waashk/dblp huggingface dataset:
texture classification through directional empirical mode decomposition. this paper presents a method for texture classification through directional empirical mode decomposition (demd).although there have been many filtering based techniques proposed for texture retrieval, problems of non-adaptivity and redundancy are still hard to solve simultaneously.as a technique being introduced into signal processing recently, empirical mode decomposition (emd) is an adaptive and approximately orthogonal filtering process.to apply emd to texture classification, we propose a new method of extending 1-d emd to 2-d case called demd.the approach adaptively decomposes images into local narrow band ingredients-intrinsic mode functions (imfs) and extracts their features including frequency and envelopes.to improve its classification ability the fractal dimensions of the imfs are also considered. decomposition of several directions is computed for rotation invariance.experiments for textures in brodatz set and usc database indicate the effectiveness of our technique.
iris recognition based on dlda. iris feature extraction is very important for an iris recognition system. this paper focuses on iris feature extraction. in this paper we propose direct linear discriminant analysis (dlda) which combines with wavelet transform to extract iris feature. in our method, firstly, we apply wavelet decomposition to the normalized iris image whose size is 64×256 and just choose the coefficients of the approximation part of the second level wavelet decomposition to represent the iris image because this part contains main feature of the original iris image but the size of this part is only 16×64. and then make use of dlda to extract the iris feature from this approximation part. during classification, the euclidean distance is applied to measure the similarity degree of two iris classes. in the end of this paper, the proposed method was tested on the second version casia iris database. we evaluate the performance by equal error rate (eer) which is the point that the false match rate (fmr) is equal to false non-match rate (fnmr) in valve. the experiment shows that the eer of our method is about 1.44% which is lower than other methods such as principle component analysis (pca) and independent component analysis (ica) etc.
a new hybrid gmm/svm for speaker verification. this paper proposes a new combination approach between gaussian mixture model (gmm) and support vector machine (svm) by feature extraction based on adapted gmm for svm in text-independent speaker verification. because of excellent scalability, adapted gmm was used to extract a small quantity of typical feature vectors from large numbers of speech data for svm speaker verification. using this new combination approach, our speaker verification system performed significantly better than the current state-of-the-art gmm-ubm system on the nist'04 1side-1side database.
nonparametric background generation. a novel background generation method based on nonparametric background model is presented for background subtraction. we introduce a new model, named as effect components description (ecd), to model the variation of the background, by which we can relate the best estimate of the background to the modes (local maxima) of the underlying distribution. based on ecd, an effective background generation method, most reliable background mode (mrbm), is developed. the basic computational module of the method is an old pattern recognition procedure, the mean shift, which can be used recursively to find the nearest stationary point of the underlying density function. the advantages of this method are threefold: first, backgrounds can be generated from image sequence with cluttered moving objects; second, backgrounds are very clear without blur effect; third, it is robust to noise and small vibration. extensive experimental results illustrate its good performance.
occlusion robust face recognition with dynamic similarity features. in this paper, we present a new scheme for face recognition. the main idea is to represent the images with the similarity features against the reference set and to provide the relative match for two images. for any image, we first compute the similarities between it and all the reference images, and then we take these similarities as its feature. based on the similarity features, a linear discriminating classifier is constructed to recognize the querying image. inspired by research in cognitive psychology, the perceptual distance based dynamic similarity function is proposed to compute the similarity features. the proposed method can be regarded as a generalization of kernel discriminant analysis, and it can well deal with the nonlinear variations, especially occlusion. extensive experiments are conducted to show its performance and robustness to occlusion.
a novel volumetric shape from silhouette algorithm based on a centripetal pentahedron model. in this paper we present a novel volumetric shape from silhouette algorithm based on a centripetal pentahedron model. the algorithm first partitions the space with a set of infinite triangular pyramids derived from a geodesic sphere. then the pyramids are cut by silhouettes into a set of pentahedrons, which together constitute the centripetal pentahedron model of the visual hull. this process is accelerated by precomputed polar silhouette graphs (psgs) and reduced psgs. finally a mesh surface model is extracted by marching pentahedrons. our algorithm has the advantages of robustness, speediness and preciseness.
