Disparity Calculation Research Articles

Measuring vergence and fixation disparity in 3D space

As the eyes continuously move in 3D space, they rarely converge at the exact depth of the plane even when fixating a 2D image or computer screen. Rather, the lines of gaze measured by eye movement recordings show some misalignment so-called fixation disparity. Fixation disparity occurs in front of or behind the plane, and the eyes may also be lagged vertically. For those reasons, vision research requires mathematical tools to calculate where exactly the lines of gaze cross the stimulus plane. Seminal research on vergence eye movements targeting stimuli lying on isovergence curves has been content with simple computation of the difference between the two eye rotation angles. Recently, the need of new calculations has emerged with the increasing use of eye-trackers providing the eye coordinates on a computer screen. Previous studies have made this attempt but with restrictions. We introduce here a complete calculation of fixation disparity in 3D space allowing vision researchers to study the precision of gaze regardless of the stimulus location in 3D space and of whether the eyes lag horizontally and/or vertically.

Trends in Racial/Ethnic Disparity of Health-Related Quality of Life in Older Adults with and without Cancer (1998-2012).

Non-White cancer survivors often report poorer health compared with Non-Hispanic Whites. Whether those disparities are changing over time is unknown. We examined changes in health-related quality of life (HRQOL) by race/ethnicity from 1998 to 2012 among older adults with and without cancer. Data from Medicare Advantage beneficiaries were obtained from the linkage between the Medicare Health Outcomes Survey and Surveillance, Epidemiology, and End Results cancer registry data (SEER-MHOS). HRQOL was assessed with the SF-36/VR-12 Physical and Mental Component Scores (PCS/MCS) and 8 scales (Physical Functioning, Role-Physical, Bodily Pain, General Health, Vitality, Social Functioning, Mental Health, Role-Emotional). Annual average HRQOL scores, adjusting for age at survey, gender, number of comorbidities, education, and SEER registry, were compared over time. Absolute (between-group variance; BGV) and relative (mean log deviation; MLD) indices of disparity were generated using the National Cancer Institute's health disparities calculator (HD*Calc). Joinpoint was used to test for significant changes in the slopes of the linear trend lines. Racial/ethnic disparities in MCS increased in absolute and relative terms over time for those with [BGV = 15.8 (95% confidence interval [CI], 10.2-21.6); MLD = 16.2 (95% CI, 10.5-22.1)] and without [BGV = 19.3 (95% CI, 14.9-23.8); MLD = 19.6 (95% CI, 15.2-24.0)] cancer. PCS disparities over time did not significantly change. Changes in disparities in 5 of 8 HRQOL scales were significant in those with and without cancer. Older adults with cancer show increasing racial/ethnic disparities in HRQOL, particularly in mental health status. Future research should evaluate trends in HRQOL and explore factors that contribute to health disparities.

How Administrative Data Collection and Analysis Can Better Reflect Racial and Ethnic Identities

Measuring race and ethnicity for administrative data sets and then analyzing these data to understand racial/ethnic disparities present many logistical and theoretical challenges. In this chapter, we conduct a synthetic review of studies on how to effectively measure race/ethnicity for administrative data purposes and then utilize these measures in analyses. Recommendations based on this synthesis include combining the measure of Hispanic ethnicity with the broader racial/ethnic measure and allowing individuals to select more than one race/ethnicity. Data collection should rely on self-reports but could be supplemented using birth certificates or equivalent sources. Collecting data over time, especially for young people, will help identify multiracial and American Indian populations. For those with more complex racial/ethnic identities, including measures of country of origin, language, and recency of immigration can be helpful in addition to asking individuals which racial/ethnic identity they most identify with. Administrative data collection could also begin to incorporate phenotype measures to facilitate the calculation of disparities within race/ethnicity by skin tone. Those analyzing racial/ethnic disparities should understand how these measures are created and attempt to develop fieldwide terminology to describe racial/ethnic identities.

