varunr14/newsummary huggingface dataset:
This paper proposes a new method for self calibrating a set of stationary non rotating zooming cameras . This is a realistic configuration usually encountered in surveillance systems in which each zooming camera is physically attached to a static structure . In particular a linear yet effective method to recover the affine structure of the observed scene from two or more such stationary zooming cameras is presented . The proposed method solely relies on point correspondences across images and no knowledge about the scene is required . Our method exploits the mostly translational displacement of the so called principal plane of each zooming camera to estimate the location of the plane at infinity . The principal plane of a camera at any given setting of its zoom is encoded in its corresponding perspective projection matrix from which it can be easily extracted . As a displacement of the principal plane of a camera under the effect of zooming allows the identification of a pair of parallel planes each zooming camera can be used to locate a line on the plane at infinity . Hence two or more such zooming cameras in general positions allow the obtainment of an estimate of the plane at infinity making it possible under the assumption of zero skew and or known aspect ratio to linearly calculate the camera s parameters . Finally the parameters of the camera and the coordinates of the plane at infinity are refined through a nonlinear least squares optimization procedure . The results of our extensive experiments using both simulated and real data are also reported in this paper . 


Summary: A simple linear method to self calibrate stationary zooming cameras Neither special camera motion nor scene knowledge required Two zoom images of a stationary camera provide two parallel principle planes. The plane at infinity can be located from two or more such zooming cameras. Simulation laboratory and real scene experiments with 3D measurements validate our method.
This paper proposes a novel active learning framework and combines it with semi supervised learning for segmenting Crohns disease tissues from abdominal magnetic resonance images . Robust fully supervised learning based classifiers require lots of labeled data of different disease severities . Obtaining such data is time consuming and requires considerable expertise . SSL methods use a few labeled samples and leverage the information from many unlabeled samples to train an accurate classifier . AL queries labels of most informative samples and maximizes gain from the labeling effort . Our primary contribution is in designing a query strategy that combines novel context information with classification uncertainty and feature similarity . Combining SSL and AL gives a robust segmentation method that optimally uses few labeled samples and many unlabeled samples and requires lower training time . Experimental results show our method achieves higher segmentation accuracy than FSL methods with fewer samples and reduced training effort . 


Summary: We propose an interactive method combining semi supervised learning SSL and active learning AL for segmenting Crohns disease affected regions in MRI. A novel query strategy for AL has been proposed that makes use of context information to identify query samples. Compared to fully supervised methods we obtain high segmentation accuracy with fewer samples and lesser computation time. Our method has the potential to be used in scenarios which pose difficulties in obtaining large numbers of accurately labeled data.
Aim Medical data mining processes for extracting patterns from large datasets . In the current study we intend to assess different medical data mining approaches to predict ischemic stroke . Materials and methods The collected dataset from Turgut Ozal Medical Centre Inonu University Malatya Turkey comprised the medical records of 80 patients and 112 healthy individuals with 17 predictors and a target variable . As data mining approaches support vector machine stochastic gradient boosting and penalized logistic regression were employed . 10 fold cross validation resampling method was utilized and model performance evaluation metrics were accuracy area under ROC curve sensitivity specificity positive predictive value and negative predictive value . The grid search method was used for optimizing tuning parameters of the models . Results The accuracy values with 95 CI were 0.9789 for SVM 0.9737 for SGB and 0.8947 for PLR . The AUC values with 95 CI were 0.9783 for SVM 0.9757 for SGB and 0.8953 for PLR . Conclusions The results of the current study demonstrated that the SVM produced the best predictive performance compared to the other models according to the majority of evaluation metrics . SVM and SGB models explained in the current study could yield remarkable predictive performance in the classification of ischemic stroke . 


