waashk/dblp huggingface dataset:
an adaptive icp registration for facial point data. an algorithm for finding coupling points plays an important role in the iterative closest point algorithm (icp) which is widely used in medical imaging and 3-d architecture applications. in recent researches of finding coupling points, approximate k-d tree search algorithm (ak-d tree) is an efficient nearest neighbor search algorithm with comparable results. we proposed adaptive dual ak-d tree search algorithm (adak-d tree) for searching and synthesizing coupling points as significant control points to improve the registration accuracy in icp registration applications. adak-d tree utilizes ak-d tree twice in different geometrical projection orders to reserve true nearest neighbor points used in later icp stages. an adaptive threshold in adak-d tree is used to reserve sufficient coupling points for a smaller alignment error. experimental results are shown that the registration accuracy of using adak-d tree is improved than of using ak-d tree and the computation time is acceptable. we also design a system gui based on the proposed algorithm to register the facial point data which are extracted from prestore ct imaging and captured via range scan equipments or a 3-d digitizer.
automated detection of solar loops by the oriented connectivity method. an automated technique to segment solar coronal loops from intensity images of the sun's corona is introduced. it exploits physical characteristics of the solar magnetic field to enable robust extraction from noisy images. the technique is a constructive curve detection approach, constrained by collections of estimates of the magnetic field's orientation. its effectiveness is evaluated through experiments on synthetic and real coronal images.
robust vehicle detection based on shadow classification. the multi-level shadow classification has been shown to provide reliable information on the presence of vehicles in traffic scenes. the method is based on classifying the shadow shapes into six categories at each threshold level. non-overlapping shadow shapes with higher priority are selected at each level. shadow-reshaping capability makes the resulting shadow information robust to the variation of operating conditions. unlike other approaches, vehicle movement information between frames is not utilized; thereby the traffic parameters can be measured quantitatively even when the vehicle movement is not observed. also the detecting performance is not affected by the abrupt change of weather because background information is not utilized.
face reconstruction with low resolution facial images by feature vector projection in kernel space. in spite of increasing interest in person identification based on biometrics, face recognition technology has not been applied into real world. it is caused by appearance changes such as illumination, noise, degradation, and occlusion. among these problems, we focus on the low resolution problem and propose a new face recognition method of extending the svdd(support vector data description). in the proposed method, we first solve the svdd problem for the data belonging to the given prototype facial images, and model the data region for the normal faces as the ball resulting from the svdd problem. next, for each input facial image in low resolution, we project its feature vector onto the decision boundary of the svdd ball so that it can be tailored enough to belong to the normal region. finally, we synthesize facial images which are obtained from the preimage of the projection, and then perform the face recognition. the applicability of the proposed method is illustrated via some experiments using general recognition algorithm.
fusion of chaotic measure into a new hybrid face-gait system for human recognition. in this paper we describe a method to recognize people using face and gait features in a novel yet natural way, using a single camera. we show that frontal-normal motion analysis yields more dynamic information as compared to fronto-parallel motion and allows us to analyse gait in a new way, using nonlinear dynamics of time series normally used in chaos theory. a set of point light sources attached to various points of a walking person allows the walker to be identified. phase-space analysis of trajectories of these moving light displays (mlds) provides sufficient information for identification of people by their gait. using chaotic measures to identify humans by their gait is a significant precedent. to demonstrate the usefulness of this result, we perform face recognition in a nonideal environment and show that by augmenting with gait data, we get better recognition rates, providing a more robust identification scheme..
robust multiclass ensemble classifiers via symmetric functions. we introduce a generalization to the multiclass framework of a previous approach to boosting by constructing symmetric functions. this approach contrasts with the usual adaboost-type boosting algorithms using linear separators. indeed, multiclass induction does not necessitate combination tricks such as those for linear separators, and it achieves some novel agnostic learning properties, as well as significant malicious noise tolerance. experiments on a large testbed against adaboost and c4.5 display the efficiency of the approach proned.
supervised image classification by som activity map comparison. this article presents a method aiming at quantifying the visual similarity between two images. this kind of problem is recurrent in many applications such as object recognition, image classification, etc. in this paper, we propose to use self-organizing feature maps (som) to measure image similarity. to reach this goal, we feed local signatures associated to salient patches into the neural network. at the end of the learning step, each neural unit is tuned to a particular local signature prototype. during the recognition step, each image presented to the network generates a neural map that can be represented by an activity histogram. image similarity is then computed by a quadratic distance between histograms. this scheme offers very promising results for image classification with a percentage of 84.47% of correct classification rates.
