waashk/dblp huggingface dataset:
a study of nonlinear shape normalization for online handwritten chinese character recognition: dot density vs. line density equalization. nonlinear shape normalization (nsn) approaches based on line density equalization have been the most popular choice for both offline and online handwritten chinese character recognition (hccr). however, in a recent study of using 8-directional features for online hccr, we discovered that an nsn approach based on dot density equalization achieved a much better performance than that of an nsn approach based on line density equalization. in this paper, we present the details of the nsn approaches we studied for online hccr, and report the comparative experimental results using an in-house developed chinese handwriting corpus as well as the popular nakayosi and kuchibue japanese character databases. we also present an improved nsn approach based on the equalization of dot densities derived from blurred character image that can be used for offline hccr.
a novel eye location algorithm based on radial symmetry transform. a novel and robust eye location algorithm is proposed in this paper. the algorithm is based on a low level, context free generalized symmetry transform. once the regions of interest are detected, characteristics of eyes can be used to improve detection results. the algorithm is tested using 1460 face images of the bioid database and 2730 images of the banca database. a fully automatic face verification system has also been developed using this eye location algorithm. the system was one of the top performers in the 2004 international face verification competition.
detection-assisted initialization, adaptation and fusion of body region trackers for robust multiperson tracking. in this paper, we present a system for simultaneous tracking of multiple persons in a smartroom using multiple cameras. robust person tracks are created, continuously adapted, and deleted by fusing cues from foreground segmentation maps and various appearance-based object detectors. tracking is performed using color histograms which are automatically filtered and adaptated based on local image characteristics. tracks from the various 2d views are merged to 3d position estimates by an intelligent fusion algorithm based on triangulation error reduction. the approach allows to robustly track moving, standing or sitting persons in cluttered environments and to successfully recover lost tracks at any point in the room. we also introduce a new set of metrics to measure multiple object tracking performance. our system reaches a high tracking accuracy with average position errors of less than 17cm.
graph matching using spectral embedding and alignment. this paper describes how graph-spectral methods can be used to transform the node correspondence problem into one of point-set alignment. we commence by using the isomap algorithm to embed the nodes of a graph in a low-dimensional euclidean space. with the nodes in the graph transformed to points in a metric space, we can recast the problem of graph-matching into that of aligning the points. here we use a variant of the scott and longuet-higgins algorithm to find point correspondences. we experiment with the resulting algorithmon a number of real-world problems.
real-time 3d articulated pose tracking using particle filters interacting through belief propagation. this article proposes a new statistical model for fast 3d articulated body tracking, similar to the loose-limbed model, but where inter-frame coherence is taken into account by using the previous marginal probability of each limb as prior information. belief propagation is used to estimate the current marginal for each limb. all probability distribution are represented as sums of weighted samples. the resulting algorithm corresponds to a set of particle filters, one for each limb, where the weight of each sample, after the standard evaluation, is recalculated by taking into account the interactions between limbs. applied to upper-body tracking in disparity and color images, the resulting algorithm estimates the body pose in quasi real-time (12hz).
a pattern recognition scheme for distributed denial of service (ddos) attacks in wireless sensor networks. we define distinct attack patterns depicting distributed denial of service (ddos) attacks against target nodes within wireless sensor networks for three most commonly used network topologies. we propose a graph neuron (gn)-based, decentralized pattern recognition scheme for attack detection. the scheme does analysis of internal traffic flow of the network for ddos attack patterns. we stipulate that the attack patterns depend on both the current energy levels, as well as the energy consumption rates of individual target nodes. the results of varying pattern update rates on the pattern recognition accuracies for the three network topologies are included in the end to test the effectiveness of our implementation.
generic detection of multi-part objects. a method is proposed to detect multi-part man-made or natural objects in complex images. it consists in first extracting simple curves and straight lines from the edge map. then, a search tree is expanded by selecting and ordering the segmented primitives on the basis of generic local and global grouping criteria. the set of partial contours provided by the parallel search are combined into more complex forms. global scores produce a sorted list of potential object silhouettes.
interaction-centric modelling for interactive virtual worlds: the apia approach. conceptual modelling studies the different abstraction methods of the real world. the conception and the execution of virtual worlds depend strongly of the type of conceptual models. existing modelling methods such as object-oriented modelling are not appropriated when the main concern is the dynamic reusability and interoperability. such a reusability and interoperability must be free of any human intervention. this paper presents a new paradigm named interaction-centric modelling (icm) that increases reusability and interoperability of virtual entities and behaviours.
