waashk/dblp huggingface dataset:
fundamental frequency gabor filters for object recognition. gabor filters are a widely used feature extraction method in image analysis. in this study, a new method is presented that utilises gabor filters for extracting fundamental frequencies of objects. the fundamental frequencies represent the shape of an object and can be used to classify objects with dissimilar spatial dimensions. theoretical results are verified by experiments with real images of electronic components. experiments indicate that the fundamental frequency gabor filters are a robust tool for rotation and translation invariant object recognition.
a hybrid license plate extraction method for complex scenes. this paper presents a hybrid method for extracting license plates from cluttered images. the proposed algorithm consists of three major components. first, a line detection method is proposed to detect straight lines in the edge map. second, a weight assignment scheme is applied to obtain a weight based edge density map. regions with the densest edges are selected as candidates. third, all candidate plates will pass to a refining selection procedure. one candidate that best satisfy the color constraints becomes the output.
photometric stereo under blurred observations. in this paper we address the problem of simultaneous estimation of structure and restoration of images from blurred photometric measurements. given the blurred observations of a static scene captured with a stationary camera, under different illuminant directions, we obtain the structure represented by the surface gradients and the albedo and also perform blind image restoration. the surface gradients and the albedo are modeled as separate markov random fields (mrf) and a suitable regularization scheme is used to estimate the different fields as well as the blur parameter.
a maximum-likelihood approach to symbolic indirect correlation. symbolic indirect correlation (sic) is a nonparametric method that offers significant advantages for recognition of ordered unsegmented signals. a previously introduced formulation of sic based on subgraph-isomorphism requires very large reference sets in the presence of noise. in this paper, we seek to address this issue by formulating sic classification as a maximum likelihood problem. we present experimental evidence that demonstrates that this new approach is more robust for the problem of online handwriting recognition using noisy input.
integration of range images in a multi-view stereo system. a novel method for integrating multiple range images in a multi-view stereo imaging system is presented here. due to self-occlusion an individual range image provides only a partial model of an object surface. therefore multiple range images from differing viewpoints must be captured and merged to extend the surface area that can be captured. in our approach range images are decomposed into subset patches and then evaluated in a "confidence competition". redundant patches are removed whilst winning patches are merged to complete a single plausible mesh that represents the acquired object surface.
bernoulli mixture models for binary images. mixture modelling is a hot area in pattern recognition. although most research in this area has focused on mixtures for continuous data, there are many pattern recognition tasks for which binary or discrete mixtures are better suited. this paper focuses on the use of bernoulli mixtures for binary data and, in particular, for binary images. results are reported on a task of handwritten indian digits.
calibrating freely moving cameras. we present a novel practical method for self-calibrating a camera which may move freely in space while changing it internal parameters by zooming. we show that point correspondences between a pair of images, and the fundamental matrix computed from these point correspondences, are sufficient to recover the internal parameters of a camera. unlike other methods, no calibration object with known 3-d shape is required and no limitation are put on the unknown motion, as long as the camera is projective. the main contribution of this paper is development of a global linear solution which is based on the well-known kruppa equations. we introduce a formulation different from the huang and faugeras constraints. the method has been extensively tested on synthetic and real data and promising results are reported.
configuring mixed reality environment. we present a practical framework for registering a mixed reality(mr) environment of an arbitrary number of agents. each agent consist of a head mounted display (hmd), which consists of a pair of stereo cameras. each agent is assumed to be moving freely in 3d space and multiple hmds need not have a common field of view (fov). we show that the plane at infinity and a common vertical vanishing point can be use to determine the exact orientation of all hmds with respect to each other, and establish a common reference frame. our method generalizes previous work which considers restricted camera motions. using minimal assumptions, we are able to successfully demonstrate promising results on real data.
