waashk/dblp huggingface dataset:
adaptive feature integration for segmentation of 3d data by unsupervised density estimation. in this paper, a novel unsupervised approach for the segmentation of unorganized 3d points sets is proposed. the method derives by the mean shift clustering paradigm devoted to separate the modes of a multimodal density by using a kernel-based technique. here, the attention is focused on the selection of the kernel bandwidth which typically strongly affects the level of accuracy of the segmentation results. in particular, a set of geometric features is computed from each 3d point of the given data. this set is projected onto a number of independent sub-spaces, each one associated to a different estimated feature, and overall forming a joint multidimensional (feature) space. in this space, we propose a method for selecting the best multidimensional kernel bandwidth in an automatic fashion, based on stability criteria. the final kernel considers each sub-space in an adaptive way in relation to the discrimination power of each feature, leading to accurate results when dealing with different types of 3d data.
an interactive trajectory synthesizer to study outlier patterns in handwriting recognition and signature verification. a software tool has been developed to simulate complex pen tip trajectories and their corresponding velocity profiles based on the kinematic theory of rapid human movements and its lognormal model. the set of equations used to generate the various signals is shortly introduced. the application interface and the functionalities of the main modules are schematized and described. finally typical simulations results are presented in the context of an interactive comparative analysis of on-line signature data.
relightning of facial video. we present a novel method to relight video sequences given known surface shape and illumanation. the method preserves fine visual details.. it requires single videoframes, approximate 3d shape and standard studio illumanation only, making it applicable in studio production. the technique is demonstrated for relightning video sequences of faces.
adaptative markov random fields for omnidirectional vision. images obtained with catadioptric sensors contain significant deformations which prevent the direct use of classical image treatments. thus, markov random fields (mrf) whose usefulness is now obvious for projective image processing, can not be used directly on catadioptric images because of the inadequacy of the neighborhood. in this paper, we propose to define a new neighborhood for mrf by using the equivalence theorem developed for central catadioptric sensors. we show the importance of this adaptation for a motion detection application.
spatial color component matching of images. color and color neighborhood statistics have been used extensively in image matching and retrieval. however, the effective incorporation of color layout information remains a challenging issue. in this paper we present a novel method for color layout based image matching called spatial color component matching (sccm). first perceptually dominant colors are extracted from an image and are back-projected to segment the image into various areas. then, each dominant color area, depending on its size, is segmented into a number of spatial units using a multilevel graph partitioning algorithm. each unit is described in terms of its colorand a set of spatial attributes to form a spatial color component (scc). all scc's form a list that summarizes the color layout information in an image. the distance between two images is then defined by the minimum distance mapping between the two corresponding scc lists. the algorithm has been evaluated using an image database of wall paper patterns and another database of natural images. it has been judged by human subjects to be highly effective in both cases.
building the topological tree by recursive fcm color clustering. in this paper we define a topological tree (tt) as a knowledge representation method that aims to describe important visual and spatial features of image regions, namely the color similarity, the inclusion and the spatial adjacency. the topological tree exhibits some interesting properties that can be exploited to extract knowledge from images for information retrieval, image understanding and diagnosis purposes. examples of applications in dermatology are described. the tt can be constructed after segmentation, by computing the spatial relationships of regions or can be generated directly during the segmentation: to this aim we present a novel recursive fuzzy c-means (fcm) clustering algorithm based on the principal component analysis of the color space. the recursive fcm proves to be effective for underlining the adjacency and inclusion property of regions.
unsupervised image segmentation using a simple mrf model with a new implementation scheme. a markov random field (mrf) model with a new implementation scheme is proposed for unsupervised image segmentation based on image features. the traditional two-component mrf model for segmentation requires training data to estimate necessary model parameters and is thus unsuitable for unsupervised segmentation. the new mrf model overcomes this problem by introducing a function-based weighting parameter between the two components. this new mrf model is able to automatically estimate model parameters and is demonstrated to produce more accurate image segmentations than the traditional model using a variety of imagery.
