varunr14/newsummary huggingface dataset:
In the spirit of recent work on contextual recognition and estimation we present a method for estimating the pose of human hands employing information about the shape of the object in the hand . Despite the fact that most applications of human hand tracking involve grasping and manipulation of objects the majority of methods in the literature assume a free hand isolated from the surrounding environment . Occlusion of the hand from grasped objects does in fact often pose a severe challenge to the estimation of hand pose . In the presented method object occlusion is not only compensated for it contributes to the pose estimation in a contextual fashion this without an explicit model of object shape . Our hand tracking method is non parametric performing a nearest neighbor search in a large database of hand poses with and without grasped objects . The system that operates in real time is robust to self occlusions object occlusions and segmentation errors and provides full hand pose reconstruction from monocular video . Temporal consistency in hand pose is taken into account without explicitly tracking the hand in the high dim pose space . Experiments show the non parametric method to outperform other state of the art regression methods while operating at a significantly lower computational cost than comparable model based hand tracking methods . 


Summary: We developed a system that estimates in real time the articulated pose of the hand. Our approach is discriminative with low computational load and fast error recovery. Our system is robust to occlusions implicitly extracting information from them. The system is thoroughly evaluated with quantitative and qualitative experiments.
In this paper we present a comparative study of two approaches for road traffic density estimation . The first approach uses the microscopic parameters which are extracted using both motion detection and tracking methods from a video sequence and the second approach uses the macroscopic parameters which are directly estimated by analyzing the global motion in the video scene . The extracted parameters are applied to three classifiers the K Nearest Neighbor classifier the LVQ classifier and the SVM classifier in order to classify the road traffic in three categories light medium and heavy . The methods are compared based on their robustness to the classification of different road traffic states . The goal of this study is to propose an algorithm for road traffic density estimation with a high precision . 


Summary: We propose an algorithm for road traffic congestion estimation from video scenes. We compare between macroscopic and microscopic parameters in terms of accuracy. The method proposed is accurate and it is computationally inexpensive. It does not require segmentation or tracking of vehicles. It is robust towards illumination changes.
Shape from focus is a passive technique widely used in image processing for obtaining depth maps . This technique is attractive since it only requires a single monocular camera with focus control thus avoiding correspondence problems typically found in stereo as well as more expensive capturing devices . However one of its main drawbacks is its poor performance when the change in the focus level is difficult to detect . Most research in SFF has focused on improving the accuracy of the depth estimation . Less attention has been paid to the problem of providing quality measures in order to predict the performance of SFF without prior knowledge of the recovered scene . This paper proposes a reliability measure aimed at assessing the quality of the depth map obtained using SFF . The proposed reliability measure analyzes the shape of the focus measure function and estimates the likelihood of obtaining an accurate depth estimation without any previous knowledge of the recovered scene . The proposed R measure is then applied for determining the image regions where SFF will not perform correctly in order to discard them . Experiments with both synthetic and real scenes are presented . 


Summary: The performance of focus measure depends on the optics and imaging conditions. The concept of reliability in focus measure is introduced. A method for computing the reliability of shape from focus is presented. The proposed reliability integrates efficiently to shape from focus. The proposed method is experimentally effective.
Background and objectives Mathematical models are suitable to simulate complex biological processes by a set of non linear differential equations . These simulation models can be used as an e learning tool in medical education . However in many cases these mathematical systems have to be treated numerically which is computationally intensive . The aim of the study was to develop a system for numerical simulation to be used in an online e learning environment . Methods In the software system the simulation is located on the server as a CGI application . The user selects the boundary conditions for the simulation on the browser . With these parameters the simulation on the server is started and the simulation result is re transferred to the browser . Results With this system two examples of e learning units were realized . The first one uses a multi compartment model of the glucose insulin control loop for the simulation of the plasma glucose level after a simulated meal or during diabetes . The second one simulates the ion transport leading to the resting and action potential in nerves . The student can vary parameters systematically to explore the biological behavior of the system . Conclusions The described system is able to simulate complex biological processes and offers the possibility to use these models in an online e learning environment . As far as the underlying principles can be described mathematically this type of system can be applied to a broad spectrum of biomedical or natural scientific topics . 


