varunr14/newsummary huggingface dataset:
This paper proposes a novel training algorithm for radial basis function neural networks based on fuzzy clustering and particle swarm optimization . So far fuzzy clustering has proven to be a very efficient tool in designing such kind of networks . The motivation of the current work is to quantify the exact effect of fuzzy cluster analysis on the network s performance and use it in order to substantially improve this performance . There are two key theoretical findings resulting from the present work . First it is analytically proved that when the standard fuzzy c means algorithm is used to generate the input space fuzzy partition the main effect this partition imposes to the network s square error can be written down in terms of a distortion function that measures the ability of the partition to recreate the original data . Second using the aforementioned distortion function an upper bound of the network s square error can be constructed . Then the particle swarm optimization is put in place to minimize the above upper bound and determine the network s parameters . To further improve the accuracy the basis function widths and the connection weights are fine tuned by employing a steepest descent approach . The main experimental findings are the implementation of the PSO obtains a significant reduction of the square error while exhibiting a smooth dynamic behavior although the steepest descent further decreases the error it finally obtains smaller reduction rates meaning that the strongest impact on the error reduction is provided by the PSO and the improved performance of the proposed network is demonstrated through an extensive comparison with other related methods using a 10 fold cross validation analysis . 


Summary: This paper quantifies the effect of fuzzy clustering in the design process of a typical RBF network. It is analytically shown that the fuzzy clustering acts to minimize an upper bound of the network s square error. The PSO algorithm is used to minimize the upper bound and to provide an estimation of the network s parameters. Finally the widths and connection weights are further tuned using a steepest descent approach.
We propose a new method to incorporate priors on the solution of nonnegative matrix factorization . The NMF solution is guided to follow the minimum mean square error estimates of the weight combinations under a Gaussian mixture model prior . The proposed algorithm can be used for denoising or single channel source separation applications . NMF is used in SCSS in two main stages the training stage and the separation stage . In the training stage NMF is used to decompose the training data spectrogram for each source into a multiplication of a trained basis and gains matrices . In the separation stage the mixed signal spectrogram is decomposed as a weighted linear combination of the trained basis matrices for the source signals . In this work to improve the separation performance of NMF the trained gains matrices are used to guide the solution of the NMF weights during the separation stage . The trained gains matrix is used to train a prior GMM that captures the statistics of the valid weight combinations that the columns of the basis matrix can receive for a given source signal . In the separation stage the prior GMMs are used to guide the NMF solution of the gains weights matrices using MMSE estimation . The NMF decomposition weights matrix is treated as a distorted image by a distortion operator which is learned directly from the observed signals . The MMSE estimate of the weights matrix under the trained GMM prior and log normal distribution for the distortion is then found to improve the NMF decomposition results . The MMSE estimate is embedded within the optimization objective to form a novel regularized NMF cost function . The corresponding update rules for the new objectives are derived in this paper . The proposed MMSE estimates based regularization avoids the problem of computing the hyper parameters and the regularization parameters . MMSE also provides a better estimate for the valid gains matrix . Experimental results show that the proposed regularized NMF algorithm improves the source separation performance compared with using NMF without a prior or with other prior models . 


Summary: Single channel source separation using nonnegative matrix factorization NMF . Using MMSE under GMM for regularizing the NMF gains online estimation of uncertainty. Less sensitive to the regularization parameter. Efficient update rules for gain parameters. Improved results in speech music separation experiments.
One of the most important challenges in the computer vision has long been to obtain three dimensional models from the information given by a projection of the model . In this work we show an automatic system which allows obtaining three dimensional models from entities that represent the conical projection of a polyhedral model with normalon or quasi normalon typology . The results obtained on a total of 160 tests with a success ratio of 100 make the method a proposal to be considered for obtaining models from conical perspectives automatically . 


