waashk/dblp huggingface dataset:
real-time face detection using boosting in hierarchical feature spaces. boosting-based methods have recently led to the state-of-the-art face detection systems. in these systems, weak classifiers to be boosted are based on simple, local, haar-like features. however, it can be empirically observed that in later stages of the boosting process, the non-face examples collected by bootstrapping become very similar to the face examples, and the classification error of haar-like feature-based weak classifiers is thus very close to 50%. as a result, the performance of a face detector cannot be further improved. this paper proposed a solution to this problem, introducing a face detection method based on boosting in hierarchical feature spaces (both local and global). we argue that global features, like those derived from principal component analysis, can be advantageously used in the later stages of boosting, when local features do not provide any further benefit. we show that weak classifiers learned in hierarchical feature spaces are better boosted. our methodology leads to a face detection system that achieves higher performance than a current state-of-the-art system, at a comparable speed.
vignetting distortion correction method for high quality digital imaging. vignetting refers to a position dependent loss of light in the output of an optical system causing gradual fading out of an image near the periphery. in this paper, we propose a method for correcting vignetting distortion by introducing nonlinear model fitting of a proposed vignetting distortion function. the proposed method aims for embedded digital imaging applications as well as conventional image enhancement. we could obtain a light intensity profile distribution without precisely controlled illumination and considerably reduce memory requirement for vignetting correction compared with traditional lookup table (lut) methods. we show the effectiveness of the proposed method by presenting experimental results obtained from commercial digital cameras.
face recognition by combining kernel associative memory and gabor transforms. kernel associative memory (kam) has previously been proposed as an efficient scheme for face recognition. in this paper, a hybrid method of combining kam and gabor wavelet transform is proposed. in this method, face images of each person are first decomposed into their spatial/ frequency domains by gabor transforms, which are then modelled by a kam. while gabor properties of orientation selectivity and spatial frequency selectivity provide discriminating features, kam offers the means to capture the important intra-class variations. experimental results obtained on two standard face databases demonstrated that the proposed method consistently improved the system performance.
shape-based discrimination and classification of cortical surfaces. advances in medical imaging technique make it possible to study shape variations of neuroanatomical structures in vivo, which has been proved useful in the study of neuropathology and neurodevelopment. in this paper, we propose the use of spherical wavelet transformation to extract shape features, as it can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a noncompact basis set. the extracted shape features can be used to statistically detect and visualize group shape differences from a coarse to fine resolution, and facilitate shape-based classification. a procedure is developed to apply this method to cortical surface models, and promising results are acquired on synthetic and real data.
weakly supervised learning on pre-image problem in kernel methods. this paper presents a novel alternative approach, namely weakly supervised learning (wsl), to learn the pre-image of a feature vector in the feature space induced by a kernel. it is known that the exact preimage may typically seldom exist, since the input space and the feature space are not isomorphic in general, and an approximate solution is required in past. the proposed wsl, however, would find an appropriate rather than only a purely approximate solution. wsl is able to involve some weakly supervised prior knowledge into the study of pre-image. the prior knowledge is weak and no class label of the sample is required, providing only information of positive class and negative class which should properly depend on applications. the proposed algorithm is demonstrated on kernel principal component analysis (kpca) with application to illumination normalization and image denoising on faces. evaluations of the performance of the proposed algorithm show notable improvement as comparing with some well-known existing approaches.
spatial-hmm: a new approach for semantic annotation of histological. this paper presents a new spatial-hmm for automatically classifying and annotating histological images. our model is a 2d generalization of hmm. given a matrix of feature vectors for all blocks in an image, the most appropriate semantic labels determined by our models are used for annotation. our experimental results showed that our model is superior to hmm in both recognition and annotation accuracy.
an efficient svm classifier for lopsided corpora. this paper explores application of svm to lopsidedcorpora in text categorization. by means of integrating kernel caching with shrinking policies effectively, an improved svm training algorithm for weightcalculation formula is proposed under the decomposition framework. extensive experiments on lopsided-corpora have been conducted. the conclusion can make it possible to apply the improved svm training algorithm to lopsided corpora in text categorization.
cryptographic key generation from biometric data using lattice mapping. crypto-biometric systems are recently emerging as an effective process of key management to address the security weakness of conventional key release systems using passcodes, tokens or pattern recognition based biometrics. this paper presents a lattice mapping based fuzzy commitment method for cryptographic key generation from biometric data. the proposed method not only outputs high entropy keys, but also conceals the original biometric data such that it is impossible to recover the biometric data even when the stored information in the system is open to an attacker. simulated results have demonstrated that its authentication accuracy is comparable to the well-known k-nearest neighbour classification.
