varunr14/newsummary huggingface dataset:
This paper introduces four classes of rotation invariant orthogonal moments by generalizing four existing moments that use harmonic functions in their radial kernels . Members of these classes share beneficial properties for image representation and pattern recognition like orthogonality and rotation invariance . The kernel sets of these generic harmonic function based moments are complete in the Hilbert space of square integrable continuous complex valued functions . Due to their resemble definition the computation of these kernels maintains the simplicity and numerical stability of existing harmonic function based moments . In addition each member of one of these classes has distinctive properties that depend on the value of a parameter making it more suitable for some particular applications . Comparison with existing orthogonal moments defined based on Jacobi polynomials and eigenfunctions has been carried out and experimental results show the effectiveness of these classes of moments in terms of representation capability and discrimination power . 


Summary: We generalize polar harmonic transforms for pattern description recognition. The generalization maintains beneficial properties of existing transforms. The completeness of the corresponding basis sets is proven. The numerical stability of the computation is discussed. The new generic transforms have superior performance to comparison methods.
In this paper a MATLAB based graphical user interface software tool for general biomedical signal processing and analysis of functional neuroimaging data is introduced . Specifically electroencephalography and electrocardiography signals can be processed and analyzed by the developed tool which incorporates commonly used temporal and frequency analysis methods . In addition to common methods the tool also provides non linear chaos analysis with Lyapunov exponents and entropies multivariate analysis with principal and independent component analyses and pattern classification with discriminant analysis . This tool can also be utilized for training in biomedical engineering education . This easy to use and easy to learn intuitive tool is described in detail in this paper . 


Summary: Design of a MATLAB based GUI tool for general biomedical signal processing and analysis. EEG and ECG signals can be processed and analyzed by the designed tool. The easy to use and easy to learn intuitive tool provides non linear chaos analysis. The tool provides entropy multivariate analysis with PCA and ICA pattern classification. The tool can also be utilized for training in biomedical engineering education.
Background and objective A markerless low cost prototype has been developed for the determination of some spatio temporal parameters of human gait step length step width and cadence have been considered . Only a smartphone and a high definition webcam have been used . Methods The signals obtained by the accelerometer embedded in the smartphone are used to recognize the heel strike events while the feet positions are calculated through image processing of the webcam stream . Step length and width are computed during gait trials on a treadmill at various speeds . Results Six subjects have been tested for a total of 504 steps . Results were compared with those obtained by a stereo photogrammetric system . The maximum average errors were 3.7cm for the right step length and 1.63cm for the right step width at 5km h. The maximum average error for step duration was 0.02s at 5km h for the right steps . Conclusion The system is characterized by a very high level of automation that allows its use by non expert users in non structured environments . A low cost system able to automatically provide a reliable and repeatable evaluation of some gait events and parameters during treadmill walking is relevant also from a clinical point of view because it allows the analysis of hundreds of steps and consequently an analysis of their variability . 


Summary: A markerless low cost system for the estimation of some spatial temporal parameters of human gait. A system usable on not instrumented treadmill. A system characterized by a very high level of automation. A system can be used to analyze up to one hundred steps. A system with precision comparable with the gold standard.
This paper presents an improved multiple instance learning tracker representing target with Distribution Fields and building a weighted geometric mean MIL classifier . Firstly we adopt DF layer as feature instead of traditional Haar like one to model the target thanks to the DF specificity and the landscape smoothness . Secondly we integrate sample importance into the weighted geometric mean MIL model and derive an online approach to maximize the bag likelihood by AnyBoost gradient framework to select the most discriminative layers . Due to the target model consisting of selected discriminative layers our tracker is more robust while needing fewer features than the traditional Haar like one and the original DFs one . The experimental results show higher performances of our tracker than those of five state of the art ones on several challenging video sequences . 


Summary: We adopt Distribution Field DF layer as feature instead of traditional Haar like one to robustly model the target. We derive an online weighted geometric mean MIL classifier to select the most discriminative layers. Our tracker is more robust while needing fewer features than the traditional Haar like one and the original DFs one. The experiments show higher performance of our tracker than five state of the art ones.
Text based image retrieval may perform poorly due to the irrelevant and or incomplete text surrounding the images in the web pages . In such situations visual content of the images can be leveraged to improve the image ranking performance . In this paper we look into this problem of image re ranking and propose a system that automatically constructs multiple candidate multi instance bags which are likely to contain relevant images . These automatically constructed bags are then utilized by ensembles of Multiple Instance Learning classifiers and the images are re ranked according to the final classification responses . Our method is unsupervised in the sense that the only input to the system is the text query itself without any user feedback or annotation . The experimental results demonstrate that constructing multiple instance bags based on the retrieval order and utilizing ensembles of MIL classifiers greatly enhance the retrieval performance achieving on par or better results compared to the state of the art . 