Virtual binocular vision sensing and control of molten pool width for gas metal arc additive manufactured thin-walled components

Gas metal arc (GMA) additive manufacturing (AM) is one of the significant wire and arc AM processes with the ability to produce large-scale metal parts in a layer by layer fashion. Despite this fact, techniques to realize process sensing and geometry control have not been perfectly developed. This study aims at molten pool width control in GMA AM using a passive vision sensing technique. A virtual binocular vision sensing system consisting of a biprism and a camera is designed to monitor the molten pool geometry. The molten pool width in a captured image pair is extracted by a series of procedures, such as sensor calibration, image pair rectification, disparity calculation, and width reconstruction. A verification test is conducted and reveals that the detection error of the sensing system is less than 3%. To keep consistent layer width in each layer, the deviation of the molten pool width is compensated by designing a fuzzy intelligent controller to adjust the arc current in real time. The effectiveness of the controller is evaluated via the deposition of thin-walled parts. The results indicate that the consistency of the molten pool width in GMA AM can be improved when employing the fuzzy controller.

Neuronal Specialization for Fine-Grained Distance Estimation Using a Real-Time Bio-Inspired Stereo Vision System

The human binocular system performs very complex operations in real-time tasks thanks to neuronal specialization and several specialized processing layers. For a classic computer vision system, being able to perform the same operation requires high computational costs that, in many cases, causes it to not work in real time: this is the case regarding distance estimation. This work details the functionality of the biological processing system, as well as the neuromorphic engineering research branch—the main purpose of which is to mimic neuronal processing. A distance estimation system based on the calculation of the binocular disparities with specialized neuron populations is developed. This system is characterized by several tests and executed in a real-time environment. The response of the system proves the similarity between it and human binocular processing. Further, the results show that the implemented system can work in a real-time environment, with a distance estimation error of 15% (8% for the characterization tests).

Real-time stereo matching on CUDA using Fourier descriptors and dynamic programming

Computation of stereoscopic depth and disparity map extraction are dynamic research topics. A large variety of algorithms has been developed, among which we cite feature matching, moment extraction, and image representation using descriptors to determine a disparity map. This paper proposes a new method for stereo matching based on Fourier descriptors. The robustness of these descriptors under photometric and geometric transformations provides a better representation of a template or a local region in the image. In our work, we specifically use generalized Fourier descriptors to compute a robust cost function. Then, a box filter is applied for cost aggregation to enforce a smoothness constraint between neighboring pixels. Optimization and disparity calculation are done using dynamic programming, with a cost based on similarity between generalized Fourier descriptors using Euclidean distance. This local cost function is used to optimize correspondences. Our stereo matching algorithm is evaluated using the Middlebury stereo benchmark; our approach has been implemented on parallel high-performance graphics hardware using CUDA to accelerate our algorithm, giving a real-time implementation.

Variance Estimation and Confidence Intervals for 11 Commonly Used Health Disparity Measures.

There is increased interest in eliminating health disparities in the United States and worldwide. Broadly defined, health disparities refer to preventable inequalities in health status, such as cancer to ethnicity, socioeconomic status, gender, education, environment, and geographic locations. To make informed health policy decisions, it is essential to precisely measure the magnitude of disparities and assess trends over time. The Health Disparities Calculator (HD*Calc) is free statistical software that calculates 11 commonly used measures of health disparities and provides corresponding 95% CIs for the 11 measures using either an analytic method or a Monte Carlo simulation-based method; however, the derivation of SEs and coverage properties of the CIs have not been formally evaluated. We used simulation studies to assess the coverage properties of these CIs. We have also conducted bias analyses for measures implemented in HD*Calc using age-adjusted cancer incidence rates from national, state, and county level SEER data. The results of these analyses indicate that HD*Calc should be used with caution to construct CIs for some health disparity measures when the proportion of zero event counts is greater than 25%.

Statistical inferences of extended concentration indices for directly standardized rates.

The relative concentration index (RCI) and the absolute concentration index (ACI) have been widely used for monitoring health disparities with ranked health determinants. The RCI has been extended to allow value judgments about inequality aversion by Pereira in 1998 and by Wagstaff in 2002. Previous studies of the extended RCI have focused on survey sample data. This paper adapts the extended RCI for use with directly standardized rates (DSRs) calculated from population-based surveillance data. A Taylor series linearization (TL)-based variance estimator is developed and evaluated using simulations. A simulation-based Monte Carlo (MC) variance estimator is also evaluated as a comparison. Following Wagstaff's approach in 1991, we extend the ACI for use with DSRs. In all simulations, both the TL and MC methods produce valid variance estimates. The TL variance estimator has a simple, closed form that is attractive to users without sophisticated programming skills. The TL and MC estimators have been incorporated into a beta version of the National Cancer Institute's Health Disparities Calculator, a free statistical software tool that enables the estimation of 11 commonly used summary measures of health disparities for DSRs.