Summary: The proposed algorithms represent a significant advance in solid geometry reconstruction. The reconstruction process is carried out automatically without user interaction. The methods have been implemented and simulated obtaining a success rate of 100 .
Object recognition systems constitute a deeply entrenched and omnipresent component of modern intelligent systems . Research on object recognition algorithms has led to advances in factory and office automation through the creation of optical character recognition systems assembly line industrial inspection systems as well as chip defect identification systems . It has also led to significant advances in medical imaging defence and biometrics . In this paper we discuss the evolution of computer based object recognition systems over the last fifty years and overview the successes and failures of proposed solutions to the problem . We survey the breadth of approaches adopted over the years in attempting to solve the problem and highlight the important role that active and attentive approaches must play in any solution that bridges the semantic gap in the proposed object representations while simultaneously leading to efficient learning and inference algorithms . From the earliest systems which dealt with the character recognition problem to modern visually guided agents that can purposively search entire rooms for objects we argue that a common thread of all such systems is their fragility and their inability to generalize as well as the human visual system can . At the same time however we demonstrate that the performance of such systems in strictly controlled environments often vastly outperforms the capabilities of the human visual system . We conclude our survey by arguing that the next step in the evolution of object recognition algorithms will require radical and bold steps forward in terms of the object representations as well as the learning and inference algorithms used . 


Summary: We survey the literature on passive and active object recognition during the last 50years. We survey some of the best performing object recognition algorithms. We discuss the limitations and drawbacks of current methodologies. We discuss future novel research directions.
We present TouchCut a robust and efficient algorithm for segmenting image and video sequences with minimal user interaction . Our algorithm requires only a single finger touch to identify the object of interest in the image or first frame of video . Our approach is based on a level set framework with an appearance model fusing edge region texture and geometric information sampled local to the touched point . We first present our image segmentation solution then extend this framework to progressive video segmentation encouraging temporal coherence by incorporating motion estimation and a shape prior learned from previous frames . This new approach to visual object cut out provides a practical solution for image and video segmentation on compact touch screen devices facilitating spatially localized media manipulation . We describe such a case study enabling users to selectively stylize video objects to create a hand painted effect . We demonstrate the advantages of TouchCut by quantitatively comparing against the state of the art both in terms of accuracy and run time performance . 


Summary: TouchCut requires only a single touch to bootstrap the object segmentation. It incorporates a new model that fuses edge region geometric and shape cues. A new fast dominant color extraction scheme to generate edge probability. Temporally propagated shape prior enables extension to video segmentation. Comprehensive qualitative and quantitative evaluations on images and videos.
This paper introduces a new variation of the p norm detector which is designed for application to coherent multilook detection in compound Gaussian clutter with inverse Gamma texture . By applying what is termed a compensator enhanced detection performance can be achieved independently of the number of looks used . This is particularly useful in the case of a fast scan rate radar where the number of looks may be quite small . Conventional coherent detectors tend to experience saturation in such scenarios and so this new detection process complements recent advances in this area . Further validation is provided by applying this new decision rule to synthetic target detection in real X band radar clutter . 


Summary: The p norm detector investigated for coherent multilook detection. Compensator used to improve its performance. Criteria established to select p norm plus compensator detector s parameters. Enhance detector performance achieved regardless of the number of looks. Application to detection in compound Gaussian clutter with inverse Gamma texture shows excellent results.
We consider the problem of joint tracking and classification using the information from radar and electronic support measure . For each target class a separate filter is operated in parallel and each class dependent filter is implemented by interacting multiple model regularized particle filter . The speed likelihood for each class is defined using a priori information about speed constraint and combined with the likelihoods from two sensors to improve tracking and classification . Moreover the output of classifier is also used for particle reassignment of different classes which might lead to better performance . Simulations show that our proposed method can provide reliable tracking and correct classification . joint tracking and classification point target motion model based JTC rigid target motion model based JTC electronic support measure transferable belief model particle filter regularized particle filter interacting multiple model interacting multiple model regularized particle filter IMMRPF based JTC probability density mass function root mean squared errors posterior joint state class probability density mass function target state vector ith target type known number of target classes measurement sequences from radar and ESM measurement at time k from radar and ESM flight envelope constraints for class i set of maneuver models for class i target Markovian maneuver transition matrix for class i target Gaussian white process noise vector with zero mean and variance Q state transition matrix gain matrix additive Gaussian white noise with zero mean and variance R set of all possible emitters where N is the total number of emitter types set of on board emitters of class i target number of the on board emitters of class i target ESM measurement space where ESM confusion matrix usage transition probability matrix for emitter j overall emitter usage transition matrix for class i target with emitter set posterior target class probability using ESM data only emitter usage status vector of class i target posterior target class probability using radar data only jth model probability of class i target at time k likelihood for jth model probability of class i target at time k speed likelihood functions speed feature measurement initial class probabilities number of particles for class i target at time k initial number of particles for class i indicator function effective sample size for class i target preset re sampling threshold parameter hybrid particle weights of particles 