Summary: A system that constructs multi instance bags from text based retrieval order. Ensemble of MI classifiers is learned using these multi instance bags. We report image re ranking performance on multiple datasets. Our system receives on par or better results than the state of the art.
Background and objective Non compartmental analysis calculates pharmacokinetic metrics related to the systemic exposure to a drug following administration e.g . area under the concentration time curve and peak concentration . We developed a new package in R called ncappc to perform a NCA and simulation based posterior predictive checks for a population PK model using NCA metrics . Methods The nca feature of ncappc package estimates the NCA metrics by NCA . The ppc feature of ncappc estimates the NCA metrics from multiple sets of simulated concentration time data and compares them with those estimated from the observed data . The diagnostic analysis is performed at the population as well as the individual level . The distribution of the simulated population means of each NCA metric is compared with the corresponding observed population mean . The individual level comparison is performed based on the deviation of the mean of any NCA metric based on simulations for an individual from the corresponding NCA metric obtained from the observed data . The ncappc package also reports the normalized prediction distribution error of the simulated NCA metrics for each individual and their distribution within a population . Results The ncappc produces two default outputs depending on the type of analysis performed i.e . NCA and PopPK diagnosis . The PopPK diagnosis feature of ncappc produces 8 sets of graphical outputs to assess the ability of a population model to simulate the concentration time profile of a drug and thereby evaluate model adequacy . In addition tabular outputs are generated showing the values of the NCA metrics estimated from the observed and the simulated data along with the deviation NPDE regression parameters used to estimate the elimination rate constant and the related population statistics . Conclusions The ncappc package is a versatile and flexible tool set written in R that successfully estimates NCA metrics from concentration time data and produces a comprehensive set of graphical and tabular output to summarize the diagnostic results including the model specific outliers . The output is easy to interpret and to use in evaluation of a population PK model . ncappc is freely available on CRAN and GitHub . 


Summary: ncappc performs i NCA and ii simulation based posterior predictive checks using NCA metrics. ncappc package is highly flexible and comprehensive. Can perform both individual and population level diagnostics. Produces output summarizing the diagnostic results including the model specific outliers. The output is easy to interpret and to use in evaluation of a population PK model.
The cardiovascular and respiratory autonomic nervous regulation has been studied mainly by hemodynamic responses during different physical stressors . In this study dynamics of autonomic response to an orthostatic challenge was investigated by hemodynamic variables and by diverse linear and nonlinear indices calculated from time series of beat to beat intervals respiratory cycle duration systolic and diastolic blood pressure . This study included 16 young female patients with vasovagal syncope and 12 age matched female controls . The subjects were enrolled in a head up tilt test breathing normally including 5min of baseline and 18min of 70 orthostatic phase . To increase the time resolution of the analysis the time series were segmented in five minute overlapping windows with a shift of 1min . Hemodynamic parameters did not show any statistical differences between SYN and CON . Time domain linear analysis revealed increased respiratory frequency and increased blood pressure variability in patients during OP meaning increased sympathetic activity and vagal withdrawal . Frequency domain analysis confirmed a predominance of sympathetic tone by steadily increased values of low over high frequency power in BBI and of low frequency power in SYS and DIA in patients during OP . The nonlinear analysis by symbolic dynamics seemed to be highly suitable for differentiation of SYN and CON in the early beginning of OP i.e . 5min after tilt up . In particular the index SYS plvar3 showed less patterns of low variability in patients reflecting a steadily increase in both BPV and sympathetic activity . The proposed dynamical analysis could lead to a better understanding of the temporal underlying mechanisms in healthy subjects and patients under orthostatic stress . 