2d grey-level skeleton computation: a discrete 3d approach. a discrete 3d binary approach to compute the skeleton in 2d grey-level images is presented. the 2d grey-level input image is converted to a 3d binary image and the top surface of the foreground is identified. this discrete surface then undergoes skeletonization. the obtained 3d curve skeleton is pruned, before being projected back to a 2d grey-level image. this is suitably post-processed, since the projection may cause spurious loops and thickening. this algorithm can find applications in optical character recognition and document analysis or in other situations where shape analysis by skeletons is desired. an important property of the suggested method is that no hard segmentation into foreground and background is needed prior to the skeletonization.
multiresolution spatial partitioning for shape representation. in this paper, an original solution for shape representation is proposed which relies on a spatial partitioning approach. the representation selects a discrete set of reference points with respect to which a relationship matrix is computed, accounting for the spatial distribution of shape pixels. this is accomplished at different levels of resolution by a tree based representation. depending on the number of points, coarse to fine region and boundary shape information are captured. properties of the representation are discussed and assessed through an experimental evaluation on a set of sample shapes.
modeling spatial relationships between 3d objects. in this paper, we propose an original modeling technique which enables quantitative non-symbolic representation and comparison of the mutual spatial positioning of extended entities in a 3d space. the representation accounts for the overall distribution of relationships among the individual points of two objects. properties of the model are expounded to develop an efficient computation technique and to motivate and assess a metric of similarity for quantitative comparison of spatial relationships between object pairs. experiments compare the proposed approach against two different solutions.
principal flow for tubular objects with non-circular cross-sections. various anatomical objects are tubular in shape. these structures can be modeled by describing their curvilinear path and the cross-sectional shape along the path. however, most research on tubular object segmentation has focused on vascular systems, and often assumes a circular cross-section. these techniques are not readily applicable to anatomy such as the cochlea, which has a non-circular cross-sectional shape. we present the principal flow filter, which calculates the flow vector (tangential to the path) in a local region of a tubular object with a non-circular cross-section. it can be used to extract the centerline orientation and thus incrementally track along the tube. we present results from generated data with a variety of cross-sectional shapes. the filter is shown to rapidly and robustly converge to the true orientation. we also analyse a ct scan of a human cochlea, with promising results.
extraction of resource descriptors for distributed content based image retrieval. content based retrieval from distributed libraries raises new and challenging issues with respect to retrieval from a single repository. in particular, an effective management of distributed libraries develops upon three main processes: resource description (extraction of descriptors that qualify the content of a given archive), resource selection (given a user query, select the resources that contain relevant documents) and results merging (organize and present items returned by individual libraries). so far, these issues have been mainly addressed for text archives. in this paper, we propose a novel approach to the extraction and verificationof resource descriptions for distributed collections of images.
pca for gender estimation: which eigenvectors contribute? a pruning schema is applied to multi-layer perceptron (mlp) gender classifier.mlp uses eigenvector coefficients of the face space created by principal component analysis (pca). we show that pruning improves the initial mlp performance by preserving the most effective input while eliminating most of the units and connections. pruning is also used as tool to monitor which eigenvectors contribute to gender estimation. in addition, by usage of feret face database, we test the pca approach on gender estimation task in bigger setting than the previous experiments.
partitioning of 3d meshes using reeb gra. in this paper, a model is proposed for partitioning of 3d objects based on reeb-graphs. the model is motivated by perceptual principles and supports identification of the main protrusions of an object. experimental results are presented to demonstrate the effectiveness of the proposed solution with respect to ground-truth data represented by manually segmented objects.
subpixel alignment of mri data under cartesian and log-polar sampling. magnetic resonance imaging (mri) allows numerous fourier domain sampling schemes such as cartesian and non-cartesian trajectories (e.g. polar, circular, and spherical). on the other hand, it provides directly the fourier spectrum of the field of view (fov) in the corresponding sampling scheme. motivated by these characteristic features of mri, we have developed a new scheme for direct fourier domain registration of mri data based on the phase difference matrix. we derive the exact relationship between the continuous and the discrete fourier phase-difference for cartesian or polar sampling schemes, and demonstrate that in each case the discrete phase diffence is a 2d sawtooth signal. subpixel alignment under rotation, translation and scale variation is then established simply by counting the number of cycles of the sawtooth signal. the problem is formulated as an over-determined system of equations, and is solved by imposing a regularity constraint, using the method of generalized cross validation (gcv).
