waashk/dblp huggingface dataset:
a new sammon algorithm for sparse data visualization. sammon's mapping is an important non-linear projection technique that has been widely applied to the visualization of high dimensional data. however when dealing with sparse data, the object relations induced by the map become often meaningless. in this paper, we present a new sammon algorithm (ssammon) that overcomes this problem by previously transforming the dissimilarity matrix in an appropriate manner. the connection between our algorithm and a kernelized version of sammon's mapping is also studied. the new model has been applied to the high dimensional and sparse problem of word relation visualization. we report that ssammon outperforms two widely used alternatives proposed in the literature.
learning an optimal naive bayes classifier. the naive bayes classifier is an efficient classification model that is easy to learn and has a high accuracy in many domains. however, it has two main drawbacks: (i) its classification accuracy decreases when the attributes are not independent, and (ii) it can not deal with nonparametric continuous attributes. in this work we propose a method that deals with both problems, and learns an optimal naive bayes classifier. the method includes two phases, discretization and structural improvement, which are repeated alternately until the classification accuracy can not be improved. discretization is based on the minimum description length principle. to deal with dependent and irrelevant attributes, we apply a structural improvement method that eliminates and/or joins attributes, based on mutual and conditional information measures. the method has been tested in two different domains with good results.
a fast and precise system for taking high-density human head measurements with surrounding range finders. this paper presents a novel head measurement system with surrounding range finders, which enables highly dense 3-d human head shape measurements to be taken quickly and precisely. since short measurement time is as important as the inherent accuracy of instruments for measuring the human body, the system concurrently controls multiple range finders at video rate. it can capture all images of the human head, including the top and thechin, within a single second. moreover, it enables measurements less than 1mm 2 in density to be taken with average error as low as 0.4mm. no conventional systems have both these advantages. the 3-d data acquired by the system will be utilized to design ergonomic products.
real-time k-means clustering for color images on reconfigurable hardware. k-means clustering is a very popular clustering technique, which is used in numerous applications. however, clustering is a time consuming task, particularly for large dataset, and large number of clusters. in this paper, we show that real-time k-means clustering can be realized for large size color images (24-bit full color rgb) and large number of clusters (up to 256) using an off-the-shelf fpga (field programmable gate arrays) board. in our current implementation with one fpga, the performance for 512 × 512 and 640 × 480 pixel images is more than 30 fps, and 20 - 30 fps for 756 × 512 pixel images in average when dividing to 256 clusters.
robust real time tracking of 3d objects. in this article the problem of tracking rigid 3d objects is addressed. the contribution of the proposed approach is an algorithm which combines: efficiency (i.e. the algorithm is designed before all to be real time using standard architecture), robustness (occlusions are allowed) and accuracy (sub-pixel accuracy is obtained). it is devoted to the tracking of 3d rigid objects, assuming that the 3d geometry as well as the texture of the surface is known. such performances can be obtained through a two levels scheme: the core of the approach consists in an efficient 2d patch tracker whose results are combined robustly to compute the 3d object pose. this article provides experimental results proving the soundness of the proposed approach.
quickstroke: an incremental on-line chinese handwriting recognition system. this paper presents quickstroke: a system for the incremental recognition of handwritten chinese characters. only a few strokes of an ideogram need to be entered in order for a character to be successfully recognized. incremental recognition is a new approach for on-line recognition of ideographic characters. it allows a user to enter characters a factor of 2 times faster than systems that require entry of full characters. incremental recognition is performed by a two-stage system which utilizes 68 neural networks with more than 5 million free parameters. to enable incremental recognition, we use specialized time-delay neural networks (tdnns) that are trained to recognize partial characters. to boost the recognition accuracy of complete characters, we also use standard fully-connected neural networks. quickstroke is 97.3% accurate for the incremental writer-independent recognition of 4400 simplified gb chinese ideograms.
combining global and local classifiers with bayesian network. this paper introduces a classification method based on feature space segmentation. since the classification task is equivalent to a probability distribution estimation, a bayesian network is used as an inference mechanism for dealing with the underling probability distribution function that, presumably, is complex and factored. the article presents a method for splitting the feature space into regions that are associated to local classifiers. after that, a bayesian network is used for combining their outputs. experimental results reveal that this is a suitable approach for speeding up the training phase for large databases as well as to ensure good recognition rates.
