varunr14/newsummary huggingface dataset:
Advanced segmentation techniques in the surveillance domain deal with shadows to avoid distortions when detecting moving objects. Most approaches for shadow detection are still typically restricted to penumbra shadows and cannot cope well with umbra shadows. Consequently umbra shadow regions are usually detected as part of moving objects thus affecting the performance of the final detection. In this paper we address the detection of both penumbra and umbra shadow regions. First a novel bottom up approach is presented based on gradient and colour models which successfully discriminates between chromatic moving cast shadow regions and those regions detected as moving objects. In essence those regions corresponding to potential shadows are detected based on edge partitioning and colour statistics. Subsequently i temporal similarities between textures and ii spatial similarities between chrominance angle and brightness distortions are analysed for each potential shadow region for detecting the umbra shadow regions. Our second contribution refines even further the segmentation results a tracking based top down approach increases the performance of our bottom up chromatic shadow detection algorithm by properly correcting non detected shadows. To do so a combination of motion filters in a data association framework exploits the temporal consistency between objects and shadows to increase the shadow detection rate. Experimental results exceed current state of the art in shadow accuracy for multiple well known surveillance image databases which contain different shadowed materials and illumination conditions. 


Summary: We address the detection of chromatic moving shadows. Gradient and colour information is exploited for separating shadow regions. Temporal gradients and spatial angle and brightness distortions are used to detect shadows. Shadows and objects are tracked using motion filters. Mutual information and data association between objects and shadows are used to recover misdetected shadows.
Sentiment analysis from data streams is aimed at detecting authors attitude emotions and opinions from texts in real time . To reduce the labeling effort needed in the data collection phase active learning is often applied in streaming scenarios where a learning algorithm is allowed to select new examples to be manually labeled in order to improve the learner s performance . Even though there are many on line platforms which perform sentiment analysis there is no publicly available interactive on line platform for dynamic adaptive sentiment analysis which would be able to handle changes in data streams and adapt its behavior over time . This paper describes ClowdFlows a cloud based scientific workflow platform and its extensions enabling the analysis of data streams and active learning . Moreover by utilizing the data and workflow sharing in ClowdFlows the labeling of examples can be distributed through crowdsourcing . The advanced features of ClowdFlows are demonstrated on a sentiment analysis use case using active learning with a linear Support Vector Machine for learning sentiment classification models to be applied to microblogging data streams . 


Summary: We present a cloud based platform for data stream processing with workflows. The ClowdFlows platform enables processing of multiple concurrent data streams. We implement an active learning scenario for sentiment analysis on data streams. Machine learning methods are shown to be suitable for sentiment analysis. Active learning improves the accuracy of sentiment classification.
We propose a real time multi view landmark detector based on Deformable Part Models . The detector is composed of a mixture of tree based DPMs each component describing landmark configurations in a specific range of viewing angles . The usage of view specific DPMs allows to capture a large range of poses and to deal with the problem of self occlusions . Parameters of the detector are learned from annotated examples by the Structured Output Support Vector Machines algorithm . The learning objective is directly related to the performance measure used for detector evaluation . The tree based DPM allows to find a globally optimal landmark configuration by the dynamic programming . We propose a coarse to fine search strategy which allows real time processing by the dynamic programming also on high resolution images . Empirical evaluation on in the wild images shows that the proposed detector is competitive with the state of the art methods in terms of speed and accuracy yet it keeps the guarantee of finding a globally optimal estimate in contrast to other methods . 


Summary: Real time multi view landmark detector independent on initialization Detector composed of a mixture of tree based Deformable Part Models Landmark detector learned by the Structured Output Support Vector Machines Coarse to fine strategy to speed up inference based on dynamic programming
Efficient feature description and classification of dynamic texture is an important problem in computer vision and pattern recognition . Recently the local binary pattern based dynamic texture descriptor has been proposed to classify DTs by extending the LBP operator used in static texture analysis to the temporal domain . However the extended LBP operator can not characterize the intrinsic motion of dynamic texture well . In this paper we propose a novel video set based collaborative representation dynamic texture classification method . First we divide the dynamic texture sequence into subsequences along the temporal axis to form the video set . For each DT we extract the video set based LBP histogram to describe it . We then propose a regularized collaborative representation model to code the LBP histograms of the query video sets over the LBP histograms of the training video sets . Finally with the coding coefficients the distance between the query video set and the training video sets can be calculated for classification . Experimental results on the benchmark dynamic texture datasets demonstrate that the proposed method can yield good performance in terms of both classification accuracy and efficiency . 


