waashk/dblp huggingface dataset:
hybrid off-line cursive handwriting word recognition. in this paper, we present an off-line cursive word handwriting recognition methodology. this is based on an additive fusion resulted after a novel combination of two different modes of word image normalization and robust hybrid feature extraction. we employ two types of features in a hybrid fashion. the first one, divides the word image into a set of zones and calculates the density of the character pixels in each zone. in the second type of features, we calculate the area that is formed from the projections of the upper and lower profile of the word. the performance of the proposed methodology is demonstrated after testing with the reference iam cursive handwriting database.
perspective pose estimation from uncertain omnidirectional image data. omnidirectional vision is highly beneficial for robot navigation. we present a novel perspective pose estimation for omnidirectional vision involving a parabolic central catadioptric sensor using small data sets. we incorporate an appropriate and approved stochastic method to deal with uncertainties in the data. our approach is robust in that it is more accurate than recent methods while using less precise hardware without rigorous calibration.
split and merge data association filter for dense multi-target tracking. bayesian target tracking methods consist in filtering successive measurements coming from a detector. in the presence of clutter or multiple targets, the filter must be coupled with an association procedure. classical bayesian multi-target tracking methods rely on the hypothesis that a target can generate at most one measurement per scan and that a measurement originates from at most one target. when tracking a high number of deformable sources, the previous assumptions are often not met, leading existing methods to fail. here, we propose an algorithm which allows to perform the tracking in the cases when a single target generates several measurements or several targets generate a single measurement. the novel idea presented in this paper is the introduction of a set that we call virtual measurement set which supersedes and extends the set of measurements. this set is chosen to optimally fit the set of predicted measurements at each time step. this is done in two stages : i) a set of feasible joint association events is built from virtual measurements that are created by successively splitting and merging real measurements; ii) the joint probability is maximized over all feasible joint association events. the method has been tested on microscopy image sequences which typically contains densely moving objects and gives satisfactory preliminary results.
genre-based search through biomedical images. we exploit the retrieval of visual information from biomedical scientific publication databases. therefore, we consider the use of domain specific genres to automatically subdivide large image databases into smaller, consistent parts. combination with latent semantic indexing onthe picture captions allows for efficient retrieval of images in specific categories. we demonstrate our approach on a large collection of images with captions from the elsevier brain research publications. initial result demonstrate the power of the proposed combination.
effect of noise on model selection criteria in visual applications. in this paper, the effect of noise level on the performance of a large number of existing model selection criteria for two important computer vision applications (motion and range segmentation) has been investigated. the results of our experiments show that although the performance of all model selection criteria deteriorates by increasing the level of noise, the surface selection criterion (ssc) remains the criterion of choice for different noise levels.
the model selection criteria as merging criteria. a comparative study of a wide range of different model selection criteria for merging 3d range data is presented. first, we measure and compared the success rate of every criterion in detecting step and crease discontinuities in 3d range data followed by measuring the performance in merging non-existent discontinuities. as a conclusion, we have recommended a small group of criteria that are suitable for all cases.
incremental vehicle 3-d modeling from video. in this paper, we present a new model-based approach for building 3-d models of vehicles from color video provided by a traffic surveillance camera. we incrementally build 3d models using a clustering technique. geometrical relations based on 3d generic vehicle model map 2d features to 3d. the 3d features are then adaptively clustered over the frame sequence to incrementally generate the 3d model of the vehicle. results are shown for both simulated and real traffic video. they are evaluated by a new structural performance measure underscoring usefulness of incremental learning.
a psychological adaptive model for video analysis. extracting key-frames is the first step for efficient content-based indexing, browsing and retrieval of the video data in commercial movies. most of the existing research deals with "how to extract representative frames?" however the unaddressed question is "how many key-frames are required to represent a video shot properly?" generally, the user defines this number a priori or some heuristic methods are used. in this paper, we propose a psychological model, which computes this number adaptively and online, from variation of visual features in a video-shot. we incorporate it with an iterative key-frame selection method to automatically select the key-frames. we compare the results of this method with two other well-known approaches, based on a novel effectiveness measure that scores each approach based on its representational power. movie-clips of varying complexity are used to underscore the success of the proposed model in real-time.
