varunr14/newsummary huggingface dataset:
In this paper we present a robust and lightweight method for the automatic fitting of deformable 3D face models on facial images . Popular fitting techniques such as those based on statistical models of shape and appearance require a training stage based on a set of facial images and their corresponding facial landmarks which have to be manually labeled . Therefore new images in which to fit the model can not differ too much in shape and appearance from those used for training . By contrast our approach can fit a generic face model in two steps the detection of facial features based on local image gradient analysis and the backprojection of a deformable 3D face model through the optimization of its deformation parameters . The proposed approach can retain the advantages of both learning free and learning based approaches . Thus we can estimate the position orientation shape and actions of faces and initialize user specific face tracking approaches such as Online Appearance Models which have shown to be more robust than generic user tracking approaches . Experimental results show that our method outperforms other fitting alternatives under challenging illumination conditions and with a computational cost that allows its implementation in devices with low hardware specifications such as smartphones and tablets . Our proposed approach lends itself nicely to many frameworks addressing semantic inference in face images and videos . 


Summary: We robustly fit deformable 3D face models on facial images. We estimate the 3D position 3D orientation shape and actions of faces. We quickly and robustly initialize user specific face tracking approaches. Our approach outperforms others under challenging illumination conditions. Our approach runs in real time in smartphones and tablets.
Building facade detection is an important problem in computer vision with applications in mobile robotics and semantic scene understanding. In particular mobile platform localization and guidance in urban environments can be enabled with accurate models of the various building facades in a scene. Toward that end we present a system for detection segmentation and parameter estimation of building facades in stereo imagery. The proposed method incorporates multilevel appearance and disparity features in a binary discriminative model and generates a set of candidate planes by sampling and clustering points from the image with Random Sample Consensus RANSAC using local normal estimates derived from Principal Component Analysis PCA to inform the planar models. These two models are incorporated into a two layer Markov Random Field MRF an appearance and disparity based discriminative classifier at the mid level and a geometric model to segment the building pixels into facades at the high level. By using object specific stereo features our discriminative classifier is able to achieve substantially higher accuracy than standard boosting or modeling with only appearance based features. Furthermore the results of our MRF classification indicate a strong improvement in accuracy for the binary building detection problem and the labeled planar surface models provide a good approximation to the ground truth planes. 


Summary: We perform automatic modeling of building facades from single view stereo. Goal is to provide semantic landmarks for mobile platform localization. Use of novel appearance model with stereo features and plane fitting to disparity. Facade segmentation by graphical model hierarchical Markov Random Field. Good performance in segmenting and modeling major facades in the scene.
Segregation models often focus on private racial preference but overlook the institutional context . This paper represents an effort to move beyond the preference centricity . In this paper an ideal Pigovian regulatory intervention is emulated and added into Schelling s classic spatial proximity model of racial segregation with an aim to preserve collective welfare against the negative externalities induced by the changing local racial compositions after individual relocations . A key discovery from a large number of cellular automata is that the Pigovian regulation tends to result in less segregated but also less efficient residential patterns than laissez faire . This finding albeit from a highly stylized model bears intellectual relations to an important practical question What are the potential racial effects of Pigovian local planning interventions such as financially motivated anti density zoning or the collection of a development impact fee On top of its modest policy implications this paper demonstrates a bottom up computational modelling approach to reconcile the preference based and institution orientated academic perspectives regarding racial residential segregation . 


Summary: A stylized Pigovian intervention into Schelling s classic segregation model. A bottom up computational approach to modelling interactions between private preferences and public institutions. A technical attempt to address the relations between race space and planning.
Over the last few years much online volunteered geographic information has emerged and has been increasingly analyzed to understand places and cities as well as human mobility and activity . However there are concerns about the quality and usability of such VGI . In this study we demonstrate a complete process that comprises the collection unification classification and validation of a type of VGI online point of interest data and develop methods to utilize such POI data to estimate disaggregated land use at a very high spatial resolution using part of the Boston metropolitan area as an example . With recent advances in activity based land use transportation and environment models such disaggregated land use data become important to allow LUTE models to analyze and simulate a person s choices of work location and activity destinations and to understand policy impacts on future cities . These data can also be used as alternatives to explore economic activities at the local level especially as government published census based disaggregated employment data have become less available in the recent decade . Our new approach provides opportunities for cities to estimate land use at high resolution with low cost by utilizing VGI while ensuring its quality with a certain accuracy threshold . The automatic classification of POI can also be utilized for other types of analyses on cities . 


