waashk/dblp huggingface dataset:
improvement of the virtual printing scheme for synthesizing ukiyo-e. in this paper, we propose a physically based model for the printing process in the virtual woodblock printing for improvement of printing quality. virtual printing is a simulation of real printing using "woodblocks", "a paper sheet", "a baren (japanese squeegee)", and "inks" in a virtual 3d space. a print is synthesized by an interaction among them, and it is very important to study physical properties and behaviors of the virtual items. we focus on the property and the behavior of ink and study a variety of effects due to the degree of moisture of the ink. this improvement enables the virtual printing to synthesize japanese traditional multicolor prints ukiyo-e.
factorized local appearance models. we propose a novel local appearance modeling method for object detection and recognition in cluttered scenes. the approach is based on the joint distribution of local feature vectors at multiple salient points and factorization with independent component analysis (ica). the resulting non-parametric densities are simple multiplicative histograms. this leads to computationally tractable joint probability densities which can model high-order dependencies. testing and evaluation shows that the factorized density model with spatial encoding improves modeling accuracy and outperforms global appearance models in image/object retrieval. furthermore, experiments in detection of substantially occluded objects in cluttered scenes have demonstrated promising results.
on-line script recognition. automatic identification of handwritten script facilitates many important applications such as automatic transcription of multi-lingual documents and search for documents on the internet containing a particular script. the increase in usage of handheld devices which accept handwritten input is creating a huge volume of handwritten data. we propose a method to classify words and lines in an on-line handwritten document into arabic, cyrillic, devnagari, han, hebrew and roman scripts. the proposed classification system, based on spatial and temporal features of the strokes, attained an overall classification accuracy of 86:5% at the word level on a dataset containing 13; 379 words. the classification accuracy improves to 95% as the number of words in the test sample is increased to five and to 95:1% for complete text lines.
agglomerative clustering for image segmentation. the paper presents an agglomerative clustering technique for image segmentation. to initiate agglomeration, a set of homogeneous segments is found in the image using level set analysis (lsa). a relational matrix is then defined establishing relations between neighbouring segments present in the image. these relations are derived based on the intensity and boundary features of the segments. the agglomeration is performed on this relationship based on asymmetric agglomerative clustering criteria. the performance is demonstrated through results on a number of natural images and through cluster validity criteria.
retrieval of on-line hand-drawn sketches. sketch matching algorithms are commonly used for indexing and retrieval of documents based on printed or hand-drawn sketches.one could use a hand-held computer to do sketch-based queries to a database containing hand-drawn and printed sketches.we present an on-line hand-drawn sketch matching algorithm based on a line-based representation of sketches.a distance measure is defined for comparing two sketches based on this representation. the algorithm is computationally efficient and achieves a recall rate of 88.44% at the same precision, when tested on a database of 150 sketches collected from 5 users.
a non-iterative approach to reconstruct face templates from match scores. regeneration of biometric templates from match scores has security and privacy implications related to any biometric based authentication system. in this paper, we propose a novel non-iterative scheme to reconstruct face templates from match scores. we use an affine transformation of the images to approximate the behavior of the given face recognition system based on an independent set of face templates termed as "break-in set. selected templates from the "break-in set are matched only once with the enrolled template of the target account and match scores are recorded. these scores are then embedded in the approximating affine space along with break-in set templates to compute the co-ordinates of the target template. the inverse transformation is used to reconstruct the original target template. we present the reconstruction of templates for three different face recognition algorithms: bayesian intrapersonal/extrapersonal classifier, elastic bunch graph matching (ebgm) algorithm and baseline algorithm used in face recognition grand challenge (2005). we also report the initial result to break an user's account using the reconstructed template for bayesian algorithm. bayesian algorithm was set to operate at 0.1% false acceptance rate and 98% true acceptance rate with 100 enrollments. we observed that with proposed scheme, at most 600 attempts is required to achieve a 0.99 probability of break-in the bayesian face recognition algorithm.
integration of feature distributions for colour texture segmentation. this paper proposes a new framework for colour texture segmentation and determines the contribution of colour and texture. the distributions of colour and texture features provides the discrimination between different colour textured regions in an image. the proposed method was tested using different mosaic and natural images. from the results, it is evident that the incorporation of colour information enhanced the colour texture segmentation and the developed framework is effective.