Summary: The weighted volume local binary pattern descriptor is formed to represent dynamic texture. The weights are learned with a regularized collaborative representation model. The representation residual is used for dynamic texture classification.
The detection of vanishing points in a monoscopic image is a first step to the extraction of 3D data . This article shows a partition of the image space in order to determine the type of perspective which is present thereby allowing the determination of the vanishing point associated with each of the axes of the special reference system . Additionally the Thales second theorem allows us to determine the position of the vanishing points of the image and to automatize the process . An algorithm has been used with the data provided by the selected edge detector which provides the location of the vanishing points contained on the image plane . The comparative study includes two variables the number of vanishing points and the image resolution . The results show that in general the Prewitt s edge detector provides the best results both positional and statistical . Increasing the image resolution improves the results although the results obtained for a resolution of 640 480 pixels and another of 1024 768 pixels are very similar . 


Summary: An algorithm has been developed for automatic detection of vanishing points. The behavior of gradient based edge detectors has been analyzed. The results have been analyzed according to the number of vanishing points and image resolution.
Automatic optical inspection plays an important role to control the appearance quality of wide range of products in the product process . Recently the high popularity of smartphones and information appliances drives significant demand of touch panels . However the traditional frequency based method which exploits the line structure feature of texture images is not effective for the defect detection of touch panels . The paper presents a novel spatial domain algorithm to inspect the defects on touch panel . By utilizing the characteristics of periodic patterns of the sensing circuits an adaptive model for each pattern is learned online to effectively extract defects . The experimental results indicate that our proposed method achieves accurate detection with efficient computation . In addition the users pay very little effort for the testing of different panel products . 


Summary: We develop a novel algorithm to inspect the defects on touch panel. Based on periodic patterns of touch panel an adaptive model is learned online to extract defects. The experimental results indicate that our proposed method achieves accurate detection with efficient computation. The users pay very little effort for the testing of different panel products.
The difficulty of face recognition FR systems to operate efficiently in diverse operational environments e.g. day and night time is aided by employing sensors covering different spectral bands i.e. visible and infrared . Biometric practitioners have identified a framework of band specific algorithms which can contribute to both assessment and intervention. While these motions are proven to achieve improvement of identification performance they traditionally result in solutions that typically fail to work efficiently across multiple spectrums. In this work we designed and developed an efficient fully automated direct matching based FR approach that is designed to operate efficiently when face data is captured using either visible or passive infrared IR sensors. Thus it can be applied in both daytime and nighttime environments. First input face images are geometrically normalized using our pre processing pipeline prior to feature extraction. Then face based features including wrinkles veins as well as edges of facial characteristics are detected and extracted for each operational band visible MWIR and LWIR . Finally global and local face based matching is applied before fusion is performed at the score level. Our approach achieves a rank 1 identification rate of at least 99.43 regardless of the spectrum of operation. This suggests that our approach results in better performance than other tested standard commercial and academic face based matchers on all spectral bands used. 


Summary: Propose new automated tri spectral visible MWIR and LWIR FR approach Design experiments to quantitatively measure benefits of global vs. local matchers Evaluation of global vs. local based matchers when fused at the score level Achieve rank 1 identification rate of at least 99.43 per spectrum of operation
A trie is one of the data structures for keyword matching . It is used in natural language processing IP address routing and so on . It is represented by the matrix form the link form the double array and LOUDS . The double array representation combines retrieval speed of the matrix form with compactness of the list form . LOUDS is a succinct data structure using bit string . Retrieval speed of LOUDS is not faster than that of the double array but its space usage is smaller . This paper proposes a compressed version of the double array by dividing the trie into multiple levels and removing the BASE array from the double array . Moreover a retrieval algorithm and a construction algorithm are proposed . According to the presented experimental results for pseudo and real data sets the retrieval speed of the presented method is almost the same as the double array and its space usage is compressed to 66 comparing with LOUDS for a large set of keywords with fixed length . 