Summary: We demonstrate the unification classification and validation of online POI data. The classified POI data is used to disaggregate land use at a high spatial resolution. The disaggregated land use data is useful for LUTE models and for economic analyses.
Background and objective Retinal blood vessel segmentation is a prominent task for the diagnosis of various retinal pathology such as hypertension diabetes glaucoma etc . In this paper a novel matched filter approach with the Gumbel probability distribution function as its kernel is introduced to improve the performance of retinal blood vessel segmentation . Methods Before applying the proposed matched filter the input retinal images are pre processed . During pre processing stage principal component analysis based gray scale conversion followed by contrast limited adaptive histogram equalization are applied for better enhancement of retinal image . After that an exhaustive experiments have been conducted for selecting the appropriate value of parameters to design a new matched filter . The post processing steps after applying the proposed matched filter include the entropy based optimal thresholding and length filtering to obtain the segmented image . Results For evaluating the performance of proposed approach the quantitative performance measures an average accuracy average true positive rate and average false positive rate are calculated . The respective values of the quantitative performance measures are 0.9522 0.7594 0.0292 for DRIVE data set and 0.9270 0.7939 0.0624 for STARE data set . To justify the effectiveness of proposed approach receiver operating characteristic curve is plotted and the average area under the curve is calculated . The average AUC for DRIVE and STARE data sets are 0.9287 and 0.9140 respectively . Conclusions The obtained experimental results confirm that the proposed approach performance better with respect to other prominent Gaussian distribution function and Cauchy PDF based matched filter approaches . 


Summary: A novel matched filter approach with the Gumbel PDF as its kernel is proposed. Pre processing includes PCA based gray scale conversion and contrast enhancement. Post processing includes the entropy based optimal thresholding and length filtering. On the basis of exhaustive experiment select the appropriate value of parameters.
We present a method for the recognition of complex actions . Our method combines automatic learning of simple actions and manual definition of complex actions in a single grammar . Contrary to the general trend in complex action recognition that consists in dividing recognition into two stages our method performs recognition of simple and complex actions in a unified way . This is performed by encoding simple action HMMs within the stochastic grammar that models complex actions . This unified approach enables a more effective influence of the higher activity layers into the recognition of simple actions which leads to a substantial improvement in the classification of complex actions . We consider the recognition of complex actions based on person transits between areas in the scene . As input our method receives crossings of tracks along a set of zones which are derived using unsupervised learning of the movement patterns of the objects in the scene . We evaluate our method on a large dataset showing normal suspicious and threat behaviour on a parking lot . Experiments show an improvement of 30 in the recognition of both high level scenarios and their composing simple actions with respect to a two stage approach . Experiments with synthetic noise simulating the most common tracking failures show that our method only experiences a limited decrease in performance when moderate amounts of noise are added . 


Summary: We present a method for the recognition of complex actions in a unified way. We encode simple action HMMs within the stochastic grammar that models complex actions. As input our method receives a sequence of crossings of tracks through a set of zones. We evaluate our method in a threat recognition setting.
The estimation of camera orientation from image lines using the anthropic environment restriction is a well known problem but traditional methods to solve it depend on line extraction a relatively complex procedure that is also incompatible with distorted images . We propose Corisco a monocular orientation estimation method based on edgels instead of lines . Edgels are points sampled from image edges with their tangential directions extracted in Corisco using a grid mask . The estimation aligns the measured edgel directions with the predicted directions calculated from the orientation using a known camera model . Corisco uses the M estimation technique to define an objective function that is optimized by two algorithms in sequence RANSAC which gives robustness and flexibility to Corisco and FilterSQP which performs a continuous optimization to refine the initial estimate using closed formulas for the function derivatives . Corisco is the first edgel based method able to analyze images with any camera model and it also allows for a compromise between speed and accuracy so that its performance can be tuned according to the application requirements . Our experiments demonstrate the effectiveness of Corisco with various camera models and its performance surpasses similar edgel based methods . The accuracy displayed a mean error below 2 for execution times above 8s in a conventional computer and above 3 for less than 2s . 