a general multichannel image restoration method using compound models. in this paper we present a multichannel image restoration method using compound gauss markov random field (cgmrf) models. information regarding the objects present in the scene is shared via the line process in the cgmrf. two new iterative algorithms to estimate the underlying multichannel image are presented, which can be considered as extensions of the classical simulated annealing and icm methods. experimental results demonstrate the effectiveness of the proposed approach.
quality-based score level fusion in multibiometric systems. the quality of biometric samples has a significant impact on the accuracy of a matcher. poor quality biometric samples often lead to incorrect matching results because the features extracted from these samples are not reliable. therefore, dynamically assigning weights to the outputs of individual matchers based on the quality of the samples presented at the input of the matchers can improve the overall recognition performance of a multibiometric system. we propose a likelihood ratio-based fusion scheme that takes into account the quality of the biometric samples while combining the match scores provided by the matchers. instead of estimating the quality of the template and query images individually, we estimate a single quality metric for each template-query pair based on the local image quality measures. experiments on a database of 320 users with iris and fingerprint modalities demonstrate the advantages of utilizing the quality information in multibiometric systems.
a cluster validity approach based on nearest-neighbor resampling. we introduce an approach for validating clustering results based on partition stability under a nearestneighbor resampling. the approach is relatively robust, efficient, and avoids conceptual problems of other common validation strategies. encouraging results compared to those of subsampling-based consensus clustering are presented for simulated data and (tumor) gene expression benchmark data sets. the proposed method is discussed in view of future applications to unsupervised learning from sample data.
a fuzzy min-max neural network classifier with compensatory neuron architecture. this paper proposes a supervised learning neural network classifier with compensatory neuron architecture. the proposed "fuzzy min-max neural network classifier with compensatory neurons" (fmcn) extends the principle of minimal disturbance. the new architecture consists of compensating neurons that are trained to handle the hyperbox overlap and containment. the fmcn is capable of learning data on-line, in a single pass through, with reduced classification and gradation error. one of the good features of fmcn is that its performance is almost independent of the expansion coefficient i.e. maximum hyperbox size. the paper demonstrates the performance of fmcn with several examples.
a reflex fuzzy min max neural network for granular data classification. granular data classification and clustering is an upcoming and important issue in the field of pattern recognition. the paper proposes a granular neural network called as "reflex fuzzy min-max neural network" for classification. reflex mechanism inspired from human brain is exploited here to handle class overlaps. this network can be trained on-line using granular or point data. the proposed neuron activation functions are designed to tackle data of different granularity (size). experimental results on real datasets show that the proposed algorithm can classify granules of different granularity more correctly compared to general fuzzy min max neural network proposed by gabrycz and bargiela.
unifying background models over complex audio using entropy. in this paper we extend an existing audio background modelling technique, leading to a more robust application to complex audio environments. the determination of background audio is used as an initial stage in the analysis of audio for surveillance and monitoring applications. knowledge of the background serves to highlight unusual or infrequent sounds. an existing modelling approach uses an online, adaptive gaussian mixture model technique that uses multiple distributions to model variations in the background. the method used to determine the background distributions of the gmm leads to a failure mode of the existing technique when applied to complex audio. we propose a method incorporating further information, the proximity of distributions determined using entropy, to determine a more complete background model. the method was successful in more robustly modelling the background for complex audio scenes.
model-based brain and tumor segmentation. combining image segmentation based on statistical classification with a geometric prior has been shown to significantly increase robustness and reproducibility. using a probabilistic geometric model and image registration serves both initialization of probability density functions and definition of spatial constraints. a strong spatial prior, however, prevents segmentation of structures that are not part of the model.our driving application is the segmentation of brain tissue and tumors from three-dimensional magnetic resonance imaging (mri). our goal is a high-quality segmentation of both healthy tissue and tumor. we present an extension to an existing expectation maximization (em) segmentation algorithm that modifies a probabilistic brain atlas with an individual subject's information about tumor location obtained from subtraction of post- and pre-contrast mri. the new method handles various types of pathology, space-occupying mass tumors and infiltr ating changes like edema. preliminary results on five cases presenting tumor types with very different characteristics demonstrate the potential of the new technique for clinical routine use for planning and monitoring in neurosurgery, radiation oncology, and radiology.
perceptual grouping for multiple view stereo using tensor voting. we address the problem of multiple view stereo from a perceptual organization perspective. currently, the leading methods in the field are volumetric. they operate at the level of scene voxels and image pixels, without considering the structures depicted in them. on the other hand, many perceptual organization methods for binocular stereo are not extensible to more images. we present an approach where feature matching and structure reconstruction are addressed within the same framework. in order to handle noise, lack of image features, anddiscontinuities, we adopt a tensor representation for the data and tensor voting for information propagation. the key contributions of this paper are twofold. first, we introduce "saliency" instead of correlation as the criterion to determine the correctness of matches; second, ourtensor representation and voting enable us to perform the complex computations associated with multiple view stereo at a reasonable computational cost. we present results on real data.