nearest-prototype relevance feedback for content based image retrieval. effective retrieval of images from databases can be attained by adopting relevance feedback mechanisms. the vast majority of such mechanisms that have been proposed so far are based on modifying either the query point, or the feature space, or the similarity measure, so that the average similarity between pairs of relevant images is as minimum as possible. in this paper, a relevance feedback technique based on the nearest-neighbor rule is proposed. for each image of the database, a relevance score is computed as the ratio between the distances from the nearest non-relevant and relevant images respectively. relevance is thus related to "local" properties of the images rather than to the estimation of global properties. reported results on the corel dataset show that the proposed mechanism allows attaining large improvements in retrieval precision compared to other mechanisms.
constructing visual taxonomies by shape. we investigate the use of statistical shape measures for segmented image regions to construct taxonomies of visual similarity. it is demonstrated that without the use of a priori knowledge, cluster analysis can be used to impose structure on heterogeneous image data sets. we develop visual taxonomies to accomplish moderate classification tasks, and provide a framework for more powerful, open-ended analysis of large data sets. the power of this method is demonstrated using a visual taxonomy of textual data, which is shown to be efficient in an mdl context.
a general framework for agglomerative hierarchical clustering algorithms. this paper presents a general framework for agglomerative hierarchical clustering based on graphs. different hierarchical agglomerative clustering algorithms can be obtained from this framework, by specifying an inter-cluster similarity measure, a subgraph of the â-similarity graph, and a cover routine. we also describe two methods obtained from this framework called hierarchical compact algorithm and hierarchical star algorithm. these algorithms have been evaluated using standard document collections. the experimental results show that our methods are faster and obtain smaller hierarchies than traditional hierarchical algorithms while achieving a similar clustering quality.
expectation-maximization for a linear combination of gaussians. we propose a modified expectation-maximization algorithm that approximates an empirical probability density function of scalar data with a linear combination of gaussians (lcg). due to both positive and negative components, the lcg approximates inter-class transitions more accurately than a conventional mixture of only positive gaussians. experiments in segmenting multi-modal medical images show the proposed lcg-approximation results in more adequate region borders.
to frame or not to frame in probabilistic texture modelling? the maximum entropy principle is a cornerstone of frame (filters, random fields, and maximum entropy) model considered at times as a first-ever step towards a universal theory of texture modelling or even as "the inevitable texture model". this paper disputes such opinions. that a wealth of exponential families of probability distributions is deduced from the me principle is well known for decades. the me property by itself in no way leads to an adequate probabilistic description, and to model a particular texture, specific limitations have to be imposed on signal statistics. frequency distributions of outputs from a bank of linear filters (the second frame's cornerstone) are hardly the only choice outperforming all other alternatives. the paper points also to other hidden drawbacks of frame.
accuracy of the regularised dynamic programming stereo. binocular stereo is an ill-posed problem that has to be regularised with respect to partial occlusions and homogeneous textures for obtaining a unique solution. we analyse typical errors of the dynamic programming stereo based on a probabilistic regularisation of partial occlusions. experiments show that the reconstruction is accurate until occlusions do not prevail over the binocularly visible terrain.
concurrent stereo under photometric image distortions. we have improved our concurrent stereo matching (csm) algorithm, which abandons the search for 'best' matches and determine matches that lie within admissible ranges using a noise model. we estimate photometric deviations between corresponding regions of stereo pairs with photometric transformations and mismatched or occluded regions. we allow for global, disparity dependent contrast and offset (gain and dark noise) distortions as well as multiple outliers. noise is estimated for each pixel at each disparity level and the csm framework applied. outliers are eliminated with a statistical model and likely matching volumes identified. then, starting in the foreground, the volumes are explored to select mutually consistent optical surfaces. finally, local, not global, surface continuity and visibility constraints are applied to generate a disparity map. this approach compares well with other matching algorithms: the more realistic matching model allows for signal contrast and offset variations over the whole image.
pixel position regression - application to medical image segmentation. pixel position regression (ppr), an automatic supervised method for image segmentation, is presented. the method uses a set of corresponding points indicated in each train image. for each point in this set, the mean position in all train images is determined. by warping the set of corresponding points to their mean positions, one can associate with each position in each train image a reference position. ppr estimates the reference position from a rich set of local image features through k-nearest-neighbor regression. the deformation field thus obtained determines the segmentation. it is demonstrated that the deformation field estimate can be improved by (weighted) blurring and more sophisticated methods such as global modeling of the deformation field through principal component analysis and iterated regression. the method is evaluated on a set of chest radio-graphs in which the lung fields, heart and clavicles are segmented.