Summary: The system uses complex numerical simulations in an online e learning environment. Complex calculations are performed on the Web server as a CGI application. The user enters simulation parameters on the browser which are sent to the server. The simulation result is re transferred as a graphical representation. Two examples were realized glucose insulin homeostasis and basic neurophysiology.
Motion segmentation refers to the problem of separating the objects in a video sequence according to their motion. It is a fundamental problem of computer vision since various systems focusing on the analysis of dynamic scenes include motion segmentation algorithms. In this paper we present a novel approach where a video shot is temporally divided in successive and overlapping windows and motion segmentation is performed on each window respectively. This attribute renders the algorithm suitable even for long video sequences. In the last stage of the algorithm the segmentation results for every window are aggregated into a final segmentation. The presented algorithm can handle effectively asynchronous trajectories on each window even when they have no temporal intersection. The evaluation of the proposed algorithm on the Berkeley motion segmentation benchmark demonstrates its scalability and accuracy compared to the state of the art. 


Summary: We divide a video sequence on overlapping subsequences. On each subsequence we perform over segmentation handling efficiently asynchronous trajectories. The number of moving objects is automatically estimated. The segmentation results are aggregated into a final segmentation. Our method is tested on the Berkeley motion segmentation benchmark.
In this paper the unsteady pulsatile magneto hydrodynamic blood flows through porous arteries concerning the influence of externally imposed periodic body acceleration and a periodic pressure gradient are numerically simulated . Blood is taken into account as the third grade non Newtonian fluid . Besides the numerical solution for small Womersley parameter the analytical perturbation method is used to solve the nonlinear governing equations . Consequently analytical expressions for the velocity profile wall shear stress and blood flow rate are obtained . Excellent agreement between the analytical and numerical predictions is evident . Also the effects of body acceleration magnetic field third grade non Newtonian parameter pressure gradient and porosity on the flow behaviors are examined . Some important conclusions are that when the Womersley parameter is low viscous forces tend to dominate the flow velocity profiles are parabolic in shape and the center line velocity oscillates in phase with the driving pressure gradient . In addition by increasing the pressure gradient the mean value of the velocity profile increases and the amplitude of the velocity remains constant . Also when non Newtonian effect increases the amplitude of the velocity profile . 


Summary: The pulsatile MHD third grade blood flow through arteries is analytically studied. The influence of externally imposed periodic body acceleration is considered. Analytical formulas for velocity profile wall shear stress and flow rate are given.
When estimating human gaze directions from captured eye appearances most existing methods assume a fixed head pose because head motion changes eye appearance greatly and makes the estimation inaccurate. To handle this difficult problem in this paper we propose a novel method that performs accurate gaze estimation without restricting the user s head motion. The key idea is to decompose the original free head motion problem into subproblems including an initial fixed head pose problem and subsequent compensations to correct the initial estimation biases. For the initial estimation automatic image rectification and joint alignment with gaze estimation are introduced. Then compensations are done by either learning based regression or geometric based calculation. The merit of using such a compensation strategy is that the training requirement to allow head motion is not significantly increased only capturing a 5 s video clip is required. Experiments are conducted and the results show that our method achieves an average accuracy of around 3 by using only a single camera. 