a two level classifier process for audio segmentation. we are dealing in this paper with audio segmentation. we propose a two level segmentation process that enables the audio tracks to be sampled in short sequences which are classified into several classes. the segmentation is performed by computing several features for each audio sequence. these features are computed either on a complete audio segment or on a frame (set of samples) which is a subset of the audio segment. the proposed approach formicrosegmentation of audio data consists of a combination of a k-means classifier at the segment level and of a multidimensional hidden markov model system using the frame decomposition of the signal. a fir st classification is obtained using the k-means classifier and segment-based features. then final result comes from the use of multi-dimensional hidden markov models and frame-based features involving temporary results. multidimensional hidden markov models are an extension of classical hidden markov model dedicated to multicomponents data. they are particularly adapted in our case where each audio segmentcan be characterized by several features of different nature.
adaboost tracker embedded in adaptive particle filtering. due to computational simplicity, low-level visual cues (such as color, contour and corner) have been widely integrated into various visual trackers. however, the robustness of these trackers will be challenged by cluttered backgrounds, partial occlusion and varying illuminations. in many applications, the classes of the interested objects to be tracked are usually known in advance. hence, high-level information from trained classifiers can be fused into the visual tracker to overcome the above limitations. in this paper, a novel approach is proposed to integrate all weak and strong classifiers of the trained adaboost cascade into the observation model of sampled particles in particle filtering. furthermore, in order to track objects undergoing non-stationary movement, decisions of the whole boosted cascade on all sampled particles are incorporated to adapt the proposal distribution of sampling for better approximation of the desired posterior distribution. experimental results on tracking different objects over indoor and outdoor video sequences have shown the proposed tracker is able to effectively handle rapid movements, partial occlusion, varying illuminations, and large changes of scale and viewpoint.
an ubm-based reference space for speaker recognition. the universal background model represents the speaker independent distribution of features, so it can be used to construct a reference space for speaker recognition. in the anchor models, one speaker utterance can be located at one point in the anchor space. we construct the reference space using the gaussian distributions in the universal background model instead of the virtual speakers in the anchor models, and one speaker's all utterances mainly locate in a small portion of the whole space. on the other hand, we use the support vector machine to separate this speaker dependent portion from the whole space while the euclidean distance measure is used in the anchor models in general. the experiments on the yoho database show that our method can get better performance comparing with the decision fashion based on the euclidean distance which is widely used in the anchor models.
efficient gaussian mixture for speech recognition. this article presents a clustering algorithm to determine the optimal number of components in a gaussian mixture. the principle is to start from an important number of mixture components then group the multivariate normal distributions into clusters using the divergence, a weighted symmetric, distortion measure based on the kullback-leibler distance. the optimal cut in the tree, i.e. the clustering, satisfies criteria based on either the minimum amount of available training data or dissimilarities between clusters. the performance of this algorithm is compared favorably against a reference system and a likelihood loss based clustering system. the tree cutting criteria are also discussed. about an hour of ester, a french broadcast news database is used for the recognition experiments. performance are significantly improved and the word error rate decreases by about 4.8%, where the confidence interval is 1%.
experiments on eigenfaces robustness. this paper is an experimental study on the robustness of the eigenfaces method for face recognition. to build a face recognition system, especially in an unconstrained surveillance system where a clear, direct, and normalized view of the face cannot be assumed, one needs to implement several image preprocessing steps like segmentation, deskewing, zooming, rotation, warping, etc., before processing the face image per se. our aim is to determine how efficient these preprocessing steps must be in order to apply the eigenfaces method with success. the experiments are conducted on a subset of the ar-face color image database.real images are used and altered synthetically to study the effects of 7 parameters that can be translated into corresponding preprocessing artifacts: horizontal and vertical translations, downsampling, zooming, rotation, morphing and lighting.