image denoising with k-nearest neighbor and support vector regression. denoising is an important application of image processing, especially for medical image data. these images tend to be very noisy when a low radiation dose, less harmful to the patient, is used for acquisition. for computed tomography (ct) data, it is possible to simulate realistic low dose images from the raw scanner data. we use this data to construct a supervised denoising system, that learns an optimal mapping from input features to denoised voxel values. as input features we use several general filters and the output of existing standard noise reduction filters, notably non-linear diffusion schemes. after feature selection, these are mapped to the denoised values by k-nearest neighbor and support vector regression. the resulting regression denoising systems are shown to perform significantly better than non-linear diffusion schemes, gaussian smoothing and median filtering in experiments on ct chest scans.
text detection in images based on unsupervised classification of high-frequency wavelet coefficients. text localization and recognition in images is important for searching information in digital photo archives, video databases and web sites. however, since text is often printed against a complex background, it is often difficult to detect. in this paper, a robust text localization approach is presented, which can automatically detect horizontally aligned text with different sizes, fonts, colors and languages. first, a wavelet transform is applied to the image and the distribution of high-frequency wavelet coefficients is considered to statistically characterize text and non-text areas. then, the k-means algorithm is used to classify text areas in the image. the detected text areas undergo a projection analysis in order to refine their localization. finally, a binary segmented text image is generated, to be used as input to an ocr engine. the detection performance of our approach is demonstrated by presenting experimental results for a set of video frames taken from the mpeg-7 video test set.
learning optimal classifier through fuzzy recognition rate maximization. the adjustment of the metric (features weighting) and the optimisation of the position of prototypes in the feature space are two of most important problems in minimum distance classifiers. this paper presents a new method to deal with these two problems based on the maximisation of a fuzzy recognition rate functional. the functional is a result of an easy to understand mathematical formulation. experimental results on the recognition of binary cursive characters and gray-level container code characters follow. a comparison with the standard lvq method is also made and discussed for the cursive character case.
comparative analysis of decision-level fusion algorithms for 3d face recognition. 3d shape-based face recognition algorithms can be improved by using decision-level fusion algorithms. in this work, we present a comparative analysis of various fusion algorithms, and also propose novel ones. the contributions of the paper can be summarized as: i) a through analysis of several decision-level fusion algorithms, ii) a dynamically estimated reliability-assisted fusion schemes, and iii) a novel implementation of lda-based cascaded serial fusion algorithm. experiments conducted on the 3d_rma dataset confirm that serial fusion offers the best solution, and dynamic calculation of reliability estimates improves the accuracy of the standard fusion schemes.
comparing optimal bounding ellipsoid and support vector machine active learning. in this paper we propose two active learning algorithms combining statistical active learning methods based on svm and optimal bounding algorithms (obe) of adaptive system identification. we unify svm and obe by demonstrating the similarities and representing svm in an obe interpretation. samples are judiciously selected based on a volume measure provided by obe using both simple heuristic and greedy optimal strategies. preliminary experiments illustrate the effectiveness of the proposed algorithms as compared to similar methods.
finding regions of interest in document images by planar hmm. we present a new stochastic approach based on planar hidden markov models (phmm) for finding regions of interest (roi) in document images. the main advantage of the proposed approach is that no explicit rules characterizing the rois have to be defined. instead, the phmm learns to find the rois based on training data which show examples of document images together with the rois. the method has been tested on the task of finding the legal amount in real check images.
estimation of 3d motion from stereo images- differential and discrete formulations. in this paper we analyze the problem of motion estimation from a sequence of stereo images. we formulate both the differential and discrete approaches of two methods. the differential approach uses differential optical flow whereas the discrete approaches uses feature correspondences. we use both methods to compute, first, the 3d velocity in the z direction and, second, the complete motion parameters. the methods were extensively tested using synthetic images as well as real images and several conclusions are drawn from the results. we point out the critical factors for the methods. the real images are used without any illumination control of the scene in order to analyze the behavior of the methods in strongly noisy environments and low resolution depth maps.
rigid motion estimation from non-central catadioptric images. this paper addresses the problem of rigid motion estimation and 3d reconstruction in vision systems where it is possible to recover the incident direction from image points. such systems include pinhole cameras and catadioptric cameras. given two images of the same scene acquired from two positions, the transformation is estimated by means of an iterative process. the estimation process aims at having corresponding incident rays intersecting at the same 3d point. geometrical relationships are derived to support the estimation method. experiments with real images are presented.