Summary: It uses information from radar and ESM to avoid the kinematic only classification. For each class a separate filter is operated in parallel and implemented by IMMRPF. Speed likelihood for each class is calculated and combined with likelihoods from two sensors. Output of classifier is also used for particle reassignment of different classes.
Acoustic echo canceller is used in communication and teleconferencing systems to reduce undesirable echoes resulting from the coupling between the loudspeaker and the microphone . In this paper we propose an improved variable step size normalized least mean square algorithm for acoustic echo cancellation applications based on adaptive filtering . The steady state error of the NLMS algorithm with a fixed step size is very large for a non stationary input . Variable step size algorithms can be used to decrease this error . The proposed algorithm named MESVSS NLMS combines the generalized sigmoid variable step size NLMS with the ratio of the estimation error to the mean history of the estimation error values . It is shown from single talk and double talk scenarios using speech signals from TIMIT database that the proposed algorithm achieves a better performance more than 3 dB of attenuation in the misalignment evaluation compared to GSVSS NLMS non parametric VSS NLMS and standard NLMS algorithms for a non stationary input in noisy environments . 


Summary: A new technique for acoustic echo cancellation. An improved variable step size normalized least mean square adaptive filtering algorithm. A comparative study using some variable step size algorithms and the proposed algorithm. Experiments in single talk and double talk scenarios using TIMIT database. Standardized performances measures Mean Square Error and Normalized misalignment.
The anisotropic diffusion is an efficient smoothing process . It is widely used in noise removing and edges preserving via different schemes . In this paper based on a mathematical background and the existing efficient anisotropic function in the literature we developed a new mathematical anisotropic diffusion function which is able to overcome the drawbacks of the traditional process such as the details loss and the image blur . The simulations results and the comparative study with other recent techniques are conducted and showed that the proposed schema generates a wide improvement in the quality of the restored images . This improvement has been shown subjectively in terms of visual quality and objectively with reference to the computation of some criteria . The simulated images are well de noised but the most important is that details and structural information are kept intact . In addition to that the proposed new function was found very interesting and presents numerous advantages like its similarity to the conventional model and the importance of the speed hence it converges faster which allows an opportunity to be well implemented in our de noising process . 


Summary: Some shortcomings of some anisotropic filtering process were cited. A new filtering process based on new adaptive diffusion function is proposed. The efficiency of the new anisotropic function in the filtering process was shown. The proposed new function was found very interesting and generates good results. The time of the filtering process was highly decreased.
Two of the main ingredients of topological persistence for shape comparison are persistence diagrams and the matching distance . Persistence diagrams are signatures capturing meaningful properties of shapes while the matching distance can be used to stably compare them . From the application viewpoint one drawback of these tools is the computational cost for evaluating the matching distance . In this paper we introduce a new framework for the matching distance estimation It preserves the reliability of the entire approach in comparing shapes extremely reducing the computational cost . Theoretical results are supported by experiments on 3D models . 


Summary: This paper deals with the concepts of persistence diagram and matching distance. We present multi scale approaches to approximate the matching distance. Experiments show the capability of the proposed methodologies for shape retrieval.
In this paper we address the problem of 2D 3D pose estimation . Specifically we propose an approach to jointly track a rigid object in a 2D image sequence and to estimate its pose in 3D space . We revisit a joint 2D segmentation 3D pose estimation technique and then extend the framework by incorporating a particle filter to robustly track the object in a challenging environment and by developing an occlusion detection and handling scheme to continuously track the object in the presence of occlusions . In particular we focus on partial occlusions that prevent the tracker from extracting an exact region properties of the object which plays a pivotal role for region based tracking methods in maintaining the track . To this end a dynamical choice of how to invoke the objective functional is performed online based on the degree of dependencies between predictions and measurements of the system in accordance with the degree of occlusion and the variation of the object s pose . This scheme provides the robustness to deal with occlusions of an obstacle with different statistical properties from that of the object of interest . Experimental results demonstrate the practical applicability and robustness of the proposed method in several challenging scenarios . 