Summary: We assessed different medical data mining approaches to predict ischemic stroke. Grid search were used for improving classification performance of the models. The accuracy and AUC values were higher than 0.8947 and 0.8953 respectively. SVM and SGB models yielded remarkable performance for classifying ischemic stroke.
Human action recognition has lots of real world applications such as natural user interface virtual reality intelligent surveillance and gaming . However it is still a very challenging problem . In action recognition using the visible light videos the spatiotemporal interest point based features are widely used with good performance . Recently with the advance of depth imaging technology a new modality has appeared for human action recognition . It is important to assess the performance and usefulness of the STIP features for action analysis on the new modality of 3D depth map . In this paper we evaluate the spatiotemporal interest point based features for depth based action recognition . Different interest point detectors and descriptors are combined to form various STIP features . The bag of words representation and the SVM classifiers are used for action learning . Our comprehensive evaluation is conducted on four challenging 3D depth databases . Further we use two schemes to refine the STIP features one is to detect the interest points in RGB videos and apply to the aligned depth sequences and the other is to use the human skeleton to remove irrelevant interest points . These refinements can help us have a deeper understanding of the STIP features on 3D depth data . Finally we investigate a fusion of the best STIP features with the prevalent skeleton features to present a complementary use of the STIP features for action recognition on 3D data . The fusion approach gives significantly higher accuracies than many state of the art results . 


Summary: A comprehensive evaluation of STIP based features on depth based action recognition Two schemes to refine STIP features for a deeper understanding of their behaviors A fusion approach is developed which outperforms many state of the art methods.
Background and objectives Angle closure disease in the eye can be detected using time domain Anterior Segment Optical Coherence Tomography . The Anterior Chamber characteristics can be quantified from AS OCT image which is dependent on the image quality at the image acquisition stage . To date to the best of our knowledge there are no objective or automated subjective measurements to assess the quality of AS OCT images . Methods To address AS OCT image quality assessment issue we define a method for objective assessment of AS OCT images using complex wavelet based local binary pattern features . These features are pooled using the Na ve Bayes classifier to obtain the final quality parameter . To evaluate the proposed method a subjective assessment has been performed by clinical AS OCT experts who graded the quality of AS OCT images on a scale of good fair and poor . This was done based on the ability to identify the AC structures including the position of the scleral spur . Results We compared the results of the proposed objective assessment with the subjective assessments . From this comparison it is validated that the proposed objective assessment has the ability of differentiating the good and fair quality AS OCT images for glaucoma diagnosis from the poor quality AS OCT images . Conclusions This proposed algorithm is an automated approach to evaluate the AS OCT images with the intention for collecting of high quality data for further medical diagnosis . Our proposed quality index has the ability of automatic objective and quantitative assessment of AS OCT image quality and this quality index is similar to glaucoma specialist . 


Summary: A new quality assessment method for AS OCT images using wavelet based LBP features. It is a first work so far there is no objective assessment of AS OCT image quality. The proposed algorithm does not require any additional information from the AS OCT device. This work aimed at collecting high quality AS OCT images for Glaucoma diagnosis. Our proposed quality index score is similar to the quality assessment of Glaucoma experts.
Anatomical cine cardiovascular magnetic resonance imaging is widely used to assess the systolic cardiac function because of its high soft tissue contrast . Assessment of diastolic LV function has not regularly been performed due the complex and time consuming procedures . This study presents a semi automated assessment of the left ventricular diastolic function using anatomical short axis cine CMR images . The proposed method is based on three main steps non rigid registration which yields a sequence of endocardial boundary points over the cardiac cycle based on a user provided contour on the first frame LV volume and filling rate computations over the cardiac cycle and automated detection of the peak values of early and late ventricular filling waves . In 47 patients cine CMR imaging and Doppler echocardiographic imaging were performed . CMR measurements of peak values of the E and A waves as well as the deceleration time were compared with the corresponding values obtained in Doppler Echocardiography . For the E A ratio the proposed algorithm for CMR yielded a Cohen s kappa measure of 0.70 and a Gwet s AC1 coefficient of 0.70 . Conclusion Semi automated assessment of the left ventricular diastolic function using anatomical short axis cine CMR images provides mitral inflow measurements comparable to Doppler Echocardiography . 


Summary: A semi automatic method was proposed to assess the cardiac LV diastolic function. A non rigid registration was applied to track the endocardial boundary points. LV volume and filling rates are computed over the entire cardiac cycle. The peaks of early and late ventricular filling waves were detected automatically. Experimental evaluations were performed over CMR and echo datasets from 47 subjects.
We propose a measure of information gained through biometric matching systems . Firstly we discuss how the information about the identity of a person is derived from biometric samples through a biometric system and define the biometric system entropy or BSE based on mutual information . We present several theoretical properties and interpretations of the BSE and show how to design a biometric system which maximizes the BSE . Then we prove that the BSE can be approximated asymptotically by the relative entropy D fI where fG and fI are probability mass functions of matching scores between samples from individuals and among population . We also discuss how to evaluate the BSE of a biometric system and show experimental evaluation of the BSE of face fingerprint and multimodal biometric systems . 


