waashk/dblp huggingface dataset:
simple shadow remova. given the location of shadows, how can we obtain highquality shadow-free images? several methods have been proposed so far, but they either introduce artifacts or can be difficult to implement. we propose here a simple method that results in virtually error and shadow-free images in a very short time. our approach is based on the insight that shadow regions differ from their shadow-free counterparts by a single scaling factor. we derive a robust method to obtain that factor. we show that for complex scenes - containing many disjointed shadow regions- our new method is faster and more robust than others previously published. the method delivers good performance on a variety of outdoor images.
3d shape reconstruction of template models using genetic algorithms. we present in this communication a method, which enables to fit a 3d object defined by a functional representation (frep) to a dataset of 3d points on its surface. a parametric frep model sketching the point-set is fitted to the point-set. the best fitted parameters of the model are obtained by using a genetic algorithm, well known for its interesting properties in non-linear optimization. the efficiency of the approach is illustrated for reverse engineering applications.
school level recognition from children's drawings and writings. this paper presents part of a work aiming at building a tool for the detection of graphomotor difficulties involving disorders in the writing of children. we have defined an experimental protocol, containing exercises, like copying figures or writing sentences under different conditions. it allows to measure simple aspects of graphomotor skill up to complex ones. a great number of features were obtained from on-line children productions. we focus here on the method we used to select low-level features that can describe the automation level of graphic activity. it is based on hierarchical clustering of features and a sequential forward selection. every exercise is represented by two relevant features at least. we show that, in most cases, the selected features allow to recognize the school level of children having a regular schooling but to discriminate children with scholar difficulties as well.
discontinuity-based simplification of free form surface from a range image. this paper improves an existing method for more accurate simplification of free form surface represented as dense points. the main improvement lies in two aspects: one is the introduction of a novel transformation of curvatures, the other is to propose sampling points using both curvature information and curvature variation information. the experimental results based on real images show that the original object surface has been better approximated and the majority of the surface discontinuities have been better preserved with considerably few sampled (5%) data points.
condensation tracking through a hough space. recent work has shown tracking groups of lines throughthe parameter space represented by a hough accumulatorarray to be efficient and insensitive to both occlusion andchanges in illumination. previous methods [object tracking througha hough space, tracking in a hough space with an extended kalman filter]have, however, been less robust in the presence of distractions. distractions are a challenge to trackers of all types, but the problem is heightened in the hough-based approach as short collinear segments give the same response as an equal length connected line. we describe the application of the condensation algorithm to tracking peaks through a hough accumulator array, producing a tracking technique that is efficient and robust under illumination changes, occlusion and distractions.
segmentation and denoising via an adaptive threshold mumford-shah-like functional. this paper introduces an adaptive threshold algorithm based on variational methods which generalizes the mumford-shah and chan-vese functionals. it assumes a piecewise smooth model of the image and a closed contour, realized as the zero level set of a function. this functional is built upon an adaptive threshold surface coupled with the smoothed image. the algorithm uses the image boundaries found during the process of calculating the adaptive threshold surface to also smooth the image while preserving object boundaries, thus also improving the thresholding result. the resulting adaptive threshold surface provides a good approximation of the illumination function and thus can also be used to flatten the image. this method provides good smoothing results even in cases where the image can't be segmented using adaptive thresholding techniques.
membershipmap: data transformation based on membership aggregation. we propose a new data-driven transformation that facilitates many data mining, interpretation, and analysis tasks. our approach, called membershipmap, strives to extract the underlying sub-concepts of each raw attribute, and uses the orthogonal union of these sub-concepts to define a new space. the sub-concept soft labels of each point in the original space determine the position of that point in the new space. since sub-concept labels are prone to uncertainty inherent in the original data and in the initial extraction process, a combination of labeling schemes that are based on different measures of uncertainty will be presented. in particular, we introduce the crispmap, softmap, and possibilisticmap. we show that the membershipmap can be used as a flexible pre-processing tool to support such tasks as: sampling, data cleaning, and outlier detection.