object localization based on directional information: case of 2d raster data. a directional spatial relationship to a reference object (e.g., "east of the post office") can be represented by a spatial template. the template partitions the space into regions where the relationship holds (to various extents) and regions where it does not hold. the objects for which the relationship holds can then be located. a template can be easily modeled. computationally, however, exact calculation of the model in case of 2d raster data is prohibitively expensive, and a tractable approximation algorithm was proposed. here, we introduce a new concept: the concept of the f-template. it leads to a new approximation algorithm, which is faster, gives better results, and is more flexible.
prototype setting for elastic matching-based image pattern recognition. the purpose of this paper is to emphasize the importance the consistency between the distance measures on prototype setting and discrimination in elastic matching (em)-based recognition. specifically, this paper focuses on the following points: (i) confirmation of performance degradation when euclidean distance is used on prototype setting whereas em-distance is used on discrimination, and (ii) proposal of new prototype setting algorithm where this inconsistency is avoided. through an experiment of handwritten character recognition, the effectiveness of the proposed algorithm was quantified.
reconciling landmarks and level sets. shape warping is a key problem in statistical shape analysis. this paper proposes a framework for geometric shape warping based on both shape distances and landmarks. our method is compatible with implicit representations and a matching between shape surfaces is provided at no additional cost. it is, to our knowledge, the frst time that landmarks and shape distances are reconciled in a pure geometric level set framework. the feasibility of the method is demonstrated with two- and three-dimensional examples. combining shape distance and landmarks, our approach reveals to need only a small number of landmarks to obtain improvements on both warping and matching.
a novel svm geometric algorithm based on reduced convex hulls. geometric methods are very intuitive and provide a theoretically solid viewpoint to many optimization problems. svm is a typical optimization task that has attracted a lot of attention over the recent years in many pattern recognition and machine learning tasks. in this work, we exploit recent results in reduced convex hulls (rch) and apply them to a nearest point algorithm (npa) leading to an elegant and efficient solution to the general (linear and nonlinear, separable and non-separable) svm classification task.
evaluation of correspondence errors for stereo. the computation of a scalar correspondence error is the fundamental step in most stereo algorithms. the quality of the results obtained by the reconstruction algorithm directly depends on the characteristics of such error. we developed a procedure to evaluate different methods proposed for the computation of the correspondence error. the evaluation is based on exploring the shape of the error surface and test it for uniqueness, isolation and compatibility. experiments are reported which assess the behaviour of three different methods for correspondence error calculation. for the scenes used, it is possible to identify the most appropriate method to compute the correspondence error.
attentive visual servoing in the mpeg compressed domain for un-calibrated motion parameter estimation of road traffic. an attentive vision system is proposed that estimates traffic motion parameters from mpeg-2 compressed video recorded using an un-calibrated camera mounted on a pantilt unit. compressed domain information from a stream of mpeg-2 video is used to estimate a traffic motion field and infer the 2d frame location of the traffic lanes. with these visual cues attentive visual servoing automatically adjusts camera pose so as to improve the motion parameter estimation process. an existing technique to acquire motion parameters from an un-calibrated camera is used after obtaining an optimal camera pose. this system provides a computationally inexpensive alternative to existing intelligent transport systems for surveillance.
seeing around occluding objects. this paper presents a novel method for the removal of unwanted image intensity due to occluding objects far from the plane of focus. such occlusions may arise in scenes with large depth discontinuities, and result in image regions where both the occluding and background objects contribute to pixel intensities. the contribution of the occluding object's radiance is modeled by reverse projection, and can be removed from this region by a simple operation on the pixel's intensity. experimental results demonstrate our ability to accurately recover the background's appearance despite significant occlusion. as compared with processing based a linear model of occlusion, the results show lower error and a more accurate contrast.
a bayesian approach to visual size classification of everyday objects. humans are adept at size classification from visual images of objects. a challenging computer vision problem is that of automatic visual size classification. current size classification systems assume controlled environments and use features geared towards a particular object category and pose. however, certain applications may require algorithms that can adapt to a variety of object categories and handle complex environments. in this paper, we propose a bayesian approach to automatic visual size classification, inspired by human visual perception, for a more generalized and robust size classifier. initial results show that the proposed approach can handle multiple object categories and is invariant to scale changes.
