waashk/dblp huggingface dataset:
progress in document reconstruction. we combine information from a language model and character image pattern matching to iteratively reduce ambiguity in document images. combining word shape information and lists of similar bitmap patterns in a document at least partially resolves the character content without optical character recognition. we present the output in various ways. suitable for human readers or for differing downstream processes.
clothed people detection in still images. we present a trainable system for locating clothed people in photographic images. people detection is a particularly challenging image understanding problem; as a result of variations in clothing and posture, the appearance of people may vary enormously from image to image.our approach attempts to construct a maximally person-like assembly of image regions, where candidate regions are provided by color-based segmentation followed by non-purposive grouping. a tree structured probability model is employed to allow efficient searches. this structure represents the pairwise configuration of body parts as a function of relative position, relative size, and adjacency. face and skin detection is also used to help the search. the problem of occlusion is addressed through a mixture of trees, where the different mixture components represent the possible subsets of visible parts. different clothing styles are accounted for by separate models. experimental results are shown to demonstrate the promise of and challenges for the current system.
a non-random data sampling method for classification model assessment. data sampling is a critical factor for building and evaluating the quality of classifiers, such as neural networks. traditional techniques, such as k-fold cross validation, exhibit limitations when dealing with small data sets. this paper introduces an alternative method that splits the data into training and testing partitions, which have similar statistical characteristics. this method is compared with a traditional technique, using a relatively small dataset and several neural network classifiers. results suggest that this new technique can reduce variability of predictive accuracies and provide consistent results across different classification models.
competitive mixtures of simple neurons. we propose a competitive finite mixture of neurons (or perceptrons) for solving binary classification problems. our classifier includes a prior for the weights between different neurons such that it prefers mixture models made up from neurons having classification boundaries as orthogonal to each other as possible. we derive an em algorithm for learning the mixing proportions and weights of each neuron, consisting of an exact e step and a partial m step, and show that our model covers the regions of high posterior probability in weight space and tends to reduce overfitting. we demonstrate the way in which our mixture classifier works using a toy 2-dimensional data set, showing the effective use of strategically positioned components in the mixture. we further compare its performance against svms and one-hidden-layer neural networks on four realworld data sets from the uci repository, and show that even a relatively small number of neurons with appopriate competitive priors can achieve superior classification accuracies on held-out test data.
small-world approximations in spectral segmentation. spectral segmentation has been shown to produce perceptually meaningful groupings. the underlying similarity matrices are usually very large. several approximations - deterministic and stochastic - areused in practice. the approximations usually use only local information. it has been shown recently that a few random long-range interactions facilitate emergence of structure in several domains like ising models. in this paper we explore the use of long-range interactions in spectral segmentation.
a dynamic approach to learning vector quantization. learning vector quantization networks are generally considered a powerful pattern recognition tool. their main drawback, however, is the competitive learning algorithm they are based upon, that suffers of the so called under-utilized or dead unit problem. to solve this problem, algorithms substantially based on a modified distance calculation, such as the frequency sensitive competitive learning (fscl), have been proposed, but their attainable performance strongly depends on the selection of an appropriate number of neurons. this choice generally require knowledge about the number of clusters in the feature space. in this paper we propose a new supervised training algorithm for lvq neural networks, which provide the optimal number of neurons for each class by dynamically adding or removing neurons on the basis of a measure of their performance. the experimental results, performed on different databases of synthetic data, confirmed the effectiveness of our approach.
improving dynamic learning vector quantization. we introduce some improvements to the dynamic learning vector quantization algorithm proposed by us for tackling the two major problems of those networks, namely neuron over-splitting and their distribution in the feature space. we suggest to explicitly estimate the potential improvement on the recognition rate achievable by splitting neurons in those regions of the feature space in which two or more classes overlap. we also suggest to compute the neuron splitting frequency, and to combine these information for selecting the most promising neuron to split. experimental results on both synthetic and real data extracted from uci machine learning repository show substantial improvements of the proposed algorithm with respect to the state of the art.
a multiresolution approach to on-line handwriting segmentation and feature extraction. we present an approach to on-line handwriting segmentation into elementary strokes. the segmentation is achieved by exploiting curvature information extracted from the electronic ink at different level of resolution. such information is then combined into a saliency map, through which the segmentation points are eventually found. information from the saliency map is also used to select the optimal resolution to be used for describing the curvature of each stroke. experiments conducted by encoding stroke curvature information into strings have shown that similar strings are associated with similar parts of the original ink, independently of the actual word they belong to.
dense stereo based on the uniqueness constraint. the paper presents the matching core of a stereo algorithm suitable to real-time applications. unlike most area-based algorithms, the proposed approach relies on a single matching phase (i.e. do not include the check for left-right consistency). unreliable disparity measuremnts areprimarly detected on the basis of the violation of the uniqueness constraint. in order to further improve the reliability of the matches we enforce additional contraints based on the behaviour of the error function that can be veryfied at a very small computational cost. experimental results show that the proposed approach provides reliable disparity measurements and that it is significantly fast.
