Machines (SVM), Sparse Representation-based classification (SRC), Deep Belief Networks (DBN) and Random Forest (RF). These widely used algorithms continue to yield unsatisfactory performance for delineating the MCI participants from the cognitively normal control (CN) group. A novel Gaussian discriminant analysis-based algorithm is thus introduced to achieve a more effective and accurate classification performance than the aforementioned state-of-the-art algorithms. This study makes use of magnetic resonance imaging (MRI) data uniquely as input to two separate high-dimensional decision spaces that reflect the structural measures of the two brain hemispheres. The data used include 190 CN, 305 MCI and 133 AD subjects as part of the AD Big Data DREAM Challenge #1. Using 80% data for a 10-fold cross-validation, the proposed algorithm achieved an average F1 score of 95.89% and an accuracy of 96.54% for discriminating AD from CN; and more importantly, an average F1 score of 92.08% and an accuracy of 90.26% for discriminating MCI from CN. Then, a true test was implemented on the remaining 20% held-out test data. For discriminating MCI from CN, an accuracy of 80.61%, a sensitivity of 81.97% and a specificity of 78.38% were obtained. These results show significant improvement over existing algorithms for discriminating the subtle differences between MCI participants and the CN group.

This study introduces a novel Gaussian discriminant analysis (GDA)-based computer aided diagnosis (CAD) system using structural magnetic resonance imaging (MRI) data uniquely as input for screening different stages of Alzheimers disease (AD) involving its prodromal stage of mild cognitive impairment (MCI) in relation to the cognitive normal control group (CN). Taking advantage of multiple modalities of biomarkers, over the past few years, several machine learning-based CAD approaches have been proposed to address this high-dimensional classification problem. This study presents a novel GDA-based CAD system on the basis of a tenfold cross validation and a held-out test data set. Subjects considered in this study included 187 CN, 301 MCI, and 131 AD subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI) database. In the tenfold cross validation, the proposed system achieved an average F1 score of 97.20%, accuracy of 96.00%, sensitivity of 99.14%, and specificity of 88.67% for discriminating together the MCI and AD groups from the CN group; and an average F1 score of 79.82%, accuracy of 87.43%, sensitivity of 79.09%, and specificity of 91.25% for discriminating AD from MCI. By testing on the held-out test data, for discriminating MCI and AD from CN, an accuracy of 93.28%, a sensitivity of 98.78%, and a specificity of 81.08% were obtained. These results also show that by separating left and right hemispheres of the brain into two decisional spaces, and then combining their outputs, the GDA-based CAD system demonstrates a high potential for clinical application.

Neuroimaging is an important research platform that can be very useful for eliciting new understanding on the complicated pathogenesis between genetics and disease phenotypes. Due to the extremely high dimensionality of image and genetic data, and considering the potential joint effect of genetic variants, multivariate techniques have been examined to detect Alzheimers disease (AD) related genetic variants expressed through single-nucleotide polymorphisms (SNPs). However, the image features used in support of those methods are not immediately related to the disease, and the detected genetic markers may not be related to AD. In this study, we propose an ensemble model based framework for firstly extracting 50 region-based image features whose values are predicted by base learners trained on raw neuroimaging morphological variables. This task is followed by performing sparse Partial Least Squares regression (sPLS) method on the extracted 50 AD related image features and pre-selected 1508 SNPs to detect the significant SNPs associated with the extracted image features. Instead of modeling a direct link between genetic variants and disease label, we captured disease information indirectly.

Patterns of interictal epileptiform activities, such as sharp waves, spikes, spike-wave complexes and polyspike-wave complexes are explored in the recorded electroencephalograms (EEG) to gauge the different functional connectivity dynamics and to assess how they could be affected by the type of a seizure. Connectivity measures were represented by the phase synchronization among scalp electrodes that were obtained by adopting a nonlinear data-driven method. These interictal epileptic activities were investigated using a graph theory analysis. The connectivity maps were compared by considering the number of connections in four main brain regions (anterior region, posterior region, left hemisphere and right hemisphere). Results revealed interesting and different network topology for the connectivity maps. Besides, a relationship between the connectivity patterns of the recorded epileptic activities and the types of seizures was observed. This relationship was statistically confirmed by analysis of variance (ANOVA) that denoted a significant difference among connectivity patterns of sharp waves and spike activities, which were seen in focal epilepsy, in contrast to the spike-wave and polyspike-wave complexes that were associated with generalized epilepsy (P-value = 0). These results augment the prospects for diagnosis and enhance the recognition of the disease type via EEG-based connectivity maps.

