varunr14/newsummary huggingface dataset:
Visual tracking is an important task in various computer vision applications including visual surveillance human computer interaction event detection video indexing and retrieval . Recent state of the art sparse representation based trackers show better robustness than many of the other existing trackers . One of the issues with these SR trackers is low execution speed . The particle filter framework is one of the major aspects responsible for slow execution and is common to most of the existing SR trackers . In this paper An earlier brief version of the paper has appeared in ICIP 13 Melbourne Australia 2013 . we propose a robust interest point based tracker in l 1 minimization framework that runs at real time with performance comparable to the state of the art trackers . In the proposed tracker the target dictionary is obtained from the patches around target interest points . Next the interest points from the candidate window of the current frame are obtained . The correspondence between target and candidate points is obtained via solving the proposed l 1 minimization problem . In order to prune the noisy matches a robust matching criterion is proposed where only the reliable candidate points that mutually match with target and candidate dictionary elements are considered for tracking . The object is localized by measuring the displacement of these interest points . The reliable candidate patches are used for updating the target dictionary . The performance and accuracy of the proposed tracker is benchmarked with several complex video sequences . The tracker is found to be considerably fast as compared to the reported state of the art trackers . The proposed tracker is further evaluated for various local patch sizes number of interest points and regularization parameters . The performance of the tracker for various challenges including illumination change occlusion and background clutter has been quantified with a benchmark dataset containing 50 videos . 


Summary: The proposed tracker combines the flexibility of interest points and robustness of sparse representation. Proposed a robust matching criteria for reliable tracking via L1 minimization The proposed tracker is computationally efficient and provides real time performance. The tracker is benchmarked with many publicly available complex video sequences. Performance is compared with many recent state of the art trackers using 50 benchmark video dataset.
The goals of this paper are 1 to enhance the quality of images of faces 2 to enable 3D Morphable Models 3DMMs to cope with severely degraded images and 3 to reconstruct textured 3D faces with details that are not in the input images. Details that are lost in the input images due to blur low resolution or occlusions are filled in by the 3DMM and an additional texture enhancement algorithm that adds high resolution details from example faces. By leveraging class specific knowledge this restoration process goes beyond what general image operations such as deblurring or inpainting can achieve. The benefit of the 3DMM for image restoration is that it can be applied to any pose and illumination unlike image based methods. However it is only with the new fitting algorithm that 3DMMs can produce realistic faces from severely degraded images. The new method includes the blurring or downsampling operator explicitly into the analysis by synthesis algorithm. 


Summary: A 3D model based algorithm for face hallucination at any pose and illumination A method for including non local effects e.g. blur in 3D analysis by synthesis The algorithm combines low spatial frequency information with details of a 3D model. Transfer of high spatial frequency details for hallucination on the level of pores Occlusion handling and seamless texture reconstruction
This paper investigates the problem of cross domain action recognition . Specifically we present a cross domain action recognition framework by utilizing some labeled data from other data sets as the auxiliary source domain . It is a challenging task as data from different domains may have different feature distribution . To map data from different domains into the same abstract space and boost the action recognition performance we propose a method named collective matrix factorization with graph Laplacian regularization . Our approach is built upon the technique of collective matrix factorization which simultaneously learns a common latent space linear projection matrices for obtaining semantic representations and an optimal linear classifier . Moreover we explore the label consistency across different domain and the local geometric consistency in each domain and obtain a graph Laplacian regularization term to enhance the discrimination of learned features . Experimental results verify that CMFGLR significantly outperforms several state of the art methods . 


Summary: We apply collective matrix factorization to cross domain action recognition. We build a novel graph Laplacian regularization term. The framework jointly learns semantic representations and a linear classifier.
The authors of this paper investigate terms of consumers diabetes based on a log from the Yahoo Answers social question and answers forum ascertain characteristics and relationships among terms related to diabetes from the consumers perspective and reveal users diabetes information seeking patterns . In this study the log analysis method data coding method and visualization multiple dimensional scaling analysis method were used for analysis . The visual analyses were conducted at two levels terms analysis within a category and category analysis among the categories in the schema . The findings show that the average number of words per question was 128.63 the average number of sentences per question was 8.23 the average number of words per response was 254.83 and the average number of sentences per response was 16.01 . There were 12 categories in the diabetes related schema which emerged from the data coding analysis . The analyses at the two levels show that terms and categories were clustered and patterns were revealed . Future research directions are also included . 