Summary: We propose an information theoretical measure for biometric systems. We present several useful properties and interpretations of the measure. We prove that the measure can be approximated by the relative entropy. We discuss how to evaluate the measure and show experimental evaluations.
Brain ageing is followed by changes of the connectivity of white matter and changes of the grey matter concentration . Neurodegenerative disease is more vulnerable to an accelerated brain ageing which is associated with prospective cognitive decline and disease severity . Accurate detection of accelerated ageing based on brain network analysis has a great potential for early interventions designed to hinder atypical brain changes . To capture the brain ageing we proposed a novel computational approach for modeling the 112 normal older subjects brain age by connectivity analyses of networks of the brain . Our proposed method applied principal component analysis to reduce the redundancy in network topological parameters . Back propagation artificial neural network improved by hybrid genetic algorithm and Levenberg Marquardt algorithm is established to model the relation among principal components and brain age . The predicted brain age is strongly correlated with chronological age . The model has mean absolute error of 4.29 years . Therefore we believe the method can provide a possible way to quantitatively describe the typical and atypical network organization of human brain and serve as a biomarker for presymptomatic detection of neurodegenerative diseases in the future . 


Summary: We proposed a novel computational approach for modeling the normal elderly subjects s brain age by connectivity analyses of networks of the brain. Principal component analysis PCA is applied to reduce the redundancy in network topological parameters. BP artificial neural network BPANN is improved by hybrid genetic algorithm GA and Levenberg Marquardt LM algorithm to model the relation among principal components PCs and brain age. The method has shown good performance for old cohort with limited samples.
Background and objective Signal segmentation and spike detection are two important biomedical signal processing applications . Often non stationary signals must be segmented into piece wise stationary epochs or spikes need to be found among a background of noise before being further analyzed . Permutation entropy has been proposed to evaluate the irregularity of a time series . PE is conceptually simple structurally robust to artifacts and computationally fast . It has been extensively used in many applications but it has two key shortcomings . First when a signal is symbolized using the Bandt Pompe procedure only the order of the amplitude values is considered and information regarding the amplitudes is discarded . Second in the PE the effect of equal amplitude values in each embedded vector is not addressed . To address these issues we propose a new entropy measure based on PE the amplitude aware permutation entropy . Methods AAPE is sensitive to the changes in the amplitude in addition to the frequency of the signals thanks to it being more flexible than the classical PE in the quantification of the signal motifs . To demonstrate how the AAPE method can enhance the quality of the signal segmentation and spike detection a set of synthetic and realistic synthetic neuronal signals electroencephalograms and neuronal data are processed . We compare the performance of AAPE in these problems against state of the art approaches and evaluate the significance of the differences with a repeated ANOVA with post hoc Tukey s test . Results In signal segmentation the accuracy of AAPE based method is higher than conventional segmentation methods . AAPE also leads to more robust results in the presence of noise . The spike detection results show that AAPE can detect spikes well even when presented with single sample spikes unlike PE . For multi sample spikes the changes in AAPE are larger than in PE . Conclusion We introduce a new entropy metric AAPE that enables us to consider amplitude information in the formulation of PE . The AAPE algorithm can be used in almost every irregularity based application in various signal and image processing fields . We also made freely available the Matlab code of the AAPE . 


Summary: Permutation entropy PE is a fast method to evaluate the irregularity of signals. PE does not consider the average of amplitude values and equal amplitude values. We propose amplitude aware PE AAPE . We evaluate the AAPE method for signal segmentation and spike detection applications.
Background and objective Heart failure due to iron overload cardiomyopathy is one of the main causes of mortality . The cardiomyopathy is reversible if intensive iron chelation treatment is done in time but the diagnosis is often delayed because the cardiac iron deposition is unpredictable and the symptoms are lately detected . There are many ways to assess iron overload . However the widely used and approved method is by using MRI which is performed by calculating the T2 . In order to compute the T2 value the region of interest is manually selected by an expert which may require considerable time and skills . The aim of this work is hence to develop the cardiac T2 measurement by using region growing algorithm for automatically segmenting the ROI in cardiac MR images . Mathematical morphologies are also used to reduce some errors . Methods Thirty MR images with free breathing and respiratory trigger technique were used in this work . The segmentation algorithm yields good results when compared with the manual segmentation performed by two experts . Results The averages of positive predictive value the sensitivity the Hausdorff distance and the Dice similarity coefficient are 0.76 0.84 7.78 pixels and 0.80 when compared with the two experts opinions . The T2 values were carried out based on the automatically segmented ROI s. The mean difference of T2 values between the proposed technique and the experts opinion is about 1.40ms . Conclusions The results demonstrate the accuracy of the proposed method in T2 value estimation . Some previous methods were implemented for comparisons . The results show that the proposed method yields better segmentation and T2 value estimation performances . 