Summary: Appearance based gaze estimation with head motion is decomposed into subproblems. Subproblems are solved by compensating for two types of estimation biases. The compensation method only requires a 5 second video for training. Eye images for different head poses are aligned via rectification and optimization. We achieve a gaze estimation accuracy of 3 with free head motion.
Models that simulate land use patterns often use either inductive data driven approaches or deductive theory based methods to describe the relative strength of the social economic and biophysical forces that drive the various sectors in the land system . An integrated framework is proposed here that incorporates both approaches based on a unified assessment for local land suitability following a monetary utility based logic . The framework is illustrated with a hedonic pricing analysis of urban land values and a net present value assessment for agricultural production system in combination with statistics based assessments of land suitability for other sectors . The results show that limited difference exists between the most commonly applied inductive approaches that use either multinomial or binomial logistic regression specifications of suitability . Land use simulations following the binomial regression based suitability values that were rescaled to bid prices perform better for all individual land use types . Performance improves even further when a land value based description of urban bid prices is added to this approach . Interestingly enough the better fitting description of suitability for urban areas also improves the ability of the model to simulate correct locations for business estates and greenhouses . The simulation alternatives that consider the net present values for agricultural types of land use show the relevance of this approach for understanding the spatial distribution of these types of land use . The combined use of urban land values and net present values for agricultural land use in defining land suitability performs best in our validation exercise . The proposed methodology can also be used to incorporate information from other research frameworks that describe the utility of land for different types of use . 


Summary: Novel multi sector framework for integrated local scale land use modeling. We apply deductive theory based and inductive data driven methods to describe land use change. We incorporate hedonic pricing net present value and statistics based methods. Utility based approaches outperform common statistics based approaches in our Dutch case study. Output from other research methods can be incorporated to describe utility of land for other uses.
Discriminative human pose estimation is the problem of inferring the 3D articulated pose of a human directly from an image feature . This is a challenging problem due to the highly non linear and multi modal mapping from the image feature space to the pose space . To address this problem we propose a model employing a mixture of Gaussian processes where each Gaussian process models a local region of the pose space . By employing the models in this way we are able to overcome the limitations of Gaussian processes applied to human pose estimation their O time complexity and their uni modal predictive distribution . Our model is able to give a multi modal predictive distribution where each mode is represented by a different Gaussian process prediction . A logistic regression model is used to give a prior over each expert prediction in a similar fashion to previous mixture of expert models . We show that this technique outperforms existing state of the art regression techniques on human pose estimation data sets for ballet dancing sign language and the HumanEva data set . 


Summary: Novel algorithm for large scale human pose estimation problems. Uses multiple Gaussian processes in a mixture of expert framework. Allows the accurate regression of Gaussian processes to be scaled to large data. Algorithm gives state of the art performance on 3 pose estimation data sets.
Cheap ubiquitous high resolution digital cameras have led to opportunities that demand camera based text understanding such as wearable computing or assistive technology. Perspective distortion is one of the main challenges for text recognition in camera captured images since the camera may often not have a fronto parallel view of the text. We present a method for perspective recovery of text in natural scenes where text can appear as isolated words short sentences or small paragraphs as found on posters billboards shop and street signs etc. . It relies on the geometry of the characters themselves to estimate a rectifying homography for every line of text irrespective of the view of the text over a large range of orientations. The horizontal perspective foreshortening is corrected by fitting two lines to the top and bottom of the text while the vertical perspective foreshortening and shearing are estimated by performing a linear regression on the shear variation of the individual characters within the text line. The proposed method is efficient and fast. We present comparative results with improved recognition accuracy against the current state of the art. 


Summary: We present a method for perspective recovery of text in natural scenes. It relies on the characters geometry to estimate a rectifying homography. The proposed method is efficient and fast. Comparative results show improved recognition accuracy against the state of the art.
Avoiding the use of complicated pre processing steps such as accurate face and body part segmentation or image normalization this paper proposes a novel face person image representation which can properly handle background and illumination variations . Denoted as gBiCov this representation relies on the combination of Biologically Inspired Features and Covariance descriptors . More precisely gBiCov is obtained by computing and encoding the difference between BIF features at different scales . The distance between two persons can then be efficiently measured by computing the Euclidean distance of their signatures avoiding some time consuming operations in Riemannian manifold required by the use of Covariance descriptors . In addition the recently proposed KISSME framework is adopted to learn a metric adapted to the representation . To show the effectiveness of gBiCov experiments are conducted on three person re identification tasks and one face verification task on which competitive results are obtained . As an example the matching rate at rank 1 on the VIPeR dataset is of 31.11 improving the best previously published result by more than 10 . 