Summary: A user based diabetes subject directory was discovered in this study. Terms and their relationships in each of the 12 categories were visually displayed and analyzed. The relationships among the 12 categories were analyzed in a visual context. Descriptive statistic data on diabetes in the Q A were revealed.
An example based face hallucination system is proposed in which given a low resolution facial image a corresponding high resolution image is automatically obtained . In practice such a problem is extremely challenging since it is often the case that two discriminative high resolution images may have similar low resolution inputs . To address this issue this study proposes an ensemble of image feature representations including various local patch or block based representations a one dimensional vector image representation a two dimensional matrix image representation and a global matrix image representation . Notably some of these representations are designed to preserve the global facial geometry of the low resolution input while others are designed to preserve the local detailed texture . For each feature representation a regression function is constructed to synthesize a high resolution image from the low resolution input image . The synthesis process is conducted in a layer by layer fashion with the output from one layer serving as the input to the following layer . Importantly each regression function is associated with a classifier in order to determine which regression functions are required in the synthesis procedure in accordance with the particular characteristics of the input image . Furthermore these classifiers can also help to deal with the individual ambiguity of system low resolution inputs . The experimental results show that the proposed framework is capable of synthesizing high resolution images from low resolution input images with a wide variety of facial poses geometry misalignments and facial expressions even when such images are not included within the original training dataset . 


Summary: Propose an example based face hallucination framework. Proposed ensemble learning for face hallucination. Reconstruct both global and local facial features. Individual ambiguity of system low resolution inputs is considered in this framework.
Automatic text summarization has been an active field of research for many years . Several approaches have been proposed ranging from simple position and word frequency methods to learning and graph based algorithms . The advent of human generated knowledge bases like Wikipedia offer a further possibility in text summarization they can be used to understand the input text in terms of salient concepts from the knowledge base . In this paper we study a novel approach that leverages Wikipedia in conjunction with graph based ranking . Our approach is to first construct a bipartite sentence concept graph and then rank the input sentences using iterative updates on this graph . We consider several models for the bipartite graph and derive convergence properties under each model . Then we take up personalized and query focused summarization where the sentence ranks additionally depend on user interests and queries respectively . Finally we present a Wikipedia based multi document summarization algorithm . An important feature of the proposed algorithms is that they enable real time incremental summarization users can first view an initial summary and then request additional content if interested . We evaluate the performance of our proposed summarizer using the ROUGE metric and the results show that leveraging Wikipedia can significantly improve summary quality . We also present results from a user study which suggests that using incremental summarization can help in better understanding news articles . 


Summary: Summarization using Wikipedia and graph based ranking. Theoretical analysis for various sentence concept models. Real time incremental summarization. Performance analysis using the ROUGE metric and user evaluations. Personalized and query focused summarization multi document summarization.
As a result of the growing demand for health services China s large city hospitals have become markedly overstretched resulting in delicate and complex operating room scheduling problems . While the operating rooms are struggling to meet demand they face idle times because of resources being pulled away for other urgent demands and cancellations for economic and health reasons . In this research we analyze the resulting stochastic operating room scheduling problems and the improvements attainable by scheduled cancellations to accommodate the large demand while avoiding the negative consequences of excessive overtime work . We present a three stage recourse model which formalizes the scheduled cancellations and is anticipative to further uncertainty . We develop a solution method for this three stage model which relies on the sample average approximation and the L shaped method . The method exploits the structure of optimal solutions to speed up the optimization . Scheduled cancellations can significantly and substantially improve the operating room schedule when the costs of cancellations are close to the costs of overtime work . Moreover the proposed methods illustrate how the adverse impact of cancellations for economic and health reasons can be largely controlled . The resource unavailability however is shown to cause a more than proportional loss of solution value for the surgery scheduling problems occurring in China s large city hospitals even when applying the proposed solution techniques and requires different management measures . 