p-channels: robust multivariate m-estimation of large datasets. in this paper we introduce a new technique that allows to estimate modes of a high-dimensional probability density function with linear time-complexity in the number of dimensions and the number of samples. the method can be implemented in an order-independent incremental way, such that the space-complexity is linear in the number of dimensions and the number of modes. the number of required samples to get reliable estimates depends linearly on the number of dimensions even if we replace the assumption of independent stochastic variables with the weaker assumption of data clustered in submanifolds. these submanifolds need not to be known, but smoothness assumptions are made. the new technique is based on representing data in what we call p-channels.
projection and visualization based on synchronization of coupled oscillators. this paper introduces a tool for mapping high dimensional data to a low-dimensional space by combining concepts from clustering and synchronization of pulse-coupled oscillators. each data point is represented by an integrate-and-fire oscillator which is characterized by a phase variable. using an appropriate coupling function, we show that the relative values of the phases evolve to reflect the proximity of the data in the original space. as a result, the oscillators phases could be used as a 1-d representation of the original space. to generate a 2-d phase space, we apply the synchronization procedure twice with different resolution parameters or different distance measures. the proposed approach is used to visualize the classical iris data and two collection of images.
fingerprint indexing using ridge invariants. indexing large fingerprint databases is an important and challenging problem. in this paper, an invariant-based fingerprint indexing scheme is proposed. minutia and surrounding ridges are combined to form a substructure. the invariants describe binary relations between substructures. experimental results on fvc2002 database demonstrate the validity of the proposed algorithm.
building a multi-modal thesaurus from annotated images. we propose an unsupervised approach to learn associations between low-level visual features and keywords. we assume that a collection of images is available and that each image is globally annotated. the objective is to extract representative visual profiles that correspond to frequent homogeneous regions, and to associate them with keywords. these labeled profiles would be used to build a multi-modal thesaurus that could serve as a foundation for hybrid navigation and search algorithms. our approach has two main steps. first, each image is coarsely segmented into regions, and visual features are extracted from each region. second, the regions are categorized using a novel algorithm that performs clustering and feature weighting simultaneously. as a result, we obtain clusters of regions that share subsets of relevant features. representatives from each cluster and their relevant visual and textual features would be used to build a thesaurus. the proposed approach is validated using a collection of 1169 images.
fingerprint representation and matching in ridge coordinate system. fingerprints are generally represented and analyzed in cartesian or polar coordinates. in this paper, however, fingerprints are represented and analyzed in a novel coordinate system, called ridge coordinate system (rcs), which is based on a ridge and an oriented point on the ridge. using rcss based on lots of ridges, we obtain a robust representation scheme of the ridge (skeleton) image of a fingerprint. based on this representation, an alignment-based fingerprint matching algorithm is proposed. experimental results on fvc2002 demonstrate the validity of the proposed algorithm.
recognizing emphysema- a neural network approach. an accurate and fully automatic method for detecting and quantifying emphysema in ct-images is presented. the method is based on an image preprocessing step followed by a neural network classifier trained to separate true emphysema from artifacts. the proposed approach is shown to be superior to an established method when applied on real patient data.
chinese handwriting recognition using hidden markov models. hidden markov model (hmm) has been applied to the problem of machine recognition of chinese handwriting. the character image is segmented into a number of local regions and feature vectors of these regions are extracted. the feature vectors are then used to get the observations for the hmm. the states of the hmm are to reflect the characteristic space structures of the character and its identities are obtained through the training samples usingsome algorithms. two kinds of hmm are built and two more simple nearest neighbor classifiers (nn) based on the vector quantification process in the discrete hmm are employed. the combination of the classifiers is presented. five kinds of features used to get the observations have been tried and three algorithms are adopted to determine the training process. the experimental result indicates the promising prospect of this approach.
pay attention when selecting features. in this paper, we propose a new, hierarchical approach to landmark selection for simultaneous robot localization and mapping based on visual sensors: a biologically motivated attention system finds salient regions of interest (rois) in images, and within these regions, harris corners are detected. this combines the advantages of the rois (reducing complexity, enabling good redetactability of regions) with the advantages of the harris corners (high stability). reducing complexity is important to meet real-time requirements and stability of features is essential to compute the depth of landmarks from structure from motion with a small baseline. we show that the number of landmarks is highly reduced compared to all harris corners while maintaining the stability of features for the mapping task.