Summary: We propose an orientation estimation method based on edgels and M estimation. Edgels are extracted with a grid mask that can compromise between speed and accuracy. Any camera model can be used and errors are calculated on the original image space. The estimation starts with a random search and ends with a continuous optimization. The method uses quaternions and all derivative calculations use closed formulas.
Background and objective Neuroimaging studies have demonstrated dysfunction in the brain reward circuit in individuals with online gaming addiction . We hypothesized that virtual reality therapy for OGA would improve the functional connectivity of the cortico striatal limbic circuit by stimulating the limbic system . Methods Twenty four adults with OGA were randomly assigned to a cognitive behavior therapy group or VRT group . Before and after the four week treatment period the severity of OGA was evaluated with Young s Internet Addiction Scale . Using functional magnetic resonance imaging the amplitude of low frequency fluctuation and FC from the posterior cingulate cortex seed to other brain areas were evaluated . Twelve casual game users were also recruited and underwent only baseline assessment . Results After treatment both CBT and VRT groups showed reductions in YIAS scores . At baseline the OGA group showed a smaller ALFF within the right middle frontal gyrus and reduced FC in the cortico striatal limbic circuit . In the VRT group connectivity from the PCC seed to the left middle frontal and bilateral temporal lobe increased after VRT . Conclusion VRT seemed to reduce the severity of OGA showing effects similar to CBT and enhanced the balance of the cortico striatal limbic circuit . 


Summary: We developed a novel virtual reality therapy VRT program for online gaming addiction OGA . We compared the treatment effect of VRT to cognitive behavior therapy for OGA. VRT reduced the severity of OGA showing effects similar to cognitive behavior therapy. VRT improved the functional connectivity of the cortico limbic circuit.
Background and objective Classification of gene expression data is the common denominator of various biomedical recognition tasks . However obtaining class labels for large training samples may be difficult or even impossible in many cases . Therefore semi supervised classification techniques are required as semi supervised classifiers take advantage of unlabeled data . Methods Gene expression data is high dimensional which gives rise to the phenomena known under the umbrella of the curse of dimensionality one of its recently explored aspects being the presence of hubs or hubness for short . Therefore hubness aware classifiers have been developed recently such as Naive Hubness Bayesian k Nearest Neighbor . In this paper we propose a semi supervised extension of NHBNN which follows the self training schema . As one of the core components of self training is the certainty score we propose a new hubness aware certainty score . Results We performed experiments on publicly available gene expression data . These experiments show that the proposed classifier outperforms its competitors . We investigated the impact of each of the components separately and showed that each of these components are relevant to the performance of the proposed approach . Conclusions Our results imply that our approach may increase classification accuracy and reduce computational costs . Based on the promising results presented in the paper we envision that hubness aware techniques will be used in various other biomedical machine learning tasks . In order to accelerate this process we made an implementation of hubness aware machine learning techniques publicly available in the PyHubs software package implemented in Python one of the most popular programming languages of data science . the set of k nearest neighbors of x. probability that x belongs to class C given its nearest neighbors the probability of the event that x appears as one of the k nearest neighbors of any labeled training instance belonging to class C the prior probability of the event that an instance belongs to class C how many times x occurs as one of the k nearest neighbors of labeled training instances belonging to class C how many times x occurs as one of the k nearest neighbors of other instances when considering lab 


Summary: A semi supervised hubness aware classifier is proposed. The classifier is evaluated on publicly available real gene expression data. We made the implementation of hubness aware machine learning techniques available in the PyHubs software package.
This paper presents a novel approach for action recognition localization and video matching based on a hierarchical codebook model of local spatio temporal video volumes. Given a single example of an activity as a query video the proposed method finds similar videos to the query in a target video dataset. The method is based on the bag of video words BOV representation and does not require prior knowledge about actions background subtraction motion estimation or tracking. It is also robust to spatial and temporal scale changes as well as some deformations. The hierarchical algorithm codes a video as a compact set of spatio temporal volumes while considering their spatio temporal compositions in order to account for spatial and temporal contextual information. This hierarchy is achieved by first constructing a codebook of spatio temporal video volumes. Then a large contextual volume containing many spatio temporal volumes ensemble of volumes is considered. These ensembles are used to construct a probabilistic model of video volumes and their spatio temporal compositions. The algorithm was applied to three available video datasets for action recognition with different complexities KTH Weizmann and MSR II and the results were superior to other approaches especially in the case of a single training example and cross dataset1 action recognition. 


