waashk/dblp huggingface dataset:
an efficient three-stage classifier for handwritten digit recognition. this paper proposes an efficient three-stage classifier for handwritten digit recognition based on nn (neural network) and svm (support vector machine) classifiers. the classification is performed by 2 nns and one svm. the first nn is designed to provide a low misclassification rate using a strong rejection criterion. it is applied on a small set of easy to extract features. rejected patterns are forwarded to the second nn that uses additional, more complex features, and utilizes a well-balanced rejection criterion. finally, rejected patterns from the second nn are forwarded to an optimized svm that considers only the "top k" classes as ranked by the nn. this way a very fast svm classification is obtained without sacrificing the classifier accuracy. the obtained recognition rate is among the best on the mnist database and the classification time is much better compared to the single svm applied on the same feature set.
graph matching using random walks. in this paper we propose a graph matching algorithm which uses random walks to compute topological features for each node, in order to identify candidate pairs of corresponding nodes in the two graphs. the algorithm automatically adapts the number of topological features required to determine the exact match among the nodes. even if the proposed technique is not guaranteed to provide an exact solution for all graphs, the experiments on a benchmark dataset show that it can outperform other state of the art algorithms with respect to the computational requirements. in fact, the proposed algorithm is polynomial in the number of graph nodes.
bayesian imitation of human behavior in interactive computer games. modern interactive computer games provide the ability to objectively record complex human behavior, offering a variety of interesting challenges to the pattern-recognition community. such recordings often represent a multiplexing of long-term strategy, mid-term tactics and short-term reactions, in addition to the more low-level details of the player's movements. in this paper, we describe our work in the field of imitation learning; more specifically, we present a mature, bayesian-based approach to the extraction of both the strategic behavior and movement patterns of a human player, and their use in realizing a cloned artificial agent. we then describe a set of experiments demonstrating the effectiveness of our model.
robust extraction of planar and quadric surfaces from range images. we present a new range image segmentation algorithm based on the extraction of planar and general quadric surfaces, which is of great relevance in man-made environments, built largely of low-order surfaces. we describe how our robust estimator can effectively reject erroneous surface hypotheses, while identifying points describing a general quadric surface, by means of an improved approximation to the true geometric distance between each point and the surface. we present thorough experimental results with quantitative evaluation against ground truth.
ocrgrid : a platform for distributed and cooperative ocr systems. developing an ocr (optical character recognition/ reader) system requires expertise in various fields of researches, and the fact has been recognized as an obstacle to expanding the range of ocr applications, improving the recognition accuracy, and to providing end users with better ocr applications and services. this paper proposes a platform for distributed and cooperative ocr systems, called "ocrgrid," that allows end users to search for and use ocr servers over networks. we discuss the potential of the platform, useful applications, and problems that need to be addressed. this paper also introduces a toolkit for realizing web-based ocrs and an implementation of the character recognition based on the majority logic.
character pattern extraction from colorful documents with complex backgrounds. today there are lots of documents in which text characters are printed on colored and/or complex backgrounds. we previously proposed a character pattern extraction method by which character paterns can be extracted from grayscale document images with comlex background. the method has unique, advantageous properties; it is capable of extracting very small characters and is tolerant of shadings of images. however, the method did not work well for some color documents since it lacks the ability of discriminating color difference. this paper proposes an enhanced version of the method, which utilizes the local color segmentation and the region growing. the experimental results have shown that the new method yields much better results for color magazine covers without sacrificing the performance of extracting small character patterns. the method is tolerant of shadings of images as well.
screen pattern removal for character pattern extraction from high-resolution color document images. screen pattern used in offset-printed documents has been one of great obstacles in developing document recognition systems that handle color documents. this paper proposes a selective smoothing method for filtering the screen patterns/noise in high-resolution color document images. experimental results show that the method yields significant improvements in character pattern extraction.
line detection and texture characterization of network patterns. this paper describes a complete approach to detect, localize and describe network patterns. such texture is automatically detected with gaussian derivative kernels and fisher linear discriminant analysis; line closure and thinning is provided by morphological masking and line luminance profile fitting provides width estimation. detection results on dermatological images are reported and discussed.
color calibration for a dermatological video camera system. in this work we describe a technique to calibrate images for skin analysis in dermatology. using a common reference we correct non-uniform illumination effects, give an estimation of the gamma correction, produce a xyz conversion matrix. the final result is then reverted to a non standard rgb color space, built from the instrument images. in this way different instruments behave uniformly allowing colorimetric characterization, while improving the results of common algorithms. the proposed techniques should be the initial support for a distributed framework where dermatological images can be consistently compared.
