waashk/dblp huggingface dataset:
a new structural constraint and its application in wide baseline matching. we introduce a new structural constraint that can be used for matching points in image pairs taken from a wide baseline. no assumption is made about the geometry of the 3d points or of surfaces in the scene, nor about the location or orientation of the cameras. this structural constraint can be used to reduce the search space when matching a number of feature points in two images, eliminating the risk of selecting a matching that is provably wrong because unrealizable.
a generic method for eager interpretation of on-line handwritten structured documents. in this paper, a new approach for on-line handwritten structured document interpretation is presented. it aims at interpreting the strokes progressively. the major component of our approach is a flexible formalism for the recognition of the document elements. its originality is the modelling of the document global structure. the system then drives dedicated recognizers and looks only for the likely symbols depending on the document structural context in which that element is located. moreover, the formalism defines a "canonical on-line signal form" of the recognizer entries to facilitate the interpretation process which is then more robust and more efficient. to highlight its genericity, this approach has been used to design two pen-based prototypes, for musical score editing and for graph editing.
multilinear principal component analysis of tensor objects for recognition. in this paper, a multilinear formulation of the popular principal component analysis (pca) is proposed, named as multilinear pca (mpca), where the input can be not only vectors, but also matrices or higher-order tensors. it is a natural extension of pca and the analogous counterparts in mpca to the eigenvalues and eigenvectors in pca are defined. the proposed mpca has wide range of applications as a higher-order generalization of pca. as an example, mpca is applied to the problem of gait recognition using a novel representation called eigentensorgait. a gait sequence is divided into half gait cycles and each half cycle, represented as a 3rd-order tensor, is considered as one data sample. experiments show that the proposed mpca performs better than the baseline algorithm in human identification on the gait challenge data sets.
lesion detection using morphological watershed segmentation and modelbased inverse filtering. in this paper, we present a method that detects lesions in two-dimensional (2d) cross-sectional brain images. use of the morphological watershed segmentation technique localizes shape variation in the gray level distribution of brain images and, in turn, identifies the regions with abnormal shape and/or texture structure. the detected brain areas are then subjected to a model-based inverse filtering to determine their physiological characteristics whether they are lesions or other types of anomalies. the proposed algorithm was tested on different images of "the whole brain atlas" database [13]. the experimental results have produced 90% classification accuracy in processing 10 arbitrary images, representing different kinds of brain lesion.
separating color and pattern information for color texture discrimination. the analysis of colored surface textures is a challenging research problem in computer vision. current approaches to this task can be roughly divided into two categories: methods that process color and texture information separately and those that utilize multispectral texture descriptions. motivated by recent psychophysical findings, we find the former approach quite auspicious. we propose the use of complementary color and texture measures that are combined on a higher level, and empirically demonstrate the validity of our proposion using a large set of natural color textures.
word spotting in chinese document images without layout analysis. an approach to searching user-specified words/phrases in chinese document images, without the requirements of layout analysis, is proposed in this paper. bounding boxes of chinese character images are fir st determined using connected component analysis. next, a suitable character from the user-specified word/phrase is chosen as the initial character to search for a matching candidate in the document. once a matched candidate is found, its adjacent characters in the horizontal and vertical directions are examined for matching with other corresponding characters in the user-specified word/phrase, subject to the constraints of positional relation and size similarity. the character matching is done in two stages. the coarse matching is carried out based on the stroke density features. a weighted hausdorff distance (whd) is proposed for the second matching phase. experimental results show that the proposed method can effectively search the user-specified chinese word/phrase from horizontal or vertical text lines of document images.
an efficient features - based license plate localization method. this paper presents a feature-based license plate localization algorithm that copes with multi-object problem in different image capturing conditions. the proposed algorithm is robust against illumination, shadow, scale, rotation, and weather condition. it extracts license plate candidates using edge statistics and morphological operations and removes the incorrect candidates according to the determined features of license plates. we have formed a rather complete database of 269 images in different conditions. the proposed algorithm successfully detecteds the accurate location of the license plates in 96.5% cases, which outperforms the other available approaches in the literature.