Summary: A hierarchical structure for accurate video to video matching and event recognition Incorporating contextual information to the bag of video words framework Coding spatio temporal compositions of video volumes by a probabilistic framework
The relationship between nonverbal behavior and severity of depression was investigated by following depressed participants over the course of treatment and video recording a series of clinical interviews. Facial expressions and head pose were analyzed from video using manual and automatic systems. Both systems were highly consistent for FACS action units AUs and showed similar effects for change over time in depression severity. When symptom severity was high participants made fewer affiliative facial expressions AUs 12 and 15 and more non affiliative facial expressions AU 14 . Participants also exhibited diminished head motion i.e. amplitude and velocity when symptom severity was high. These results are consistent with the Social Withdrawal hypothesis that depressed individuals use nonverbal behavior to maintain or increase interpersonal distance. As individuals recover they send more signals indicating a willingness to affiliate. The finding that automatic facial expression analysis was both consistent with manual coding and revealed the same pattern of findings suggests that automatic facial expression analysis may be ready to relieve the burden of manual coding in behavioral and clinical science. 


Summary: We investigated the relation between nonverbal behavior and severity of depression. When symptoms were severe participants showed less AU 12 and AU 15 and more AU 14. When symptoms were severe participants head motion was reduced in size and speed. The pattern of findings was highly consistent for automated and manual measurements. The findings support the hypothesis of nonverbal social withdrawal in depression.
Engineers have proposed many watermark mechanisms for protecting the content of digital media from unauthorized use . The visible watermark scheme indicates the copyright of digital media posted over the Internet by embedding an inconspicuous but recognizable pattern into media . However the embedding process often results in serious distortion of the protected image . Since the strength of the watermark in conventional methods mainly depends on the feature of protected media this may lead to unsatisfactory transparency of watermarked images . This paper proposes a removable solution for visible watermark mechanism . By adopting the subsampling technique the method proposes a contrast adaptive strategy to solve this problem . This method can also guarantee the essentials of general visible watermark schemes . Experimental results show that the proposed method outperforms related works in terms of preserving the quality of the restored image . 


Summary: The transparency of the watermark is applicable to the various host images. The browsers can recognize the watermark and the host image is not seriously obscured. The new scheme can remove the embedded watermark from the watermarked image. The PSNR values of the various restored images are around 56dB after the visible watermark has been removed.
Equivalence testing is recommended as a better alternative to the traditional difference based methods for demonstrating the comparability of two or more treatment effects . Although equivalent tests of two groups are widely discussed the natural extensions for assessing equivalence between several groups have not been well examined . This article provides a detailed and schematic comparison of the ANOVA F and the studentized range tests for evaluating the comparability of several standardized effects . Power and sample size appraisals of the two grossly distinct approaches are conducted in terms of a constraint on the range of the standardized means when the standard deviation of the standardized means is fixed . Although neither method is uniformly more powerful the studentized range test has a clear advantage in sample size requirements necessary to achieve a given power when the underlying effect configurations are close to the priori minimum difference for determining equivalence . For actual application of equivalence tests and advance planning of equivalence studies both SAS and R computer codes are available as supplementary files to implement the calculations of critical values p values power levels and sample sizes . 


Summary: This article explicates and evaluates the features of the ANOVA F test and the studentized range test for determining the equivalence of multiple standardized effects. The primary emphasis is to reveal the underlying properties of the two methods with regard to power behavior and sample size requirement across a variety of design configurations. To enhance the practical usefulness complete sets of SAS and R computer algorithms for calculating the critical values p values power levels and sample sizes are also developed.