minimum enclosing and maximum excluding machine for pattern description and discrimination. this work addresses the description problem of a target class in the presence of negative samples or outliers. traditional support vector machines (svm) has strong discrimination capability to distinguish the target class but does not reject the uncharacteristic patterns well. the one-class svm, on the other hand, provides good representation for the class of interest but overlooks the discrimination issue between the class and outliers. this paper presents a new one-class classifier named minimum enclosing and maximum excluding machine (memem), which offers capabilities for both pattern description and discrimination. the properties of memem are analyzed and the performance comparisons using synthetic and real data are presented.
fingerprint reference point detection based on local axial symmetry. reference point detection is an important process for fingerprint analysis. in this paper, we propose a novel feature which is named local axial symmetry (las) and present an algorithm to calculate reference point of a fingerprint based on this kind of feature. experimental results demonstrate its feasibility, validity and the ability to detect reference points of all classes of fingerprints including arch-type fingerprints which is difficult to locate a stable reference point with other methods.
word extraction from on-line handwritten text lines. in this paper we investigate the word extraction task in on-line recognition of cursively handwritten text lines. for the segmentation we propose a method which is based on the assumption that the size of gaps between consecutive words may considerably vary, but humans usually leave more whitespace between two consecutive words than between two connected components that belong to the same word. we use several metrics known from off-line word segmentation for measuring the distances between two adjacent components. then we apply different procedures to get the initial threshold for segmentation. using these techniques we could significantly increase the segmentation rate compared to methods which are usually applied in on-line text recognition systems.
euclidean reconstruction of deformable structure using a perspective camera with varying intrinsic parameters. in this paper we present a novel approach for the 3d euclidean reconstruction of deformable objects observed by a perspective camera with variable intrinsic parameters. we formulate the non-rigid shape and motion estimation problem as a non-linear optimization where the objective function to be minimised is the image reprojection error. our approach is based on the observation that often some of the points on the observed object behave rigidly, while others deform from frame to frame. we propose to use the set of rigid points to obtain an initial estimate of the camera's varying internal parameters and the overall rigid motion. the prior information that some of the points in the object are rigid can also be added to the non-linear minimization scheme in order to avoid ambiguous configurations. results on synthetic and real data prove the performance of our algorithm even when using a minimal set of rigid points and when varying the intrinsic camera paremeters.
an illumination insensitive representation for face verification in the frequency domain. we present a comparison of several face representation methods from the point of view of their sensitivity to illumination changes. the sensitivity is measured in terms of the overlap of the distributions of normalized correlations used for inter class and intra class image comparisons. the result suggests, that better illumination invariance could be achieved in feature spaces in the frequency domain derived for a differentiated image rather than using the original input image.
evaluation of three optical flow-based observation models for tracking. in this paper, we study the use of optical flow as a characteristic for tracking. we analyze the behavior of three flow-based observation models for particle filter algorithms, and compare the results with those obtained using a well-known, gradient-based, observation model. although in theory, optical flow could be used directly to displace an object model, in practice, flow estimation techniques lack the necessary precision. in view of the fact that probabilistic tracking algorithms enable imprecise or incomplete information to be handled naturally, these models have been used as a natural way of incorporating flow information into the tracking.
approximation of digital curves using a multi-objective genetic algorithm. in this paper, a digital planar curve approximation method based on a multi-objective genetic algorithm is proposed. in this method, the optimization/exploration algorithm locates breakpoints on the digital curve by minimizing simultaneously the number of breakpoints and the approximation error. using such an approach, the algorithm proposes a set of solutions at its end. the user may choose his own solution according to its objective. the proposed approach is evaluated on curves issued from the literature and compared successfully with many classical approaches.
robust detection of region-duplication forgery in digital image. region duplication forgery, in which a part of a digital image is copied and then pasted to another portion of the same image in order to conceal an important object in the scene, is one of the common image forgery techniques. in this paper, we describe an efficient and robust algorithm for detecting and localizing this type of malicious tampering. we present experimental results which show that our method is robust and can successfully detect this type of tampering for images that have been subjected to various forms of post region duplication image processing, including blurring, noise contamination, severe lossy compression, and a mixture of these processing operations.
refining 3d models using a two-stage neural network-based iterative process. this paper presents a refinement method that supplements the 3d model construction process. the refinement method addresses the issue of using inaccurate 3d positional information to construct the 3d model. in the context of this paper, the inaccuracies in the 3d information come from a low-cost and low-precision range finder system. the core component of the refinement system is a neural network architecture termed ifosart that attempts to associate particular corrections to the 3d model given range and intensity information. results presented show the refinement system successfully reduces the inaccuracies in real-world 3d models.