Summary: This paper proposes a novel person face image representation. The representation avoids the use of body segmentation or image normalization. The representation relies on the combination of BIF and Covariance descriptor. The representation can handle background and illumination variations. The matching rate at rank 1 on VIPeR is 31.11 and the accuracy on LFW is 84.48 .
Many recent image retrieval methods are based on the bag of words model with some additional spatial consistency checking . This paper proposes a more accurate similarity measurement that takes into account spatial layout of visual words in an offline manner . The similarity measurement is embedded in the standard pipeline of the BoW model and improves two features of the model i latent visual words are added to a query based on spatial co occurrence to improve query recall and ii weights of reliable visual words are increased to improve the precision . The combination of these methods leads to a more accurate measurement of image similarity . This is similar in concept to the combination of query expansion and spatial verification but does not require query time processing which is too expensive to apply to full list of ranked results . Experimental results demonstrate the effectiveness of our proposed method on three public datasets . 


Summary: We propose a more accurate similarity measurement for object retrieval. Our method improves two features of the BoW model. Spatial expansion can incorporate more latent visual words into a query. Visual word re weighting can increase weights of reliable visual words. The combination of them can improve both precision and recall.
Background and objective Percutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy . Cardiologists use intravascular ultrasound for screening risk assessment and stratification of coronary artery disease . We hypothesize that plaque components are vulnerable to rupture due to plaque progression . Currently there are no standard grayscale IVUS tools for risk assessment of plaque rupture . This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis for plaque feature dimensionality reduction and dominant feature selection technique . The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup . Method This system consists of a machine learning paradigm which uses a support vector machine combined with PCA for optimal and dominant coronary artery morphological feature extraction . Carotid artery proven intima media thickness biomarker is adapted as a gold standard during the training phase of the machine learning system . For the performance evaluation K fold cross validation protocol is adapted with 20 trials per fold . For choosing the dominant features out of the 56 grayscale features a polling strategy of PCA is adapted where the original value of the features is unaltered . Different protocols are designed for establishing the stability and reliability criteria of the coronary risk assessment system . Results Using the PCA based machine learning paradigm and cross validation protocol a classification accuracy of 98.43 with K 10 folds using an SVM radial basis function kernel was achieved . A reliability index of 97.32 and machine learning stability criteria of 5 were met for the cRAS . Conclusions This is the first Computer aided design system of its kind that is able to demonstrate the ability of coronary risk assessment and stratification while demonstrating a successful design of the machine learning system based on our assumptions . 


Summary: Coronary artery disease risk assessment in intravascular ultrasound. A link between carotid and coronary grayscale plaque morphology. Principal component analysis PCA for dominant feature selection. Classification accuracy of 98.43 and reliability index of 97.32 .
We introduce a robust framework for learning and fusing of orientation appearance models based on both texture and depth information for rigid object tracking . Our framework fuses data obtained from a standard visual camera and dense depth maps obtained by low cost consumer depth cameras such as the Kinect . To combine these two completely different modalities we propose to use features that do not depend on the data representation angles . More specifically our framework combines image gradient orientations as extracted from intensity images with the directions of surface normals computed from dense depth fields . We propose to capture the correlations between the obtained orientation appearance models using a fusion approach motivated by the original Active Appearance Models . To incorporate these features in a learning framework we use a robust kernel based on the Euler representation of angles which does not require off line training and can be efficiently implemented online . The robustness of learning from orientation appearance models is presented both theoretically and experimentally in this work . This kernel enables us to cope with gross measurement errors missing data as well as other typical problems such as illumination changes and occlusions . By combining the proposed models with a particle filter the proposed framework was used for performing 2D plus 3D rigid object tracking achieving robust performance in very difficult tracking scenarios including extreme pose variations . 


Summary: Robust learning and fusing of orientation appearance models. Combination of image gradient orientations with the directions of surface normals. Use of a robust kernel based on the Euler representation of angles. Performing 2D plus 3D rigid object tracking achieving robust performance.