multi feature path modeling for video surveillance. this paper proposes a novel method for detecting nonconforming trajectories of objects as they pass through a scene. existing methods mostly use spatial features to solve this problem. using only spatial information is not adequate; we need to take into consideration velocity and curvature information of a trajectory along with the spatial information for an elegant solution. our method has the ability to distinguish between objects traversing spatially dissimilar paths, or objects traversing spatially proximal paths but having different spatio-temporal characteristics. the method consists of a path building training phase and a testing phase. during the training phase, we use graph-cuts for clustering the trajectories, where the hausdorff distance metric is used to calculate the edge weights. each cluster represents a path. an envelope boundary and an average trajectory are computed for each path. during the testing phase we use three features for trajectory matching in a hierarchical fashion. the first feature measures the spatial similarity while the second feature compares the velocity characteristics of trajectories. finally, the curvature features capture discontinuities in velocity, acceleration, and position of the trajectory. we use real-world pedestrian sequences to demonstrate the practicality of our method.
frame grouping measure for factorization-based projective reconstruction. the factorization-based method generally suffers less from drift and error accumulation than the merging. however, the factorization method assumes that all correspondences must remain in all frames. in order to overcome the limitation, we present a new factorization-based projective reconstruction from un-calibrated image sequences. the proposed method breaks the full sequence into sub-sequences based on a quantitative measure considering the number of matching points between frames, the homography error, and the distribution of matching points in the image. all of projective reconstructions in sub-sequences are registered into the same coordinate frame for a complete description of the scene. experimental results showed our algorithm can recover more precise 3d structure than the merging method.
radial distortion refinement by inverse mapping-based extrapolation. "caltech calibration toolbox" by jean-yves bouguet is one of the most famous open-source calibration tools [1]. although it works well for general camera lenses, it reveals problems in case of wide-angle lens. this paper analyzes the problem and shows that the insufficient calibration of radial distortion is caused by lease-square based polynomial fitting. to overcome the problem, this paper proposes novel refinement method using inverse mapping-based extrapolation. the proposed method is expected to be an efficient supplement and is opened to the public in the appendix as a short matlab script.
selective sampling based on the variation in label assignments. in this paper, a new selective sampling method for the active learning framework is presented.initially, a small training set t and a large unlabeled set ¿ are given.the goal is to select, one by one, the most informative objects from ¿ such that, after labeling by an expert, they will guarantee the best improvement in the classifier performance. our sampling strategy relies on measuring the variation in label assignments (of the unlabeled set) between the classifier trained on t and the classifiers trained on t with a single unlabeled object added with all possible labels. we compare the performance of our algorithm with two traditional procedures random sampling and uncertainty sampling. we show empirically across a range of datasets that the proposed selective sampling method decreases the number of labeled instances needed to achieve the desired error for the fixed size of t.experimental results on toy problems and the uci datasets are presented.
domain based lda and qda. we propose an alternative to probability density classifiers based on normal distributions lda and qda. instead of estimating covariance matrices using the standard maximum likelihood estimator we estimate class domains by the minimum volume enclosing ellipsoid (í-mvee). the í-mvee is a robust statistic rejecting a specified fraction í of the data. the performance of the domain and density approaches are compared in small sample size problems and in situations where sampling of a training and test sets is not i.i.d..
affine parameter estimation from the trace transform. in this paper, we assume that we are given the images of two segmented objects, one of which may be an affinely distorted version of the other, and wish to recover the values of the parameters of the affine transformation between the two images. the images may also differ by the overall level of illumination. the multiplicative constant of such difference may also be recovered. we present a generic theoretical framework to solve this problem. in terms of this framework, other proposed methods may be interpreted. we show how, in this framework, one can recover the affine parameters in a way that is robust to various effects, such as occlusion and illumination variation. the proposed method is generic enough to be applicable also to matching two images that do not depict the same scene or object.
reverse engineering the human vision system: a possible explanation for the role of microsaccades. we present a method of image reconstruction which is invariant to the chosen group of transformations of the spline grid used to reconstruct the image. integration over a group of transformations may be what the human eye does during microsaccades which may be an explanation of why the images we see are not aliased although the sensors with which we record them are irregularly placed in the retina.
address-block extraction by bayesian rule. a method for extracting a recipient address-block from a mail image has been developed. the method is composed of two steps: nomination of address-block candidates and evaluation of these candidates by using the bayesian rule according to each of address-block type. accordingly, the proposed method can cope with various types of address-blocks. the effectiveness of the method was confirmed in several address extraction experiments. these experiments show that the top-five extraction results include one correct address -block in 94% of total number of printed-mail cases and 89% in of handwritten- mail cases.