active learning to recognize multiple types of plankton. this paper presents an active learning method which reduces the labeling effort of domain experts in multi-class classification problems. active learning is applied in conjunction with support vector machines to recognize underwater zooplankton from higher-resolution, new generation sipper ii images. most previous work on active learning with support vector machines only deals with two class problems. in this paper, we propose an active learning approach "breaking ties" for multi-class support vector machines using the one-vs-one approach with a probability approximation. experimental results indicate that our approach often requires significantly less labeled images to reach a given accuracy than the approach of labeling the least certain test example and random sampling. it can also be applied in batch mode resulting in an accuracy comparable to labeling one image at a time and retraining.
architectural design issues for bayesian contextual vision. sensor fusion technology has been so far developed for the fusion of camera and different sensors, e.g. radar, sonar, etc. the same techniques apply to integrating several vision algorithms into a multi-modular system. in this paper, we abstract our attempt on the matter and propose a uniform paradigm to integrate both "vision modules" directly observing targets (e.g. intruders in video surveillance) and "accessory modules" observing scene features that may trigger system adaptation to the current context. to be concrete, we completely develop a real example in re-designing a previous context-dependent video surveillance system.
design and implementation of a card reader based on build-in camera. with the availability of high-resolution cameras and increased computation power, it becomes possible to implement ocr applications such as business card reader in the mobile device. in this paper we introduced the design and implementation of a business card reader based on build-in camera. in order to deal with the challenge of limited resource in mobile device, we proposed a new method based on multi-resolution analysis of document images. this method improves computation speed and reduces memory requirement of the image-processing step by detecting the text areas in the downscaled image and then analyzing each detected area in the original image. for the ocr engine, we used a two-layer classifier to improve speed. our experiment gives satisfactory result.
a hybrid model for invariant and perceptual texture mapping. texture is an important visual feature for computer vision tasks. in applications such as image retrieval and computer image understanding, texture similarity should be measured in a manner that is invariant to texture scale and orientation, as well as consistent with human's perception. however, most existing computational features and similarity measures are not perceptually consistent. a solution is to map textures into an invariant and perceptual space such that similarity measured in the space is perceptually consistent. this paper presents a hybrid method, using convolutional neural network and svm, to perform the invariant and perceptual mapping. test results show that it's overall performance is better than that of an individual neural network and a svm.
conditional linear discriminant analysis. dimensionality reduction by means of linear discriminant analysis (lda) can generally lead to considerable improvements in classification accuracy and computation time. however, in supervised, pixel-based, image segmentation, the limiting factor of lda that it cannot extract more than k - 1 features (k the number of classes) often prevents successfully employing it as k is typically small. based on the observation that the kind of feature to extract should often depend on the kind of image structure that is in the vicinity, we propose to condition lda on auxiliary variables extracted from the manual segmentations (which are only available in the training phase). the conditioned fisher criteria obtained through this are subsequently combined to construct our final global fisher-like dimensionality reduction criterion. this conditional lda is capable of extracting more features than standard lda, which can considerably improve the segmentation accuracy as our experiments show.
static posterior probability fusion for signal detection: applications in the detection of interstitial diseases in chest radiographs. this work presents general signal detection schemes based on the static fusion of posterior probabilities. starting with the assumption that for every pixel in an image there is a posterior probability-indicating the probability of the presence or the absence of the signal to be detected, some well-known probability fusion schemes and generalizations thereof are proposed to come to an overall decision regarding the presence or absence of the signal. in addition to these well-known static fusion schemes-i.e.,voting, averaging, maximum rule, etcetera, a quantile-based combination rule is presented as well. the performance of the several rules is evaluated on two real-world, medical image analysis task. both tasks consider the computer-aided diagnosis (cad) of standard posteroanterior chest radiographs. more specifically, in the first task the general detection of interstitial diseases is studied, while in the second task the focus is on the detection of tuberculosis.