Summary: A new compression method of the double array is proposed. The BASE array is removed from the double array. The retrieval and construction algorithms are proposed. The space usage of our method is more compact than that of the double array. The retrieval speed of our method is almost the same as the double array.
Pose estimation is a key concern in 3D urban surveying mapping and navigation . Although Global Positioning System technologies can be used to estimate a robot s or vehicle s pose there are many urban environments in which GPS functions poorly or not at all . For these situations we offer a novel approach based on a careful fusion of panoramic camera data and 2D laser scanner input . First a Constrained Bundle Adjustment is introduced to handle scale and loop closure constraints . The fusion of a panoramic image series and laser data then enables an accurate scale to be estimated and loop closures detected . Finally the two geometric constraints are enforced on the global CBA solution which in turn produces a robust pose estimate . Experiments show that the proposed method is practicable and more accurate than vision only methods with an average error of just 0.2m in the horizontal plane over a 580m trajectory . 


Summary: Accurate scale is estimated with laser data and features extracted from camera. Many more loop closures are detected by sensor than that of camera only methods. Scale and loop closure geometric constraints are enforced on Constrained Bundle Adjustment which produces a robust pose estimate. Experiments show that the proposed method is practicable and more accurate than vision only methods.
Locally affine transformation with globally elastic interpolation is a common strategy for non rigid registration. Current techniques improve the registration accuracy by only processing the sub images that contain well defined structures quantified by Moran s spatial correlation. As an indicator Moran s metric successfully excludes noisy structures that result in misleading global optimum in terms of similarity. However some well defined structures with intensity only varying in one direction may also cause mis registration. In this paper we propose a new metric based on the response of a similarity function to quantify the ability of being correctly registered for each sub image. Using receiver operating characteristic analysis we show that the proposed metric more accurately reflects such ability than Moran s metric. Incorporating the proposed metric into a hierarchical non rigid registration scheme we show that registration accuracy is improved relative to Moran s metric. 


Summary: For non rigid registration a hierarchical piece wise affine transform is examined. We propose a metric to quantify the ability of being correctly registered. We examine ROC analysis on the proposed metric and Moran s spatial correlation. Proposed metric reflects the ability of being registered better than Moran s. Registration error is reduced by using the proposed metric as a sub image indicator.
In this paper we propose a method for face recognition by using the two dimensional discrete wavelet transform and a new patch strategy . Based on the average image of all training samples by using integral projection technique for two top level s high frequency sub bands of 2D DWT we propose a non uniform patch strategy for the top level s low frequency sub band . This patch strategy is more suitable to reflect the structure feature of face image and it is better for retaining the integrity of local information . By applying the obtained patch strategy to all samples we obtain patches of training samples and testing samples and then give the final decision by using the nearest neighbor classifier and the majority voting . Experiments are run on the AR FERET Extended Yale B and LFW face databases . The obtained numerical results show that the new face recognition method outperforms the traditional 2D DWT method and some state of the art patch based methods . 


Summary: A new strategy of overlapped non uniform patch is proposed. 2D DWT and integral projection technique are used to obtain the patch strategy. The overlapped patches are elected to keep the patches robust. The proposed face recognition method processes good performance.
The norm of practice in estimating graph properties is to use uniform random node samples whenever possible . Many graphs are large and scale free inducing large degree variance and estimator variance . This paper shows that random edge sampling and the corresponding harmonic mean estimator for average degree can reduce the estimation variance significantly . First we demonstrate that the degree variance and consequently the variance of the RN estimator can grow almost linearly with data size for typical scale free graphs . Then we prove that the RE estimator has a variance bounded from above . Therefore the variance ratio between RN and RE samplings can be very large for big data . The analytical result is supported by both simulation studies and 18 real networks . We observe that the variance reduction ratio can be more than a hundred for some real networks such as Twitter . Furthermore we show that random walk sampling is always worse than RE sampling and it can reduce the variance of RN method only when its performance is close to that of RE sampling . 