entropy estimation and multiscale processing in meteorological satellite images. a new model for the multiscale characterization of turbulence and chaotic information in digital images is presented. the model is applied to infrared satellite images for the determination of specific areas inside the clouds. these images are difficult to manipulate however due to their intrinsically chaotic character, consequence of the extreme turbulent regime of the atmospheric flow . in this paper we briefly review some known techniques for processing suchdata and we will justify the necessity of multiscale methods to extract the relevant features. in the theory presented herein, one main attribute is determined for every image: the most singular manifold (msm, of fractal nature), characterizing the sharpest changes in graylevel values. we will see that the most important set (from the statistical point of view) is that which both contains the sharpest transitions (msm) and maximizes the local entropy. for that reason, images can be reconstructed to a good quality from the value of the gradient over that set of maximal information. the results are interpreted according to their relevance for determining meteorological features.
propagating segmented regions during a camera saccade. in this paper, we present a method for propagating segmentation information across a saccade for a foveating camera. in particular, we take a region of interest from a wide-angle, low-fidelity image and propagate its segmentation information to a zoomed, high-fidelity image containing that region. our method uses normalized greyscale templates to estimate the change in translation and magnification required to transform the segmented region. this process is particularly useful for systems whose segmentation methods rely on prior images, such as motion or difference segmentation, because the segmentation methods cannot be simply be rerun to obtain a segmentation in the high-fidelity images.
location and recognition of flashlight projections for visual interfaces. we consider the location and recognition of flashlight projections given the image sequence supplied by a fixed camera monitoring a physical surface. a quotient method extracts a description of the flashlight projection that is independent of the reflectance of the illuminated surface. the information recovered is used to recognise individual flashlights and trigger audiovisual events in response to users' actions. a demonstration application, an interactive poster, is described and directions for future work identified.
probabilistic models for generating, modelling and matching image categories. in this paper we present a probabilistic and continuous framework for supervised image category modelling and matching as well as unsupervised clustering of image space into image categories. a generalized gmm-kl framework is described in which each image or image-set (category) is represented as a gaussian mixture distribution and images (categories) are compared and matched via a probabilistic measure of similarity between distributions. image-to-category matching is investigated and unsupervised clustering of a random image set into visually coherent image categories is demonstrated.
a subspace approach to texture modelling by using gaussian mixtures. assuming local and shift-invariant texture properties we describe the statistical dependencies between pixels by a joint probability density of gray-levels within a suitably chosen observation window. we estimate the unknown multivariate density in the form of a gaussian mixture of product components from data obtained by shifting the observation window. obviously, the size of the window should be large to capture the low-frequency properties of textures but, on the other hand, the increasing dimension of the estimated mixture may become prohibitive. by considering a subspace approach based on a structural mixture model we can increase the size of the observation window while keeping the computational complexity in reasonable bounds.
on the use of anthropometry in the invariant analysis of human actions. in this paper, we propose a novel approach to matching human actions using semantic correspondence between human bodies with an eye towards invariant analysis of activity. the correspondences are used to provide geometric constraints between multiple anatomical landmarks (e.g. hands, shoulders and feet) to match actions performed from different viewpoints and in different environments. the fact that the human body has certain anthropometric proportion allows innovative use of the machinery of epipolar geometry to provide constraints to accurately analyze actions performed by different people leading to some interesting results. temporally invariant matching is performed, using non-linear time warping, to ensure that similar actions performed at different rates are accurately matched as well. thus, the proposed algorithm guarantees that both temporal and view invariance is maintained in matching. we demonstrate the versatility of our algorithm in a number of challenging sequences and applications.
robust omniview-based probabilistic self-localization for mobile robots in large maze-like environments. this paper extends our previous work on omniview-based monte carlo localization. it presents a number of improvements addressing challenges arising from the characteristics of the given real-world application, the self-localization of a mobile robot in a regularly structured, maze-like and populated operation area, a home store. the contribution of this paper can be summarized as follows: we introduce a more specific extraction of color-based appearance features and propose a novel selective observation comparison method to determine the similarity between expected and actual observation allowing a better handling of severe occlusions or disturbances. moreover, we present the results of a series of localization experiments studying the impact of the appearance-feature extraction and the observation comparison on the localization accuracy. our improved approach can successfully demonstrate its omniview-based localization capabilities for a demanding, large operation area - a home store with a size up to 100 × 60^2. to the best of our knowledge, this is the most complex operation area that has been studied experimentally so far using appearance-based localization techniques.