3d object digitization: majority interpolation and marching cube. in a previous paper we showed that a 3d object can be digitized without changing the topology if the object is r-regular and if the reconstruction method fulfills certain requirements. in this paper we give two important examples for such reconstruction methods. first, we introduce majority interpolation, an algorithm to interpolate sampling points at doubled resolution such that topological ambiguities are resolved. second, we show how the well-known marching cubes algorithm has to be modified such that it is topology preserving. this is the first approach of digitizing 3d objects which guarantees topology preservation for voxel-based or polygonal surface-based reconstructions.
3d object digitization: majority interpolation and marching cubes. in a previous paper we showed that a 3d object can be digitized without changing the topology if the object is r-regular and if the reconstruction method fulfills certain requirements. in this paper we give two important examples for such reconstruction methods. first, we introduce majority interpolation, an algorithm to interpolate sampling points at doubled resolution such that topological ambiguities are resolved. second, we show how the well-known marching cubes algorithm has to be modified such that it is topology preserving. this is the first approach of digitizing 3d objects which guarantees topology preservation for voxel-based or polygonal surface-based reconstructions.
3d object digitization: topology preserving reconstruction. in this paper we derive a sampling theorem, which is the first one to guarantee topology preservation during digitization of 3d objects. this new theorem is applicable to several reconstruction methods, e.g. a unionof- balls reconstruction and the trilinear interpolation.
a visual attention estimator applied to image subject enhancement and colour and grey level compression. image segmentation technology has immediate application to compression where image regions can be identified and economically coded for storage or transmission. normally segmentation identifies regions that are uniform and homogeneous with respect to some characteristic such as colour or texture and ideally these regions coincide with image objects and thereby offer the potential of huge compression ratios. this paper proposes a technique for colour variability reduction that only affects background material and leaves perceptually important areas unchanged.
class dependent cluster refinement. unsupervised classification is a very common problem in pattern recognition even when the classes are known. in many areas intra-class variations may be greater than the inter-class variations causing a need for a subdivision of the training set of a class into smaller subunits often referred to as clusters. the subdivision or clustering is often performed independently of the relative properties of the other present classes in the recognition task. this paper presents a novel class-dependent approach to the clustering problem. experiments with online handwriting data show that the novel clustering approach cdcr produces a clustering better suited for the task of pattern recognition. although only validated for two recognition methods in this paper, the same approach could be applied to other methods as well as to other pattern recognition problems.
motion imagery navigation using terrain estimates. all aircraft rely on on-board sensor systems for navigation. the drawback to inertial sensors is that their position error compounds over time. global positioning systems (gps) overcome that problem for location but not orientation; also, gps is sensitive to signal dropout and hostile jamming. in this paper, we present a system capable of estimating the six-degree-of-freedom (6dof) state (geo-location and orientation) of an air vehicle given digital terrain elevation data (dted), an estimate of the vehicle's velocity, and a sequence of images from an onboard video camera. this system first reconstructs individual terrain map sections from pairs of images taken by the onboard video camera as it flies over the terrain. the velocity estimate is used to convert the metric units produced by the reconstruction to euclidean units (such as meters). the individual reconstructed map sections are then stitched together to form a larger terrain map. this reconstructed terrain map is then matched against the dted to produce an estimate of the 6dof state of the vehicle.
identifying vehicles using vibrometry signatures. laser vibrometry is an exciting new sensor technology that promises to improve automatic target recognition (atr) performance. sensors such as micro-laser interferometer doppler (mlid) measure minute vibrations on the surface of an object from a large standoff distance. in this paper, we demonstrate a baseline atr algorithm designed for vibrometry data that can reliably recognize military vehicles when trained and tested on similar operating conditions.