Summary: A robust algorithm for 2D visual tracking and 3D pose estimation is proposed. We focus on partial occlusions that distort the region properties of an object. Optimal pose of an object is estimated via particle filters in a decoupled manner. The degree of trust between system s predictions and measurements is controlled. The resulting methodology improves tracking performance in clutter and occlusion.
First order Riesz transform based monogenic signal representation has been widely used in image processing and computer vision however it only characterizes image intrinsic one dimensional structure and is incapable of describing intrinsic two dimensional structure . To this end a novel feature extraction approach named Riesz Binary Pattern is proposed for face recognition based on image multi scale analysis and multi order Riesz transform . RBP consists of two complementary components i.e . local Riesz binary pattern and global Riesz binary pattern . LRBP is obtained by performing local binary coding operator on each Riesz transform response to extract image intrinsic two dimensional structure features . While GRBP is the global binary coding of joint information of image pixel multi scale analysis and multi order Riesz transform . Histogram of LRBP and GRBP are concatenated to form face image RBP description . Experimental results on three databases demonstrate that our proposed RBP descriptor is more discriminant in extracting image information and can provide a higher classification rate compared to some state of the art image representation methods . 


Summary: A new image descriptor RBP is presented for face recognition in this paper. RBP is based on image multi scale analysis and multi order Riesz transform. RBP consists of two complementary components i.e. local Riesz binary pattern LRBP and global Riesz binary pattern GRBP . Experimental results on four databases demonstrate the superiority of our RBP compared with other image representation methods.
Saliency detection has been researched a lot in recent years . Traditional methods are mostly conducted and evaluated on conventional RGB images . Few work has considered the incorporation of multi spectral clues . Considering the success of including near infrared spectrum in applications such as face recognition and scene categorization this paper presents a multi spectral dataset and applies it in saliency detection . Experiments demonstrate that the incorporation of near infrared band is effective in the saliency detection procedure . We also test the combinational models for integrating visible and near infrared bands . Results show that there is no single model to effect on every saliency detection method . Models should be selected according to the specific employed method . 


Summary: A multi spectral dataset is constructed containing RGB and near infrared images. The incorporation of near infrared is proved to be valuable for saliency detection. The best model for integrating RGB and near infrared clues is analyzed.
We address the problem of predicting category labels for unlabeled videos in a large video dataset by using a ground truth set of objectively labeled videos that we have created . Large video databases like YouTube require that a user uploading a new video assign to it a category label from a prescribed set of labels . Such category labeling is likely to be corrupted by the subjective biases of the uploader . Despite their noisy nature these subjective labels are frequently used as gold standard in algorithms for multimedia classification and retrieval . Our goal in this paper is NOT to propose yet another algorithm that predicts labels for unseen videos based on the subjective ground truth . On the other hand our goal is to demonstrate that the video classification performance can be improved if instead of using subjective labels we first create an objectively labeled ground truth set of videos and then train a classifier based on such a ground truth so as to predict objective labels for the set of unlabeled videos . With regard to how we generate the objectively labeled ground truth dataset we base it on the notion that when a video is labeled by a panel of diverse individuals the majority opinion rendered by the panel may be taken to be the objective opinion . In this manner using judgments provided by multiple human annotators we have collected objective labels for a ground truth dataset consisting of randomly selected 1000 videos from the TinyVideos database that contains roughly 52 000 videos from YouTube . Through a fourfold cross validation experiment on the ground truth set we demonstrate that the objective labels have a superior consistency compared to the subjective labels when used for video classification . We show that this claim is valid for several different kinds of feature sets that one can use to compare videos and with two different types of classifiers that one can use for label prediction . Subsequently we use the ground truth dataset of 1000 videos to predict the objective category labels of the remaining 51 000 videos . We compare the objective labels thus determined with the subjective labels provided by the video uploaders and qualitatively argue for the more informative nature of the objective labels . 


Summary: We present a strategy to create objectively labeled ground truth set of videos. Such a strategy mitigates the subjective biases of the annotation process. Objective labels show superior consistency than subjective labels. A classifier is trained to predict objective labels for 51K unlabeled videos.