Summary: Novel automatic cardiac T2 relaxation time estimation algorithm from MR images. Novel automatic ROI segmentation algorithm in cardiac MR images. Segmentation results from our technique are very close to the experts opinions. Cardiac T2 values from our technique are very close to that calculated by experts. Our method does not need manual processes in ROI segmentation and T2 calculation.
Background and objective Hypokinetic dysarthria is a frequent speech disorder associated with idiopathic Parkinson s disease . It affects all dimensions of speech production . One of the most common features of HD is dysprosody that is characterized by alterations of rhythm and speech rate flat speech melody and impairment of speech intensity control . Dysprosody has a detrimental impact on speech naturalness and intelligibility . Methods This paper deals with quantitative prosodic analysis of neutral stress modified and rhymed speech in patients with PD . The analysis of prosody is based on quantification of monopitch monoloudness and speech rate abnormalities . Experimental dataset consists of 98 patients with PD and 51 healthy speakers . For the purpose of HD identification sequential floating feature selection algorithm and random forests classifier is used . In this paper we also introduce a concept of permutation test applied in the field of acoustic analysis of dysarthric speech . Results Prosodic features obtained from stress modified reading task provided higher classification accuracies compared to the ones extracted from reading task with neutral emotion demonstrating the importance of stress in speech prosody . Features calculated from poem recitation task outperformed both reading tasks in the case of gender undifferentiated analysis showing that rhythmical demands can in general lead to more precise identification of HD . Additionally some gender related patterns of dysprosody has been observed . Conclusions This paper confirms reduced variation of fundamental frequency in PD patients with HD . Interestingly increased variability of speech intensity compared to healthy speakers has been detected . Regarding speech rate disturbances our results does not report any particular pattern . We conclude further development of prosodic features quantifying the relationship between monopitch monoloudness and speech rate disruptions in HD can have a great potential in future PD analysis . 


Summary: We analysed neutral stress modified and rhymed speech in Parkinson s disease. We proposed quantitative prosodic analysis of poem recitation task. We showed rhythmical demands improve identification of hypokinetic dysarthria. We introduced a concept of permutation test in dysarthric speech analysis.
This paper presents a matching strategy to improve the discriminative power of histogram based keypoint descriptors by constraining the range of allowable dominant orientations according to the context of the scene under observation . This can be done when the descriptor uses a circular grid and quantized orientation steps by computing or providing a global reference orientation based on the feature matches . The proposed matching strategy is compared with the standard approaches used with the SIFT and GLOH descriptors and the recent rotation invariant MROGH and LIOP descriptors . A new evaluation protocol based on an approximated overlap error is presented to provide an effective analysis in the case of non planar scenes thus extending the current state of the art results . 


Summary: Novel matching strategies for histogram based descriptors are presented. Global dominant orientation is used by exploiting the image context. A new 3D extensible framework to evaluate feature descriptors is introduced. 2D 3D comparisons with state of the art rotational invariant descriptors are reported. Results show the effectiveness of the proposed matching approaches.
The analysis of regular texture images is cast in a model comparison framework . Texel lattice hypotheses are used to define statistical models which are compared in terms of their ability to explain the images . This approach is used to estimate lattice geometry from patterns that exhibit translational symmetry . It is also used to determine whether images consist of such regular textures . A method based on this approach is described in which lattice hypotheses are generated using analysis of peaks in the image autocorrelation function statistical models are based on Gaussian or Gaussian mixture clusters and model comparison is performed using the marginal likelihood as approximated by the Bayes Information Criterion . Experiments on public domain images and a commercial textile image archive demonstrate substantially improved accuracy compared to several alternative methods . 


Summary: Regular texture patterns exhibiting translational symmetry were modelled. Model comparison was implemented using Bayes Information Criterion. Texel lattice geometry was estimated from e.g. images from a textile archive. Accuracy was better than other methods with which it was compared.