varunr14/newsummary huggingface dataset:
Recent developments in low cost CMOS cameras have created the opportunity of bringing imaging capabilities to sensor networks and a new field called visual sensor networks has emerged . VSNs consist of image sensors embedded processors and wireless transceivers which are powered by batteries . Since energy and bandwidth resources are limited setting up a tracking system in VSNs is a challenging problem . In this paper we present a framework for human tracking in VSN environments . The traditional approach of sending compressed images to a central node has certain disadvantages such as decreasing the performance of further processing because of low quality images . Instead in our decentralized tracking framework each camera node performs feature extraction and obtains likelihood functions . We propose a sparsity driven method that can obtain bandwidth efficient representation of likelihoods extracted by the camera nodes . Our approach involves the design of special overcomplete dictionaries that match the structure of the likelihoods and the transmission of likelihood information in the network through sparse representation in such dictionaries . We have applied our method for indoor and outdoor people tracking scenarios and have shown that it can provide major savings in communication bandwidth without significant degradation in tracking performance . We have compared the tracking results and communication loads with a block based likelihood compression scheme a decentralized tracking method and a distributed tracking method . Experimental results show that our sparse representation framework is an effective approach that can be used together with any probabilistic tracker in VSNs . 


Summary: A decentralized approach for multi view multi person tracking in VSNs. Design overcomplete dictionaries matched to the structure of likelihoods functions. Obtain sparse representation of likelihoods to reduce communication in the network. Comparison on well known L1 solvers to choose a fast and reliable method. Qualitatively and quantitatively our framework outperforms previous approaches.
Computer vision is hard because of a large variability in lighting shape and texture in addition the image signal is non additive due to occlusion . Generative models promised to account for this variability by accurately modelling the image formation process as a function of latent variables with prior beliefs . Bayesian posterior inference could then in principle explain the observation . While intuitively appealing generative models for computer vision have largely failed to deliver on that promise due to the difficulty of posterior inference . As a result the community has favoured efficient discriminative approaches . We still believe in the usefulness of generative models in computer vision but argue that we need to leverage existing discriminative or even heuristic computer vision methods . We implement this idea in a principled way with an informed sampler and in careful experiments demonstrate it on challenging generative models which contain renderer programs as their components . We concentrate on the problem of inverting an existing graphics rendering engine an approach that can be understood as Inverse Graphics . The informed sampler using simple discriminative proposals based on existing computer vision technology achieves significant improvements of inference . 


Summary: The informed sampler a general inference technique for Bayesian posterior inference in generative models. This method leverages discriminative computer vision models for faster probabilistic inference in generative models. Three different applications that highlight common challenges of posterior inference. Detailed comparisons and analysis with respect to different baseline sampling based methods. Informed sampling is found to converge faster than all baseline samplers across diverse problems.
Classical image segmentation techniques in computer vision exploit visual cues such as image edges lines color and texture . Due to the complexity of real scenarios the main challenge is achieving meaningful segmentation of the imaged scene since real objects have substantial discontinuities in these visual cues . In this paper a new focus based perceptual cue is introduced the focus signal . The focus signal captures the variations of the focus level of every image pixel as a function of time and is directly related to the geometry of the scene . In a practical application a sequence of images corresponding to an autofocus sequence is processed in order to infer geometric information of the imaged scene using the focus signal . This information is integrated with the segmentation obtained using classical cues such as color and texture in order to yield an improved scene segmentation . Experiments have been performed using different off the shelf cameras including a webcam a compact digital photography camera and a surveillance camera . Obtained results using Dice s similarity coefficient and the pixel labeling error show that a significant improvement in the final segmentation can be achieved by incorporating the information obtained from the focus signal in the segmentation process . 