a clustering approach to corner point analysis in hand drawn images. drawing tasks are used widely within neuropsychology for the assessment and monitoring of a variety of conditions. automated assessment of these tasks improves their repeatability and accuracy in use alongside a reduction in trained therapist resource loading. using images fromtest responses collected in an assessment of the condition of visuo-spatial neglect, this paper presents a method utilising a standard corner detection routine (susan) to establish the presence and location of corner points within an image of a drawn geometric shape. it is demonstrated that by applying a series of standard clustering systems to these located corners, the accuracy in terms of number and spatial position of corner points found assessed against actual corner position is improved.
activity discovery from surveillance videos. multi-agent interactions often result in mutual occlusion sequences which constitute a visual signature for the event. we define six qualitative occlusion primitives based on the persistence hypothesis (objects continue to exist even when hidden from view): isolated, occlude with foreground, occlude by background, disappear, enter and exit. variable length temporal sequences of occlusion primitives are shown to be useful features for categorizing many classes of semantically significant events. occlusion primitive labels depend on agent positions in the image, which are determined by combining foreground blob tracking and image motion. no prior knowledge of domain or camera calibration is necessary. new foreground blobs are identified as putative agents which may undergo occlusions, split into multiple agents, merge back again, etc. transition sequences are mined to identify semantic categories (e.g. people disembarking from a vehicle involve a series of splits). occlusion features alone may be useful for distinguishing some broad categories of interaction states, and together with features such as agent shape and motion histories, these form a rich signature for different event types that can be classified without camera calibration or any environment/ agent/action model priors.
a bimodal face and body gesture database for automatic analysis of human nonverbal affective behavior. to be able to develop and test robust affective multimodal systems, researchers need access to novel databases containing representative samples of human multi-modal expressive behavior. the creation of such databases requires a major effort in the definition of representative behaviors, the choice of expressive modalities, and the collection and labeling of large amount of data. at present, public databases only exist for single expressive modalities such as facial expression analysis. there also exist a number of gesture databases of static and dynamic hand postures and dynamic hand gestures. however, there is not a readily available database combining affective face and body information in a genuine bimodal manner. accordingly, in this paper, we present a bimodal database recorded by two highresolution cameras simultaneously for use in automatic analysis of human nonverbal affective behavior.
perceptual audio watermarking by learning in wavelet domain. conventional blind watermark (wm) decoding schemes use correlation-based decision rules because of their simplicity. drawback of the correlator decoders is their performance relies on the decision threshold. existence of an undesirable correlation between the wm data embedded through a secret key and the host signal makes the decision threshold specification harder, especially in noisy channels. to overcome this drawback, we propose a svm-based decoding scheme which is capable of learning the embedded wm data in wavelet domain. it is shown that both decoding and detection performance of the introduced wm extraction technique outperforms state-of-the-art correlation-based schemes. test results demonstrate that learning in the wavelet domain improves robustness to attacks while reducing complexity.
an evaluation of ensemble methods in handwritten word recognition based on feature selection. handwritten text recognition is one of the most difficult problems in the field of pattern recognition. the combination of multiple classifiers has been proven to be able to increase the recognition rate in difficult problems when compared to single classifiers. in this paper several novel methods for the creation of classifier ensembles are compared where the individual classifiers use different feature subsets. the methods are evaluated in the context of handwritten word recognition, using a hidden markov model recognizer as basic classifier.
multi-modality image registration using mutual information based on gradient vector flow. similarity measure plays a critical role in image registration. mutual information (mi) has been proved to be a promising measure used widely in multi-modality image registration. however, mutual information only takes statistical information into consideration, while spatial information is not even considered. in this paper, a novel approach is proposed to incorporate spatial information into mi through gradient vector flow (gvf). mutual information now is calculated from the gvf-intensity (gvfi) map of the original images instead of their intensity values. multimodality brain image registration was performed to test the accuracy and robustness of the proposed method. experimental results showed that the success rate of our method is higher than that of traditional mi-based registration.