Typically, neuropsychological testing helps medical experts situate a given patient in continuum of the Alzheimer's disease (AD) spectrum, especially in the continuum between cognitively normal controls (CN) and the prodromal stage of mild cognitive impairment (MCI). As a well-known early symptom, some linguistic complexity changes of language have been associated with the progression of AD. Currently, the investigations of linguistic manifestations of MCI do mostly rely on manual analysis, yet linguistic complexity-based neuropsychological assessment cannot be effectively attained by using manual approaches. Taking advantage of the existing natural language processing (NLP) techniques to extract key features in linguistic complexity changes associated with the progression of MCI, this study develops an NLP-oriented computerized neuropsychological assessment to automatically analyze the distinguishing characteristics of data in the MCI group versus those in cognitively normal control (CN) group. By using the transcripts of news conferences from President Ronald Reagan, who was diagnosed with Alzheimer's disease in 1994, and President Dwight D. Eisenhower (DE), and President George H. W. Bush (GB), who have no known diagnosis of AD, this study found the significantly different patterns of the linguistic complexity changes between CN and MCI subjects, which can be applied into the diagnosis of MCI.

Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). For solving this high dimensional classification problem, the widely used algorithm remains to be Support Vector Machines (SVM). But due to the high variance of the data, the classification performance of SVM remains unsatisfactory, especially for delineating the MCI group from the cognitively normal control (CN) group. This study introduces a novel algorithm based on the Gaussian discriminant analysis (GDA) for a more effective and accurate classification performance. Subjects considered in this study included 190 CN, 305 MCI, and 133 AD subjects. Using 75% of the data as the training set with a tenfold cross validation, the proposed algorithm achieved an average accuracy of 94.17%, a sensitivity of 93.00%, and a specificity of 95.00% for discriminating AD from CN; and an average accuracy of 84.86%, a sensitivity of 84.78%, and a specificity of 85.00% for discriminating MCI from CN. Then a true test was implemented for the remaining 25% data, for discriminating specifically MCI from CN, resulting in an accuracy of 82.20%, a sensitivity of 83.10%, and a specificity of 80.85%. As revealed through the literature, these results involving the delineation of the MCI group from CN could be considered as the best classification performance obtained so far. This study also shows that by separating left and right hemispheres of the brain into two decision spaces, then combining the results of these two spaces, the classification performance can be improved significantly; an assertion proven in this study.

Background: Deposition of beta amyloid protein (Aβ) is known to be an early event that is closely associated with the pathogenesis of Alzheimer's disease (AD), along with related downstream events such as neuronal loss, neurofibrillary tangles, cortical thinning and cognitive deficits. APOE e4 allele (E4) is also known to be associated with increased risk for AD. Objectives: The goal of this study is to examine the association of Aβ deposition to cortical thickness (CoTh), in healthy control (CN), early MCI (EMCI), late MCI (LMCI) and AD stages by controlling for E4 load, both in regional and hemispheric levels, and to interpret patterns of different brain regions based on their correlation performance among the four groups. Methods: We analyzed Amyloid PET Scan, Volumetric MRI (CoTh) data from participants in the ADNIGO/ADNI2 cohort whose APOE gene information are available. Statistical analysis includes Pearson partial correlations, Analysis of Covariance (ANCOVA) with post-hoc Tukey HSD. Complete-linkage hierarchical clustering analysis was further performed to group brain regions based on their significant correlation performance. Results: 25 out of 68 regions showed significant correlation of Aβ load and CoTh at least in one diagnostic group. Furthermore, 6 main clusters were recognized based on the performance patterns of those 25 regions across 4 diagnosis groups. Conclusion: Our major finding clustered the cortical regions into 2 general groups, positive correlation in CN or AD, and negative correlation in EMCI and/or LMCI, and 6 more specific groups were then recognized, confirming the interplay between of Aβ and CoTh in the different stages of the disease.

CircGR is a multi-touch non-symbolic gesture recognition algorithm, which utilizes circular statistic measures to implement linearithmic (O(n log n)) template-based matching. CircGR provides a solution to gesture designers, which allows for building complex multi-touch gestures with high-confidence accuracy. We demonstrated the algorithm and described a user study with 60 subjects and over 12,000 gestures collected for an original gesture set of 36. The accuracy is over 99% with the Matthews correlation coefficient of 0.95. In addition, early gesture detection was successful in CircGR as well.

