waashk/dblp huggingface dataset:
fft snake: a robust and efficient method for the segmentation of arbitrarily shaped objects in image sequences. a robust and efficient algorithm for segmenting arbitrarily shaped objects in images, which is called fft snake, is proposed in this paper. a low-pass filter with the fast fourier transform (fft) of the curve as theoretic internal force is first introduced to smooth the contours. in real algorithm, it is composed of the curves trimming and crossing chains cutting. at last the contours are evolved in the direction of normal vectors of the curve to match the feature-map. the algorithm is then applied to the rapid video feedback on the motion for the real-time diving training. the results are highly encouraging to capture the contours of arbitrarily shaped objects for real-time tracking systems. we believe that fft snake has wide uses in video compression, multimedia applications, and so on.
shadow detection by integrating multiple features. cast shadows of moving foreground objects in a scene often result in problems for many applications such as surveillance, object tracking/recognition, video content analysis and intelligent transportation systems. in this paper we presented an algorithm exploiting information of color, shading, texture, neighborhoods and temporal consistency to detect shadows in a scene efficiently and reliably. the experimental results showed that the proposed method can detect umbra as well as penumbra in different kinds of scenarios under various illumination conditions.
latent layout analysis for discovering objects in images. latent layout analysis (lla) is a novel unsupervised learning technique to discover objects in unseen images using a set of un-annotated training images. lla defines a generative model that associates latent aspects to local appearances. the dependency between aspects and position is captured by a spatial sensitive aspect model. this dependency distinguishes lla from probabilistic latent semantic analysis (plsa). the latent aspects together with the latent layout constitute a compact scene representation. we demonstrate that the proposed lla significantly outperforms probabilistic latent semantic analysis in two tasks: object discovery (detection) and object localization.
distance based kernel pca image reconstruction. principal component analysis (pca) is widely used in data compression, de-noising and reconstruction, but it is inadequate to describe real images with complex nonlinear variations, such as illumination, distortion, etc, because it is a linear method in nature. in this paper, kernel pca (kpca) is presented to describe real images, which combines the nonlinear kernel trick with pca. first, the kernel trick is used to map the input data into an implicit feature space f, and then pca is performed in f to produce nonlinear principal components of the input data. however, there exists a problem for kpca reconstruction, as the feature space f is implicit and unknown. in order to deal with this problem, we propose to employ a new kernel called the distance kernel to set up a corresponding relation based on distance between the input space and the implicit feature space f. experimental results illustrate that the proposed method has an encouraging performance.
accurate dense optical flow estimation using adaptive structure tensors and a parametric model. an accurate optical flow estimation algorithm is proposed in this paper. by using a 3d structure tensor and a parametric flow model, the optical flow estimation is converted to generalized eigenvalue problem to avoid solving a linear system explicitly. the optical flow can be accurately estimated from the generalized eigenvectors. the confidence measurement derived from the generalized eigenvalues is used to adaptively adjust the coherent motion region to further improve the accuracy. experiments on both synthetic sequences with ground truth and real sequences are used to test our method. comparison with classical and recently published methods are given to demonstrate that our algorithm is accurate and robust to the aperture problem.
improve handwritten character recognition performance by heteroscedastic linear discriminant analysis. in this paper, we propose a new linear dimensionality reduction method to deal with heteroscedastic feature distribution in handwritten character recognition. marc loog's between-class scatter matrix decomposition and directed distance matrix (ddm) concept is adopted, while the chernoff criterion he used is replaced by a new mahalanobis criterion proposed in this paper, and the pairwiseclass calculation is removed to reduce computational cost. we experiment our heteroscedastic linear discriminant analysis algorithm on different character recognition problems, and demonstrate its superiority over conventional linear discriminant analysis.
a bayesian predictive method for automatic speech segmentation. implicit speech segmentation is basically to find time instances when the spectral distortion is large. spectral variation function is a widely used measure of spectral distortion. however, svf is a data-dependent measure. in order to make the measurement data-independent, a likelihood ratio is constructed to measure the spectral distortion. this ratio can be computed efficiently with a bayesian predictive model. the prior of the bayesian predictive model is estimated from unlabeled data via an unsupervised machine learning technique . gaussian mixture model(gmm). the experimental results show that effectiveness of this novel method. the performance on timit corpus indicate the potential applications in speech recognition, synthesis and coding.
