waashk/dblp huggingface dataset:
string-like occluding region extraction for background restoration. in this paper, we propose a method for extracting stringlike objects in a still image for background restoration. we assume that the object regions occluding the background are long and narrow, and contrasted in intensity with background. first the method introduces a circle contrast, the intensity difference between a pixel and those on a circle around, to find the occluding string-like regions. then the signs of the circle contrast are decided so that the occluding regions and backgrounds are well separated, and further enhanced by an optimization process. extracted regions are removed with interpolation (inpainting) for background restoration. experimental results on real images show the validity of the proposed method.
smartalbum - towards unification of approaches for image retrieval. in this paper we present a novel application (called smartalbum) for photo indexing and retrieval that unifies two different image indexing approaches. the system uses two modalities to extract information about a digital photograph; i.e. content-based and speech annotation forimage description. the result is a powerful image retrieval tool that has capabilities beyond what current single-mode retrieval systems can offer. we show on a corpus of 1200 images the interest of our approach.
robust image denoising using kernel-induced measures. in this paper, we propose a class of novel nonlinear robust filters for image denoising by incorporating kernel -induced measures into classical linearmean filter. particularly, we place more focus on gaussian kernel based filter (gk) due to its simplicity. the gk filter not only generalizes and makes the original linear mean filter highly resistant to outliers but also outperforms a typical and powerful mean-logcauchy filter recently developed by hamza et al in the mixed noise removal in certain specific conditions in the normalized mean square error (nmse) sense. also the experimental results illustrate that the kernel-based nonlinear filters are promising.
efficient night gait recognition based on template matching. gait is a useful biometric which can be used to recognize people at a distance when other biometrics are incapable. however, most work on gait recognition has been visible spectrum-oriented over the past decade, ignoring recognition at night which is in reality demandimperative. this paper deals with the problem of night gait recognition via thermal infrared imagery. first of all, human detection is accomplished, based on the gaussian mixture modeling of the background. then, human silhouettes are extracted on the basis of preceding detection results. moreover, a new gait representation called hti is proposed to characterize gait signatures for recognition. an infrared night gait database was built to provide a foundation for night gait recognition. experimental results on two gait datasets show the effectiveness of this method.
a theoretical and experimental consideration on interference in resolutions between sampling theorem and ok-quantization theory. ok-quantization theory for the digitization in value ensures the reconstruction of the probabilistic density function of the image. this paper shows some experimental demonstrations to reduce the number of the gray levels, and shows mainly that there is a necessary but very important analytical relationship between sampling and quantization based on the equivalence relationship between two kinds of the integral, riemann and lebesgue integrals for calculating the volume of the image. experimental demonstrations are also shown in this paper.
use of viewpoint information for example selection in cbir. images play important role in telerobotic interface systems. a set of images taken by an image sensor equipped on a mobile robot is helpful for a remote user to recognize the conditions of the robot as well as its operating environments. we view such an image set as a large-scale image database, and apply content-based image retrieval (cbir) to visualize the image database. we will propose a novel cbir method that efficiently searches such relevant objects based not only on their features, but also on their locations with respect to the operating environment.
constructing speech processing systems on universal phonetic codes accompanied with reference acoustic models. this paper proposes a novel speech processing framework, where all of the speech data are once encoded into universal phonetic code (upc) sequences and speech processing systems, such as speech recognition, retrieval, digesting, are constructed on this upc domain. first of all, we introduce an ipa-based sub-phonetic segment (sps) set as the upc to deal withmultilingual speech. in the upc (sps) domain, each upc accompanies a reference acoustic model which is independent of real acoustic models used in the encoding process. processing, such as recognition, in the upc domain is conducted based on the distance between upc sequences estimated by using the reference acoustic models. we confirm the proposed framework by constructing a speech recognition and a vocabulary-free speech retrieval system on the sps domain. we show several experimental results on these systems, using japanese and english speech data sets.
neighbor pixel mixture. pixel mixture is a technique to reduce the read-out time of an image by mixing multiple pixel values on an imager. ordinary pixel mixtures mix the values of equicolor pixels. however, the equicolor pixels are not contiguous on the bayer pattern. mixing non-contiguous pixels degrades the resolution of the mixed image. this paper proposes a novel pixel mixture method which we call a "neighbor pixel mixture". the resolution of the neighbor pixel mixture image is superior to that of exisiting pixel mixtures. the proposed method mixes the values of the neighbor pixels with different colors. in the proposed method, a weighted average is used for the mixing operation, whereas ordinary pixel mixtures apply a simple average. we also discuss a guideline to design weights for averaging.