Most of the computer users with ocular aberrations such as myopia, hyperopia and other high-order aberrations are subject to visual blurring, which may impede the efficient interactions with the computers. Conventional methods used to counter visual blurring include spectacles and contact lenses. In this paper, we introduce an image preprocessing method that is designed to counteract the visual blurring caused by the aberration of the eye. In this method, the presented images are preprocessed by performing personalised compensation according to the ocular aberration of the computer user. In order to overcome the mismatch between the aberration used to generate the precompensation and the actual aberrations at the time of viewing, dynamic ocular aberrations are derived from the resizing of the initial aberration data measured by the wavefront analyzer. The dynamic ocular aberrations are used to update the image precompensation in real time. Results of human subject experiment show that the image recognition accuracy was significantly increased after the dynamic precompensation was applied. Subjective impressions from the participants confirmed the effectiveness of the method.

Previous research found that the pupil diameter (PD) can be an indication of affective state, but this approach to the detection of the affective state of a computer user has not been investigated fully. We propose a new affective sensing approach to evaluate the computer user's affective states as they transition from "relaxation” to "stress,” through processing the PD signal. Wavelet denoising and Kalman filtering were used to preprocess the PD signal. Then, three features were extracted from it and five classification algorithms were used to evaluate the overall performance of the identification of "stress” states in the computer users, achieving an average accuracy of 83.16 percent, with the highest accuracy of 84.21 percent reached with a Multilayer Perceptron and a Naive Bayes classifier. The Galvanic Skin Response (GSR) signal was also analyzed to study the comparative efficiency of affective sensing through the PD signal. We compared the discriminating power of the three features derived from the preprocessed PD signal to three features derived from the preprocessed GSR signal in terms of their Receiver Operating Characteristic curves. The results confirm that the PD signal should be considered a powerful physiological factor to involve in future automated affective classification systems for human-computer interaction.

A new thermal imaging framework with unique feature extraction and similarity measurements for face recognition is presented. The research premise is to design specialized algorithms that would extract vasculature information, create a thermal facial signature and identify the individual. The proposed algorithm is fully integrated and consolidates the critical steps of feature extraction through the use of morphological operators, registration using the Linear Image Registration Tool and matching through unique similarity measures designed for this task. The novel approach at developing a thermal signature template using four images taken at various instants of time ensured that unforeseen changes in the vasculature over time did not affect the biometric matching process as the authentication process relied only on consistent thermal features. Thirteen subjects were used for testing the developed technique on an in-house thermal imaging system. The matching using the similarity measures showed an average accuracy of 88.46% for skeletonized signatures and 90.39% for anisotropically diffused signatures. The highly accurate results obtained in the matching process clearly demonstrate the ability of the thermal infrared system to extend in application to other thermal imaging based systems. Empirical results applying this approach to an existing database of thermal images proves this assertion.

Visual distortion and blurring impede the efficient interaction between computers and their users. Visual problems can be caused by eye diseases, severe refractive errors or combinations of both. Several image enhancement methods based on contrast sensitivity have been used to help people with eye diseases (e.g., age-related macular degeneration and cataracts), whereas few methods have been designed for people with severe refractive errors. This paper describes a new pre-compensation method to counter the visual blurring caused by the severe refractive errors of a specific computer user. It preprocesses the pictorial information through dynamic pre-compensation in advance, aiming to present customized images on the basis of the ocular aberrations of the specific computer user. The new method improves the previous static pre-compensation method by updating the aberration data according to pupil size variations, in real-time. The real-time aberration data enable us to generate better suited pre-compensated images, as the pre-compensation model is updated dynamically. An empirical study was conducted to evaluate the efficiency of the new pre-compensation method, through an icon recognition test. From the results of statistical analysis, we found that participants achieved significantly higher accuracy levels in recognizing the icons with dynamic pre-compensation, than when viewing the original icons. The accuracy is also significantly boosted when the icons were processed with dynamic pre-compensation method, in comparison with the previous static pre-compensation method.