cast shadow removal with gmm for surface reflectance component. cast shadow on the background is generated by an object moving between a light source and the background. the position and illumination of the source always change with time, while the background is stable. therefore, features connected with light source always change with time, such as geometry and color. in this paper, we present a shadow removal method by homomorphic model to extract surface reflectance component, which is only connected with background of the scene and is robust to change of light source. we assume that reflectance component fits gaussian distribution, and then use gmm to model it. experimental results show that, except dealing with shadow, our method is not sensitive to the change of illumination.
robust local scoring function for text-independent speaker verification. traditionally, the universal background model (ubm) is viewed as the background model of the entire acoustic feature space. we propose a novel interpretation of the ubm model, and consider it as a mapping function that transforms the variable length observations (speech utterances) into a fixed dimensional feature vector (sufficient statistics). after this mapping, a similarity measurement is computed on the fixed dimensional features. with this novel interpretation, we proposed a new similarity measurement which produces more than 10% relative improvement over the conventional ubm-map framework in both equal error rate and detection cost function.
fingerprint retrieval by complex filter responses. this paper proposes an approach of fingerprint retrieval based on the continuous classification of two complex filter responses. two complex filters are introduced and applied on the fingerprint orientation field to extract the local singularities, the similarities to the singular points. a numerical feature vector from the aligned fingerprint local singularities is constructed as the global feature for fingerprint retrieval. the continuous classification is employed to retrieve a subset of fingerprints similar to the query fingerprint for the finer matching. experimental results on nist fingerprint database 4 (nist-4) show the effectiveness of the proposed fingerprint retrieval approach.
inverse validation for accurate range image registration with structured data. automatic range image registration is a fundamental yet extremely difficult problem in machine vision. in this paper, we extend a promising registration algorithm (gicp) [6] for structured image data. the extended algorithm is based on a relaxation method combining the motion estimation results from the possible correspondences established by the gicp algorithm and those that are further ev aluated by an in verse validation procedure. a comparative studybased on real images has shown that the extended algorithm is more accurate and robust than the original algorithm.
genus-zero shape classification using spherical normal image. a new method for three dimensional (3d) genus-zero shape classification is proposed. it conformally maps a 3d mesh onto a unit sphere and uses normal vectors to generate a spherical normal image (sni). unlike extended gaussian images which have an ambiguity problem, the sni is unique for each shape. spherical harmonics coefficients of snis are used as feature vectors and a self-organizing map is adopted to explore the structure of a shape model database. since the method compares only the snis of different objects, it is computationally more efficient than the methods which compare multiple 2d views of 3d objects. the experimental results show that the proposed method can discriminate collected 3d shapes very well, and is robust to mesh resolution and pose difference.
free form shape matching using deterministic annealing and softassign. free form shape matching is a fundamental problem in both the machine vision and pattern recognition literatures. however, the automatic approach to free form shape matching still remains open. in this paper, we improve an existing deterministic annealing and softassign method for automatic free form shape matching through incorporating rigid motion constraints. the experiments based on both synthetic data and real images show the effectiveness of the improved algorithm.
object tracking using globally coordinated nonlinear manifolds. we present a dynamic inference algorithm in a globally parameterized nonlinear manifold and demonstrate it on the problem of visual tracking. an appearance manifold is usually nonlinear, embedded in a high dimensional space, and can be approximated by a mixture of locally linear models. existing methods for nonlinear dimensionality reduction, which map an appearance manifold to a single low dimensional coordinate system, preserve only spatial relationships among manifold points and render low dimensional embeddings rather than mapping functions. in this paper, we parameterize the mixture of linear appearance subspaces of an object in a global coordinate system, and apply it to visual tracking using a rao-blackwellized particle filter. experimental results demonstrate that the proposed approach performs well on object tracking problem in scenes with significant clutter and temporary occlusions which pose difficulties for other methods.