The minimum spanning tree in stereo vision

Image can be presented in graph or grid graph. Segmentation is widely used in computer vision. Image segmentation is done in various approaches. One approach to image segmentation is the use of a minimum spanning tree. Almost every method of determining disparity in stereo vision uses image segmentation. The method in this paper is in accordance with the method of Segment-Based Stereo Matching using belief propagation and self-adapting dissimilarity measure with improvements for segmentation, and apply local window-based matching steps. The disparity calculation method built with minimum spanning tree approach is one alternative that can be used to calculate disparity for image pair with simple stereo configuration.

Statistical Inference on Health Disparity Indices for Complex Surveys.

The National Cancer Institute developed the Health Disparities Calculator (HD*Calc) to facilitate research on health disparities. HD*Calc calculates multiple measures of health disparities using data collected from population-based disease surveillance systems, such as cancer registries. In this paper, we extend the use of HD*Calc to complex survey data by developing plug-in point estimators and Taylor linearization variance estimators that consider complex designs: stratification, multistage clustering, and differential weighting. Our simulation indicates that the plug-in estimators are approximately unbiased and the Taylor linearization variance estimators are accurate. Using 2011-2016 data from the National Health and Nutrition Examination Survey, we demonstrate the use of these estimators in evaluating socioeconomic disparities in the prevalence of child and adolescent (ages 2-18 years) obesity in the United States. Statistical software has been developed for ease of disparity analyses using complex survey data.

250 Evidence for race/ethnic incidence rate disparity in mycosis fungoides: A report from the NCI surveillance, epidemiology, and end results (SEER) program

Mycosis fungoides (MF) is the most common form of cutaneous T-cell lymphoma, and some studies have shown that the incidence rate (IR) and prognosis varies between race/ethnic (R/E) groups. This study aims to investigate whether R/E disparities in MF IR have changed from 2000 to 2014 using the Health Disparities Calculator (HD*calc) produced by the SEER Program. SEER data, excluding Alaska Native and Louisiana registries, from 2000 to 2014 were used to estimate age-adjusted IR of MF by gender and R/E group. R/E categories were limited to White, Black, Asian/Pacific Islander, American Indian, or Hispanic. IRs were inputted into HD*calc to generate the range difference (RD), a measure of disparity. RD is calculated as the difference in IR of MF between the R/E group with the highest IR and lowest IR (a RD of 0 indicates no disparity exists). RD measures the absolute range of IR across all R/E groups. TheBlack non-Hispanic R/E group had the highest age-adjusted IR of MF, ranging from 0.53 to 1.30 cases per 100,000. Moreover, RD increased for males: 0.79 to 1.06, a 34.2% change; and for females: 0.77 to 1.02, a 31.9% change. These nation-wide findings indicate that R/E disparities in IR for MF have increased using the range difference measure, particularly for the Black non-Hispanic R/E group when comparing data from 2000 to 2014. However, it is important to note that HD*Calc allows researchers to compare measures in order to select the set of measures that best capture the nuances of the data. Hence, these findings point to the need for ongoing analysis in order to better understand which, and to what extent, race/ethnic groups are most disparate.

Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors.

Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison.

Crowd Segmentation Using both Appearance and Stereo Information

Crowd segmentation is an important issue in video surveillance. With the decrease in their cost, stereo cameras can be used to help develop new algorithms to achieve better accuracy in crowd segmentation. This paper aims to develop a method to explore the depth cues for crowd segmentation in video surveillance. The contributions of this paper are twofold. First, a novel crowd segmentation method closely coupling appearance and stereo information has been developed. Instead of performing disparity calculation as a preprocessing step, stereo information is obtained concurrently with appearance-based crowd segmentation. Second, an object-level disparity algorithm is proposed for object segmentation in surveillance scenarios. Only one disparity value for each hypothetical object greatly reduces the computational complexity and simplifies the segmentation method. Experimental results and quantitative evaluations based on two surveillance scenarios are presented in this paper. The results consistently show the effectiveness of the algorithm in exploring depth cues for crowd segmentation.