a template-matching approach for protein surface clustering. surface-based techniques for protein comparison and classification typically require a compact surface representation, capable of effectively condensing its description. in this paper we propose an original template-matching algorithm for multi-feature surface clustering in the biochemical context. the effectiveness of our clustering algorithm in capturing surface similarities is then discussed within a larger framework for protein classification based on surface comparison, with the support of tests performed on a dataset including 25 proteins.
object-based and event-based semantic video adaptation. semantic video adaptation allows to transmit video content with different viewing quality, depending on the relevance of the content from the user's viewpoint. to this end, an automatic annotation subsystem must be employed that automatically detect relevant objects and events in the video stream. in this paper we present a composite framework that is made of an automatic annotation engine and a semantics-based adaptation module. three new different compression solutions are proposed that work at the object or event level. their performance is compared according to a new measure that takes into account the user's satisfaction and the effects on it of the errors in the annotation module.
early feature stream integration versus decision level combination in a multiple classifier system for text line recognition. this paper compares two different methods to combine feature streams to improve the performance of offline handwritten text line recognition systems. in both methods a pixel-based and a geometric feature stream are combined. the first method integrates the feature streams at an early stage whereas in the second method a combination step at the decision level is applied. in the experiments, the early integration approach outperforms the decision level combination as well as recognisers built from the individual feature streams.
switching particle filters for efficient real-time visual tracking. particle filtering is an approach to bayesian estimation of intractable posterior distributions from time series signals distributed by non-gaussian noise. a couple of variant particle filters have been proposed to approximate bayesian computation with finite particles. however, the performance of such algorithms has not been fully evaluated under circumstances specific to real-time vision systems. in this article, we focus on two filters: condensation and auxiliary particle filter (apf). we show their contrasting characteristics in terms of accuracy and robustness. we then propose a novel filtering scheme that switches these filters, according to a simple criterion, for realizing more robust and accurate real-time visual tracking. the eectiveness of our scheme is demonstrated by real visual tracking experiments. we also show that our simple switching method significantly helps online learning of the target dynamics, which greatly improves tracking accuracy.
mining for implications in medical data. accruing patients for clinical trials has been a tedious and time consuming task for clinicians. it requires extensive knowledge of the specific criteria for all available clinical trials. through interviews with clinicians, implications were discovered which reduced the number of required questions/answers to determine eligibility. after gathering and recording data on past breast cancer patients, the answers to the questions asked by an expert system were extracted. an association rule learner, was used to generate implication rules such as: male => not pregnant. it was determined that all current implication rules could be recovered with 100% confidence. further searching for additional rules resulted in the discovery of several which provided an improvement in the clinical ease of use of the webbased clinical trial assignment expert system.
corner detection using support vector machines. a support vector machine based algorithm for corner detection is presented. it is based on computing the direction of maximum gray-level change for each edge pixel in an image, and then representing the edge pixel by a four dimensional feature vector constituted by the count of other edge pixels lying in a window centred about and having each of the possible four directions as their direction of maximum local gray-level change. a support vector machine is designed using this feature vectors and the support vectors, representing critical points in a classification problem, correspond to the corner points. the algorithm is straightforward and does not involve computation of complex differential geometric operators. it has implicit learning capability resulting in good performance for a wide range of images.
a comparison of pixel, edge andwavelet features for face detection using a semi-naive bayesian classifier. henry schneiderman at carnegie mellon university developed a face detection algorithm based upon a semi-naive bayesian classifier and 5/3 linear phase wavelets. this paper explores the relative value of these wavelet features compared to simpler pixel and edge features. experiments suggest edge features are superior for highly controlled lighting, while pixel features are better and more stable for uncontrolled lighting. tests use the notre dame face data collected in fall 2003 and spring 2004 and use over 400, 000 face and non-face test image chips.
rule extraction from support vector machines: measuring the explanation capability using the area under the roc curve. recently, the area of rule extraction from support vector machines (svms) has been explored. one important indication of the success of a rule extraction method is the performance of extracted rules as compared to the original svm. in this paper, we describe the use of the area under the receiver operating characteristics (roc) curve (auc) to assess the quality of rules extracted from an svm. in particular, we directly compare auc to the more commonly used measures of accuracy and fidelity and show that auc is both a more reliable and meaningful measure to use.