document flattening through grid modeling and regularization. for document images captured by a digital camera, perspective and geometric distortions make it hard to recognize the document content properly. in this paper, we propose an integrated document restoration technique, which is capable of removing perspective and geometric distortions, and producing a flattened and fronto-parallel text image that is friendly to the generic ocr systems. the proposed document restoration is accomplished through grid modeling, which divides camera images into multiple quadrilateral grids using vertical text directions and the x lines and base lines. the global distortions are then removed through grid regularization that transforms the quadrilateral grids together with the pixel contents to the regular square grids. experimental results show the proposed method is fast and easy for implementation.
the influence of the noise in the restoration of solar radio images using adaptive regularization techniques based on clustering. this article describes the efficiency analysis of the new restoration algorithm applied to solar radio images and the influence that an additive noise has in the roughness classification process of the degraded image pixels. as the deconvolution problems are ill-conditioned, small disturbances, caused by the noise can imply in great variations in the estimate of the solution of the problem. therefore, several simulations were performed to evaluate the efficiency of the proposed algorithm in cases that the noise exerts significant influence in the result of the processing.
camera text recognition based on perspective invariants. as camera resolution increases, high-speed non-contact text capture through a digital camera is opening up a new channel for document capture and understanding. unfortunately, perspective and geometric distortions in camera image of documents make it hard to recognize the document content properly. in this paper, we propose a character recognition technique, which is capable of recognizing camera text lying over a planar or smoothly curved surface in perspective views. in our proposed method, a few perspective invariants including character ascender and descender, centroid intersection numbers, and water reservior are first detected. camera texts are then recognized using a classification and regression tree (cart) structure. experimental results show our method is fast and improves recognition performance greatly.
dense estimation of surface reflectance properties based on inverse global illumination rendering. in augmented virtuality, estimating object surface reflectance properties is important when rendering objects under arbitrary illumination conditions. however, faithfully estimating surface reflectance properties is difficult for objects having interreflections. the present paper describes a new method for densely estimating the non-uniform surface reflectance properties of real objects constructed of convex and concave surfaces having diffuse and specular interreflections. the registered range and surface color texture images were obtained using a laser rangefinder. in the proposed method, the light positions are first determined in order to take color images, which are then used to discriminate diffuse and specular reflection components of surface reflection. surface reflectance parameters are then estimated based on an inverse global illumination rendering. experiments were conducted to reveal the usefulness of the proposed method.
dense estimation of surface reflectance properties of objects with interreflections. in augmented virtuality which virtualizes real objects to construct a mixed reality environment, it is important to estimate object surface reflectance properties to render objects under arbitrary illumination conditions. the authors developed a method to estimate reflectance properties ofobject surfaces densely. however, it was difficult to estimate surface reflectance properties faithfully for objects with interreflections. this paper describes a new method of densely estimating non-uniform surface reflectance properties of real objects constructed of convex and concave surfaces with interreflections. we use registered range and surface color texture images obtained by a laser range finder. the proposed method first determines positions oflight to take color images for discriminating diffuse and specular reflection components of surface reflection. then, surface reflectance parameters are estimated based on radiosity. experiments show the usefulness of the proposed method.
a target detection method in range-doppler domain from sar echo data. the direct evidence of radar target recognition is the backscatter energy and its distribution of objects, which is concluded by imaging process of synthetic aperture radar (sar) in the two-dimension image domain. so the issues about automatic target recognition (atr) of sar are often a "post-process" following sar imaging. a method of target detection in the state of non-imaging based on analyzing the features of all kinds of targets on sar echo data is presented in this paper, where the targets are detected in range-doppler domain (rdd). based on analysis of target features in rdd, an algorithm of target detection in a rdd image is developed. the experimental results indicate the validity of the algorithm for special kinds of target detection. the algorithm is simple, able to recurrent, easy for real-time processing and hardware implementation. it can also apply to getting target alarm with real-time sar data forthe pre-selected targets and do the imaging process selectively at the same time.
a pde-based method for optical flow estimation. in this paper, we proposed a new method for accurate optical flow estimation. the significance of this work is twofold. first in this paper we showed that the optical flow estimation could be viewed as a diffusion-reaction process (pde-based) instead of a variational problem of minimizing some energy functions. the minimization often need the corresponding regularizer be restricted to some forms with convex and differentiable in view of convergence and the euler-lagrange equation. second, we adopted coherence enhancing scheme as the diffusion part of the diffusion-reaction equation to encourage smoothing along motion boundaries and preserving them. qualitative and quantitative results for synthetic and real-world scenes demonstrate that the new method can produce an accurate result.