graph spectral approach for learning view structure. in this paper we explore how to represent object view-structure by embedding the neighbourhood graphs of feature points in a pattern-space. we adopt a graph-spectral approach. we use the leading eigenvectors of the graph adjacency matrix to define clusters of nodes. for each cluster, we compute vectors of cluster properties. we embed these vectors in a pattern-space using two contrasting approaches. the first of these involves performing principal components analysis on the covariance matrix for the spectral pattern vectors. the second approach involves performing multidimensional scaling on the l2 norm for pairs of pattern vectors. we demonstrate the both methods result in well-structured view spaces for graph-data extracted from 2d views of 3d objects.
local discriminant analysis. the main objective of the work presented here is to introduce a supervised, nonlinear dimensionality reduction technique which, performs well-known linear discriminant analysis in a local way and which is able to provide a powerful mapping with less computational effort than other nonlinear reduction methods. additionally, because of the close connection of the new approach to fisher's lda, it is more clear that it acts discriminatively, which is not immediately apparent from previous formulations. the method makes use of the optimal scoring framework advocated by hastie et al. and it is coined local discriminant analysis (eda).
unified model for omnidirectional vision using the conformal geometric algebra framework. this work presents the application of the unified model for handling diverse catadioptric mirrors using the conformal geometric algebra framewok. this framefork is well equiped with incidence algebra operations (duality, meet and join) necessary for reflecting and projecting geometric entities of the visual space through the mirror to the camera. in this paper we show that our mathematical reduces the algebraic burden, as a result the development of algorithms for omnidirectrional vision is easier and effective.
omnidirectional vision and invariant theory for robot navigation using conformal geometric algebra. the automatic landmark identification is very important in autonomous robot navigation tasks. in this work we use a monocular omnidirectional vision system to extract images features, and with help of the conformal geometric algebra (cga), we show how these features can be used to calculate projective and permutation p2-invariants. these p2-invariants represent scene sub-landmarks and a set of them characterize a landmark.
pattern matching by sequential subdivision of transformation space. pattern matching is a well-known pattern recognition technique. this paper proposes a novel pattern matching algorithm that searches transformation space by sequential subdivision. the algorithm subdivides the transformation space in depth-first manner by conducting boolean operations on the constraint sets that are defined by pairs of template points and target points. for constrained polynomial transformations that have no more than two parameters on each coordinate, a constraint set can be represented as a 2d polygon or a cartesian product of 2d polygons. then, the boolean operations can be computed through generic polygon clipping algorithms. preliminary experiments on randomly generated point patterns show that the algorithm is effective and efficient under practical conditions.
region of interest watermarking based on fractal dimension. an image watermarking algorithm that aims to protect the region of interest (roi) is proposed based on fractal dimension. the roi in an image is determined by the user, and is mapped to the subband of wavelet decomposition. a visually significative watermark is scaled according to the size of the roi. embedding is completed by enforcing the relationship between the subband coefficients and approximate coefficients. the parameters and strength is determined according to the feature analysis to the image which is based on fractal dimension. extraction is blind that requires neither the host image nor the original watermark. experimental results show that this algorithm can robustly resist cropping, patching, and compression.
efficient monocular 3d reconstruction from segments for visual navigation in structured environments. we present an algorithm for performing real time 3d reconstructions of indoor scenes from a single image. the technique has been successfully used in indoor robot visual navigation applications. to solve the depth ambiguity inherent to the process of image formation, the procedure interprets the scene in terms of a set of vertical planes in arbitrary orientations (e.g. walls and doors) lying on a common horizontal plane (the floor). interpretation is based on a zone classification algorithm that divides the image into a set of disjoint patches, each one belonging to a different plane. in order to make the reconstruction possible in real time, we use a very fast feature extraction algorithm based on robust extraction of the most salient segments of the scene, augmented with local color information. the extracted segments help in both the construction of the zone classification machine and the posterior 3d reconstruction, which is in turn based on a powerful single image projective geometry result.
a physics-motivated approach to detecting sky in photographs. sky is among the subject matters frequently seen in photographs and useful for image understanding, processing, and retrieval. we propose a novel approach to sky detection based on color classification, region extraction, and physics-motivated sky signature validation. first, the color classification is performed to generate a belief map of sky colored pixels. next, connected components are extracted from the sky color belief map to generate candidate sky regions. finally and most importantly, we determine the orientation of a candidate sky region, analyze traces within the region based on a physics-motivated model, and compute the sky belief of the region. for a database of 1800 amateur photos of variable content and quality, the recall ratte is 96% with a precision rate of 98% on a per region basis.