motion estimation using dynamic programming with selective path search. a novel dynamic programming-based motion estimation algorithm is presented in this paper. during matching cost calculation, the algorithm selectively keeps the cost values for the best n candidates only. the required memory space is therefore considerably reduced. in addition, a new path searching approach is applied. when searching for the optimal path from pixel p, the algorithm considers both the nearby candidates and the best n candidates at pixel p-1. as a result, better estimations can be produced around motion boundaries. the experimental results show that the motions estimated through considering all the candidates are only slightly better than those estimated through selectively considering a small number of candidates, even though the former approach requires significantly more computational time and memory space.
multi-resolution genetic algorithm and its application in motion estimation. many computer vision problems can be formulated into optimization problems. in this paper, we propose a multi-resolution genetic algorithm, which can be applied to solving many of these problems. the motion estimation problem is used as a vehicle for demonstration. a matching-based estimator is proposed through setting up a proper fitness evaluation function and letting the multi-resolution genetic algorithm do the global searching. the results show that the proposed estimator is robust and can produce consistent velocities for real image sequences.
disparity flow estimation using orthogonal reliability-based dynamic programming. disparity flow depicts the 3d motion of a scene in disparity space of a given view and can be considered as view-dependent scene flow. the disparity flow map of a given view is a 2d array of 3d vectors that depicts the 3d motion observed at different pixel locations. estimating 3d motion in form of disparity flow map limits all computations in the 2d image space and converts the 3d motion estimation problem into a 2d labeling problem. a novel algorithm is presented in this paper for disparity flow estimation using the orthogonal reliability-based dynamic programming technique. experimental results using captured stereo sequences show that the new algorithm can generate dense and smooth 3d motion for dynamic scenes.
robust algorithm for pupil-glint vector detection in a video-oculography eyetracking system. this paper presents a robust real time algorithm for an eye tracking system employing the well-known bright-pupil technique that performs an effective detection of the pupil and glint positions in the image. the accuracy in the processing is essential if a good determination of the eye gaze is desired. this algorithm is competent for unconstrained images and presents an unmatchable behaviour for users using glasses. the algorithm employs mostly commands from matrox imaging library (mil) that present a wide sort of functions for image processing and pattern recognition.
pose correction and subject-specific features for face authentication. in this paper, we present a face authentication system that can be broken down in three stages. prior to feature extraction, a pose correction step is applied, so that frontal face images are synthesized through the use of flexible shape models and thin plate splines warping. once the pre-processing is completed, a set of subject-specific key points is located in face images by means of a two-layer strategy. in the first level, locations are chosen from lines that depict facial structure, and gabor features (jets) are extracted at these positions. in the second step, we evaluate these jets as individual classifiers over a training dataset, so that only the subset of points whose jets are good at discriminating between clients and imposters are preserved.
fast dichotomic multiple search algorithm for shortest cirular path. circular shortest path algorithms for polar objects segmentation have been proposed in [1, 11, 10] to address discrete case and extended in [2] to the continuous domain for closed global optimal geodesic calculation. the best method up to date relies on a branch and bound approach and runs in o(u1.6v) on average while o(u2v) in worst case for a u × v discrete trellis warped in the direction of v. we propose an new algorithm called dichotomic multiple search (dms) which finds the global minimum with a o(ulog2(u)v) worst case scenario complexity. our algorithm relies on the fact that two minimal paths never cross more than once. this allows to sequentially partition the trellis in a dichotomic manner. each computed circular minimal path with chosen starting point allows cutting the trellis into two sub trellis. the algorithm is then recursively applied on each sub trellis. application to object segmentation is presented.
combining svm classifiers for handwritten digit recognition. in this paper, we investigate the advantages and weaknesses of various decision fusion schemes using statistical and rule-based reasoning. the cooperation schemes are applied on two svm (support vector machine) classifiers performing classification task on two feature families referenced as structural and statistical features. the obtained results show that it is difficult to exceed the recognition rate of a single classifier applied strightforwardly on both feature families as one set. the rule based cooperation schemes enable an easy and efficient implementation of various rejection criteria. on the other hand, the statistical cooperation schemes provide higher recognition rates and offer possibility for fine-tuning of the recognition versuses the reliability tradeoff.