varunr14/newsummary huggingface dataset:
Background and objective Feature extraction of electroencephalogram plays a vital role in brain computer interfaces . In recent years common spatial pattern has been proven to be an effective feature extraction method . However the traditional CSP has disadvantages of requiring a lot of input channels and the lack of frequency information . In order to remedy the defects of CSP wavelet packet decomposition and CSP are combined to extract effective features . But WPD CSP method considers less about extracting specific features that are fitted for the specific subject . So a subject based feature extraction method using fisher WPD CSP is proposed in this paper . Methods The idea of proposed method is to adapt fisher WPD CSP to each subject separately . It mainly includes the following six steps original EEG signals from all channels are decomposed into a series of sub bands using WPD average power values of obtained sub bands are computed the specified sub bands with larger values of fisher distance according to average power are selected for that particular subject each selected sub band is reconstructed to be regarded as a new EEG channel all new EEG channels are used as input of the CSP and a six dimensional feature vector is obtained by the CSP . The subject based feature extraction model is so formed the probabilistic neural network is used as the classifier and the classification accuracy is obtained . Results Data from six subjects are processed by the subject based fisher WPD CSP the non subject based fisher WPD CSP and WPD CSP respectively . Compared with non subject based fisher WPD CSP and WPD CSP the results show that the proposed method yields better performance and the classification accuracy from subject based fisher WPD CSP is increased by 6 12 and 14 respectively . Conclusions The proposed subject based fisher WPD CSP method can not only remedy disadvantages of CSP by WPD but also discriminate helpless sub bands for each subject and make remaining fewer sub bands keep better separability by fisher distance which leads to a higher classification accuracy than WPD CSP method . 


Summary: A subject based feature extraction method is discussed. The feature extraction method combines wavelet packet decomposition common spatial patterns and fisher distance. The selection of sub bands based on fisher distance tells apart those useless sub bands and remain best sub bands for each subject. The proposed method can extract suitable features for specific subject and achieve a higher classification accuracy than non subject based method.
Answering to the growing demand of machine vision applications for the latest generation of electronic devices endowed with camera platforms several moving object detection strategies have been proposed in recent years. Among them spatio temporal based non parametric methods have recently drawn the attention of many researchers. These methods by combining a background model and a foreground model achieve high quality detections in sequences recorded with non completely static cameras and in scenarios containing complex backgrounds. However since they have very high memory and computational associated costs they apply some simplifications in the background modeling process therefore decreasing the quality of the modeling. Here we propose a novel background modeling that is applicable to any spatio temporal non parametric moving object detection strategy. Through an efficient and robust method to dynamically estimate the bandwidth of the kernels used in the modeling both the usability and the quality of previous approaches are improved. Furthermore by adding a novel mechanism to selectively update the background model the number of misdetections is significantly reduced achieving an additional quality improvement. Empirical studies on a wide variety of video sequences demonstrate that the proposed background modeling significantly improves the quality of previous strategies while maintaining the computational requirements of the detection process. 


Summary: Real time dynamic bandwidth estimation that reduces the amount of false detections. Selective update mechanism that significantly reduces the number of misdetections. The quality provided by previous background modeling strategies is improved. The computational cost of the simplest modeling strategy is barely increased. The proposed methods can be used by any spatio temporal non parametric strategy.
We present a novel approach for the estimation of a person s overall body orientation 3D shape and texture from overlapping cameras. A distinguishing aspect of our approach is the use of spherical harmonics for 3D shape and texture representation it offers a compact low dimensional representation which elegantly copes with rotation estimation. The estimation process alternates between the estimation of texture orientation and shape. Texture is estimated by sampling image intensities with the predicted 3D shape i.e. torso and head and the predicted orientation from the last time step. Orientation i.e. rotation around torso major axis is estimated by minimizing the difference between a learned texture model in a canonical orientation and the current texture estimate. The newly estimated orientation allows to update the 3D shape estimate taking into account the new 3D shape measurement obtained by volume carving. We investigate various components of our approach in experiments on synthetic and real world data. We show that our proposed method has lower orientation estimation error than other methods that use fixed 3D shape models for data involving persons. 