Summary: Focus has been typically exploited in computer vision as depth cue. A new focus based perceptual cue is introduced the focus signal. The focus signal corresponds to the change in focus level as a function of time. The focus signal can be integrated with classical cues for image segmentation. The proposed focus aided methodology yields improved scene segmentation.
With the rapidly increasing demands from surveillance and security industries crowd behaviour analysis has become one of the hotly pursued video event detection frontiers within the computer vision arena in recent years. This research has investigated innovative crowd behaviour detection approaches based on statistical crowd features extracted from video footages. In this paper a new crowd video anomaly detection algorithm has been developed based on analysing the extracted spatio temporal textures. The algorithm has been designed for real time applications by deploying low level statistical features and alleviating complicated machine learning and recognition processes. In the experiments the system has been proven a valid solution for detecting anomaly behaviours without strong assumptions on the nature of crowds for example subjects and density. The developed prototype shows improved adaptability and efficiency against chosen benchmark systems. 


Summary: A real time crowd anomaly detection algorithm for video surveillance is proposed. The research has developed a spatio temporal texture model for feature extraction. A redundant texture feature space has been composed by using wavelet transform. Detection algorithm is based on 3 sigma rule which is fast and robust. The system shows improved accuracy and efficiency against many benchmark systems.
With the advent of the digital camera a common popular imaging processing is high dynamic range that aims to overcome the technological limitations of the irradiance sensor dynamic range . In this paper we will present a new method to combine low dynamic range images for HDR processing . This method is based on the theory of evidence . Without a prior knowledge of the sensor intrinsic parameters and no extra data it allows to locally maximizing the signal to noise ratio over the entire acquisition dynamic . In addition our method is less sensitive to object or people in motion into the scene that are causing ghost like artifacts with the conventional methods . This technique require that the camera be absolutely still between exposures or need a translational alignment . Simulation and experimental results are presented to demonstrate both the accuracy and efficiency of our algorithm . 


Summary: We propose a new unbiased method for HDR reconstruction based on evidence theory. Our method allows to locally minimizing the acquisition noise in the HDR image. Our method is adapted for very high dynamic range acquisition. For average user no setting and no additional information are needed. Our method is less sensitive to object or people in motion into the scene.
We present a passive forensics method to distinguish photorealistic computer graphics from natural images . The goals of our work are to improve the detection accuracy and the robustness to content preserving image manipulations . In the proposed method Homomorphic filtering is used to highlight the detail information of image . We find that the texture changes are different between photographs and PRCG images under same Homomorphic filtering transformation and then we use the difference matrixes to describe the differences of texture changes . We define a customized statistical feature named texture similarity and combine it with the statistical features extracted from the co occurrence matrixes of differential matrixes to construct forensics features . Then we develop a statistical model and use SVM as classifier to distinguish PRCG from photographs . Experimental results show that the proposed method enjoys following advantages Proposed method reaches higher detection accuracy synchronously it is robust to tolerate content preserving manipulations such as JPEG compression adding noise histogram equalization and filtering . Proposed method is provided with satisfactory generalization capability it will be available when the training samples and the testing samples come from different sources . 


Summary: A method to distinguish photographic images and photorealistic computer graphics is proposed. A customized statistical feature named texture similarity is defined. Proposed method used Homomorphic filtering to highlight the image detail information. Proposed method is robust for content preserving manipulations. Proposed method provides satisfactory detection accuracy and generalization capability.
The minimum barrier distance MBD introduced recently in is a pseudo metric defined on a compact subset D of the Euclidean space and whose values depend on a fixed map f from D into . The MBD is defined as the minimal value of the barrier strength of a path between the points which constitutes the length of the smallest interval containing all values of f along the path . In this paper we present a polynomial time algorithm that provably calculates the exact values of MBD for the digital images . We compare this new algorithm theoretically and experimentally with the algorithm presented in which computes the approximate values of the MBD . Moreover we notice that every generalized distance function can be naturally translated to an image segmentation algorithm . The algorithms that fall under such category include Relative Fuzzy Connectedness and those associated with the minimum barrier fuzzy distance and geodesic distance functions . In particular we compare experimentally these four algorithms on the 2D and 3D natural and medical images with known ground truth and at varying level of noise blur and inhomogeneity . 