probabilistic people tracking for occlusion handling. this work presents a novel people tracking approach, able to cope with frequent shape changes and large occlusions. in particular, the tracks are described by means of probabilistic masks and appearance models. occlusions due to other tracks or due to background objects and false occlusions are discriminated. the tracking system is general enough to be applied with any motion segmentation module, it can track people interacting each other and it maintains the pixel assignment to track even with large occlusions. at the same time, the update model is very reactive, so as to cope with sudden body motion and silhouette's shape changes. due to its robustness, it has been used in many experiments of people behavior control in indoor situations.
probability table compression using distributional clustering for scanning n-tuple classifiers. a method for compressing tables of probability distributions using distributional clustering is presented and applied to shrink the look-up tables of a scanning n-tuple handwritten character recognizer. lossy compression is realized by clustering n-tuples that are observed to induce similar class probability distributions. a new distance metric called "weighted mean kl divergence" is introduced to assess similarity and account for the cumulative effect of merging two distributions. after compression, cluster membership is rebalanced in an annealing-like process. the proposed method is evaluated on three isolated-character subsets of the unipen database. compression ratios in excess of 2000:1 are demonstrated for 5-tuple classifiers.
feature extraction methods applied to the clustering of electrocardiographic signals. a comparative study. in this paper, a method to automatically extract the main information from a long-term electrocardiographic signal is presented. this method is based on techniques of pattern recognition applied to speech processing, like dynamic time warping, and trace segmentation. in order to fulfill this objective, a clustering process is applied to the set of beats present within the electrocardiographic signal. from each group obtained, one beat is taken as representative of all the beats in that cluster.since the discrete sequences of beat features can have different length, the clustering process takes place in a pseudo-metric space, and the dissimilarity measure is calculated using dynamic programming. due to the same reason, the clustering algorithm employed is thekmedians, including some optimizations to reduce the computational cost. an experimental comparative study, using four different feature extraction methods, linear, and non-linear temporal alignment of sequences, is performed using labeled registers from the mit database.
some pattern recognition challenges in data-intensive astronomy. we review some of the recent developments and challenges posed by the data analysis in modern digital sky surveys, which are representative of the information-rich astronomy in the context of virtual observatory. illustrative examples include the problems of an automated star-galaxy classification in complex and heterogeneous panoramic imaging data sets, and an automated, iterative, dynamical classification of transient events detected in synoptic sky surveys. these problems offer good opportunities for productive collaborations between astronomers and applied computer scientists and statisticians, and are representative of the kind of challenges now present in all data-intensive fields. we discuss briefly some emergent types of scalable scientific data analysis systems with a broad applicability.
support vector machines for face recognition with two-layer generated virtual data. this paper presents support vector machines (svm) for few samples-based face recognition with two-layer artificially generated virtual training data. the few samples cannot express all the conditions of the test data. thus, we generalize the samples and the feature data to other conditions according to the distribution. first, correspond to the original face images, by locating the eyes center on the face images and facemask template; second is to the feature vectors, we get the feature data by principal component analysis to the face images, then use linear interpolate and extrapolate methods to generate new data. after all the data drawn, use svm to train and test. in the ict-ycnc face database, the proposed system obtains competitive results, and shows the methods are available.
an iris image synthesis method based on pca and super-resolution. it is very important for the performance evaluation of iris recognition algorithms to construct very large iris databases. however, limited by the real conditions, there are no very large common iris databases now. in this paper, an iris image synthesis method based on principal component analysis (pca) and super-resolution is proposed. the iris recognition algorithm based on pca is first introduced and then, iris image synthesis method is presented. the synthesis method first constructs coarse iris images with the given coefficients. then, synthesized iris images are enhanced using super-resolution. through controlling the coefficients, we can create many iris images with specified classes. extensive experiments show that the synthesized iris images have satisfactory cluster and the synthesized iris databases can be very large.
skin color detection through estimation and conversion of illuminant color using sclera region of eye under varying illumination. the skin color in an image captured by a camera undergoes variation as the illumination changes. therefore, the skin color detection based only on a static model often decreases the detection rate. for enhancing the robust skin color detection using a static model, the illuminant color estimated from images captured under various illumination conditions is converted to a canonical illuminant. this paper first estimates the illuminant color from the pixels in the sclera region of the eyes and then converts it to a canonical color for robust skin color detection. experimental results show that the skin color detection using the proposed method increases the detection rate especially for the images taken in the low or high correlated color temperature of the illumination.