Summary: Estimation of a person s orientation 3D shape and texture from overlapping cameras Spherical harmonics are used as low dimensional 3D shape and texture representation. Spherical harmonics properties allow to cope with orientation estimation elegantly. Outperformance of orientation estimation methods that use a fixed 3D shape model
Despite the widespread use of cardiotocography in foetal monitoring the evaluation of foetal status suffers from a considerable inter and intra observer variability . In order to overcome the main limitations of visual cardiotocographic assessment computerised methods to analyse cardiotocographic recordings have been recently developed . In this study a new software for automated analysis of foetal heart rate is presented . It allows an automatic procedure for measuring the most relevant parameters derivable from cardiotocographic traces . Simulated and real cardiotocographic traces were analysed to test software reliability . In artificial traces we simulated a set number of events to be recognised . In the case of real signals instead results of the computerised analysis were compared with the visual assessment performed by 18 expert clinicians and three performance indexes were computed to gain information about performances of the proposed software . The software showed preliminary performance we judged satisfactory in that the results matched completely the requirements as proved by tests on artificial signals in which all simulated events were detected from the software . Performance indexes computed in comparison with obstetricians evaluations are on the contrary not so satisfactory in fact they led to obtain the following values of the statistical parameters sensitivity equal to 93 positive predictive value equal to 82 and accuracy equal to 77 . Very probably this arises from the high variability of trace annotation carried out by clinicians . 


Summary: A software for automatic CTG analysis which could be a useful clinical tool is shown. The software provides important classical and nonlinear parameters. Simulated and real CTG traces were analysed to test software reliability. The software showed good performance in fact all simulated events were detected. On real CTG sensitivity equal to 93 positive predictive value 82 accuracy 77 .
Registration of mammograms plays an important role in breast cancer computer aided diagnosis systems . Radiologists usually compare mammogram images in order to detect abnormalities . The comparison of mammograms requires a registration between them . A temporal mammogram registration method is proposed in this paper . It is based on the curvilinear coordinates which are utilized to cope both with global and local deformations in the breast area . Temporal mammogram pairs are used to validate the proposed method . After registration the similarity between the mammograms is maximized and the distance between manually defined landmarks is decreased . In addition a thorough comparison with the state of the art mammogram registration methods is performed to show its effectiveness . 


Summary: The comparison of mammograms requires a registration between them. A mammogram registration method based on the curvilinear coordinates is proposed. Temporal mammogram pairs are used to validate the proposed method.
In this paper we present an unsupervised distance learning approach for improving the effectiveness of image retrieval tasks . We propose a Reciprocal kNN Graph algorithm that considers the relationships among ranked lists in the context of a k reciprocal neighborhood . The similarity is propagated among neighbors considering the geometry of the dataset manifold . The proposed method can be used both for re ranking and rank aggregation tasks . Unlike traditional diffusion process methods which require matrix multiplication operations our algorithm takes only a subset of ranked lists as input presenting linear complexity in terms of computational and storage requirements . We conducted a large evaluation protocol involving shape color and texture descriptors various datasets and comparisons with other post processing approaches . The re ranking and rank aggregation algorithms yield better results in terms of effectiveness performance than various state of the art algorithms recently proposed in the literature achieving bull s eye and MAP scores of 100 on the well known MPEG 7 shape dataset . 