Summary: Introduction of fast algorithm for the exact values of minimum barrier distance MBD. Comparison of the novel algorithm with its approximate versions. Experimental comparison MBD induced segmentations with other segmentation algorithms.
Appearance model is a key part of tracking algorithms . To attain robustness many complex appearance models are proposed to capture discriminative information of object . However such models are difficult to maintain accurately and efficiently . In this paper we observe that hashing techniques can be used to represent object by compact binary code which is efficient for processing . However during tracking online updating hash functions is still inefficient with large number of samples . To deal with this bottleneck a novel hashing method called two dimensional hashing is proposed . In our tracker samples and templates are hashed to binary matrices and the hamming distance is used to measure confidence of candidate samples . In addition the designed incremental learning model is applied to update hash functions for both adapting situation change and saving training time . Experiments on our tracker and other eight state of the art trackers demonstrate that the proposed algorithm is more robust in dealing with various types of scenarios . 


Summary: In this paper we propose a 2D based hashing method which could fast extract the binary feature of samples. We successfully apply the hashing method into tracking model by some details. We design an effective and suitable learning model to update hash functions at every frame. Comparison experiments are done to demonstrate the effectiveness and efficiency of our tracker.
This paper describes a method of gait recognition by suppressing and using gait fluctuations . Inconsistent phasing between a matching pair of gait image sequences because of temporal fluctuations degrades the performance of gait recognition . We remove the temporal fluctuations by generating a phase normalized gait image sequence with equal phase intervals . If inter period gait fluctuations within a gait image sequence are repeatedly observed for the same subject they can be regarded as a useful distinguishing gait feature . We extract phase fluctuations as temporal fluctuations as well as gait fluctuation image and trajectory fluctuations as spatial fluctuations . We combine them with the matching score using the phase normalized image sequence as additional matching scores in the score level fusion framework or as quality measures in the score normalization framework . We evaluated the methods in experiments using large scale publicly available databases and showed the effectiveness of the proposed methods . 


Summary: Introduction of the phase trajectory fluctuations and gait fluctuation image. Using gait fluctuation as a quality measure or an additional matching score. Evaluation using large scale publicly available gait databases. Suppressing and utilizing gait fluctuations improve the gait recognition performance.
This paper presents a disparity calculation algorithm based on stereo vision for obstacle detection and free space calculation . This algorithm incorporates line segmentation multi pass aggregation and efficient local optimisation in order to produce accurate disparity values . It is specifically designed for traffic scenes where most of the objects can be represented by planes in the disparity domain . The accurate horizontal disparity gradient for the side planes are also extracted during the disparity optimisation stage . Then an obstacle detection algorithm based on the U V disparity is introduced . Instead of using the Hough transform for line detection which is extremely sensitive to the parameter settings the G disparity image is proposed for the detection of side planes . Then the vertical planes are detected separately after removing all the side planes . Faster detection speed lower parameter sensitivity and improved performance are achieved comparing with the Hough transform based detection . After the obstacles are located and removed from the disparity map most of the remaining pixels are projections from the road surface . Using a spline as the road model the vertical profile of the road surface is estimated . Finally the free space is calculated based on the vertical road profile which is not restricted by the planar road surface assumption . 


Summary: We propose a disparity calculation algorithm based on multi pass aggregation and local optimisation. Disparity calculation is fast and accurate in real world scenarios. We propose the G disparity image which can be used with U V disparity for obstacle detection. Obstacle detection is more efficient and accurate. Free space calculation is simplified after obstacle detection.
Would it be possible to automatically associate ancient pictures to modern ones and create fancy cultural heritage city maps We introduce here the task of recognizing the location depicted in an old photo given modern annotated images collected from the Internet . We present an extensive analysis on different features looking for the most discriminative and most robust to the image variability induced by large time lags . Moreover we show that the described task benefits from domain adaptation . 