view-based 3-d object recognition using shock graphs. the shock graph is an emerging shape representation for object recognition, in which a 2-d silhouette is decomposed into a set of qualitative parts, captured in a directed acyclic graph. although a number of approaches have been proposed for shock graph matching, these approaches do not address the equally important indexing problem. we extend our previous work in both shock graph matching and hierarchical structure indexing to propose the first unified framework for view-based 3-d object recognition using shock graphs. the heart of the framework is an improved spectral characterization of shock graph structure that not only drives a powerful indexing mechanism (to retrieve similar candidates from a large database), but also drives a matching algorithm that can accommodate noise and occlusion. we describe the components of our system and evaluate its performance using both unoccluded and occluded queries. the large set of recognition trials (over 25,000) from a large database (over 1400 views) represents one of the most ambitious shock graph-based recognition experiments conducted to date.
a hybrid recognition scheme based on partially labeled som and mlp. we propose a hybrid system for bangla handwritten numeral recognition based on partially labeled two-layer som and mlp classifiers. partially labeled mechanism is introduced to the kohonen's som for reducing recognition error rate, and two-layer structure is applied for improving the performance of the som classifier. the directional and density features are utilized in our system, and the partially labeled som is applied first. in the case that the character cannot be recognized by the partially labeled som, it will be feed to a multi-layer perceptron classifier for further processing. the experiments on the bangla handwritten numeral samples captured from real envelopes have found that the hybrid system achieves 96.7% correct recognition rate.
augmented lagrangian approach for projective reconstruction from multiple views. in this paper, we propose a new factorizationbased algorithm for projective reconstruction by minimizing the 2d reprojection error in multiple images. reformulating the projective reconstruction problem into a constrained minimization one, we estimate the projective depths, the projection matrix and the projective motion together by the solving a sequence of unconstrained minimization problems using the augmented lagrangian method. the proposed algorithm is ready to handle missing data and it is guaranteed to converge more robustly and rapidly than the algorithm of [6].
symbolic graph matching using the em algorithm and singular value decomposition. this paper describes an efficient algorithm for inexact graph matching. the method is purely structural, that is, it uses only the edge or connectivity structure of the graph and does not draw on node or edge attributes. we make two contributions: 1) commencing from a probability distribution for matching errors, we show how the problem of graph matching can be posed as maximum-likelihood estimation using the apparatus of the em algorithm; and 2) we cast the recovery of correspondence matches between the graph nodes in a matrix framework. this allows one to efficiently recover correspondence matches using the singular value decomposition. we experiment with the method on both real-world and synthetic data. here, we demonstrate that the method offers comparable performance to more computationally demanding methods
a novel caption extraction scheme for various sports captions. the study proposes a novel scheme to extract various captions in sports videos. a caption detection process based on an iteratively temporal averaging technique is used to automatically detect and locate a caption region in a series of video frames. moreover, a caption identification approach is used to classify some caption candidates into one of the caption types in order to extract accurately the contents of the caption rather than a projection-based segmentation process. experimental results show that the proposed caption extraction approach is robust for generalizing the learning knowledge to identify various sports captions.
activity recognition from silhouettes using linear systems and model (in)validation techniques. in this work we propose a model (in)validation approach to gait recognition, using a system that tries to discriminate specific activities of people. the recognition process departs from an abstraction obtained from video image sequences for different activities performed by different people, by first using a suitable representation for each frame and for each frame sequence. for each frame two commonly used models for describing silhouettes are employed: fourier descriptors and vectors of widths. then each sequence is modeled as a linear time invariant (lti) system that captures the dynamics of the evolution of the frame description vectors in time. finally a standard classification tool, svm, is used to recognize activities using similarity measures obtained through model (in)validation. the main contribution of this work is the provision of an activity recognition model and the performance evaluation of this model using two different feature spaces.
singer identification based on vocal and instrumental models. in this paper, we propose a novel method to identify the singer of a query song from the audio database. the database contains over 100 popular songs of solo singers. the rhythm structure of the song is analyzed using our proposed rhythm tracking method and the song is segmented into beat space time frames, where within the beat space time length the harmonic structure is quasi stationary. this inter-beat time resolution of the song is used for both feature extraction and training of the classifiers (i.e. support vector machine (svm) for vocal/instrumental boundary detection and gaussian mixture models (gmms) for modeling the singer). combining the instrumental music similarities in the songs of the same singer with the vocal model can improve the identification of the singer with an accuracy of over 87%.