Summary: Presentation of an unsupervised manifold learning algorithm using Reciprocal kNN Graphs Presentation of the Reciprocal kNN Graph ReRanking for improving the effectiveness of CBIR systems Description of how Reciprocal kNN Graph algorithm can be used for rank aggregation tasks Discussion about the computational complexity and the convergence of proposed algorithm Experimental evaluation considering different datasets descriptors and baselines
Characterizing urban landscapes is important given the present and future projections of global population that favor urban growth. The definition of urban on a thematic map has proven to be problematic since urban areas are heterogeneous in terms of land use and land cover. Further certain urban classes are inherently imprecise due to the difficulty in integrating various social and environmental inputs into a precise definition. Social components often include demographic patterns transportation building type and density while ecological components include soils elevation hydrology climate vegetation and tree cover. In this paper we adopt a coupled human and natural system CHANS integrated scientific framework for characterizing urban landscapes. We implement the framework by adopting a fuzzy sets concept of urban characterization since fuzzy sets relate to classes of object with imprecise boundaries in which membership is a matter of degree. For dynamic mapping applications user defined classification schemes involving rules combining different social and ecological inputs can lead to a degree of quantification in class labeling varying from highly urban to least urban . A socio economic perspective of urban may include threshold values for population and road network density while a more ecological perspective of urban may utilize the ratio of natural versus built area and percent forest cover. Threshold values are defined to derive the fuzzy rules of membership in each case and various combinations of rules offer a greater flexibility to characterize the many facets of the urban landscape. We illustrate the flexibility and utility of this fuzzy inference approach called the Fuzzy Urban Index for the Boston Metro region with five inputs and eighteen rules. The resulting classification map shows levels of fuzzy membership ranging from highly urban to least urban or rural in the Boston study region. We validate our approach using two experts assessing accuracy of the resulting fuzzy urban map. We discuss how our approach can be applied in other urban contexts with newly emerging descriptors of urban sustainability urban ecology and urban metabolism. 


Summary: Fuzzy Sets to Characterize Urban Landcover. Fuzzy Urban Index consisting of combinations of rules. Flexible user specified fuzzy inference and mapping platform.
Topological Active Nets are promising parametric deformable models that integrate features of region based and boundary based segmentation techniques . Problems associated with the complexity of the model however have limited their utility . This paper introduces an extension of the model defining a new behavior for changing its topology as well as a novel external force definition and a new local search optimization procedure . In particular we propose a new automatic pre processing phase a new external energy term based on the Extended Vector Field Convolution node movement constraints to avoid crossing links and different procedures to perform link cuts and hole detection . Moreover the new local search procedure also incorporates heuristics to correct the position of eventually misplaced nodes . The proposal has been tested on 18 synthetic images which present different segmentation difficulties along with 3 real medical images . Its performance has been compared with that of the original Topological Active Net optimization approach along with both state of the art parametric and geometric active contours two snakes and two level sets . Our new method outperforms all the others for the given image sets in terms of segmentation accuracy measured by using four standard segmentation metrics . 


Summary: ETANs overcome the limitations of TANs while keeping their promising features. ETANs combine the best capabilities of two DMs TANs and EVFC snakes. ETANs employ novel mechanisms to tackle topological changes as link cuts and holes. ETANs employ node movement constraints to avoid crossing links. ETANs employ a new local search procedure and a new automatic pre processing phase.
Most current tracking approaches utilize only one type of feature to represent the target and learn the appearance model of the target just by using the current frame or a few recent ones . The limited representation of one single type of feature might not represent the target well . What s more the appearance model learning from the current frame or a few recent ones is intolerant of abrupt appearance changes in short time intervals . These two factors might cause the track s failure . To overcome these two limitations in this paper we apply the Augmented Kernel Matrix classification to combine two complementary features pixel intensity and LBP features to enrich the target s representation . Meanwhile we employ the AKM clustering to group the tracking results into a few aspects . And then the representative patches are selected and added into the training set to learn the appearance model . This makes the appearance model cover more aspects of the target appearance and more robust to abrupt appearance changes . Experiments compared with several state of the art methods on challenging sequences demonstrate the effectiveness and robustness of the proposed algorithm . 


Summary: Augmented Kernel Matrix AKM is applied to combine complementary features. AKM clustering is utilized to group the tracking results into a few aspects. Representative patches are selected to learn the appearance model. Adding representative patches our tracker more robust to abrupt appearance changes.
This paper presents an on line adaptive metric to estimate the similarity between the target representation model and new image received at every time instant . The similarity measure also known as observation likelihood plays a crucial role in the accuracy and robustness of visual tracking . In this work an L2 norm is adaptively weighted at every matching step to calculate the similarity between the target model and image descriptors . A histogram based classifier is learned on line to categorize the matching errors into three classes namely i image noise ii significant appearance changes and iii outliers . A robust weight is assigned to each matching error based on the class label . Therefore the proposed similarity measure is able to reject outliers and adapt to the target model by discriminating the appearance changes from the undesired outliers . The experimental results show the superiority of the proposed method with respect to accuracy and robustness in the presence of severe and long term occlusion and image noise in comparison with commonly used robust regressors . 