robust tracking of multiple people in crowds using laser range scanners. laser based people tracking systems have been developed for mobile robotic or intelligent surveillance areas. existing systems rely on laser point clustering to extract object locations. however, in a crowded environment, laser points of different objects are often interlaced and undistinguishable and can not provide reliable features. this paper presents a novel and robust laser-based tracking method for people in crowds. firstly, we propose a stable feature extraction method based on accumulated distribution of successive laser frames. then a robust tracking filter is proposed based on the combination of independent bayesian filter and sampling based data association filter. evaluations with real data show that the proposed method is robust and effective. it achieves a significant improvement compared with existing trackers.
feature extraction for the analysis of gait and human motion. in this work we introduce a new model-based approach towards the 3-d tracking and extraction of gait patterns in human motion. we suggest the use of a hierarchical, structural model of the human body with a novel derivation of system dynamics from hard and soft kinematic constraints. the hard constraints place physical limitations on possible model configurations while the soft constraints represent probabilistic distributions learned from previous examples of human motion. using the parameters of the structural and dynamic models, we derive a methodology for extracting a number of gait variables at both coarse and fine resolutions with coincident robustness and precision. in particular, we demonstrate an ability to accurately measure gait velocity, stance width, stride length, arm swing, cadence, and stance times from multi-view, video sequences of human movement captured in a complex home environment.
estimating the optimal quantization parameter in h.264. the quantization parameter (qp) has a very important impact on the compression rate in h.264. in this paper we show that in order to achieve efficient rate-control coding a good estimate for the initial qp parameter is necessary. an extensive altering of this value, to keep the required bitrate, results in significant fluctuation of the image quality and decreases the average quality of the whole coded sequence. we propose a simple and fast method to decide the starting value for qp for each part of the video sequence. experimental results show stabilized image quality and significant gain in psnr.
object detection in video via particle filters. we propose an object detection method using particle filters. our approach estimates the probability of object presence in the current image given the history of observations up to current time. to do so, object presence is modelled by a two-state markov chain, and the problem is translated into sequential bayesian estimation which can be solved by particle filters. the observation density, required by the particle filter is based on selected discriminative haar-like features that were introduced by viola and jones [7] for object detection in static images. we illustrate the approach on the problem of face detection. experiments on real video sequences show the feasability of the approach.
nearest neighbor ensemble. recent empirical work has shown that combining predictors can lead to significant reduction in generalization error. the individual predictors (weak learners) can be very simple, such as two terminal-node trees; it is the aggregating scheme that gives them the power of increasing prediction accuracy. unfortunately, many combining methods do not improve nearest neighbor (nn) classifiers at all. this is because nn methods are very robust with respect to variations of a data set. in contrast, they are sensitive to input features. we exploit the instability of nn classifiers with respect to different choices of features to generate an effective and diverse set of nn classifiers with possibly uncorrelated errors. interestingly, the approach takes advantage of the high dimensionality of the data. the experimental results show that our technique offers significant performance improvements with respect to competitive methods.
detection of microcalcifications clusters in mammograms through ts-mrf segmentation and svm-based classification. at present, mammography is the only not invasive diagnostic technique allowing the diagnosis of a breast cancer at a very early stage. a visual clue of such disease particularly significant is the presence of clusters of microcalcifications. reliable methods for an automatic detection of such clusters are very difficult to accomplish because of the small size of the microcalcifications and of the poor quality of the digital mammograms. a method designed for this task is described. the mammograms are firstly segmented by means of the tree structured markov random field algorithm which extracts the elementary homogeneous regions of interest on the image. such regions are then submitted to a further analysis (based both on heuristic rules and support vector classification) in order to reduce the false positives. the approach has been successfully tested on a standard database of 40 mammographic images, publicly available.
classification of textures distorted by waterwaves. in this paper, we approach the novel problem of classifying images of underwater textures as observed from outside the water. our main contribution is to combine a geometric distortion removal algorithm with a texture classification method to solve the problem of classifying images of submerged textures when the water is disturbed by waves. we show that by modeling the separate types of distortion, we can extract enough texture information to correctly classify textures using spatial statistical measurements on the texton representations. we evaluate our algorithm on both natural and artificial textures acquired in our laboratory. results are promising and show the feasibility of our algorithm.
