waashk/dblp huggingface dataset:
a new linear calibration method for paracatadioptric cameras. we propose a new calibration method for the paracatadioptric cameras using one image of at least three observed lines. this method is based on the geyer & daniilidis [2] one but has two main advantages. first, a geometric distance is used to compute the camera parameters instead of an algebraic distance. second, it allows to deal with lines that are projected to straight lines or to circular arcs in an unified manner. we provide a geometric interpretation of the algorithm: the line images are firstly projected to a virtual paraboloid, then planes are fitted on these projections and their intersection finally provides the camera parameters. thanks to this new formulation, the method is also able to deal very efficiently with outliers. we compare results with existing methods from geyer & daniilidis [2] and from barreto & araujo [3].
nonlinear multiscale graph theory based segmentation of color images. in this paper the issue of image segmentation within the framework of nonlinear multiscale watersheds in combination with graph theory based techniques is addressed. first, a graph is created which decomposes the image in scale and space using the concept of multiscale watersheds. in the subsequent step the obtained graph is partitioned using recursive graph cuts in a coarse to fine manner. in this way, we are able to combine scale and feature measures in a flexible way: the feature-set that is used to measure the dissimilarities may change as we progress in scale. we employ the earth mover's distance on a featureset that combines color, scale and contrast features to measure the dissimilarity between the nodes in the graph. experimental results demonstrate the efficiency of the proposed method for natural scene images.
off-line handwritten textline recognition using a mixture of natural and synthetic training data. in this paper the problem of off-line handwritten cursive text recognition is considered. a method for expanding the set of available training textlines by applying random perturbations is presented. the goal is to improve the recognition performance of an off-line handwritten textline recognizer by providing it with additional synthetic training data. three important issues - quality, variability, and capacity - relatedto this method are discussed, and a basic strategy to make use of the possibility of expanding the training set by synthetic textlines is proposed. it is shown that significant improvement of the recognition performance is possible even when the original training set is large and the textlines are provided by many different writers.
tool wear estimation from acoustic emissions: a model incorporating wear-rate. almost all prior work on modeling the dependence of acoustic emissions on tool wear have concentrated on the effect of wear-level on the sound. we give justification for including the wear-rate information contained in the sound to improve estimation of wear. a physically meaningful model is proposed which results in a hidden markov model (hmm) whose states are a combination of the wear-level and rate and observations are the feature vectors extracted from the sound. we also present an efficient method for picking feature vectors that are most useful for the classification problem.
detecting irregularities in regular patterns. this study compares three different methods designed for detecting irregularities from regular dot patterns. frequency domain information is used to split an original regular pattern into two images: the first image contains the perfect repeating pattern and the second one includes all irregularities in the original image. the methods are based on the fourier transform, but they differ in how they separate or utilize the regular and irregular image parts. performances of these methods are compared, and their strengths and weaknesses are discussed.
human motion signatures: analysis, synthesis, recognition. human motion is the composite consequence of multiple elements, including the action performed and a motion signature that captures the distinctive pattern of movement of a particular individual. we develop a new algorithm that is capable of extracting these motion elements and recombining them in novel ways. the algorithm analyzes motion data spanning multiple subjects performing different actions. the analysis yields a generative motion model that can synthesize new motions in the distinctive styles of these individuals. our algorithms can also recognize people and actions from new motions by comparing motion signaturesand action parameters.
classification probability analysis of principal component null space analysis. in a previous paper [a linear classifier for gaussian class conditional distributions with unequal covariance matrices], we have presented a new linear classification algorithm, principal component null space analysis (pc-nsa) which is designed for problems like object recognition where different classes have unequal and non-white noise covariance matrices. pcnsa first obtains a principal components space (pca space) for the entire data and in this pca space, it finds for each class 'i', and m{i} dimensional subspace along which the class's intra-class variance is the smallest.we call this subspace an approximate null space (ans) since the lowest variance is usually "much smaller" than the highest.a query is classified into class 'i' if its distance from the class's mean in the class's ans is a minimum.in this paper, we discuss the pcnsa algorithm more precisely and derive tight upper bounds on its classification error probability.we use these expressions to compare classification performance of pcnsa with that of subspace linear discriminant analysis (slda)[subspace linear discriminant analysis for face recognition].