handwritten numeral string recognition: character-level vs. string-level classifier training. the performance of handwritten numeral string recognition integrating segmentation and classification relies on the classification accuracy and the resistance to non-characters of the underlying classifier. the classifier can be trained at either character level (with character and non-character samples) or string level (with string samples). we show that both character-level and string-level training yield superior string recognition performance. string-level training improves segmentation but deteriorates classification. by combining the character-level trained classifier and the string-level trained classifier, we have achieved higher string recognition performance. we show the experimental results of three classifier structures on the numeral strings of nist special database 19.
detecting virulent cells of cryptococcus neoformans yeast: clustering experiments. the yeast cryptococcus neoformans can cause dangerous infections such as meningitis. the presence of a thick capsule is shown to be correlated with virulence of a yeast cell. this paper reports on our approach towards developing a classifier for detecting virulent cells in images. we present our methods for creating samples, collecting images, preprocessing the images, identifying cells and creating features for each cell. unsupervised clustering experiments have provided preliminary evidence that our methods results in features that can successfully be used to group and distinguish virulent from normal cells. in our future work we plan to use the same methods and feature set to build supervised classification models.
a spectral representation for appearance-based classification and recognition. we present a spectral representation for appearance-based image classification and object recognition. based on a generative process, the representation is derived by partitioning the frequency domain into small disjoint regions. this gives rise to a set of filters and a representation consisting of marginal distributions of those filter responses. we use a neural network to learn a classifier through training examples. we propose a filter selection algorithm by maximizing the performance over training data. a distinct advantage of our representation is that it can be effectively used for different classification and recognition tasks, which is demonstrated by experiments and comparisons in texture classification, face recognition, and appearance-based 3d object recognition.
reinforcement learning-based feature learning for object tracking. feature learning in object tracking is important because the choice of the features significantly affects system's performance. in this paper, a novel online feature learning approach based on reinforcement learning is proposed. reinforcement learning has been extensively used as a generative model of sequential decision-making that interacts with uncertain environment. we extend this technique to feature selection for object tracking, and further add human-computer interaction to reinforcement learning to reduce the learning complexity and speed the convergence rate. experiments of the object tracking are provided to verify the effectiveness of the proposed approach.
simplest representation yet for gait recognition: averaged silhouette. we present a robust representation for gait recognition that is compact, easy to construct, and affords efficient matching. instead of a time series based representation comprising of a sequence of raw silhouette frames or of features extracted therein, as has been the practice, we simply align and average the silhouettes over one gait cycle. we then base recognition on the euclidean distance between these averaged silhouette representations. we show, using the recently formulated gait challenge problem (www.gaitchallenge.org), that the improvement in execution time is 30 times while possessing recognition power that is comparable to the gait baseline algorithm, which is becoming the comparison standard in gait recognition. experiments with portions of the average silhouette representation show that recognition power is not entirely derived from upper body shape, rather the dynamics of the legs also contribute equally to recognition. however, this study does raise intriguing doubts about the need for accurate shape and dynamics representations for gait recognition.
personalized face verification system using owner-specific cluster-dependent lda-subspace. in this paper, we propose an owner-specific cluster-dependent linear discriminant analysis (oscd-lda) method, and apply it to develop a personalized face verification system. before the owner enrollment, our system first divides all the training face images into a number of clusters, each containing a subset of face images having similar characteristics. once the owner completes the enrollment procedure, the system will assign the owner to the cluster that contains faces most similar to the owner's training faces. then, the system uses the training faces in this most similar cluster to determine the oscd-lda subspace for computing the matching score. this oscd-lda subspace can be considered as a personalized subspace, trained specifically for this owner in order to best discriminate this particular owner from other non-owners. our experimental results have shown that the proposed oscd-lda method outperforms the conventional lda method, and can reduce false acceptance rate and false rejection rate by about 40 percent when using the xm2vts database.
image complexity and feature extraction for steganalysis of lsb matching steganography. in this paper, we present a scheme for steganalysis of lsb matching steganography based on feature extraction and pattern recognition techniques. shape parameter of generalized gaussian distribution (ggd) in the wavelet domain is introduced to measure image complexity. several statistical pattern recognition algorithms are applied to train and classify the feature sets. comparison of our method and others indicates our method is highly competitive. it is highly efficient for color image steganalysis. it is also efficient for grayscale steganalysis in the low image complexity domain.