Summary: Presents multi stage stochastic programming models the complex stochastic nature of operating room scheduling in overcrowded Chinese big city hospitals. Develops mathematical results and techniques to solve the formulated models to almost optimality. Attains substantial and significant schedule improvements for single operating room instances derived from real life data.
In this paper we consider the two stage scheduling problem in which n jobs are first processed on m identical machines at a manufacturing facility and then delivered to their customers by one vehicle which can deliver one job at each shipment . In the problem a set of n delivery times is given in advance and in a schedule the n delivery times should be assigned to the n jobs respectively . The objective is to minimize the maximum delivery completion time i.e . the time when all jobs are delivered to their respective customers and the vehicle returns to the facility . For this problem we present a approximation algorithm and a polynomial time approximation scheme . 


Summary: Scheduling with delivery coordination. Identical machines and assignable delivery times. Minimize the maximum delivery completion time. A 3 2 approximation algorithm and a PTAS.
Bipartite graph matching has been demonstrated to be one of the most efficient algorithms to solve error tolerant graph matching . This algorithm is based on defining a cost matrix between the whole nodes of both graphs and solving the nodes correspondence through a linear assignment method . Recently two versions of this algorithm have been published called Fast Bipartite and Square Fast Bipartite . They compute the same distance value than Bipartite but with a reduced runtime if some restrictions on the edit costs are considered . In this paper we do not present a new algorithm but we compare the three versions of Bipartite algorithm and show how the violation of the theoretically imposed restrictions in Fast Bipartite and Square Fast Bipartite do not affect the algorithm s performance . That is in practice we show that these restrictions do not affect the optimality of the algorithm and so the three algorithms obtain similar distances and recognition ratios in classification applications although the restrictions do not hold . Moreover we conclude that the Square Fast Bipartite with the Jonker Volgenant solver is the fastest algorithm . 


Summary: Graph Edit Distance GED is the most used error tolerant graph matching method. Bipartite graph matching algorithm BP is the most used algorithm to solve GED. 2 new versions of BP published that reduce the runtime but restrictions are imposed. We study how much extend these restrictions limits their applicability. Empirical validation shows new versions reduce runtime but keep recognition ratio.
We present a multi view face detector based on Cascade Deformable Part Models CDPM . Over the last decade there have been several attempts to extend the well established Viola Jones face detector algorithm to solve the problem of multi view face detection. Recently a tree structure model for multi view face detection was proposed. This method is primarily designed for facial landmark detection and consequently a face detection is provided. However the effort to model inner facial structures by using a detailed facial landmark labelling resulted on a complex and suboptimal system for face detection. Instead we adopt CDPMs where the models are learned from partially labelled images using Latent Support Vector Machines LSVM . Furthermore LSVM is enhanced with data mining and bootstrapping procedures to enrich models during the training. Furthermore a post optimization procedure is derived to improve the performance. This semi supervised methodology allows us to build models based on weakly labelled data while incrementally learning latent positive and negative samples. Our results show that the proposed model can deal with highly expressive and partially occluded faces while outperforming the state of the art face detectors by a large margin on challenging benchmarks such as the Face Detection Data Set and Benchmark FDDB 1 and the Annotated Facial Landmarks in the Wild AFLW 2 databases. In addition we validate the accuracy of our models under large head pose variation and facial occlusions in the Head Pose Image Database HPID 3 and Caltech Occluded Faces in the Wild COFW datasets 4 respectively. We also outline the suitability of our models to support facial landmark detection algorithms. 


Summary: We present a state of the art multi view face detector based on Cascade Deformable Part Models CDPM . We propose to combine data mining and bootstrapping to learn CDPM models from weakly labelled data. We report extensive validation of our models in the FDDB AFLW HDDB and COFW databases. We show the suitability of our models for face alignment initialization and face detection under partial occlusions.
We present a deeply integrated method of exploiting low cost gyroscopes to improve general purpose feature tracking. Most previous methods use gyroscopes to initialize and bound the search for features. In contrast we use them to regularize the tracking energy function so that they can directly assist in the tracking of ambiguous and poor quality features. We demonstrate that our simple technique offers significant improvements in performance over conventional template based tracking methods and is in fact competitive with more complex and computationally expensive state of the art trackers but at a fraction of the computational cost. Additionally we show that the practice of initializing template based feature trackers like KLT Kanade Lucas Tomasi using gyro predicted optical flow offers no advantage over using a careful optical only initialization method suggesting that some deeper level of integration like the method we propose is needed in order to realize a genuine improvement in tracking performance from these inertial sensors. 