probabilistic localization for mobile robots using incomplete maps. this paper addresses the problem of global localization for mobile robots in changeable environments, i.e. to estimate self-position using a map that is partially or completely different from the environment. it is difficult to detect changes when both of the self-position and the map have large uncertainties. to solve the problem, in this paper, we extend monte carlo localization (mcl) and sensor resetting localization (srl), so as to generate a number of hypotheses about the change as well as the self-position. as a result of tests in a number of environments as well as changes, we found the proposed method is effective even when "rate of changes (roc)" is high in the environment.
retrieval method for multi-category images. we propose an image retrieval technique based on multiple categories. generally, an image can be associated with many concepts or objects. when concepts or objects are regarded as categories, an image could have multiple labels corresponding to these categories. thus, image retrieval based on multiple categories would be an important technology for an image database system. to realize multi-category image retrieval, we employ the pmm (parametric mixture model) of multi-category text classification. we carried out multi-category image classification experiments using pmm and the color histogram feature. the experimental results show the possibility of multi-category image classification and retrieval using pmm.
probabilistic image processing based on the q-ising model by means of the mean-field method and loopy belief propagation. the framework is presented of bayesian image restoration for multi-valued images by means of the q-ising model. hyperparameters in the probabilistic model are determined so as to maximize the marginal likelihood. practical algorithms are described based the conventional mean-field approximation and loopy belief propagation. we compare the results empirically with those provided by conventional filters and the new methodsarefound to be superior.
active mass estimation with haptic vision. real-world objects exhibit rich physical interaction behaviors on contact. such behaviors depend on how heavy and hard it is when hold, how its surface feels when touched, how it deforms on contact, etc. recently, there are growing needs for haptic exploration to estimate and extract such physical object properties as mass, friction, elasticity, etc.. in this paper, we propose an active mass estimation method based on haptic vision. we first observe an object with active vision to extract its 3d shape and posture. next, we estimate a contact point and contact force and apply it to cause the object to move without rotation by a robot hand. we observe changes in the contact force using a force feedback sensor, and also observe its straight movement using a ccd camera. then we estimate the mass of the object using known static and dynamic friction coefficients. experimental results show that the mass of solids such as wood, iron, and ceramic objects were estimated efficiently within 10% error bound.
recognition of unconstrained legal amounts handwritten on chinese bank checks. this paper presents a novel research investigation on legal amount recognition of unconstrained cursive handwritten chinese character in the environment of a2ia checkreader¿ - a commercial bank check recognition system. the following problems and their solutions are described: character set of chinese legal amounts, preprocessing (slant detection and correction), segmentation, feature extraction, grammar, automatic annotation of chinese characters before and during training, and neural network/hidden markov model training and recognition. the system is trained with 47.8 thousand real bank checks, and validated with 12 thousand real bank checks. the recognition rate at the character level is 93.5%, and the recognition rate at the legal amount level is 60%. this is the first successful commercial product in this domain.
a novel method for harmonic geometric transformation model based on wavelet collocation. geometric distortion may occur in the data acquisition phase in information systems, and it can be characterized by some geometric transformation models. once the distorted image is approximated by a certain geometric transformation model, we can apply its inverse transformation for the geometric restoration to remove the distortion. harmonic model is a very important one, which can cover other linear and nonlinear geometric models. however, its implementation is very complicated, because it can not be described by any fixed functions in mathematics. in fact, it is represented by partial differential equation with a given boundary condition. in this paper, a novel wavelet-based method is presented to handle the harmonic model. our approach has two main advantages, the shape of an image is arbitrary and the program code is independent of the boundary. the performances are evaluated by experiments.
automatic segmentation of the papilla in a fundus image based on the c-v model and a shape restraint. for computer aided glaucoma diagnostics it is essential to robustly and automatically detect and segment the main regions, e.g. the papilla (optic nerve head), in a fundus image. in this paper an effective method for automatic papilla segmentation based on the c-v model and a shape restraint is proposed. the method is a combination between the c-v model using level sets and the elliptic shape restraint for papilla segmentation. the combination of the level set framework with a shape restraint ensures that the evolving curve stays an ellipse. experiments verify that the method shows a good performance in detecting the papilla shapes and computing the shape feature parameters within a broad variety of fundus images. the experiment results also show that the method is robust to noise and object deformity.