Analysis of Interrelationships among Voluntary and Prosthetic Leg Joint Parameters Using Cyclograms.

The walking mechanism of a prosthetic leg user is a tightly coordinated movement of several joints and limb segments. The interaction among the voluntary and mechanical joints and segments requires particular biomechanical insight. This study aims to analyze the inter-relationship between amputees' voluntary and mechanical coupled leg joints variables using cyclograms. From this analysis, the critical gait parameters in each gait phase were determined and analyzed if they contribute to a better powered prosthetic knee control design. To develop the cyclogram model, 20 healthy able-bodied subjects and 25 prosthesis and orthosis users (10 transtibial amputees, 5 transfemoral amputees, and 10 different pathological profiles of orthosis users) walked at their comfortable speed in a 3D motion analysis lab setting. The gait parameters (i.e., angle, moment and power for the ankle, knee and hip joints) were coupled to form 36 cyclograms relationship. The model was validated by quantifying the gait disparities of all the pathological walking by analyzing each cyclograms pairs using feed-forward neural network with backpropagation. Subsequently, the cyclogram pairs that contributed to the highest gait disparity of each gait phase were manipulated by replacing it with normal values and re-analyzed. The manipulated cyclograms relationship that showed highest improvement in terms of gait disparity calculation suggested that they are the most dominant parameters in powered-knee control. In case of transfemoral amputee walking, it was identified using this approach that at each gait sub-phase, the knee variables most responsible for closest to normal walking were: knee power during loading response and mid-stance, knee moment and knee angle during terminal stance phase, knee angle and knee power during pre-swing, knee angle at initial swing, and knee power at terminal swing. No variable was dominant during mid-swing phase implying natural pendulum effect of the lower limb between the initial and terminal swing phases. The outcome of this cyclogram adoption approach proposed an insight into the method of determining the causal effect of manipulating a particular joint's mechanical properties toward the joint behavior in an amputee's gait by determining the curve closeness, C, of the modified cyclogram curve to the normal conventional curve, to enable quantitative judgment of the effect of changing a particular parameter in the prosthetic leg gait.

Precise Error Rate Analysis of Wireless Relay Networks

A new method to analyze the bit error rate (BER) performance of two-hop multiple relay networks is presented in this paper. For this work, a flat fading channel is considered for exchanging signals between two wireless users which communicate with each other through set of bidirectional relays that operate as repeaters (amplify-and-forward). Each user adopts a relay selection scheme to choose the relay with the highest signal-to-noise-ratio (SNR). The proposed method aims to reduce the analysis of the two-hop relay network to the BER analysis of one-hop (direct link) and this allows to improve the asymptotic BER (ABER) performance of two-hop. Thereby, the ABER would be more suitable for wide SNR range. From this ABER analysis, the exact BER (EBER) can be obtained directly and this gives a standard BER analysis for bidirectional relay networks. The analytic expressions for both ABER and EBER are derived and then confirmed by simulation results. It is found that the proposal gives low disparity calculation errors between ABER and EBER. It hence provides an accurate BER analysis for a bidirectional relay networks.

Assessing disparities in colorectal cancer mortality by socioeconomic status using new tools: health disparities calculator and socioeconomic quintiles

PurposeColorectal cancer mortality rates dropped by half in the past three decades, but these gains were accompanied by striking differences in colorectal cancer mortality by socioeconomic status (SES). Our research objective is to examine disparities in colorectal cancer mortality by SES, using a scientifically rigorous and reproducible approach with publicly available online tools, HD*Calc and NCI SES Quintiles.MethodsAll reported colorectal cancer deaths in the United States from 1980 to 2010 were categorized into NCI SES quintiles and assessed at the county level. Joinpoint was used to test for significant changes in trends. Absolute and relative concentration indices (CI) were computed with HD*Calc to graph change in disparity over time.ResultsDisparities by SES significantly declined until 1993–1995, and then increased until 2010, due to a mortality drop in populations living in high SES areas that exceeded the mortality drop in lower SES areas. HD*Calc results were consistent for both absolute and relative concentration indices. Inequality aversion parameter weights of 2, 4, 6 and 8 were compared to explore how much colorectal cancer mortality was concentrated in the poorest quintile compared to the richest quintile. Weights larger than 4 did not increase the slope of the disparities trend.ConclusionsThere is consistent evidence for a significant crossover in colorectal cancer disparity from 1980 to 2010. Trends in disparity can be accurately and readily summarized using the HD*Calc tool. The disparity trend, combined with published information on the timing of screening and treatment uptake, is concordant with the idea that introduction of medical screening and treatment leads to lower uptake in lower compared to higher SES populations and that differential uptake yields disparity in population mortality.