Summary: We present an adaptive metric for measuring the similarity of a target for the purpose of visual tracking. This metric assigns a robust weight to each matching error based on the error type. A histogram based classifier is learned on line to determine the error type. The proposed robust metric dynamically adapts to the actual appearance changes by tuning its parameters.
Changes in eyebrow configuration in conjunction with other facial expressions and head gestures are used to signal essential grammatical information in signed languages. This paper proposes an automatic recognition system for non manual grammatical markers in American Sign Language ASL based on a multi scale spatio temporal analysis of head pose and facial expressions. The analysis takes account of gestural components of these markers such as raised or lowered eyebrows and different types of periodic head movements. To advance the state of the art in non manual grammatical marker recognition we propose a novel multi scale learning approach that exploits spatio temporally low level and high level facial features. Low level features are based on information about facial geometry and appearance as well as head pose and are obtained through accurate 3D deformable model based face tracking. High level features are based on the identification of gestural events of varying duration that constitute the components of linguistic non manual markers. Specifically we recognize events such as raised and lowered eyebrows head nods and head shakes. We also partition these events into temporal phases. We separate the anticipatory transitional movement the onset from the linguistically significant portion of the event and we further separate the core of the event from the transitional movement that occurs as the articulators return to the neutral position towards the end of the event the offset . This partitioning is essential for the temporally accurate localization of the grammatical markers which could not be achieved at this level of precision with previous computer vision methods. In addition we analyze and use the motion patterns of these non manual events. Those patterns together with the information about the type of event and its temporal phases are defined as the high level features. Using this multi scale spatio temporal combination of low and high level features we employ learning methods for accurate recognition of non manual grammatical markers in ASL sentences. 


Summary: Eyebrow gestures and periodic head movements convey linguistic information. We propose multi scale approach for recognizing non manual grammatical marker. We obtain high level features from spatio temporal analysis of non manual events. We use two stage CRFs to recognize events and partition them into phases. Experiments demonstrate superior improvements in ASL.
This article was motivated by the doctors demand to make a technical support in pathologies of gastrointestinal tract research which would be based on machine vision tools . Proposed solution should be less expensive alternative to already existing RF methods . The objective of whole experiment was to evaluate the amount of animal motion dependent on degree of pathology . In the theoretical part of the article several methods of animal trajectory tracking are presented two differential methods based on background subtraction the thresholding methods based on global and local threshold and the last method used for animal tracking was the color matching with a chosen template containing a searched spectrum of colors . The methods were tested offline on five video samples . Each sample contained situation with moving guinea pig locked in a cage under various lighting conditions . 


Summary: Five methods used for animal trajectory tracking which were proposed with the usage of visual system. All the algorithms are proposed for poor quality images non homogenous lightning presence of grid etc. . All methods are compared and their effectiveness is evaluated. Proposed methods are compared with standard used methods for animal tracking as GPS tracking or RFID tracking. They bring several advantages such as user friendly and simple interface appropriate for medical non technical specialists too.
Human age gender and ethnicity are valuable demographic characteristics . They are also important soft biometric traits useful for human identification or verification . We present a framework that can estimate the three traits jointly . The joint estimation framework could deal with the mutual influence of age gender and ethnicity implicitly . Under this joint estimation framework we explore different methods for simultaneous estimation of age gender and ethnicity . The canonical correlation analysis based methods and partial least squares models are explored under our joint estimation framework . Both the linear and nonlinear methods are investigated to measure the performance . We also validate some extensions of these methods such as the least squares formulations of the CCA methods . We found some consistent ranking of these methods under our joint estimation framework . More importantly we found that the CCA based methods can derive an extremely low dimensionality in estimating age gender and ethnicity . An analysis of this property is given based on the rank theory . The experiments are conducted on a very large database containing more than 55 000 face images . 


Summary: A framework for joint estimation of age gender and ethnicity in a single step A novel finding on feature dimensionality in estimating age gender and ethnicity A rank theory based analysis of dimensionality problem in using CCA based methods A ranking of CCA and PLS based methods under our joint estimation framework Investigation of LS formulations of the CCA based methods for our problem.