This study describes a new 3-D liver segmentation method in support of the selective internal radiation treatment as a treatment for liver tumors. This 3-D segmentation is based on coupling a modified k-means segmentation method with a special localized contouring algorithm. In the segmentation process, five separate regions are identified on the computerized tomography image frames. The merit of the proposed method lays in its potential to provide fast and accurate liver segmentation and 3-D rendering as well as in delineating tumor region(s), all with minimal user interaction. Leveraging of multicore platforms is shown to speed up the processing of medical images considerably, making this method more suitable in clinical settings. Experiments were performed to assess the effect of parallelization using up to 442 slices. Empirical results, using a single workstation, show a reduction in processing time from 4.5 h to almost 1 h for a 78% gain. Most important is the accuracy achieved in estimating the volumes of the liver and tumor region(s), yielding an average error of less than 2% in volume estimation over volumes generated on the basis of the current manually guided segmentation processes. Results were assessed using the analysis of variance statistical analysis.

Head Related Transfer Functions (HRTFs) are signal processing models that represent the modifications undergone by the acoustic signal as it interacts with the listener's body. HRTFs can be used to generate binaural sound as they contain all the information about the sound source's location. This paper describes a database of head-related transfer functions based on ear/head measurements measured at the FIU DSP Lab. HRTF data from 15 subjects at 12 different azimuths and 6 different elevations are included. This database also includes 3-D images of the subject's pinnae (outer ears). Anthropometric measurements of the various parts of the pinna are included.

Spatialized ("3D") audio may be useful as an alternative sensory channel to provide blind individuals with relevant spatial information in the physical world or while interacting with computers. However, an important limitation of this approach is the lower spatial resolution achievable through sound localization. While this limitation is widely acknowledged, few empirical evaluations of the sound localization achievable through audio spatialization techniques have been performed, particularly with respect to elevation localization. We performed such an empirical study and found quantitative confirmation that the localization accuracy deteriorates as the virtual sound position is set farther above or below the ear (height) level. This information may be valuable to HCI designers planning to use 3D sound.

In this paper, we propose the possibility of using real-time pupil diameter measurements, which are provided by most Eye Gaze Tracking (EGT) systems utilized for computer input by many individuals with severe motor impairments, to obtain an on-line estimation of the quality of the interaction with the computer, as perceived by the user. Increased computational power available to process, in real-time, pupil diameter data could, in the future, enable an assessment of the level of frustration (stress) that an EGT user may experience when the quality of the interaction deteriorates. This would be a critical first step in the direction of enabling EGT systems to initiate a re-calibration process, under those circumstances.

In this paper, we present statistical results from testing our precompensation algorithms with 20 human subjects. A factorial experiment was designed and tested to evaluate the significance that the method, icon size, and subject group have on the ability of users to identify icons. These results reinforce software and "artificial eye" test findings indicating that the methods used for precompensation provide a significant increase in retinal image quality for users that have visual aberrations present in the optical systems of their eyes. Significant interactions were also found that reveal circumstances under which the method may perform best.

Eye Gaze Tracking (EGT) systems allow individuals with motor disabilities to quickly move a screen cursor on a PC. However, there are limitations in the steadiness and the accuracy of cursor control and clicking capabilities they provide. On the other hand, a cursor control system to step the cursor up, down, left or right in response to voluntary contractions of specific facial muscles, developed by our group, provides steady and precise, albeit slow, cursor control, along with a reliable clicking mechanism. This system identifies muscle contractions by performing digital processing of the Electromyogram (EMG) signals generated by the facial muscles. Based on the complementary nature of the strengths of these two cursor control modalities we have developed an integrated EGT/EMG system in an attempt to consolidate the advantages of both input modalities. We have compared the selection accuracy and speed of an EGT-only cursor control implementation, our integrated EGT/EMG cursor control system and a standard handheld mouse in point-and click trials.

In this paper, we describe a multi-domain approach for enhancing text displayed on a computer screen for users with visual aberrations. This research is based on a priori knowledge of the user's visual aberration, as measured by a wavefront analyzer. With this information it is possible to generate text that, when displayed to this user, will counteract his/her visual aberration. The method described in this paper advances the development of techniques for providing such compensation by integrating spatial information in the image as a means to eliminate some of the shortcomings inherent in using display devices such as monitors or LCD panels.

Face recognition has been a focus in research for the last couple of decades because of its wide potential applications and its importance to meet the security needs of today's world. With the development of 3D imaging technology, 3D face recognition emerges as an alternative to overcome the difficulties inherent to 2D face recognition, i.e. sensitivity to illumination conditions and positions of a subject. But 3D face recognition still needs to tackle the problem of deformation of facial geometry that results from the expression changes of a subject. To deal with this issue, a 3D face recognition framework is proposed in this paper. It is composed of three subsystems: expression recognition system, expressional face recognition system and neutral face recognition system. A system for the recognition of faces with one type of expression (smile) and neutral faces was implemented and tested on a database of 30 subjects. The results proved the feasibility of this framework.