fast linear discriminant analysis using binary bases. linear discriminant analysis (lda) is a widely used technique for pattern classification. it seeks the linear projection of the data to a low dimensional subspace where the data features can be modelled with maximal discriminative power. the main computation in lda is the dot product between lda base vector and the data point which involves costly element-wise floating point multiplications. in this paper, we present a fast linear discriminant analysis method called binary lda (b-lda), which possesses the desirable property that the subspace projection operation can be computed very efficiently. we investigate the lda guided non-orthogonal binary subspace method to find the binary lda bases, each of which is a linear combination of a small number of haar-like box functions. we also show that b-lda base vectors are nearly orthogonal to each other. as a result, in the non-orthogonal vector decomposition process, the computationally intensive pseudo-inverse projection operator can be approximated by the direct dot product without causing significant distance distortion. this direct dot product projection can be computed as a linear combination of the dot products with a small number of haar-like box functions which can be efficiently evaluated using the integral image. the proposed approach is applied to face recognition on orl and feret dataset. experiments show that the discriminative power of binary lda is preserved and the projection computation is significantly reduced.
learning a sparse representation from multiple still images for on-line face recognition in an unconstrained environment. in a real-world environment a face detector can be applied to extract multiple face images from multiple video streams without constraints on pose and illumination. the extracted face images will have varying image quality and resolution. moreover, also the detected faces will not be precisely aligned. this paper presents a new approach to on-line face identification from multiple still images obtained under such unconstrained conditions. our method learns a sparse representation of the most discriminative descriptors of the detected face images according to their classification accuracies. on-line face recognition is supported using a single descriptor of a face image as a query. we apply our method to our newly introduced bhg descriptor, the sift descriptor, and the lbp descriptor, which obtain limited robustness against illumination, pose and alignment errors. our experimental results using a video face database of pairs of unconstrained low resolution video clips of ten subjects, show that our method achieves a recognition rate of 94% with a sparse representation containing 10% of all available data, at a false acceptance rate of 4%.
human motion de-noising via greedy kernel principal component analysis filtering. kernel principal component analysis (kpca) has been shown to be a powerful non-linear de-noising technique. a disadvantage of kpca, however, is that the storage of the kernel matrix grows quadratically, and the evaluation cost grows linearly with the number of exemplars. the size of the training set composing of these exemplars is therefore vital in any real system incorporating kpca. given long human motion sequences, we show how the greedy kpca algorithm can be applied to filter exemplar poses to build a reduced training set that optimally describes the entire sequence. we compare motion de-noising between standard kpca using all poses in the original sequence as training exemplars and de-noising using the reduced set filtered by the greedy algorithm. we show how both have superior denoising qualities over pca, whilst greedy kpca results in lower evaluation cost due to the reduced training set.
reconstruction of medical images by perspective shape-from-shading. shape-from-shading (sfs) is a fundamental problem in computer vision; it is based upon the image irradiance equation. recently, the authors proposed to solve the image irradiance equation under the assumption of perspective projection rather than the common orthographic one. the solution was a modification of the fast marching method of kimmel and sethian. this paper presents an application of this novel perspective algorithm to reconstruction of medical images. we focus on gastrointestinal endoscopy and compare the two versions of the fast marching method (orthographic vs. perspective). the examples and comparison show that, unlike orthographic sfs, perspective sfs is robust and can be utilized for real-life applications.
nonparametric discriminant analysis in relevance feedback for content-based image retrieval. relevance feedback (rf) has been wildely used to improve the performance of content-based image retrieval (cbir). how to select a subset of features from a large-scale feature pool and to construct a suitable dissimilarity measure are key steps in rf. biased discriminant analysis (bda) has been proposed to select features during relevance feedback iterations. however, bda assumes all positive feedbacks form a single gaussian distribution which may not be the case for cbir. although kernel bda can overcome the drawback to some extent, the kernel parameter tuning makes the online learning unfeasible. to avoid the parameter tuning problem and the single gaussian distribution assumption in bda, we construct a new nonparametric discriminant analysis (nda). to address the small sample size problem in nda, we introduce the regularization method and the null-space method. because the regularization method may meet the ill-posed problem and the null-space method will lose somediscriminant information, we proposed here a full-space method. the proposedfull-space nda is demonstrated to outperform bda based rf significantly based on a large number of experiments in corel database with 17,800 images.