estimating the location of illuminants in realist master paintings computer image analysis addresses a debate in art history of the baroque. we address the problem of estimating the position illuminants in realist master paintings. to this end, we apply an algorithm from computer vision that has been used to detect forgeries and composites in digital photographs. our contributions are three-fold: 1) we introduce the application of a two-dimensional, modelfree occluding contour algorithm to the problem of locating the source of illumination in realist master paintings, 2) we introduce a maximum-likelihood criterion and computational method for integrating estimates from multiple occluding contours, and 3) we apply our methods to a key painting adduced as evidence in a recent controversial theory about painting praxis in the renaissance and baroque.
surface skeletons in grids with non-cubic voxels. an algorithm for computing surface skeletons on the face-centered cubic (fcc) grid and the body-centered cubic (bcc) grid is presented. the fcc grid and the bcc grid are three-dimensional grids where the voxels are rhombic do-decahedra and truncated octahedra, respectively. the dt is used to generate the set of centres of maximal balls (cmbs) which will be "anchor points" when constructing the skeleton. simple points are used in order to make the skeleton topologically correct and cmbs to produce a fully reversible skeleton. using only simple points and the cmbs generates a skeleton with a lot of branches. by using a set of additional conditions for removal and preservation of gridpoints, most of these branches are merged into surfaces. for comparison, the algorithm is also implemented for the cubic grid.
using the hexagonal grid for three-dimensional images: direct fourier method reconstruction and weighted distance transform. an image reconstruction technique for computed tomography (ct) images, the direct fourier method, is shown to apply to non-standard grids. in ct, the 3d image is obtained by reconstructing 2d slices separately. we propose to use the hexagonal grid for the 2d slices, resulting in 3d images on non-standard grids. low-level image processing is also considered for these grids - optimal weights to be used for computing the weighted distance transform are calculated.
scale adaptive complexity measure of 2d shapes. in this paper, we describe a complexity (or irregularity) measure of 2d shapes. three properties are first calculated to separately describe the complexity of the boundary, the global structure, and the symmetry of the shape. then, a model consisting of the above parameters are developed to describe the entire complexity of the shape. this model further incorporates the scale information into the boundary complexity definition and also into the determination of weights associated with different properties. finally, we test our complexity model on a synthetic dataset, and demonstrate its application on screening shapes extracted from noisy shoeprint images.
patch-based gabor fisher classifier for face recognition. face representations based on gabor features have achieved great success in face recognition, such as elastic graph matching, gabor fisher classifier (gfc), and adaboosted gabor fisher classifier (agfc). in gfc and agfc, either down-sampled or selected gabor features are analyzed in holistic mode by a single classifier. in this paper, we propose a novel patch-based gfc (pgfc) method, in which gabor features are spatially partitioned into a number of patches, and on each patch one gfc is constructed as component classifier to form the final ensemble classifier using sum rule. the positions and sizes of the patches are learned from a training data using adaboost. experiments on two large-scale face databases (feret and cas-peal-r1) show that the proposed pgfc with only tens of patches outperforms the gfc and agfc impressively.
a multimodal and multistage face recognition method for simulated portrait. recognition of simulated portrait obtained through face composition technique is a challenging task in public security area. an innovative method for simulated portrait recognition is presented in this paper. this method can be used to perform recognition of simulated portraits from anonymous cadaver, from the description of a witness, and from surveillance video. based on principal component analysis (pca), we have constructed eigenface, eigenbrow+ eye, eigeneye, eigennose, and eigenmouth. altogether 31 recognition modes can be formed through different weighted combinations of these eigen-parts. face recognition of simulated portrait is first performed using this multimodal part based pca (mmp-pca)technique, results of which are then used as inputs for further recognition based on modified line segment hausdorff distance (lhd). experiment results show that this innovative recognition method has achieved good results for the recognition of simulated portraits in a database of 100,000 face images.
detecting a gazing region by visual direction and stereo cameras. we develop a wearable vision system that consists of a user's visual direction sensor and stereo cameras. first, we establish a method for calibrating the system so that it can detect user's blink points even in a real situation such that the depth of blink points changes. next, we propose a method for detecting a gazing region of a user in terms of the planar convex polygon. in our method, the system first identifies the fixation point of a use1; and then appliesa stereo algorithm and robust statistics to detect his gazing region. now the system can detect the gazing region of a user and provide him with its 3d position.