newspaper headlines extraction from microfilm images. automatic indexing is important for a digital library to provide digitized manuscripts of old document images and their electronic text. as an essential step in creating such a system, this paper discusses the issue of extracting headlines from old newspaper microfilms. most research on document layout analysis has largely assumed relatively clean images. however microfilm images of old newspapers present a challenge. such images are usually insufficiently illuminated and considerably dirty. to overcome the problem we propose a new effective method for separating characters from noisy background since conventional threshold selection techniques are inadequate to deal with these kinds of images. a run length smearing algorithm (rlsa) is applied in the headline extraction. experiment shows that our approach has improved the recall, precision and combined rates.
kernel scatter-difference based discriminant analysis for face recognition. there are two problems with the fisher linear discriminant analysis (flda) for face recognition. one is the singularity problem of the within-class scatter matrix due to small training sample size. the other is that flda cannot efficiently describe complex nonlinear variations of face images with illumination, pose and facial expression variations, due to its linear property. in this paper, a kernel scatter-difference based discriminant analysis is proposed to overcome these two problems. we first use the nonlinear kernel trick to map the input data into an implicit feature space f. then a scatter-difference based discriminant rule is defined to analysis the data in f. the proposed method can not only produce nonlinear discriminant features in accordance with the principle of maximizing between-class scatter and minimizing within-class scatter, but also avoid the singularity problem of the within class scatter matrix. experiments on the feret database show an encouraging recognition performance of the new algorithm.
feature extraction with genetic algorithms based nonlinear principal component analysis for face recognition. principal component analysis (pca) and linear discriminant analysis (lda) are two commonly used feature extraction techniques. in this paper, a nonlinear evolutionary weighted principal component analysis (ewpca) based on genetic algorithms is proposed. similar to lda, the ewpca maximizes the ratio of between-class variations to that of within-class variations, and achieves better classification performance than that of traditional pca. genetic algorithms are chosen as the searching method to select optimal weights for the ewpca. in face recognition, evolutionary facial feature obtained by performing ewpca is used as the representation of original face images. experimental results on orl and combo face databases prove that ewpca outperforms both pca, kernel pca and lda.
facial expression recognition based on fusion of multiple gabor features. in order to accomplish subject-independent facial expression recognition task, a multiple gabor features based facial expression recognition method is presented in this paper. different channels of gabor filters have different contributions on the facial expression recognition and reasonable combination of these features can improve the performance of a facial expression recognition system. nn based data fusion method is designed for facial expression recognition in this paper. experimental results show that the facial expression recognition rate can be improved by using multiple channel features and neural network fusion.
low resolution character recognition by image quality evaluation. the character image database plays an important role for the evaluation of a character recognition system. but there is no measure which tells the level of recognition difficulty of a given database. this paper proposes a novel approach for the low resolution character recognition, which fits the input character for the appropriate character database according to the input image quality. it is composed of two stems: character image quality evaluation, character recognition. firstly, it presents the gray distribution feature to evaluate the character image quality. secondly, according to the evaluation result the appropriate character database and recognition method are selected for the input character image which makes the classification have the higher probability of being the correct decision. experiment results demonstrate the proposed approach highly improved the performance of the degraded character recognition system.
winner update on walsh-hadamard domain for fast motion estimation. motion estimation (me) plays an important role in video compression. block-based me has been adopted in most video compression standards due to its efficiency. in this paper, we propose a novel and fast block-based me algorithm that is based on applying the winner-update search in the walsh-hadamard transform domain. due to the energy packing capability of the walsh-hadamard transform, the modified winner update procedure is performed only on a small number of most representative transform coefficients. in addition, our algorithm can skip the search at some locations based on the predicted sad of the current block. the superior performance of the proposed algorithm is demonstrated through experimental comparison with some representative motion estimation methods.
nearest intra-class space classifier for face recognition. in this paper, we propose a novel classification method, called nearest intra-class space (nics), for face recognition. in our method, the distribution of face patterns of each person is represented by the intra-class space to capture all intra-class variations. then, a regular principal subspace is derived from each intra-class space using principal component analysis. the classification is based on the nearest weighted distance, combining distance-from-subspace and distance-in-subspace,between the query face and each intra-class subspace. experimental results show that the nics classifier outperforms other classifiers in terms of recognition performance.