recognition of non-negative patterns. principal component analysis (pca) is one of the most important and popular tools used to recognize and compress patterns. the principal components are the basis functions that minimize the mean-squared error and they can be used as matched filters. in many pca applications it has been observed that the eigenvector with the largest eigen-value has only non-negative entries when the vectors of the underlying stochastic process have only non-negative values. this has been used to show that the coordinate vectors in pca are all located in a cone. this in turn can be used to construct invariants, for tracking or compression (see [estimation of illumination characteristics, two stage principal component analysis of color]). in this paper we will show how this empirical observation can be rigourously proved. for the case of patterns described by vectors we use the perron-frobenius theory of non-negative matrices to investigate stochastic processes of finite-dimensional patterns that assume only non-negative function values. we will show that they always have a first eigenfunction that assumes only non-negative values. we will also describe the conditions under which the first eigenfunction has strictly positive values. for stochastic processes of patterns in hilbert (or banach) spaces we will use versions of the krein-rutman theory to prove the non-negativity of the first eigenfunction. in contrast to the finite-dimensional case we will see that this formulation gives a more direct connection to the conical structure of the underlying pattern space. as a concrete example we will sketch how these results can be used in multi-spectral color processing.
lie methods in color signal processing: illumination effects. in this paper we describe illumination changes with the help of elements in the lorentz group su(1,1). we show how lie-theoretical methods can be applied to solve problems related to illumination changes. we derive partial differential equations that describe the changes in the space of color signals. we show how these changes effect the induced variations in the space of rgb vectors. we illustrate the application of these methods with two examples: in the first example we derive a simple linear equation system that links the pointwise pixel changes to the parameters of the illumination change. in the second example we construct operators in the rgb space that either compensate illumination changes or predict the effects of illumination changes.
complex human activity recognition for monitoring wide outdoor environments. the problem of automatic recognition of human activities is among the most important and challenging open areas of research in computer vision. this paper presents a new approach to automatically recognize complex human activities embedded in video sequences acquired with a large scale view in order to monitoring wide area (car parking, archeological site. etc) with a single static camera. the recognition process is performed in two steps: at first the human body posture isestimated frame by frame and then the temporal sequences of the detected postures are statistically modeled. body postures are estimated starting from the binary shapes associatedto humans, selecting as features the horizontal and vertical histograms and supplying them as input to an unsupervised clustering algorithm. the manhattan distance is used for both clusters building and run-time classification. statistical modeling of the detected postures is performed by discrete hiddenmarkov models. the system has been tested on image sequences acquired in an outdoor archaeological site. four kinds of activities have been automatically classified with high percentage of right decisions.
continuous activity recognition with missing data. human activity recognition involves several problems like changes when an activity is performed by different persons. this means the people can perform the same activity faster or slower and also the way that an activity is performed can change, therefore we can have different trajectories representing the same activity. another problem exists when we do not have the whole trajectory because of occlusion or noise. in this work, an approach for human activity recognition based on the fourier transform and bayesian networks is presented. this approach can recognize activities performed at different velocities by different people and can work with missing data. it performs continuous activity recognition without the necessity of manually indicating when the activity starts or finishes.
non-overlapping distributed tracking using particle filter. tracking people or objects across multiple cameras is a challenging research area in visual computing especially when these cameras have non-overlapping field-of-views. the important task is to associate a target of interest with its previous appearances across time and space within the camera network. in this paper, we propose a unified tracking framework using particle filter to efficiently switch between track prediction (to deal with non-overlapping region tracking) and visual tracking. the particle filter tracking system uses a map to provide the possible trajectory information of the target as it moves within the non-overlapping regions. we implemented and tested this tracking approach in an in-house multiple cameras system. promising results were obtained which suggested the feasibility of such an approach.
linear and non-linear geometric object matching with implicit representation. this paper deals with the matching of geometric objects including points, curves, surfaces, and subvolumes using implicit object representations in both linear and non-linear settings. this framework can be applied to feature-based non-linear image warping in biomedical imaging with the deformation constrained to be one-to-one, onto, and diffeomorphic. moreover, a theoretical connection is established between the well known hausdorff metric and the framework proposed in this paper. a general strategy for matching geometric objects in both 2d and 3d is discussed. the corresponding euler-lagrange equations are presented and gradient descent method is employed to solve the time dependent partial differential equations.
learning bayesian networks for cytogenetic image classification. we experimentally learn structures of bayesian networks classifying signals enabling genetic abnormality diagnosis. structures learned based on the naive bayesian classifier, expert knowledge or using the k2 algorithm are compared. inferiority of the k2-based classifier has motivated an investigation of the algorithm initial ordering, search procedure and metric. replacing the k2 search with hill-climbing search improves accuracy as does the inclusion of hidden variables into the structure. however, it is proved experimentally that this inferiority of the k2-based classifier is mainly due to the k2 metric soliciting structures having enhanced representability but limited classification accuracy.