object predetection based on kernel parametric distribution fitting. multimodal distribution fitting is an important task in pattern recognition. for instance, the predetection which is the preliminary stage that limits image areas to be processed in the detection stage amounts to the modeling of a multimodal distribution. different techniques are available for such modeling. we propose a pros and cons analysis of multimodal distribution fitting techniques convenient for object predetection in images. this analysis leads us to propose efficient and accurate variants over the previously proposed techniques as shown by our experiments. these variants are based on parametric distribution fitting in the rkhs space induced by a positive definite kernel.
uncertainties-driven surface morphing: the case of photo-realistic transitions between facial expressions. reproduction of facial animation play a fundamental role in applications requiring human-computer interactions the objective of this paper is to introduce a geometric mechanism that exploits a fix number of states and is able to execute a subsequent number of transitions between facial expressions. standard stereo-based techniques are used to reproduce the geometry and appearance of the most characteristic facial expressions. a novel free-form-deformation technique based on uncertainty driven local geometric registration in the space of distance transforms is used to produce a one-to-one mapping between the surfaces and the associated textures. standard techniques from image morphing introduce the temporal aspect in the process. experimental results and comparisons with actual observation demonstrate the potentials of such an approach.
robust recursive learning for foreground region detection in videos with quasi-stationary backgrounds. detecting regions of interest in video sequences is the most important task in many high level video processing applications. in this paper a robust technique based on recursive learning of video background and foreground models is presented. our contributions can be described along four directions. first, a recursive learning scheme is developed to build pixel models based on their colors. second, we generate background and foreground models to enforce the temporal consistency of detected foregrounds. third, we exploit dependencies between pixel colors to insure that the model is not restricted to using only independent features. finally, an adaptive pixel-wise criterion is proposed that incorporates different spatial situations in the scene.
linear model combining by optimizing the area under the roc curve. in some classification problems, like the detection of illnesses in patients, classes are very unbalanced and the misclassification costs for different classes vary significantly. then it is better not to minimize the classification error, but to optimize the ordering of the data, or to optimize the area under the roc curve (auc). in this paper we propose to optimize a linear combination of features (or base model outputs) by optimizing auc. the advantages are that a relatively small training set is required for the optimization and that the training set can have a large class imbalance. furthermore, the classifier does not make distributional assumptions, making it very suitable to combine the outputs of base classifiers. in the application of the detection of interstitial lung diseases it is shown to be very advantageous and to outperform standard classification rules.
a consistency-based model selection for one-class classification. model selection in unsupervised learning is a hard problem. in this paper a simple selection criterion for hyper-parameters in one-class classifiers (occs) is proposed. it makes use of the particular structure of the one-class problem. the mean idea is that the complexity of the classifier is increased until the classifier becomes inconsistent on the target class. this defines the most complex classifier which can still reliably be trained on the data. experiments indicated the usefulness of the approach.
optimal inference for hierarchical skeleton abstraction. skeletons are well-known representations that accommodate shape abstraction and qualitative shape matching. however, skeletons are sometimes unstable to compute and sensitive to shape detail, thus making shape abstraction and matching difficult. to address these problems, we propose a principled framework that generates a simplified, abstracted skeleton hierarchy by analyzing the quasi-stable points of a bayesian-inspired energy function. the resulting model is parameterized by both boundary and internal structure variations corresponding to object scale and abstraction dimensions, and trades-off reconstruction accuracy and representation parsimony. our experimental results show that the method can produce useful multi-scale skeleton representations at a variety of abstraction levels.
robust computation of optical flow under non-uniform illumination variations. in this paper, an energy minimization method is proposed to estimate the optical flow of an image sequence in the presence of non-uniform illumination variations. the energy function is formulated by combining a data constraint energy that considers the illumination variations and a smoothness constraint, which minimizes the pixel-to-pixel variation of the velocity and illumination fields. minimization of this energy function is equivalent to solving a linear system, which is accomplished by using an incomplete cholesky preconditioned conjugate gradientalgorithm. a dynamic weighting scheme, which considers the statistical properties of estimated optical flow. is also combined with this algorithm to improve the robustness of our algorithm. this algorithm has been successfully applied to synthetic and real image sequences and someexperimental results demonstrate that this algorithm can estimate the optical flow under non-uniform illumination variations accurately.