learning spatial context from tracking using penalised likelihoods. map estimation of gaussian mixtures through maximisation of penalised likelihoods was used to learn models of spatial context. this enabled prior beliefs about the scale, orientation and elongation of semantic regions to be encoded, encouraging one-to-one correspondences between mixture components and these regions. in conjunction with minimum description length this enabled automatic learning of inactivity zones and entry zones from track data in a supportive home environment.
part-based probabilistic point matching. we present a probabilistic technique for matching partbased shapes. shapes are represented by unlabeled point sets, so discontinuous boundaries and non-boundary points do not pose a problem. occlusions and significant dissimilarities between shapes are explained by a 'background model' and hence, their impact on the overall match is limited. using a part-based model, we can successfully match shapes which differ as a result of independent part transformations a form of variation common amongst real objects of the same class. a greedy algorithm that learns the parts sequentially can be used to estimate the number of parts and the initial parameters for the main algorithm.
automatic color space selection for biological image segmentation. in this paper, we have tested criteria designed by liu and borsotti to automatically evaluate the quality of a color segmentation. as they do not correctly answer our microscopy image problems, we propose two modified criteria adapted to two different biological applications. penalizing inhomogeneity, numerous small regions and misclassified regions, our modified criteria help to select the best color space, for a given segmentation method.
color image segmentation based on markov random field clustering for histological image analysis. in order to characterise the virulence factors of different mycobacterium tuberculosis strains responsible of tuberculosis disease, the quantification, by cell counting, of immune cell recruitment is necessary. however, this task by microscopic observations is very tedious and difficult to reproduce. hence we propose an automatic counting approach, consisting in color image segmentation to discriminate three regions: cell nuclei, immune cells and background,followed by the extraction of each cell entity. for color segmentation, a markov random field clustering approach taking simultaneously into account both color and spatial information is chosen. our technique was sucessfully applied to several color images of different strains, andan evaluation of the results has been performed, showing the robustness of the method against noise, marker color changes, illumination changes and blurring.
generation of a 3-d face model from one camera. the generation of a fully textured 3-d model of a person's face presents difficult technical challenges, buthas many applications in several fields, such as video games, immersive telepresence, and medicine. current commercial systems rely on booth-like set-ups, equippedwith laser-based scanners, or project a pattern on the subject's face.the major drawbacks of such systems are the cost of the hardware they require, and the lack of operational flexibility. we present here a fully automatic system to generate a 3-d model from a sequence of images taken by a single camera. unlike other methods, we do not use a generic 3-d face subject to deformation, but instead proceed in a fully bottom-up fashion.the approach is a two-stage process. first, we estimate for each view the pose of the object with respect to the camera. this is accomplished by robust feature matching and global bundle adjustment. then, we consider sets of adjacent views, which we treat as stereo pairs, and generate partial depth maps, which are then integrated into a single 3-d model. the texture is obtained by merging the images themselves. we describe the algorithm in detail, and show results on a number of real datasets.
affine propagation for surface reconstruction in wide baseline stereo. the problem of dense matching in wide baseline stereo is considered. it is assumed that the scene is formed by piecewise-smooth, lambertian, textured surfaces and that rectified images of the scene are available. we present a novel dense matching algorithm that accounts for local affine distortion and propagates the best matching affine parameters on each surface until a surface discontinuity is reached. disparity maps and projective reconstructions for real-world wide baseline stereo images are shown.
anchor point thinning using a skeleton based on the euclidean distance transformation. thinning is one of the most frequently used methods to know the geometrical feature of objects. it also provides the topological feature and length measurements about an object. for example, the tree structure of the bronchus is determined by using the thinned result of it. this paper presents a three dimensional thinning method which can control the quality of result concerning appearance of spurious short branches by a parameter value.this method is constructed by integrating the anchor point thinning algorithm and the skeletonization algorithm based upon the euclidean metric. we applied the proposed method to artificial figures and a three dimensional bronchus region extracted from a real chest x-ray ct image and confirmed that the proposed method could control the number of spike branches and shrinkage of branches.dr. toyofumi saito, an associate professor who was one of the most active researchers in the field of image processing in japan and a young leader of the author's laboratory, passed away on 26 october 2000. this paper describes one of his last works. we have lost a most reliable and most promising colleague, an experienced supervisor, and a very sincere friend. we would like to dedicate this paper to dr. toyofumi saito and to all those who have shared and cherished the memories of him.