3d scanning using spatiotemporal orientation. we present a new approach to volumetric scene reconstruction which can produce accurate models from turntable image sequences. instead of an epipolar plane image (epi) volume, we consider a function on the 4d spatiotemporal volume valued with the intensity back projection of the camera (time) to the particular voxel. using an optical flow technique we compute the local orientation of this spatiotemporal image and decide on occupancy based on the relative orientation between the viewing ray of the voxel at the particular time and the local image structure. our method does not require a background compensation like the silhouette-based methods and is comparable in performance with space carving.
estimation and analysis of the deformation of the cardiac wall using doppler tissue imaging. this paper presents different ways to use the doppler tissue imaging (dti) in order to determine deformation of the cardiac wall. as an extra information added to the ultrasound images, the dti gives the velocity in the direction of the probe. we first show a way to track points along the cardiac wall in a m-mode image (1d+t). this is based on energy minimization similar to a deformable grid. we then extend the ideas to finding the deformation field in a sequence of 2d images (2d+t). this is based on energy minimization including spatio-temporal regularization.
a variational approach for color image segmentation. in this paper we use a variational bayesian framework for color image segmentation. each image is represented in the l*u*v color coordinate system before being segmented by the variational algorithm. the model chosen to describe the color images is a gaussian mixture model. the parameter estimation uses variational learning by taking into account the uncertainty in parameter estimation. in the variational bayesian approach we integrate over distributions of parameters. we propose a maximum log-likelihood initialization approach for the variational expectation-maximization (vem) algorithm and we apply it to color image segmentation. the segmentation task in our approach consists of the estimation of the distribution hyperparameters.
pen pressure features for writer-independent on-line handwriting recognition based on substroke hmm. this paper discusses the use of pen pressure as a feature in writer-independent on-line handwriting recognition. we propose two kinds of features related to pen pressure: one is the pressure representing pen ups and downs in a continuous manner; the other is the time-derivative of the pressure representing the temporal pattern of the pen pressure. combining either of them with the existing feature (velocity vector), a 3-dimensional feature is composed for character recognition. some techniques of interpolating the pen pressure during the pen-up interval is also proposed for a pre-processing purpose. through experimental evaluation using 1,016 elementary kanji characters compared with the baseline performance using velocity vector only, the additional use of pen pressure improved the performance from97.5% to 98.1% for careful writings and from 91.1% to 93.1% for cursive writings.
decomposing chinese characters into stroke segments using sogd filters and orientation normalization. the paper presents a novel directional feature extraction approach based on a directional filtering technique for chinese character recognition. the proposed filtering technique uses a set of the second-order gaussian derivative (sogd) filters to decompose a character into a number of stroke segments. moreover, a gaussian function is used to extract the stroke segments along arbitrary orientations. the optimal orientation of each stroke segment can be estimated by finding the maximal power response of the stroke segment from the gaussian function. finally, the effects of decomposition process are analyzed using some simple structural and statistical features extracted from the stroke segments. experimental results indicate that the proposed sogd filtering-based approach is very efficient to decompose noisy and degraded character images into a number of stroke segments along an arbitrary orientation. furthermore, the recognition performance from the application of decomposition process can be improved about 17.31% in test character set.
integration of shape and a multihypotheses fisher color model for figure-ground segmentation in non-stationary environments. in this paper a new technique to perform figure-ground segmentation in image sequences of scenarios with varying illumination conditions is proposed. the set of color points of both the target and background are modelled with mixture of gaussians (mog), which optimum number is automatically initialized. based on the 'linear discriminant analysis' (lda) a new colorspace that maximizes the foreground/background classseparability is presented. moreover, there is no need to assume gradual change of the viewing conditions over time, because the method works with multiple hypotheses about the next state of the color distribution (some considering small changes and other more abrupt variations). the hypothesis that generates the best object segmentation and the shape information in the previous iteration are fused to accurately detect the object boundary, in a stage denominated 'sample concentration', introduced as a final step to the classical condensation algorithm.