a novel fingerprint matching scheme based on local structure compatibility. most minutiae-based matching algorithms confront with the challenge of missing and fake minutiae, and especially the non-linear distortion. many efforts have been made to cope with these problems while relatively slow improvements achieved. in this paper, we proposed a novel minutiae-based matching scheme which introduced a concept of compatibility to the minutiae triangle structures. and based on the compatibility, we further adopted a relaxation process to adjust the similarity matrix of the minutiae triangle cells between the query and template images. to reduce the effect of non-linear distortion evidently, an extended searching step independent of any linear models was proposed. results obtained on the fvc2004 b1_a show that the proposed algorithm overcomes the influence of missing and fake minutiae, and meanwhile decreases the time cost of matching saliently.
automatic segmentation of the knee bones using 3d active shape models. this paper presents an automated segmentation approach for mr images of the knee bones. the bones are the first stage of a segmentation system for the knee, primarily aimed at the automated segmentation of the cartilages. the segmentation is performed using 3d active shape models (asm), which are initialized using an affine registration to an atlas. the 3d asms of the bones are created automatically using a point distribution model optimization scheme. the accuracy and robustness of the segmentation approach was experimentally validated using an mr database of fat suppressed spoiled gradient recall images.
iris localization with dual coarse-to-fine strategy. iris-based personal recognition is highly dependent on the accurate iris localization. in this paper, an effective and efficient iris localization algorithm is proposed to overcome the drawback of the traditional localization methods which are time-consuming and sensitive to the occlusion caused by eyelids and eyelashes. the coarse-to-fine strategy is deployed in both the inner boundary localization and the outer boundary localization. in the coarse localization of the inner boundary, the lower contour of the pupil is introduced to estimate the parameters of the pupil since it is stable even when the iris image is seriously occluded. while in the coarse localization of the outer boundary, the average intensity signals on both sides of the pupil are utilized to estimate the parameters of the sclera after the fine localization of the inner boundary. in the fine stage, the hough transform is adopted to localize both boundaries precisely with the gradient information. experimental results indicate that the proposed method is more effective and efficient.
confidence guided progressive search and fast match techniques for high performance chinese/english ocr. in the past several years, we've been developing a high performance ocr engine for machine printed chinese/english documents. in this paper, we present two innovative techniques that contribute to the high efficiency in recognition of the mixed chinese/english text line. they are (1) a progressive search strategy based on character verification, and (2) a tree-based fast match technique with a confidence-guided adaptive stopping mechanism. the efficacy of the proposed techniques is confirmed by experiments in a benchmark test.
object recognition using local information content. object identification from local information has recently been investigated with respect to its potential for robust recognition, e.g., in case of partial object occlusions, scale variation, noise, and background clutter in detection tasks. this work contributes to this research by a thorough analysis of the discriminative power of local appearance patterns and by proposing to exploit local information content for object representation and recognition. in a first processing stage, we localize discriminative regions in the object views from a posterior entropy measure, and then derive object models from selected discriminative local patterns. object recognition is then applied to test patterns with associated low entropy using an efficient voting process. the method is evaluated by various degrees of partial occlusion and gaussian image noise, resulting in highly robust recognition even in the presence of severe occlusion effects.
a statistical assembled model for segmentation of entire 3d vasculature. we introduce a novel statistical deformable model called samtus for the segmentation of soft tissue tubular structures. the model is composed of an assembly of statistically deformable tubular segments whereby the junctions of the tubular branches are used as landmarks for constructing the underlying point distribution model. the flexibility of samtus is governed by two independent statistical models that describe the axis variation (statistical axis model, or sam) and the cross-sectional radius variation (statistical surface model, or ssm) respectively. we also propose a samtus based segmentation algorithm for an entire tubular structure. the approach has been applied to the segmentation of the three-dimensional vasculature of zebrafish embryo. the efficiency and robustness of this method is evaluated through quantification results on both sectional level and volumetric level.