image classification for genetic diagnosis using fuzzy artmap. we investigate the fuzzy artmap (fa) in off and online image classification for diagnosis of genetic abnormalities. we evaluate the classification task (detecting abnormalities separately or simultaneously), classifier paradigm (monolithic or hierarchical), ordering strategy (averaging or voting), training mode (for one epoch, with validation or until completion) and sensitivity to parameters. we find the fa accurate in achieving the tasks requiring only few training epochs. superiority is found for the voting strategy and training until completion mode. compared to other classifiers, the fa does not loose but gain accuracy when overtrained. its accuracy is comparable with those of the multi-layer perceptron and support vector machine and superior to those of the naive bayesian and linear classifiers.
texture and profile features for drawing media recognition in underdrawings. in this study we analyze texture and profile features of painted strokes in order to identify the drawing media used for sketching underdrawings. underdrawings are preliminary drawings on the panel prepared for paintings and are unseen in the finished work. cameras working in the near infrared range allow the visualization of underdrawings. due to the tiny width of the strokes we perform an alignment of the feature extraction windows in order to obtain a major content of the stroke texture. the method is tested on strokes applied on test panels and underdrawing strokes in ir images of medieval paintings.
selecting vantage objects for similarity indexing. to make similarity searching in multimedia databases practical, indexing has become a necessity. vantage indexing is an indexing technique which maps a dissimilarity space onto a vector space such that each object is represented by a vector of dissimilarities to a small set of m reference objects, the vantage objects. querying takes place within this vector space, reducing the number of distance calculations to m. the retrieval performance of a system based on this technique can be improved significantly through a proper choice of vantage objects. we propose a new technique for selecting vantage objects and present experimental results based on data sets of different modality.
an energy minimisation approach to stereo-temporal dense reconstruction. we propose a novel energy minimisation framework for the dense reconstruction of stereo image sequences that incorporates data delity as well as spatial and temporal regularity. an iterated dynamic programming scheme is proposed to minimise the energy function. we also present an efficient implementation of the minimisation scheme by introducing morphological decomposition techniques to solve the dynamic programming subproblem. our proposed method is capable of reconstructing dynamic scenes with complex motion. results are presented demonstrating the strength of our proposed algorithm.
b-spline snakes in two stages. in using snake algorithms, the slow convergence speed is due to the large number of control points to be selected, as well as difficulties in setting the weighting factors that comprise the internal energies of the curve. even in using the b-spline snakes, splines cannot be fitted into the corner of the object completely. in this paper, a novel two-stage method based on b-spline snakes is proposed. it is superior both in accuracy and fast convergence speed over previous b-spline snakes. the first stage reduces the number of control points using potential function v(x,y) minimization. hence, it allows the spline to quickly approach the minimum energy state. the second stage is designed to refine the b-spline snakes based on the node points of the polynomials without knots. in other words, an elasticity spline is controlled by node points where knots are fixed. simulation and validation of results are presented. compared to the traditional b-spline snakes, better performance was achieved using the method proposed in this paper.
normalization of contrast in document images using generalized fuzzy operator with least square method. the visual effect of non-uniform contrast and brightness surrounds in the image is a very common problem in the applications of photocopying, ic manufacture and medicine. in using the digital/ccd camera to capture documents and photos based on non-uniform illumination condition, the poor image will be seen. the poor image can result in achieving the inaccurate reading from the optical character recognition (ocr) system. this paper present a new approach to normalize the local contrast in documentation based on the least square method and also enhance the object of interest using generalized fuzzy operator (gfo). two typical examples are used for evaluating the method.
ica-fx features for classification of singing voice and instrumental sound. this paper describes a new approach in locating the segments of singing voice in pop musical songs. initially, glr distance measure is employed to temporally detect the boundaries of singing voices and instrumental sounds. ica-fx is then adopted to extract the independent components of acoustic features for svm classification. experimental results indicate that ica-fx can improve the classification performance by significantly reducing the independent components that are not related to class label information.
sparse scene structure recovery from atmospheric degradation. scene structure can be recovered from images taken under different visibility conditions. we propose a new method using derivatives of images in order to accomplish this. two images of the same scene are needed, each acquired from the same viewpoint under different conditions of visibility. our approach was tested with images of both virtual and real scenes and results confirm its validity.