spectral image distortion map. in this paper a novel technique of spectral image quality evaluation using spectral image distortion map (sidm) is proposed. the method is based on a recent approach to evaluation of color differences in a spectral space. what is calculated here, in fact, is a pixelwise spectral distortion. as the measure of the dissimilarity a novel kernel based similarity measure is used. the metric produces comparable values of differences for perceptually equally disparate colors. as a result a gray-scale spectral distortion image is obtained, where the intensity of each of the pixels is a difference between the original image and the distorted one. a perceptual image distortion map (pidm) has also been constructed to show the accuracy of sidm. a comparison of pidm and sidm shows that the latter provides an excellent fit to the response of the human visual system.
3d acquisition system using uncalibrated line-laser projec. in this paper, we propose a new 3d scanner, which is easy to operate. till date several 3d scanners, including commercial products, are available; however, most of these are usually large, heavy, and expensive. although inexpensive 3d scanners manufactured using vision-based techniques, such as stereo vision, are available, they cannot be used in actual measurements because the techniques are still being researched. our proposed scanner consists of only a laser projector and a single camera and is based on the self-calibarating technique; thus it does not require both precalibration and expensive mechanical devices. this implies that the system to be low-cost and the occlusions can be effectively reduced by moving the projector while scanning. experiments of our method were performed using both synthesized and real data and the 3d information of the scene was successfully reconstructed.
conformal method for quantitative shape extraction: performance evaluation. we evaluate our recently developed conformal method for quantitative shape extraction from unorganized 3d oriented point clouds. the conformal method has been tested previously on real, noisy, 3d data. here we focus on the empirical evaluation of its performance on synthetic, ground truth data, and comparisons with other methods for quantitative extraction of mean and gauss curvatures presented in the literature.
automated segmentation of archaeological profiles or classification. classification and reconstruction of archaeological fragments is based on the profile, which is the cross-section of the fragment in the direction of the rotational axis of symmetry. in order to segment the profile into primitives like rim, wall, and base, rules based on expert knowledge are created. the input data for the estimation of the profile is a set of points produced by the acquisition system. a function fitting this set is constructed and later on processed to find the characteristic points necessary to classify the original fragment. the one we propose is based on b-splines or bell-shaped splines.
a new color image segmentation algorithm based on watershed transformation. a new color segmentation method is presented in this paper. the method is specified for color images that have both large and small objects, and objects with both step and ramp edges. scanned pages of color magazines and newspapers are the examples of this kind of images. watershed transformation algorithm is the basis of the proposed method. our method incorporates the original multi-scale analysis that allows to segment edges of different slope. this analysis uses fine-to-coarse strategy and prevents the already detected sharp edges from smoothing while moving to coarser scales. in the same time the introduced algorithm allows to detect ramp edges successfully at coarse scales. for fine scales we propose a special gradient operator and a modification of watershed transformation for small objects segmentation.
on 3d mosaicing of rotationally symmetric ceramic fragments. a major obstacle to the wider use of 3d object reconstruction and modeling is the extent of manual intervention needed. such interventions are currently massive and exist throughout every phase of a 3d reconstruction project: collection of images, image management, establishment of sensor position and image orientation, extracting the geometric detail describing an object, merging geometric, texture and semantic data. this work aims to develop a solution for automated documentation of archaeological pottery, which also leads to a more complete 3d model out of multiple fragments. generally the 3d reconstruction of arbitrary objects from their fragments can be regarded as a 3d puzzle. in order to solve it we identified the following main tasks: 3d data acquisition, orientation of the object, classification of the object and reconstruction.we demonstrate the method and give results on synthetic and real data.
modification of watershed transformation for images, containing small objects. we present a new method, which allows successful application of watershed transformation for images containing small objects. the method can be divided into two stages. during the first stage of segmentation a special gradient operator is applied to get the gradient of the image. it gives us a possibility to increase the resolution of the gradient doubly in comparison with the resolution of the original image. we can achieve this by approximation of the first-order derivative not only in image pixels, but also between pixels. during the second stage the author's modification of watershed transformation is applied. this modification enables us make optimal use of the image gradient obtained at the first stage. the method to be introduced was probed on a large amount of images with small details, and showed better results than the traditional watershed algorithm.