a ball detection algorithm for real soccer image sequences. a large number of methods for circle detection has been studied in the last years for numerous image processing applications. the application domain considered in this paper is the soccer game. to identify the ball in soccer images is very important in order to evaluate the goal event. this domain is challenging as a great number of problems has to be managed, such as occlusions, shadows, objects similar to the ball, real time processing. aim of this work is to present the results of a number of experiments obtained by using a modified version of the directional circle hough transform. different lighting conditions have been considered since they introduce strong modifications on the appearance of the ball in the image: when the image sequences are taken with natural light the ball appears as a spherical cap then the search of the ball has been modified in order to manage those situations. a large number of experiments has been carried out showing that the proposed method obtains an high detection score.
discriminant features for model-based image databases. a challenging topic in content-based image retrieval is to determine the discriminant features that improve classification performance.an approach to learn concepts is by estimating mixture model for image databases using em algorithm; however, this approach is impractical to be implemented for large databases due to the high dimensionality of the feature space.based on the over-splitting nature of our em algorithm and the bayesian analysis of the multiple users' labelling information derived from their relevance feedbacks, we propose a probabilistic mda to find the discriminating features, and integrate it with the em framework.the experimental results on corel images show the effectiveness of concept learning with the probabilistic mda, and the improvement of the retrieval performance.
an algorithm for real time eye detection in face images. the problem of eye detection in face images is very important for a large number of applications ranging from face recognition to gaze tracking. in this paper we propose a new algorithm for eyes detection that uses iris geometrical information for determining in the whole image the region candidate to contain an eye, and then the symmetry for selecting the couple of eyes. the novelty of this work is that the algorithm works on complex images without constraints on the background, skin color segmentation and so on. different experiments, carried out on images of subjects with different eyes colors, some of them wearing glasses, demonstrate the effectiveness and robustness of the proposed algorithm.
gesture segmentation from a video sequence using greedy similarity measure. we propose a novel method of greedy similarity measure to segment long spatial-temporal video sequences. firstly, a principal curve of motion region along frames of a video sequence is constructed to represent trajectory. then from the constructed principal curves of trajectories of predefined gestures, hmms are applied to modeling them. for a long input video sequence, greedy similarity measure is established to automatically segment it into gestures along with gesture recognition, where true breakpoints of its principal curve are found by maximizing the joint probability of two successive candidate segments conditioned on the gesture models obtained from hmms. the method is flexible, of high accuracy, and robust to noise due to the exploitation of principal curves, the combination of two successive candidate segments, and the simultaneous recognition. experiments including comparison with two established methods demonstrate the effectiveness of the proposed method.
a framework for grid-based image retrieval. in this paper we present a grid-based framework for image retrieval. in order to represent the intricate composition of images, the grid-based approach partitions each image into blocks from which a feature representation is derived from the local low-level content. since the background often dominates the subject in the foreground, a special query selection method was developed. it combines the salient region-of-interest/query-by-exampleparadigm with coarse segmentation to remove the irrelevant background regions. the proposed search method looks for similar features across all block positions and at several scales. existing local grid-based methods are constrained by searching for objects in the same position as the query object. using this framework, the spatial constraint can be eliminated, and steps toward scale invariance can be taken. promising results show that the grid-based method performs better than global search.
a computational framework for automatic determination of morphological parameters of proximal femur from intraoperative fluoroscopic images. determination of morphological parameters of proximal femur using different image modalities is discussed. in this paper, a computational framework based on particle filter is proposed for automatic determination of morphological parameters of proximal femur from intraoperative fluoroscopic images. in this framework, the proximal femur is decomposed into three constrained components: (1) femoral head, (2) femoral neck, and (3) femoral shaft. each component is represented by a set of parameters describing its three-dimensional (3d) spatial position and 3d geometrical shape. particle filter based inference is then used to estimate those parameters from the acquired fluoroscopic images. the constraints between different components are modeled by a single directional rational network to improve the efficiency and robustness of the proposed approach. we report the initial quantitative and qualitative evaluation results on a plastic femur, which indicate the validity of our approach.