Summary: Nonlinear dynamic analysis shows sympathetic and vagal activities fluctuations. Hemodynamic analysis did not show any statistical differences between subjects. Blood pressure variability showed the highest differences between CON and SYN. In patients systolic blood pressure variability increased immediately after tilt up. Healthy women show a stable sympatho vagal balance during orthostatic phase.
Flooding is a widely occurring natural hazard that noticeably damages property people and the environment . In the context of climate change the integration of spatial planning with flood risk management has gained prominence as an approach to mitigating the risks of flooding . The absence of easy access to integrated and high quality information and the technologies and tools to use information are among the factors that impede this integration . Limited research has been conducted to develop a framework and to investigate the role of information and technologies in this integration . This study draws primarily on the European experiences and literature and identifies three dimensions of the integration of spatial planning with flood risk management territorial policy and institutional . To facilitate integration and in accord with these three dimensions a Spatially Integrated Policy Infrastructure is conceptualised that encompasses data and information decision support and analysis tools and access tools and protocols . This study presents the connections between SIPI elements and integration dimensions which is important for a better understanding of roles of geographic information and technologies in integration . The conceptual framework of SIPI will govern further development and evaluation of SIPI . 


Summary: Integration of spatial planning and flood risk management has three dimensions territorial policy and institutional. Developing infrastructures sharing both data and models and systems is a promising way to support integration. This paper conceptualises the Spatially Integrated Policy Infrastructure SIPI which focuses on policy integration.
Dense disparity map is required by many great 3D applications. In this paper a novel stereo matching algorithm is presented. The main contributions of this work are three fold. Firstly a new cost volume filtering method is proposed. A novel concept named two level local adaptation is introduced to guide the proposed filtering approach. Secondly a novel post processing method is proposed to handle both occlusions and textureless regions. Thirdly a parallel algorithm is proposed to efficiently calculate an integral image on GPU and it accelerates the whole cost volume filtering process. The overall stereo matching algorithm generates the state of the art results. At the time of submission it ranks the 10th among about 152 algorithms on the Middlebury stereo evaluation benchmark and takes the 1st place in all local methods. By implementing the entire algorithm on the NVIDIA Tesla C2050 GPU it can achieve over 30 million disparity estimates per second MDE s . 


Summary: Hghlights A novel local stereo matching algorithm with linear complexity is proposed. The overall algorithm generates the state of the art results. Two level local adaptation is introduced to guide the adaptive guided filtering. The novel post processing method handles both occlusions and textureless regions. A parallel algorithm is proposed to speed up the algorithm on GPU.
Since 2005 human and computer performance has been systematically compared as part of face recognition competitions with results being reported for both still and video imagery. The key results from these competitions are reviewed. To analyze performance across studies the cross modal performance analysis CMPA framework is introduced. The CMPA framework is applied to experiments that were part of face a recognition competition. The analysis shows that for matching frontal faces in still images algorithms are consistently superior to humans. For video and difficult still face pairs humans are superior. Finally based on the CMPA framework and a face performance index we outline a challenge problem for developing algorithms that are superior to humans for the general face recognition problem. 


Summary: We review experiments comparing humans and machines in NIST face recognition tests. We introduce the cross modal performance analysis CMPA framework. We apply the CMPA framework to the human machine experiments in the NIST tests. We propose a challenge problem to develop algorithms with human level performance.
Background and objectives The diagnosis of Developmental Dysplasia of the Hip in infants is currently made primarily by ultrasound . However two dimensional ultrasound images capture only an incomplete portion of the acetabular shape and the alpha and beta angles measured on 2DUS for the Graf classification technique show high inter scan and inter observer variability . This variability relates partly to the manual determination of the apex point separating the acetabular roof from the ilium during index measurement . This study proposes a new 2DUS image processing technique for semi automated tracing of the bony surface followed by automatic calculation of two indices a contour based alpha angle and a new modality independent quantitative rounding index . The new index M is independent of the apex point and can be directly extended to 3D surface models . Methods We tested the proposed indices on a dataset of 114 2DUS scans of infant hips aged between 4 and 183 days scanned using a 12MHz linear transducer . We calculated the manual alpha angle coverage contour based alpha angle and rounding index for each of the recordings and statistically evaluated these indices based on regression analysis area under the receiver operating characteristic curve and analysis of variance . Results Processing time for calculating and M was similar to manual alpha angle measurement 30s per image . Reliability of the new indices was high with inter observer intraclass correlation coefficients 0.90 for and 0.89 for M. For a diagnostic test classifying hips as normal or dysplastic AUC was 93.0 for vs. 92.7 for 91.6 for M alone and up to 95.7 for combination of M with or coverage . Conclusions The rounding index provides complimentary information to conventional indices such as alpha angle and coverage . Calculation of the contour based alpha angle and rounding index is rapid shows potential to improve the reliability and accuracy of DDH diagnosis from 2DUS and could be extended to 3D ultrasound in future . 