inspecting ingredients of starches in starch-noodle based on image processing and pattern recognition. inspecting what sort of starch in commercial starchnoodles is important to international trade, food safety and protecting consumer benefit. at present, the inspection of components of starches in starch-noodle mainly relies on sensory perception, and which is fallibility or trustless. because the microstructure pattern of starches in starchnoodles depends mainly on a kind or blend of starches from which the starch-noodle was made, this paper presents an approach to classify the starch-noodles by using computer system automatically based on recognizing the microstructure pattern of the starches and components in starch-noodle. the method consists of three step: 1) take the micrograph of starch-noodles with scanning electron microscopy and preprocessing. 2) extract features of fractal geometry and gray-level co-occurrence from micrograph. 3) distinguish a sort of starch-noodles by using these combined features as input vector of artificial neural networks. the experiments has been conducted with starch-noodles of mungbean blending pachyrhizus, and the experimental results show that the method is practicable and effective.
learning and inference of 3d human poses from gaussian mixture modeled silhouettes. in this paper, we present a learning and inference framework for 3d human pose recovery using silhouettes represented by gaussian mixtures. a bayesian mixture of experts is learnt to conduct multimodal pose regression. the major contribution of this paper is the use of gaussian mixtures as silhouette shape descriptor and kullback-leibler divergence (kld) for silhouette distance and kernel computation. using gaussian mixtures and kld makes the learning and inference robust to errors in silhouettes extraction. it also allows likelihood evaluation of different pose estimates. this is done by computing the similarity of the observed silhouette and the predicted silhouettes by a generic body model onto the image plane. the system was trained with silhouettes rendered using animation software driven by motion capture data. experimental results using both synthetic and real image silhouettes illustrate the usefulness of the proposed framework.
planar motion of a parabolic catadioptric camera. camera motion is said to be planar if the direction of translation is perpendicular to the axis of rotation. a parabolic catadioptric camera is a camera realizing the orthogonal projection of rays reflected on a parabolic mirror. in this paper we consider the planar motion of a parabolic catadioptric camera, especially the motion restricted to a plane perpendicular to the optical axis, a common case in mobile robots working in urban environments. we begin by deriving the catadioptric fundamental matrix for such a motion and the intrinsic degrees of freedom in this matrix which turn out to be 8. we show that the camera intrinsics and the 3d motion can be recovered from the fundamental matrix. we derive the necessary and sufficient condition for a fundamental matrix to be induced by a planar motion. based on the additional constraint for a planar motion, we present an algorithm to compute epipolar geometry and recover the camera parameters and motion.
texture edge detection using multi-resolution features and som. texture boundaries or edges are useful information for segmenting a texture image. we propose a texture edge detection algorithm using a bank of 1-d multi-channel, multiresolution filters and self organizing map (som). 2-d filtering smears the effect of an edge in all directions, and hence we used 1-d filtering that will only smear the edge in the direction of filtering. som reduces the dimension of a feature vector by producing a 1-d map which plots the similarities of data by grouping similar items together. the output of som is processed to obtain the texture edge map. the proposed methodology is tested on simulated as well as natural texture images and produces satisfactory results.
classifiers for motion. in this paper, we present a supervised learning based approach for sub-pixel motion estimation. the novelty of this work is the learning based method itself which tries to learn the shifts from a large training database. integer pixel shift is sub-divided and discretized to levels in both the horizontal and vertical direction. we pose the problem of motion estimation in a polar coordinate system. shift estimation in the x and y direction has been posed as a problem of estimating r and è. the ordinal property of r has been used, and consequently, we employ a ranking based approach for estimating r. for è estimation we employ multi-class classification techniques. we demonstrate how very simplistic features can be used to differentiate between different subpixel shifts.
a method of image recognition based on the fusion of reduced invariant representations: mathematical substantiation. it is proposed an approach to solution of image recognition problems based on the following principal thesises: a) images are represented by multiple partial models widely used in pattern recognition ¿ feature sets; b) algorithms are multiple classifiers: each algorithm use its own data ¿ some partial image model; c) there are two kinds of fusion ¿ data (partial models) fusion and algorithm fusion; d) fusion processes of both kinds are implemented by algebraic techniques in the framework of descriptive theory of image analysis. the specific aspects of the proposed approach: a) a partial image model (a feature set) includes only image invariants; b) concept of image equivalence is a base for recognition through exploiting the partial image models; c) the final solution is obtained by the algorithm fusion methods using algebra of images and algebra of algorithms.