Summary: A 3 axis gyro mounted to a video camera can frequently predict the main component of optical flow. The gyro predicted flow can be used to regularize feature tracking to help track ambiguous features through bad imagery. Gyro regularization does not require all features to belong to a rigid scene. Gyro regularization adds very little computational cost to feature tracking. The common practice of using gyros to initialize trackers offers no advantage over careful optical only initialization.
In recent years much effort has been put into the development of novel algorithms to solve the person re identification problem. The goal is to match a given person s image against a gallery of people. In this paper we propose a single shot supervised method to compute a scoring function that when applied to a pair of images provides a score expressing the likelihood that they depict the same individual. The method is characterized by i the usage of a set of local image descriptors based on Fisher vectors ii the training of a pool of scoring functions based on the local descriptors and iii the construction of a strong scoring function by means of an adaptive boosting procedure. The method has been tested on four data sets and results have been compared with state of the art methods clearly showing superior performance. 


Summary: We propose BFiVe a new supervised algorithm for single shot person re identification. The descriptors are a set of compressed local Fisher vectors extracted from a coarse to fine image subdivision. In the training step each region gives rise to a learnt weak ranking function. The ranking function of the image gallery is obtained by a boosted selection of a weak learner subset. The matching rate at rank 1 on VIPeR is 38.9 on 3DPes 41.7 on PRID 2011 19.6 and on i LIDS 119 48.1 .
Modeling collaboration processes is a challenging task . Existing modeling approaches are not capable of expressing the unpredictable non routine nature of human collaboration which is influenced by the social context of involved collaborators . We propose a modeling approach which considers collaboration processes as the evolution of a network of collaborative documents along with a social network of collaborators . Our modeling approach accompanied by a graphical notation and formalization allows to capture the influence of complex social structures formed by collaborators and therefore facilitates such activities as the discovery of socially coherent teams social hubs or unbiased experts . We demonstrate the applicability and expressiveness of our approach and notation and discuss their strengths and weaknesses . 


Summary: We propose a modeling approach and a visual modeling notation for social collaboration processes. We consider collaboration processes as the evolution of a network of documents and people. The visual modeling notation is a fusion of statecharts and graph query languages. The approach is supported by a formal definition to enable automatic reasoning and verification. We present sensitive use cases to demonstrate expressiveness and usefulness of our approach.
Time of flight depth cameras have widely been used in many applications such as 3D imaging 3D reconstruction human interaction and robot navigation . However conventional depth cameras are incapable of imaging a translucent object which occupies a substantial portion of a real world scene . Such a limitation prohibits realistic imaging using depth cameras . In this work we propose a new skewed stereo ToF camera for detecting and imaging translucent objects under minimal prior of environment . We find that the depth calculation of a ToF camera with a translucent object presents a systematic distortion due to the superposed reflected light ray observation from multiple surfaces . We propose to use a stereo ToF camera setup and derive a generalized depth imaging formulation for translucent objects . Distorted depth value is refined using an iterative optimization . Experimental evaluation shows that our proposed method reasonably recovers the depth image of translucent objects . 


Summary: A new skewed stereo ToF camera for detecting and imaging translucent objects under minimal prior of environment Translucent region detection method Translucent region recovery method Extensive analysis and evaluations
In this paper we address the document image binarization problem with a three stage procedure . First possible stains and general document background information are removed from the image through a background removal stage . The remaining misclassified background and character pixels are then separated using a Local Co occurrence Mapping local contrast and a two state Gaussian Mixture Model . Finally some isolated misclassified components are removed by a morphology operator . The proposed scheme offers robust and fast performance especially for both handwritten and printed documents which compares favorably with other binarization methods . 


Summary: Background removal technique based on adaptive median filtering and thresholding A Local Co occurrence Map with local contrast can distinguish between document text and document stains and background. Low complexity approach with fast and accurate binarization results