Summary: We introduce a new dataset where the objective is to recognize a location of an old photograph using modern digital images. We evaluate a large number of existing features and encoding methods for this task. We show that existing domain adaptation methods can help to improve results for this specific task. We further show that there is the need for further research in cross domain image retrieval task.
Radio frequency identification technology has been used in manufacturing industries to create a RFID enabled ubiquitous environment in where ultimate real time advanced production planning and scheduling will be achieved with the goal of collective intelligence . A particular focus has been placed upon using the vast amount of RFID production shop floor data to obtain more precise and reasonable estimates of APPS parameters such as the arrival of customer orders and standard operation times . The resulting APPS model is based on hierarchical production decision making principle to formulate planning and scheduling levels . A RFID event driven mechanism is adopted to integrate these two levels for collective intelligence . A heuristic approach using a set of rules is utilized to solve the problem . The model is tested through four dimensions including the impact of rule sequences on decisions evaluation of released strategy to control the amount of production order from planning to scheduling comparison with another model and practical operations as well as model robustness . Two key findings are observed . First release strategy based on the RFID enabled real time information is efficient and effective to reduce the total tardiness by 44.46 averagely . Second it is observed that the model has the immune ability on disturbances like defects . However as the increasing of the problem size the model robustness against emergency orders becomes weak while the resistance to machine breakdown is strong oppositely . Findings and observations are summarized into a number of managerial implications for guiding associated end users for purchasing collective intelligence in practice . 


Summary: An RFID event driven mechanism is used to integrate planning and scheduling. RFID production shopfloor data was used to obtain APPS parameters. Release strategy is efficient to reduce the total tardiness by 44.46 averagely.
Recognizing scene text is a challenging problem even more so than the recognition of scanned documents . This problem has gained significant attention from the computer vision community in recent years and several methods based on energy minimization frameworks and deep learning approaches have been proposed . In this work we focus on the energy minimization framework and propose a model that exploits both bottom up and top down cues for recognizing cropped words extracted from street images . The bottom up cues are derived from individual character detections from an image . We build a conditional random field model on these detections to jointly model the strength of the detections and the interactions between them . These interactions are top down cues obtained from a lexicon based prior i.e . language statistics . The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model . We evaluate our proposed algorithm extensively on a number of cropped scene text benchmark datasets namely Street View Text ICDAR 2003 2011 and 2013 datasets and IIIT 5K word and show better performance than comparable methods . We perform a rigorous analysis of all the steps in our approach and analyze the results . We also show that state of the art convolutional neural network features can be integrated in our framework to further improve the recognition performance . 


Summary: An energy minimization based approach for scene text recognition with seamless integration of multiple cues. Applied also to the challenging open vocabulary setting where a word specific lexicon is unavailable. Comprehensive experimental evaluation on several state of the art benchmarks.
We propose an automated framework for predicting gestational age and neurodevelopmental maturation of a fetus based on 3D ultrasound brain image appearance . Our method capitalizes on age related sonographic image patterns in conjunction with clinical measurements to develop for the first time a predictive age model which improves on the GA prediction potential of US images . The framework benefits from a manifold surface representation of the fetal head which delineates the inner skull boundary and serves as a common coordinate system based on cranial position . This allows for fast and efficient sampling of anatomically corresponding brain regions to achieve like for like structural comparison of different developmental stages . We develop bespoke features which capture neurosonographic patterns in 3D images and using a regression forest classifier we characterize structural brain development both spatially and temporally to capture the natural variation existing in a healthy population 447 over an age range of active brain maturation . On a routine clinical dataset 187 our age prediction results strongly correlate with true GA 0.98 accurate within 6.10 days confirming the link between maturational progression and neurosonographic activity observable across gestation . Our model also outperforms current clinical methods by 4.57 days in the third trimester a period complicated by biological variations in the fetal population . Through feature selection the model successfully identified the most age discriminating anatomies over this age range as being the Sylvian fissure cingulate and callosal sulci . 


Summary: We present a model to predict gestational age from 3D fetal brain ultrasound images. A feature based model characterizes spatial and temporal brain development. We capitalize on sonographic image patterns and clinical measures to predict age. Use of clinical measurements and neuroimage information improves age predictions. We identify the most age discriminating brain anatomies in early brain development.