Visual discomfort prediction on stereoscopic 3D images without explicit disparities

Almost all existing 3D visual discomfort prediction models are based, at least in part, on features that are extracted from computed disparity maps. These include such estimated quantities such as the maximum disparity, disparity range, disparity energy and other measures of the disparity distribution. A common first step when implementing a 3D visual discomfort model is some form of disparity calculation, whence the accuracy of prediction largely depends on the accuracy of the disparity result. Unfortunately, most algorithms that compute disparity maps are expensive, and are not guaranteed to deliver sufficiently accurate or perceptually relevant disparity data. This raises the question of whether it is possible to build a 3D discomfort prediction model without explicit disparity calculation. Towards this possibility, we have developed a new feature map, called the percentage of un-linked pixels (PUP), that is descriptive of the presence of disparity, and which can be used to accurately predict experienced 3D visual discomfort without the need for actually calculating disparity values. Instead, PUP features are extracted by predicting the percentage of un-linked pixels in corresponding retinal patches of image pairs. The un-linked pixels are determined by feature classification on orientation and luminance distributions. Calculation of PUP maps is much faster than traditional disparity computation, and the experimental results demonstrate that the predictive power attained using the PUP map is highly competitive with prior models that rely on computed disparity maps.

3D visual discomfort prediction using low complexity disparity algorithms

Algorithms that predict the degree of visual discomfort experienced when viewing stereoscopic 3D (S3D) images usually first execute some form of disparity calculation. Following that, features are extracted on these disparity maps to build discomfort prediction models. These features may include, for example, the maximum disparity, disparity range, disparity energy, and other measures of the disparity distribution. Hence, the accuracy of prediction largely depends on the accuracy of disparity calculation. Unfortunately, computing disparity maps is expensive and difficult and most leading assessment models are based on features drawn from the outputs of high complexity disparity calculation algorithms that deliver high quality disparity maps. There is no consensus on the type of stereo matching algorithm that should be used for this type of model. Towards filling this gap, we study the relative performances of discomfort prediction models that use disparity algorithms having different levels of complexity. We also propose a set of new discomfort predictive features with good performance even when using low complexity disparity algorithms.

Extraction of Region with Excessive Disparities Using Block Based Disparity Calculation

Extraction of Region with Excessive Disparities Using Block Based Disparity Calculation

The Use of the Health Disparities Calculator in Analysis of the Lung Cancer Burden in Urban and Rural Populations in the Świętokrzyskie Province (Poland): 1999–2010

Abstract The purpose of the study was a comparative analysis of the burden of lung cancer in urban and rural areas in the Świętokrzyskie Province in the years 1999–2010. The material for the study accounted for information about the number of new cases of lung cancer obtained from the Świętokrzyskie Cancer Registration Office in Kielce and data from the Central Statistical Office concerning the number of deaths due to lung cancer in the years 1999–2010. Crude and age-standardized incidence, mortality, and potential years of life lost rates were calculated separately for groups of men and women in urban and rural areas. A comparative analysis of coefficients in urban and rural areas based on Rate Ratio (RR) and Rate Difference (RD) was performed by using the Health Disparities Calculator. The average annual incidence of lung cancer among men was 18% lower in the urban population than in the rural, while the mortality rate was 16% lower. For women, the incidence and mortality rates were higher in urban areas as opposed to rural areas – by 48% and 54% respectively. The comparative analysis of the burden of lung cancer showed great diversity between urban and rural populations in the Świętokrzyskie Province.