experiments on gait analysis by exploiting nonstationarity in the distribution of feature relationships. we consider the use of nonstationarity in the distribution of feature relationships over time for walking gait-based recognition. we statistically model the features of a person by computing the distribution of the relations among the features, rather than the features themselves. theserelational distributions of feature relations are represented as points in a space of probability functions (sopf). our database presently consists of twenty subjects walking out-doors along three different paths at 0° (frontal-parallel), 22° and 45° with respect to the image plane and walking in both directions, left to right and right to left. we performed statistical tests to demonstrate that variations between persons are statistically more significant than the variations due to walking angles and walking directions. we also present identification results on people walking at different directions and different angles.
an improved approach to generating realistic kanji character images from on-line characters and its benefit to off-line recognition performance. this paper proposes a method for generating realistic calligraphic kanji character images from on-line data. the proposed method is an improvement of our former method presented in [1]. our new method can cope also with connected on-line strokes, i.e., stroke number variations, which were not correctly painted in our previous method. the new method decomposes strokes into three different parts (end part, bend part, connecting part), and paints each part according to a prototypical shape assigned to it from a calligraphic stroke shape library.our generated calligraphic off-line images serve two purposes: first, they provide additional training samples for off-line recognition. second, they allow application of off-line methods in on-line recognition. this paper also presents some experiments for these purposes.
an evaluation of face and ear biometrics. face recognition based on principal component analysis is a heavily researched topic in computer vision. the ear has been proposed as a biometric, with claimed advantages over the face. we have applied the pca approach to images of the face and ear using the same set of subjects. testing wass done with three different gallery/probe combinations. for faces we have: 1) probes of same day but different expression, 2) probes of a different day but similar expression, and 3) probes of different day and different expression. analogously, for ears, we have: 1) probes of same day but other ear, 2) probes of a different day but same ear,and 3) probes of different day and other ear. results indicate that the face provides a more reliable biometric than the ear.
accelerating the computation of 3d gradient vector flow fields. in 3d segmentation, a deformable model can be efficiently guided by a gradient vector flow (gvf) field. the computation of a gvf field consists mainly of solving a huge discretized system of elliptic partial differential equations. these discrete equations have several properties that can be utilized to accelerate the process of finding an approximate solution. here, stationary iterative methods, preconditioned conjugate gradient methods, and multigrid methods are considered in order to compute the gvf field at computational times acceptable for interactive 3d segmentation.
biometric hash based on statistical features of online signatures. this paper presents a new approach to generate biometric hash values based on statistical features in online signature signals. whilst the output of typical online signature verification systems are threshold-based true-false decisions, based on a comparison between test sample signals and sets of reference signals, our system responds to a signature input with a biometric hash vector, which is calculated based on an individual interval matrix.especially for applications, which require key management strategies (e.g. e-commerce, smart cards), hash values are of great interest, as keys can be derived directly from the hash value, whereas a verification decision can only grant or refuse access to a stored key. further, our new approach does not require storage of templates for reference signatures, thus increases the security of the system.in our prototype implementation, the generated biometric hash values are calculated on a pen-based pda and used for key generation for a future secure data communication between a pda and a server by encryption. first tests show that the system is actually able to generate stable biometric hash values of the users and although the system was exposed to skilled forgeries, no test person was able to reproduce another subject's hash vector. during tests, we were able to tune the system to a far of 0% at a frr level of 7.05%.
an algorithm for cutting 3d surface meshes. the cutting operation of 3d surface meshes plays an important role in surgery simulators. one of the important requirements for surgical simulators is the faithful representation of interaction paths of a surgical tool. we propose a new strategy for cutting on surface meshes: refinement and separate strategy consisting of the refinement followed by the separation of the refined mesh element.the proposed strategy gives the faithful representation of interaction paths.
motion from focus. based on the triangulation method, the 3d motion of an object can be completely recognized by a stereo camera. however, the question whether or not the 3d motion of an object can be completely recognized by a motionless / fixed monocular camera is the yet-unanswered question. in this paper we propose a method using a motionless monocular camera of which the focus is changed in cycle to recognize the absolute 3d motion of an object.
on the influence of fixing the principal point in frame-by-frame multiplanar calibration. calibrating sequences were a camera moves without zooming is often considered as a pure viewpoint estimation problem. here, we demonstrate that considering varying principal point while keeping the focal length constant allows us to obtain a reprojection error similar to the one obtained with full calibration while imposing existing constraints on the camera (no zooming). then, we propose a robust calibration process which can be used for any scene that contains planar structures. robustness is obtained by estimating independently the principal point position and the viewpoint.