The possibility of pre-compensating images in a computer display according to the visual aberrations previously assessed in an optical system (e.g., the computer user's eye) has been confirmed for a simple "artificial eye". This device has been constructed from optical components, which include a plano-convex lens, an adjustable aperture, and a Charged-Couple Device (CCD) array that mimics the retina of a real eye. While the CCD array allows for the inspection of the image as it would form on the retina of a real eye, its specular reflection does not allow the resulting "artificial eye" to be measured appropriately in a wavefront analyzer (a necessary pre-requisite for the image precompensation process). Therefore, an alternative, interchangeable CCD array covered with gray paint (i.e., disabled) was also created to provide the diffuse reflectivity that is presumed in the operation of the wavefront analyzer. Experiments with this system show that the visual aberrations in a properly characterized optical system can, in fact, be precompensated by the methods proposed by Alonso et al., [1]. These same experiments, however, reveal the need to adjust the precompensation method according to the effective pupil diameter in the system during viewing.

Computer technologies, frequently employed for everyday tasks, often use Graphical User Interfaces (GUIs), presented through monitors or LCD displays. This type of visual interface is not well suited for users with refractive visual limitations, particularly when they are severe and not correctable by common means. In order to facilitate computer access for users with refractive deficiencies, an algorithm has been developed, using a priori knowledge of the visual aberration, to generate on-screen images that counter the effect of the aberration. When the user observes the screen displaying a pre-compensated image, the image perceived in the retina will be similar to the original image. The algorithm was tested by artificially introducing a spherical aberration in the field of view of 14 subjects, totaling 28 individual eyes. This use of pre-compensation improves the visual performance of the subjects with respect to that achieved with no compensation.

The use of computer technology for everyday tasks has become increasingly important in today's world. Frequently, computer technology makes use of Graphical User Interfaces (GUIs), presented through monitors or LCD displays. This type of visual interface is not well suited for users with visual limitations due to refractive errors, particularly when they are severe and not correctable by common means. In order to facilitate computer access for users with refractive deficiencies, an algorithm was developed, using a priori knowledge of the visual aberration, to generate an inverse transformation of the images that are then displayed on-screen, countering the effect of the aberration. The result is that when the user observes the screen displaying the transformed images, the image perceived in the retina will be similar to the original image. The algorithm was tested by artificially introducing a spherical aberration in the field of view of 14 subjects, totaling 28 individual eyes. Results show that when viewing the screen, this method of compensation improves the visual performance of the subjects tested in comparison to viewing uncompensated images.

Head-Related Transfer Functions (HRTFs) are commonly used to impose a virtual location of origin to mono-aural digital sounds. In listening tests, however, subjects tend to confuse the virtual location of sounds from the front hemisphere with symmetric locations in the back hemisphere, and vice versa, in an effect called “Cone of Confusion”. Preliminary observations suggested that the HRTFs of individuals with particularly protruding ears show more accentuated spectral differences between symmetric locations. When these HRTFs were used for the spatialization of sounds in listening tests performed by other subjects, the cone of confusion effect decreased. This paper describes how to synthesize attenuation/amplification profiles based on those seen in HRTFs from protruding ears and how they are applied to modify the HRTFs of several subjects. Listening tests showed that the HRTFs so modified yielded enhanced front-back differentiation, with respect to the original HRTFs of the test subjects.

Graphical user interfaces are one of the more prevalent interface types which exist today. The popularity of this interface type has caused problems for users with poor vision. Because usage strategies of low vision users differ from blind users, existing research focusing on blind users is not sufficient in describing the techniques employed by low vision users.

No abstract available.

Researchers have shown that computer users with impaired vision perform visual search more slowly than their fully sighted counterparts. However, little is known about intermediate stages of visual search that exist between stimuli detection and identification. Therefore, the primary focus of this research is to investigate 2 intermediate stages of visual search in visually impaired computer users: preattention and focal attention. A total of 10 volunteers, 1 participant who possessed normal vision and 9 participants possessing impaired vision, underwent clinical visual examinations consisting of assessments of visual acuity, contrast sensitivity, visual field, and color perception. The involvement of each participant's visual cortex while performing a continuous matching task of visual icons on a computer screen was recorded. Results demonstrated that additional time required by visually impaired computer users is not a result of delayed engagement of the visual cortex, but rather due to time spent in active search once the visual cortex has already been engaged. Directions for future research are provided.