piecewise linear skeletonization using principal curves. we propose an algorithm to find piecewise linear skeletons of handwritten characters by using principal curves. the development of the method was inspired by the apparent similarity between the definition of principal curves (smooth curves which pass through the "middle" of a cloud of points) and the medial axis (smooth curves that go equidistantly from the contours of a character image). the central fitting-and-smoothing step of the algorithm is an extension of the polygonal line algorithm which approximates principal curves of data sets by piecewise linear curves. the polygonal line algorithm is extended to find principal graphs and complemented with two steps specific to the task of skeletonization: an initialization method to capture the approximate topology of the character, and a collection of restructuring operations to improve the structural quality of the skeleton produced by the initialization method. an advantage of our approach over existing methods is that we optimize the skeleton graph by minimizing an intuitive and explicit objective function that captures the two competing criteria of smoothing the skeleton and fitting it closely to the pixels of the character image. we tested the algorithm on isolated handwritten digits and images of continuous handwriting. the results indicate that the proposed algorithm finds a smooth medial axis of the great majority of a wide variety of character templates and substantially improves the pixelwise skeleton obtained by traditional thinning methods.
an efficient face recognition system using a new optimized localization method. in this paper a system is developed for face recognition processes. preprocessing and face localization is necessary to obtain a high classification rate in face recognition tasks. in this study after preprocessing of face images, for omitting the redundant information such as background and hair, the oval shape of face is approximated by an ellipse using shape information. then the parameters (orientation and center coordinates) of this ellipse are optimized using genetic algorithm (ga). high order pseudo zernike moment invariant (pzmi) which has useful properties is utilized to produce feature vectors. also radial basis function neural network (rbfnn) with hla learning rule has been used as a classifier. simulation results on orl database indicate that the error rate of proposed system which uses genetic algorithm for optimizing the face localization step is lower than an older system which described in [2].
robust phase correlation. phase-correlation (pc) is a computationally efficient method for two and three dimensional translation estimation. we presents a projection operator which significantly improves the accuracy and robustness of the pc scheme. the operator projects the estimated correlation function into the space of correlation functions resulting from a certain range of translations, while rejecting components which are unrelated to the estimated motion.thus, the registration accuracy is improved by an order of magnitude, especially in the registration of noisy images and volumes. in addition, this approach is shown to be complementary with other subpixel phase correlation based techniques.
recognizing facial expressions by tracking feature shapes. reliable facial expression recognition by machine is still a challenging task. we propose a framework to recognise various expressions by tracking facial features. our method uses localized active shape models to track feature points in the subspace obtained from localized non-negative matrix factorization. the tracked feature points are used to train conditional model for recognising prototypic expressions like anger, disgust, fear, joy, surprise and sadness. we formulate the task as a sequence labelling problem and use conditional random fields(crf) to probabilistically predict expressions. in crf, the distribution is conditioned on the entire sequence rather than a single observation. for the joint probability defined for the entire sequence, crf does global normalization of the exponential model, as opposed to memm, for which the per state exponential distribution is locally normalized. unlike generative models(hmm), no prior dependencies between the features are assumed. we adopt a simplistic approach to classify expressions without explicitly monitoring the change in shapes of the individual facial features. instead, we allow crf to learn the complex dependencies between the features and recognize the expressions directly. experimental results demonstrate that accurately tracked feature shapes provide reliable discriminative cues to robustly recognize facial expressions for an image sequence.
an algebraic approach to symmetry detection. we present an algorithm for detecting cyclic and diherdral symmetries of an object.both symmetry types can be detected by the special patterns they generate in the object's fourier transform.these patterns are effectively detected and analyzed using the "angular difference function" (adf), which measures the difference in the angular content of images.the adf is accurately computed by using the pseudo-polar fourier transform, which rapidly computes the fourier transform of an object on a near-polar grid.the algorithm detects all the axes of centered and non-centered symmetries.the proposed algorithm is algebraically accurate and uses no interpolations.