an efficient technique for protein sequence clustering and classification. in this paper, a technique to reduce time and space during protein sequence clustering and classification is presented. during training and testing phase, the similarity score value between a pair of sequences is determined by selecting a portion of the sequence instead of the entire sequence.it is like selecting a subset of features for sequence data sets.the experimental results of the proposed method shows that the classification accuracy (ca) using the prototypes generated/used do not degrade much but the training and testing time are reduced significantly. thus the experimental results indicate that the similarity score need not be calculated by considering the entire length of the sequence for achieving a good ca.even space requirement is reduced during execution phase.we have tested this using k-medians, supervised k-medians and nearest neighbour classifier (nnc) techniques.
automatic detection of intestinal juices in wireless capsule video endoscopy. wireless capsule video endoscopy is a novel and challenging clinical technique, whose major reported drawback relates to the high amount of time needed for video visualization. in this paper, we propose a method for the rejection of the parts of the video resulting not valid for analysis by means of automatic detection of intestinal juices. we applied gabor filters for the characterization of the bubble-like shape of intestinal juices in fasting patients. our method achieves a significant reduction in visualization time, with no relevant loss of valid frames. the proposed approach is easily extensible to other image analysis scenarios where the described pattern of bubbles can be found.
data mining applied to acoustic bird species recognition. in this work we explore the application of data mining techniques to the problem of acoustic recognition of bird species. most bird song analysis tools produce a large amount of spectral and temporal attributes from the acoustic signal. the identification of distinctive features has become critical in resource constrained applications such as habitat monitoring by sensor networks. reducing computational requirements makes affordable to run a classifier on devices with power consumption constraints, such as nodes in a sensor network. experimental results demonstrate that considerable dimensionality reduction can be achieved without significant loss in classification efficiency.
computation of rotation local invariant features using the integral image for real time object detection. we present a framework for object detection that is invariant to object translation, scale, rotation, and to some degree, occlusion, achieving high detection rates, at 14 fps in color images and at 30 fps in gray scale images. our approach is based on boosting over a set of simple local features. in contrast to previous approaches, and to effi- ciently cope with orientation changes, we propose the use of non-gaussian steerable filters, together with a new orientation integral image for a speedy computation of local orientation.
image disocclusion using a probabilistic gradient orientation. in this paper we devise a new method to remove occlusions in an image by using its level-lines. we take into account the error in the computation of their orientation by introducing a field of probabilities for the level-lines orientations. we use second order partial differential equations for this field and the image to interpolate in the occluded part.
noisy text categorization. this work presents categorization experiments performed over noisy texts. by noisy, we mean any text obtained through an extraction process (affected by errors) from media other than digital texts (e.g., transcriptions of speech recordings extracted with a recognition system). the performance of a categorization system over the clean and noisy (word error rate between \sim 10 and \sim 50 percent) versions of the same documents is compared. the noisy texts are obtained through handwriting recognition and simulation of optical character recognition. the results show that the performance loss is acceptable for recall values up to 60-70 percent depending on the noise sources. new measures of the extraction process performance, allowing a better explanation of the categorization results, are proposed.
sociometry based multiparty audio recordings summarization. this paper shows how social network analysis, the study of relational data in specific social environments, can be used to summarize multiparty radio news recordings. a social network is extracted from each recording and it is analyzed in order to detect the role of each speaker (e.g. anchorman, guest, etc.). the role is then used as a criterion to select the segments that are more representative of the recording content. the results show that the length of the recordings can be reduced by more than 90 percent while still preserving most of the information about their content.
offline cursive word recognition using continuous density hidden markov models trained with pca or ica features. this work presents an offline cursive word recognition system dealing with single writer samples. the system is based on a continuous density hiddden markov model trained using either the raw data, or data transformed using principal component analysis or independent component analysis. both techniques significantly improved the recognition rate of the system.preprocessing, normalization and feature extraction are described as well as the training technique adopted. several experiments were performed using a publicly available database. the accuracy obtained is the highest presented in the literature over the same data.