Summary: We derive an upper bound for the variance of RE sampling. We show that variance of RN sampling grows with data size for scale free networks. Thereby RE sampling excels when data size is large. We support the analytical results with simulation studies and 18 real networks.
Several Web 2.0 applications allow users to assign keywords to provide better organization and description of the shared content . Tag recommendation methods may assist users in this task improving the quality of the available information and thus the effectiveness of various tag based information retrieval services such as searching content recommendation and classification . This work addresses the tag recommendation problem from two perspectives . The first perspective centered at the object aims at suggesting relevant tags to a target object jointly exploiting the following three dimensions tag co occurrences terms extracted from multiple textual features and various metrics to estimate tag relevance . The second perspective centered at both object and user aims at performing personalized tag recommendation to a target object user pair exploiting in addition to the three aforementioned dimensions a metric that captures user interests . In particular we propose new heuristic methods that extend state of the art strategies by including new metrics that estimate how accurately a candidate tag describes the target object . We also exploit three learning to rank based techniques namely RankSVM Genetic Programming and Random Forest for generating ranking functions that exploit multiple metrics as attributes to estimate the relevance of a tag to a given object or object user pair . We evaluate the proposed methods using data from four popular Web 2.0 applications namely Bibsonomy LastFM YouTube and YahooVideo . Our new heuristics for object centered tag recommendation provide improvements in precision over the best state of the art alternative of 12 on average while our new heuristics for personalized tag recommendation produce average gains in precision of 121 over the baseline . Similar performance gains are also achieved in terms of other metrics notably recall Normalized Discounted Cumulative Gain and Mean Reciprocal Rank . Further improvements for both object centered and personalized tag recommendation can also be achieved with our new L2R based strategies which are flexible and can be easily extended to exploit other aspects of the tag recommendation problem . Finally we also quantify the benefits of personalized tag recommendation to provide better descriptions of the target object when compared to object centered recommendation by focusing only on the relevance of the suggested tags to the object . We find that our best personalized method outperforms the best object centered strategy with average gains in precision of 10 . 


Summary: We propose new heuristics for object centered and personalized tag recommendation. We also propose new learning to rank L2R based strategies for the same tasks. They exploit tag co occurrences textual features relevance metrics and user history. Our solutions greatly outperform state of the art methods on real datasets. Tag personalization produces better descriptions of the objects.
In this paper a tracking method based on sequential Bayesian inference is proposed. The proposed method focuses on solving both the problem of tracking under partial occlusions and the problem of non rigid object tracking in real time on a desktop personal computer PC . The proposed method is mainly composed of two parts 1 modeling the target object using elastic structure of local patches for robust performance and 2 efficient hierarchical diffusion method to perform the tracking procedure in real time. The elastic structure of local patches allows the proposed method to handle partial occlusions and non rigid deformations through the relationship among neighboring patches. The proposed hierarchical diffusion method generates samples from the region where the posterior is concentrated to reduce computation time. The method is extensively tested on a number of challenging image sequences with occlusion and non rigid deformation. The experimental results show the real time capability and the robustness of the proposed method under various situations. 


Summary: We propose a tracking method to solve both the problem of partial occlusions and non rigid deformations in real time. The target object is modeled through an elastic structure of local patches for robust performance. Hierarchical diffusion method is proposed to obtain an acceptable solution in real time. Extensive evaluation shows that the proposed method outperforms state of the art.
In robot localization particle filtering can estimate the position of a robot in a known environment with the help of sensor data. In this paper we present an approach based on particle filtering for accurate stereo matching. The proposed method consists of three parts. First we utilize multiple disparity maps in order to acquire a very distinctive set of features called landmarks and then we use segmentation as a grouping technique. Secondly we apply scan line particle filtering using the corresponding landmarks as a virtual sensor data to estimate the best disparity value. Lastly we reduce the computational redundancy of particle filtering in our stereo correspondence with a Markov chain model given the previous scan line values. More precisely we assist particle filtering convergence by adding a proportional weight in the predicted disparity value estimated by Markov chains. In addition to this we optimize our results by applying a plane fitting algorithm along with a histogram technique to refine any outliers. This work provides new insights into stereo matching methodologies by taking advantage of global geometrical and spatial information from distinctive landmarks. Experimental results show that our approach is capable of providing high quality disparity maps comparable to other well known contemporary techniques. 


Summary: A stereo matching approach motivated by the particle filter framework in robot localization. Highly accurate GCPs acquired by the computation of multiple cost efficient disparity maps. A Markov chain model has been introduced in the process to reduce the computational complexity of particle filtering. Application of RANSAC algorithm along with a histogram technique to refine any outliers.
In this paper a local approach for 3D object recognition is presented . It is based on the topological invariants provided by the critical points of the 3D object . The critical points and the links between them are represented by a set of size functions obtained after splitting the 3D object into portions . A suitable similarity measure is used to compare the sets of size functions associated with the 3D objects . In order to validate our approach s recognition performance we used different collections of 3D objects . The obtained scores are favourably comparable to the related work . 


Summary: The 3D object is split into 18 portions according to its principal axes. Each portion is represented by a size function. A well suited similarity measure is used to compare between the 3D objects.