a three-frame approach to constraint-consistent motion estimation. we study the motion estimation across three images by simultaneously enforcing the geometric constraint of trilinearity and the constant-brightness constraint. an iterative and hierarchical three-frame scheme is proposed to solve the geometry-consistent and appearance-consistent motion fields. it provides an uncalibrated approach to dense motion estimation, applicable to both 2-d and 3-d rigid scenes under perspective projection with unconstrained camera motion. valid and consistent constraints are explored to alleviate the inherent aperture and over smoothing problems.
a target dependent colorspace for robust tracking. the selection of the appropriate colorspace for tracking applications has not been an issue previously considered in the literature. many color representations have been suggested, based on the invariance to illumination changes. nevertheless, none of them is invariant enough to deal with general and unconstrained environments. in tracking tasks, we might prefer to represent image pixels into a colorspace where the distance between the target and background colorpoints were maximized, simplifying the task of the tracker. based on this criterion, we propose an 'object dependent' colorspace, which is computed as a simple calibration procedure before tracking. furthermore, this colorspace may be easily adapted at each frame. synthetic and real experiments show how this colorspace allows for a better discrimination of the foreground and background, and permits to track in circumstances where the same tracking algorithm relying on other colorspaces would fail.
a practical stereo scheme for obstacle detection in automotive use. we propose a novel stereo scheme for obstacle detection which is aimed at practical automotive use. the basic methodology involves simple region matching between images, observed from a stereo camera rig, where it is assumed the images are related by a pseudo-projective transform. it provides an effective solution for determining boundaries of obstacles in noisy conditions, e.g. caused by weather or poor illumination, which conventional planar projection approaches cannot cope with. the linearity of the camera model also contributes significantly to compensation of road inclination. essentially, precise lane detection and prior knowledge concerning obstacles or ambient conditions are unnecessary and the proposed scheme is therefore applicable to a wide variety of outdoor scenes. we have also developed a multi-vliwprocessor that fulfills the essential specifications for automotive use. our scheme for obstacle detection is largely reflected in the processor design so that real-time on-board processing can be realized with acceptable cost to both automobile users and manufacturers. the implementation of a prototype and experimental results illustrate our method.
a hierarchical palmprint identification method using hand geometry and grayscale distribution features. palmprint identification, as an emerging biometric technique, has been actively researched in recent years. in existing palmprint identification algorithms, roi segmentation is always a must step. this paper presents a novel hierarchical palmprint identification method without roi extraction, which measures hand geometry and angle values in coarse-level feature extraction, and calculates unit information entropy of each subimage to describe grayscale distribution as the fine-level feature. we utilize the grayscale distribution variance caused by particular positions of principle lines, wrinkles and minutiae in primitive hand images as the palm descriptor instead of roi-based features. experiments were developed on a database of 990 images from 99 individuals. accuracy up to 99.24% has been obtained when using 6 samples per class for training. a performance comparison between the proposed method and roi-based pca method was made also.
a new classification rule based on nearest neighbour search. the nearest neighbour (nn) classification rule is usually chosen in a large number of pattern recognition systems due to its simplicity and good properties. as the problem of finding the nearest neighbour of an unknown sample is also of interest in other scientific communities (very large databases, data mining, computational geometry, ...), a vast number of fast nearest neighbour search algorithms have been developed during the last years. in order to improve classification rates, the k-nn rule is often used instead of the nn rule, but it yields higher classification times. in this work we introduce a new classification rule applicable to many of those algorithms in order to obtain classification rates better than those of the nearest neighbour (similar to those of the k-nn rule) without significantly increasing classification time.
3d real-time head tracking fusing color histograms and stereovision. a system that performs the tracking of a human head in 3d in real time is presented. the head shape is modeled by an ellipse with a trained color histogram of skin and hair samples. the color histogram is dynamically updated based on incoming image data in order to accommodate for varying illumination conditions. on the other hand, the size of the searched ellipse projected on the image is scaled depending on the depth information gathered from stereo vision. the strength of our method resides on the use of a predictive filter to fuse color and depth information, iteratively refining the location of the head in 3d and the parametersof the head color histogram.