multiple rectangle model for vuildings segmentation and 3d scene reconstruction. this paper introduces a method for automatic extraction of buildings in aerial images. we first present a method based on rectangular buildings, which are the most common constructions. after a rough segmentation, we estimate a criterion of similarity of each region with the best matching rectangle. for buildings of complex shapes, we introduce an iterative way to divide a region in order to optimize its approximation by a set of rectangles. we use a parametric deformable model for refining rectangle size and positions. these rectangles are then used to enhance a 3d realistic reconstruction of the scene including building models.
divide-and-conquer algorithm for creating neighborhood graph for clustering. k-nearest neighbor graph has been used for reducing the number of distance calculations in pnn-based clustering. the bottleneck of the approach is the creation of the graph. in this paper, we develop a fast divide-and-conquer method for graph creation based on algorithm previously used in the closest pair problem. the proposed algorithm is then applied to agglomerative clustering, in which it outperforms previous projection-based algorithm for high dimensional spatial data sets.
a fast and efficient ensemble clustering method. ensemble of clustering methods is recently shown to perform better than conventional clustering methods. one of the drawback of the ensemble is, its computational requirements can be very large and hence may not be suitable for large data sets. the paper presents an ensemble of leaders clustering methods where the entire ensemble requires only a single scan of the data set. further, the component leaders complement each other while deriving individual partitions. a heuristic based consensus method to combine the individual partitions is presented and is compared with a well known consensus method called co-association based consensus. experimentally the proposed methods are shown to perform well.
a pattern synthesis technique with an efficient nearest neighbor classifier for binary pattern recognition. important factors affecting the efficiency and performance of the nearest neighbor classifier (nnc) are space, classification time requirements and for high dimensional data, due to the curse of dimensionality, the training set size should be large. in this paper we propose novel techniques to improve the performance of nnc and at the same time to reduce its computational burden. a compact representation of the training set along with an efficient nnc which does implicit pattern synthesis is presented. a comparison of empirical results is made with relevant methods.
l-dbscan : a fast hybrid density based clustering method. density based clustering techniques like dbscan can find arbitrary shaped clusters along with noisy outliers. a severe drawback of the method is its huge time requirement which makes it a unsuitable one for large data sets. one solution is to apply dbscan using only a few selected prototypes. but because of this the clustering result can deviate from that which uses the full data set. a novel method proposed in the paper is to use two types of prototypes, one at a coarser level meant to reduce the time requirement, and the other at a finer level meant to reduce the deviation of the result. prototypes are derived using leaders clustering method. the proposed hybrid clustering method called l-dbscan is analyzed and experimentally compared with dbscan which shows that it could be a suitable one for large data sets.
robust local max-min filters by normalized power-weighted filtering. a normalized, power-weighted averaging filter (npf) is a very good approximation to the well-know local maximum and minimum filters along the object edges and offers noise reduction in foreground and background regions. this favorable combination turns out to be very effective in image smoothing, edges detection and image sharpening. it offers a clear improvement over the existing operators based on true max-min filtering. our filter can be implemented very efficiently by a table lookup (for the power) and two (separable) convolutions.
multi-orientation analysis by decomposing the structure tensor and clustering. the structure tensor yields an excellent characterization of the local dimensionality and the corresponding orientation for simple neighborhoods, i.e. neighborhoods exhibiting a single orientation. we show that we can disentangle crossing structures if the tensor scale is much larger than the gradient scale. mapping the gradient vectors to a continuous orientation representation yields a ½d(d+1)- dimensional feature vector per pixel. clustering of the vectors in this new space allows identification of multiple orientations. each cluster of gradient vectors can be analyzed separately using the structure tensor approach. proper clustering yields an unbiased estimate of the underlying orientations.
a statistical shape model without using landmarks. this paper describes the construction of a statistical shape model based on the iterative closest point algorithm. the method does not require manual nor automatic identification of explicit landmarks on example shapes. corresponding features are found by retrieving the nearest points via interpolation along the surface. the application to analyse carpal bone shape renders evidence that the lunate bone occurs in distinct types.
a comparison of techniques for automatic clustering of handwritten characters. this work reports experiments with four hierarchical clustering algorithms and two clustering indices for on-line handwritten characters. the main motivation of the work is to develop an automatic method for finding a set of prototypical characters which would represent well the different writing styles present in a large international database. one of the major obstacles in achieving this goal is the uneven representation of different writing styles in the database. on the basis of the results of the experiments, we claim that a good set of prototypes can be formed from the combined results of the different clustering algorithms. however, the number of clusters cannot be determined automatically but some human intervention is required.