web-based evaluation and deployment of pattern recognizers. this paper describes a framework for the automatic web-based evaluation and deployment of pattern recognizers. several usage modes are identified and the issues and tradeoffs involved in designing web-based evaluation systems are discussed. the system operates by exchanging xml messages between a recognition client and an evaluation server. http post is used for message exchange, which works through most firewalls.to use the system a researcher downloads a simple evaluation client, and then uses this client to invoke their recognizer. the recognizer must implement a simple problem-specificinterface. the system is illustrated with the aid of a sequence recognition example.
sequence recognition with scanning n-tuple ensembles. the scanning n-tuple classifier (snt) is a fast and accurate method for classifying sequences.applications include both on-line and off-line hand-written character recognition.snts have conventionally been trained using maximum likelihood parameter estimation.this paper describes a disciminative training rule that can be applied to ensembles of snts.results demonstrate a significant improvement for the discriminative ensemble method.for comparison purpose we also implemented a support vector machine (svm) operating in the sequence domain.we tested each method on a chain-coded version of the mnist hand-written digit dataset.the snt is not quite as accurate as the svm, but is much faster both in training and recognition.
gabor wavelet correlogram algorithm for image indexing and retrieval. in this paper, a new algorithm called gabor wavelet correlogram (gwc) is proposed for image indexing and retrieval. gwc is an effort to extend our former wavelet correlogram algorithm by introducing rotation invariant features using gabor wavelets. we also present some ideas in order to handle effectively the redundancy problem due to non-orthogonal decomposition of gabor wavelets. additionally, we use an optimized weighted relative distance measure to improve the retrieval performance. the retrieval results obtained by applying gwc to a 1000 image database demonstrated significant improvements in rank, precision, and recall compared to the wavelet correlogram and enhanced wavelet correlogram algorithms.
video modelling and segmentation using gaussian mixture models. this paper describes a new approach to the video modelling and segmentation problem using gaussian mixture model descriptors. these have several advantages over conventional, histogram-based techniques, including: a rigorous statistical basis; the possibility of encoding spatial, colour, texture and motion features in a unified system; and the ability to trade off accuracy of representation against data volume. after a brief introduction to the class of models, results are presented to show their efficacy.
intelligibility of children with cleft lip and palate: evaluation by speech recognition techniques. cleft lip and palate (clp) may cause functional limitations even after adequate surgical and non-surgical treatment, speech disorder being one of them. until now, an objective means to determine and quantify the intelligibility does not exist. an automatic speech recognition system was applied to 31 recordings of clp children who spoke a german standard test for articulation disorders. the speech recognition system was trained with normal adult speakers' and children's speech. a subjective evaluation of the intelligibility was performed by a panel of 3 experts and confronted to the automatic speech evaluation. the automatic speech recognition yielded word accuracies between 1.2% and 75.8% (48.0% ± 19.6%) with sufficient discrimination. it complied with experts' rating of intelligibility. thus we show that automatic speech recognition serves as a good means to objectify and quantify global speech outcome of children with clp.
approximate nearest neighbor search using a single space-filling curve and multiple representations of the data points. in this work, a fast approximate nearest neighbour search algorithm using single space-filling curve (spfc) mapping and a set of synthetic prototype representations is presented. the results are comparable to a multiplespacefilling scheme, but achieving a much faster execution time, since computing multiple transformations and spfc mapping's is avoided, at the expense of having a more densely populated one-dimensional representation of the data-set. the advantages and limitations of the model are discussed, and an experimental evaluation with synthetic data and with a large, real high-dimensional optical character recognition data-set is presented.
fvc2002: second fingerprint verification competition. two years after the first edition, a new fingerprint verification competition (fvc2002) was organized by the authors, with the aim of determining the state-of-the-art in this challenging pattern recognition application. the experience and the feedback received from fvc2000allowed the authors to improve the organization of fvc2002 and to capture the attention of a significantly higher number of academic and commercial organizations ( 33 algorithms were submitted). this paper discusses the fvc2002 database, the test protocol and the main differences between fvc2000 and fvc2002. the algorithm performance evaluation will be presented at the 16th icpr.