adaptive step size window matching for detection. an often overlooked problem in matching lies in selecting an appropriate step size. the selection of the step size for real-time applications is critical both from the point of view of computational efficiency and detection performance. current systems set the step size in an ad hoc manner. this paper describes an algorithm for selecting the step size based on a theoretical worst case analysis. we have implemented this adaptive step size method in an object detection algorithm. experimental evaluation demonstrates the effectiveness of our proposed algorithm.
image categorization using local probabilistic descriptors. image categorization involves the well known difficulties with different visual appearances of a single object, but introduces also the problem of within-category variation. this within-category variation makes highly distinctive local descriptors less appropriate for categorization. in this paper we propose a family of local image descriptors, called probabilistic patch descriptors (ppds). ppds encode the appearance of image fragments as well as their variability within a category. ppds extend the usual local descriptors by modelling also the variance of the descriptors' elements, e.g. pixels or bins in a histogram. we apply ppds to image categorization by using machine learning where the features are the matching scores between images and ppds. we experiment with two variants of ppds that are based on complementary local descriptors. an interesting observation is that combining the two ppd variants improves categorization accuracy. experiments indicate benefits of modelling the within-category variation and show good robustness with respect to noise.
using signal/residual information of eigenfaces for pca face space dimensionality characteristics. principal component analysis has been used since 1990 [1] in many recognition algorithms to get a face feature representation and to exploit the dimensionality reduction characteristic of the principal component analysis (pca). the way to determine the optimal dimension of the reduced space is still not available. another critical point when working with pca is the influence of the training set, denoted here as pca construction set. in this paper we are working on the behaviour of the signal/residual information of the pcaeigenspectrum in order to determine an optimal threshold that could be used for the dimensionality reduction. we also study the influence of different sets used to construct the pca representation. our experiments are done on the frgcv21 database, using the bee pca baseline software. we also use images from the banca database for the construction of the pca respresentations.
data fusion for 3d gestures tracking using a camera mounted on a robot. this article describes a multiple feature data fusion applied to an auxiliary particle filter for markerless tracking of 3d two-arm gestures by using a single camera mounted on a mobile robot. the human limbs are modelled by a set of linked degenerated quadrics which are truncated by pairs of planes also modelled as degenerated quadrics. the method relies on the projection of both the model's silhouette and local features located on the model surface, to validate the particles (associated configurations) which generate the best model-to-image fittings. our cost metric combines robustly two imaging cues i.e. model contours and colour or texture based patches located on the model surface, subject to 3d joint limits and also non self-intersection constraints. the results show the robustness and versatility of our data fusion based approach.
local visual primitives (lvp) for face modelling and recognition. this paper proposes a novel simple yet effective generative model based on local visual primitives (lvp) for face modeling and classification. the lvps, as the pattern of local face region, are learnt by clustering a great number of local patches. visually, these lvps correspond to intuitive low-level micro visual structures very well, and they are expected to constitute those high-level semantic features, such as eyes, nose and mouth. we show that, though face appearances vary dramatically, these lvps are very effective for face image reconstruction. for face recognition, block-based histograms of the lvps indexes are extracted as the face representation to compare for classification. primary experiments on feret face database have shown that the lvp method can achieve encouraging recognition rate.
ant colony system with extremal dynamics for point matching and pose estimation. for a point-based image registration method, point matching is a hard and a computationally intensive task to handle especially when issues of noisy and outlying data have to be considered. in this paper we cast the problem as a combinatorial optimization task and wedescribe a global optimization method to achieve robust point matching and pose estimation for image registration purpose. the basic idea is to use ant colony system (acs) as a population based search strategy to evolve promising starting solutions i.e affine transformations. an appropriate local search inspired from extremal optimization is developed and embedded within the search strategy to refine the solutions found. experimental results are very promising and show the ability of the method to cope with outliers and to achieverobust pose estimation.
human-robot eye contact through observations and actions. eye contact is an effective means of controlling communication for humans, such as starting communication. it seems that we can make eye contact if we look at each other. in addition, we need to be aware of being watched by each other. in this paper, we propose a method of eye contact between humans and robots considering the above two conditions. then, we present a robot that can recognize hand gestures after making eye contact with the human. experimental results show that the robot can recognize hand gestures without false detection, proving the effectiveness of eye contact as a means of controlling communication.