age-related skin analysis by capacitance images. the skin surface characterization has a great importance for dermatologists as well as for cosmetic scientists in order to evaluate the effectiveness of medical or cosmetic treatments. skin topography characterization has been faced by using profilometers and skin replicas to achieve some measurements related to either skin 3d profile or wrinkles. so far, no in vivo measurements regarding skin topography changes have been achieved to evaluate skin ageing. this work describes how a portable capacitive device, normally used for fingerprint acquisition, can be utilized to achieve measures of skin ageing routinely. the capacitive images give a high resolution representation of skin topography, in terms of wrinkles and cells. in this work we have addressed the latter: cells have been segmented using the watershed approach and a feature related to their area distribution has been generated. accurate experiments accomplished in vivo show how the feature we conceived is linearly related to skin ageing.
comparing rank-inducing scoring systems. several methods for comparing rankings are available. these rankings are generally performed on the basis of some scores available for each item. we suggest that it may be better to compare the underlying scores themselves. to this effect, a metric has been provided which is equivalent to comparing the scores after fusing with another set of scores, making it theoretically interesting. this metric is a generalization of kendall distance. in the present article, we assume the distribution of the scores to be fused with to be unknown. some characteristics of the proposed methodology are studied and preliminary experimental results are reported.
graph classification using genetic algorithm and graph probing application to symbol recognition. we present in this paper a graph classification approach using genetic algorithm and a fast dissimilarity measure between graphs called graph probing. the approach consists in the learning of a set of synthetic graph prototypes which are used for a 1nn classification step. some experiments are performed on real data sets, representing 10 symbols. these tests demonstrate the interest to produce prototypes instead of finding representatives which simply belong to the data set.
individual recognition by kinematic-based gait analysis. current gait recognition approaches only consider individuals walking frontoparallel to the image plane. this makes them inapplicable for recognizing individuals walking from different angles with respect to the image plane. in this paper, we propose a kinematic-based approach to recognize individuals by gait. the proposed approach eastimates 3d human walking parameters by performing a least squares fit of the 3d kinematic model to the 2d silhouette extracted from a monocular image sequence. a genetic algorithm is used for feature selection from the estimated parameters, and the individuals are then recognized from the feature vectors using a nearest neighbor method. experimental results show that the proposed approach achieves good performance in recognizing individuals walking from different angles with respect to the image plane.
face recognition from face profile using dynamic time warping. most of the current profile recognition algorithms depend on the correct detection of fiducial points and the determination of relationships among these fiducial points. unfortunately, some features such as concave nose, protruding lips, flat chin, etc., make detection of such points difficult and unreliable. also, the number and position of fiducial points vary when expression changes even for the same person. in this paper, a curvature-based matching approach is presented, which does not require the extraction of all the fiducial points, but uses information contained in the profile. the scale space filtering is used to smooth the profile and then the curvature of the filtered profile is computed. using the curvature value, the fiducial points, such as nasion and throat can be reliably extracted using a fast and simple method. then a dynamic time warping method is applied to match the face profile portion from nasion to throat based on the curvature value. experiments are performed on two profile face image databases. recognition rates and conclusion are presented and discussed.
an fpga-based architecture for real time image feature extraction. we propose a novel fpga-based architecture for the extraction of four texture features using gray level cooccurrence matrix (glcm) analysis. these features are angular second moment, correlation, inverse difference moment, and entropy. the proposed architecture consists of a hardware and a software module. the hardware module is implemented on xilinx virtex-e v2000 fpga using vhdl. it calculates many glcms and glcm integer features in parallel. the software retrieves the feature vectors calculated in hardware and performs complementary computations. the architecture was evaluated using standard grayscale images and video clips. the results show that it can be efficiently used in realtime pattern recognition applications.
robust playfield segmentation using map adaptation. a vital task in sports video annotation is to detect and segment areas of the playfield. this is an important first step in player or ball tracking and detecting the location of the play on the playfield. in this paper we present a technique using statistical models, gaussian mixture models (gmms) and maximum a posteriori (map) adaptation. this involves first creating a generic model of the playfield colour and then using unsupervised map adaptation to adapt this model to the colour of the playfield in each game. this technique provides a robust and accurate segmentation of the playfield. to demonstrate the robustness of the method we tested it on a number of different sports that have grass playfields, rugby, soccer and field hockey.