boosted band ratio feature selection for hyperspectral image classification. band ratios have many useful applications in hyperspectral image analysis. while optimal ratios have been chosen empirically in previous research, we propose a principled algorithm for the automatic selection of ratios directly from data. first, a robust method is used to estimate the kullback-leibler divergence (kld) between different sample distributions and evaluate the optimality of individual ratio features. then, the boosting framework is adopted to select multiple ratio features iteratively. multiclass classification is handled by using a pairwise classification framework. the algorithm can also be applied to the selection of discriminant bands. experimental results on both simple material identification and complex land cover classification demonstrate the potential of this ratio selection algorithm.
self-validated and spatially coherent clustering with net-structured mrf and graph cuts. self-validation and efficient global optimization are two important objectives for feature space clustering. the gibbs energy minimization of markov random field (mrf) provides a general framework for the clustering problem. however, the large computational burdenmakes mostmrfbased methods cannot efficiently achieve these two targets simultaneously. in this paper, we propose a fast clustering approach which is self-validated and guarantees stepwise global optimum. we use the net-structured mrf (ns-mrf) to model the feature space and present an iterative cluster evolution algorithm. for each iteration, the cluster evolving is chosen from three hypotheses, i.e., cluster remaining, cluster merging or cluster splitting, in terms of energy minimization. graph cuts are used to obtain the optimal binary splitting while taking spatial coherence into account. we terminate the evolution process when the whole energy of ns-mrf stops decreasing, thus solve the validation problem. we also provide experimental results and compare our approach with the state of arts.
a novel segmentation and recognition algorithm for chinese handwritten address character strings. this paper presents a new method for segmenting and recognizing chinese handwritten address character strings. first, a dissection algorithm is applied to over-segment string image into radical series so that the correct segmentation could be achieved by merging those radicals according to the correct merging path. then, the method synthesizes layout analysis, isolated character classifier and bigram language model to find the best merging path and the best recognition result. the classifier used in this paper will give every isolated character image ten recognition candidates, each with corresponding recognition confidence. the parameter of bi-gram model is obtained from address database which contains more than one hundred thousand address items. in experiments on 946 mail images, the proposed method achieves correct rate of 87.2 percent.
vision-based preceding vehicle detection and tracking. this paper presents a preceding vehicle detection and tracking system by using support vector machine- based particle filtering (svmpf). svmpf integrates the support vector machine (svm) score with sampling weights. the sample weights, which are used to construct a probability distribution of samples, are measured by the svm score. once the vehicle is detected and tracked, it changes to svm tracking mode which is simpler than the previous svmpf mode. in the experiments, we demonstrate that our system can track the preceding vehicles under different whether conditions.
a clustering-based algorithm for extracting the centerlines of 2d and 3d objects. this paper presents a new algorithm for extracting the centerlines of 2d and 3d objects, based on clustering. the algorithm computes the centerline from all points of the object in order to remain faithful to the structure of the shape. the idea is to cluster a data set constituted of the points composing the object and their relative distance transforms. the centerline is derived from the set of computed clusters. the proposed method is accurate and robust to noisy boundaries.
content-based image retrieval using gabor-zernike features. content-based image retrieval (cbir) is an important research area for manipulating large amount of image databases and archives. extraction of invariant features is the basis of cbir. this paper focuses on the problem of texture and shape feature extractions. we investigate texture feature and shape feature for cbir by successfully combining the gabor filters and zernike moments (gf+zm). gf is used for texture feature extraction and zm extracts shape features. comprehensive performance evaluation of our method is based on three different databases: face database, fingerprint database, and mpeg-7 shape database. the experimental results demonstrate that gf+zm presents robustness to all of the three databases with the best average retrieval rate while the gf and zm are limited for certain databases. gf is effective for face database and fingerprint database but is weak for mpeg-7 shape database. zm achieves high retrieval rate for face database and mpeg-7 shape database but gives relatively low retrieval rate for fingerprint database.
mixture clustering using multidimensional histograms for skin detection. mixture models are frequently used to fit skin color distributions in various color spaces. however, the high computational cost of the conventional em algorithm makes it intractable for large data sets. in this paper, we propose a novel algorithm for estimating the parameters of mixture models. multidimensional histograms are incorporated into the em framework to group neighboring datapoints and reduce the size of the data set. we adopt this method to build gaussian mixture models of skin color and compare the performance of models with different number of components. further experiments on synthetic data show the efficiency of our method as a general approach to data clustering.