Summary: New method to calculate alpha angle for DDH classification from ultrasound 2DUS . New modality independent rounding index M to quantify acetabular rounding. Proposed indices tested on 114 scans and gave 95.7 AUC for normal vs dysplastic. Indices are modality independent and extendable to modalities like CT and MRI.
Background and objective Progress in biomedical engineering has improved the hardware available for diagnosis and treatment of cardiac arrhythmias . But although huge amounts of intracardiac electrograms can be acquired during electrophysiological examinations there is still a lack of software aiding diagnosis . The development of novel algorithms for the automated analysis of EGMs has proven difficult due to the highly interdisciplinary nature of this task and hampered data access in clinical systems . Thus we developed a software platform which allows rapid implementation of new algorithms verification of their functionality and suitable visualization for discussion in the clinical environment . Methods A software for visualization was developed in Qt5 and C utilizing the class library of VTK . The algorithms for signal analysis were implemented in MATLAB . Clinical data for analysis was exported from electroanatomical mapping systems . Results The visualization software KaPAVIE was implemented and tested on several clinical datasets . Both common and novel algorithms were implemented which address important clinical questions in diagnosis of different arrhythmias . It proved useful in discussions with clinicians due to its interactive and user friendly design . Time after export from the clinical mapping system to visualization is below 5min . Conclusion KaPAVIE See http www.ibt.kit.edu hardundsoftware.php . is a powerful platform for the development of novel algorithms in the clinical environment . Simultaneous and interactive visualization of measured EGM data and the results of analysis will aid diagnosis and help understanding the underlying mechanisms of complex arrhythmias like atrial fibrillation . 


Summary: Need for novel algorithms in diagnosis and treatment of cardiac arrhythmias. Combined development using MATLAB and C VTK library. Allows for rapid implementation of new algorithms. Interactive visualization of cardiac anatomy measured data and analysis results. Promotes interdisciplinary research and discussion.
In this paper we propose an album oriented face recognition model that exploits the album structure for face recognition in online social networks. Albums usually associated with pictures of a small group of people at a certain event or occasion provide vital information that can be used to effectively reduce the possible list of candidate labels. We show how this intuition can be formalized into a model that expresses a prior on how albums tend to have many pictures of a small number of people. We also show how it can be extended to include other information available in a social network. Using two real world datasets independently drawn from Facebook we show that this model is broadly applicable and can significantly improve recognition rates. 


Summary: We propose a face recognition model based on albums in online social networks. Albums tend to be composed of many pictures of a small group of people. Limiting the set of unique labels and using album level features improve recognition. We present two systems to learn how best to use these social features. We validate our model on two datasets independently downloaded from Facebook.
Background and objective Methods used in image processing should reflect any multilevel structures inherent in the image dataset or they run the risk of functioning inadequately . We wish to test the feasibility of multilevel principal components analysis to build active shape models for cases relevant to medical and dental imaging . Methods Multilevel PCA was used to carry out model fitting to sets of landmark points and it was compared to the results of standard PCA . Proof of principle was tested by applying mPCA to model basic peri oral expressions approximated to the junction between the mouth lips . Monte Carlo simulations were used to create this data which allowed exploration of practical implementation issues such as the number of landmark points number of images and number of groups . To further test the robustness of the method mPCA was subsequently applied to a dental imaging dataset utilising landmark points along the boundary of mandibular cortical bone in panoramic radiographs of the face . Results Changes of expression that varied between groups were modelled correctly at one level of the model and changes in lip width that varied within groups at another for the Monte Carlo dataset . Extreme cases in the test dataset were modelled adequately by mPCA but not by standard PCA . Similarly variations in the shape of the cortical bone were modelled by one level of mPCA and variations between the experts at another for the panoramic radiographs dataset . Results for mPCA were found to be comparable to those of standard PCA for point to point errors via miss one out testing for this dataset . These errors reduce with increasing number of eigenvectors values retained as expected . Conclusions We have shown that mPCA can be used in shape models for dental and medical image processing . mPCA was found to provide more control and flexibility when compared to standard single level PCA . Specifically mPCA is preferable to standard PCA when multiple levels occur naturally in the dataset . 


Summary: mPCA in ASMs can be used in medical and dental image analysis. Results provided by mPCA in initial studies appear to be sensible. Between and within subject variations are modelled correctly using mPCA. mPCA has more flexibility control and accuracy than standard PCA. mPCA is the correct method of combining sets of landmark points from different experts.