obstacle detection using millimeter-wave radar and its visualization on image sequence. sensor fusion of millimeter-wave radar and a camera is beneficial for advanced driver assistance functions such as obstacle avoidance and stop&go. however, millimeter-wave radar has low directional resolution which engenders low measurement accuracy of object position and difficulty of calibration between radar and camera. in this paper, we first propose a calibration method between millimeter-wave radar and ccd camera using homography. the proposed method does not require estimation of rotation and translation between them, or intrinsic parameters of the camera. then, we propose an obstacle detection method which consists of an occupancy-grid representation, and a segmentation technique which divides data acquired by radar into clusters(obstacles); thereafter we display them as an image sequence using calibration results. we demonstrate the validity of the proposed methods through experiments using sensors that are mounted on a vehicle.
camera calibration and reconstruction from the chain connection of mutual camera projections. the relative position and orientation of multiple cameras are very important for reconstructing 3d objects and for generating arbitrary views of the scene. the relationship of these multiple cameras can be described by the epipolar geometry. recently, it has been shown that the epipolar geometry can be computed accurately and efficiently by using the mutual projection of cameras. in this paper, we propose a method for calibrating a large amount of multiple cameras efficiently by extending the mutual projection method for three views. we also show that the proposed method can be applied for reconstructing 3d objects accurately.
graph method for generating affine moment invariants. a general method of systematic derivation of affine moment invariants of any weights and orders is introduced. each invariant is expressed by its generating graph. techniques for elimination of reducible invariants and dependent invariants are discussed. this approach is illustrated on the set of all affine moment invariants up to the weight ten.
hyper frame vision: a real-time vision system for 6-dof object localization. a new system for robot vision is proposed that integrates a 3-d object recognition task and a 3-d object tracking task, enabling real-time 6-dof localization of a known continuously moving object. a computational time-lag between the two tasks is absorbed by a large amount of frame memory. for 3-d sensing, calibrated trinocular stereo cameras are used to employ stereo-vision-based object recognition and tracking methods that allow objects of any shape to be dealt with and to provide robust performance in an environment with partial occlusions and cluttered backgrounds. the effectiveness of the system is demonstrated by experimentalresults.
multi-linguistic optical font recognition using stroke templates. one of the essential distinctions between different fonts is their stroke shape. a method is presented to automatically extract representative stroke templates from a text image, which contains characters of the same typeface. the collected stroke templates are classified and saved to a font database. to recognize an unknown font for an input text image, a bayes decision rule is used to determine which font entrant in the database provides the best matching to the unknown font. the experiment demonstrates that this approach can distinguish between chinese and english fonts without the prior information of their script. another advantage is that it can learn a new font very quickly. forty fonts (twenty english and twenty chinese) are used in our experiment. an average recognition accuracy of 97 percent can be achieved in the present system.
bayesian image segmentation based on an inhomogeneous hidden markov random field. this paper introduces a bayesian image segmentation algorithm with the consideration of label scale variability in many images. an inhomogeneous hidden markov random field is adopted in this algorithm to model the label scale variability as a prior probability. an em algorithm is developed to estimate parameters for both the prior probability and likelihood probability. the image segmentation is established by a map estimator. different images are tested to verify our algorithm. and comparisons with other segmentation algorithms are made. the segmentation results show our algorithm has better performance than others.
active discriminant functions for handwriting recognition. a novel active discriminant functions (adfs) for handwriting recognition is presented in this paper. first, statistical feature based deformable model in principal subspace is proposed and a minimum distance between an unknown pattern and the deformable model is given. second, to improve the accuracy of recognition, the minor subspace is also considered in adfs. third, as parameters of the adfs, the optimal constraints of a deformable model are searched by applying minimum classification error (mce) criterion. finally, empirical experiments are conducted on handwritten chinese characters used in banking and the results show that our proposed adfs outperform other representative techniques, such as support vector machine, multiplayer perceptron, etc.
3d reconstruction of indoor and outdoor scenes using a mobile range scanner. this paper describes 3d mapping of indoor and outdoor environment using a mobile range scanner. in the raw range data preprocessing stage, we propose to use area decreasing flow for surface mesh smoothing instead of the mean curvature flow. although the proposed area decreasing flow approach is mathematically equivalent to the mean curvature flow, it can avoid the difficulty in curvature estimation and provide an optimal flowing step size. geometric details are preserved by adaptive smoothing on crease edges. in the multi-view 3d reconstruction stage, we combine the space carving with hilton's implicit surface-based method to generate watertight 3d models from limited number of scans. volumetric deformation by mean curvature flow using the level set method is applied in the post-processing stage to remove outliers. we present results of 3d reconstruction of real indoor and outdoor environment from multi-view noisy range data.