an approximative calculation of relative convex hulls for surface area estimation of 3d digital objects. relative convex hulls have been suggested for multigrid-convergent surface area estimation. besides the existence of a convergence theorem there is no efficient algorithmic solution so far for calculating 3d relative convex hulls. this article discusses an approximative solution based on minimum-length polygon calculations. it is illustrated that this approximative calculation also proves (experimentally) to provide a multigrid convergent measurement.
removing temporal stationary blur in route panoramas. the route panorama is a continuous, compact and complete image representation of scenes along a route. it is generated continuously from reading a preset line in a camera frame that moves along a smooth path. more complicated than the mathematical model of slit scanning, the physical width of a sampling line may yield a temporal blur, named stationary blur, in the route panorama. it is the counterpart of the motion blur and appears at distant scenes. we analyze the sampling of the route panorama, and recover the intrinsic high frequency components from spatiotemporal slit data. the sharpened results enhance the cityscapes archiving and visualization in virtual tour and navigation.
shape recognition using curve segment hausdorff distance. a novel shape recognition approach (cshd) combining the template and syntactic matching schemes is proposed in this paper to generalize the hausdorff distance (hd) from matching two sets of points to matching two sets of lines/curves. the new approach retains the desirable features of hd. these include nonexplicit feature correspondence (natural allowance for portions of one shape to be compared with another) and simplicity in computation. in addition, a fast searching technique is employed to identify a list of likely matches. the proposed system has been applied to logo, palmprint and stationery recognitions with superior results. it has greatly out-performed the hd and is robust to noise, occlusion, skewing and minor broken line effect.
italic font recognition using stroke pattern analysis on wavelet decomposed word images. this paper describes an italic font recognition method using stroke pattern analysis on wavelet decomposed word images. the word images are extracted from scanned text documents containing word objects in various fonts and styles. earlier font recognition methods mainly focus on slanted texture or pattern analysis on single character or large text blocks, which are sensitive to noise and subject to font and style variations such as size, serifness, boldness, etc. our method takes advantage of 2-d wavelet decomposition on each word image and performs statistical analysis on stroke patterns obtained from wavelet decomposed sub-images. experiments are carried out with 22,384 frequently used word images in both normal and italic styles of four different fonts. on average, a recognition accuracy of 95.76% for normal style and 96.49% for italic style is achieved. experiments conducted on word images extracted from scanned documents with scattered italic words also show an encouraging result.
multiresolution block sampling-based method for texture synthesis. we present an efficient multiresolution block sampling-based texture synthesis algorithm that works well for a wide variety of challenging textures. we consider the texture synthesis as a procedure that first estimates and then resamples a probability function. to achieve the effect of good and fast estimation of the probabaility function for a given texture as well as to retain the structural information during synthesis, we propose a concept of block sampling and a corresponding novel scheme of texture synthesis based on multiresolution block sampling (mbs). the computational complexity of the proposed algorithm is much lower than that of existing algorithms. experimental results have shown that this novel texture synthesis algorithm produces superior results for many challenging textures.
recognition of expression variant faces using weighted subspaces. in the past decade or so, subspace methods have been largely used in face recognition - generally with quite success. subspace approaches, however, generally assume the training data represents the full spectrum of image variations. unfortunately, in face recognition applications one usually has an under-represented training set. a known example is that possed by images bearing different expressions; i.e., where the facial expression in the training image and in the testing image diverge. if the goal is to recognize the identity of the person in the picture, facial expressions will be seen as distracters. subspace methods do not address this problem successfully, because the feature-space learned is dependent over the set of training images available - leadingto poor generalization results. in this communication, we show how one can use the deformation of the face (between the training and testing images) to solve the above defined problem. to achieve this, we calculate the facial deformation between the testing and each of the training images, project this result onto the (learned) sub-space, and there weight each of the features (dimensions) inverse-proportionally to the estimated deformation. we show experimental results of our approach on those representations given by the following subspace techniques: principal components analysis (pca), independent components analysis (ica) and linear discriminant analysis (lda). we also present comparison results with a number of known techniques and show the superiority of our weighted lda algorithm over the rest.
accurate 3-d motion tracking with an application to super-resolution. many of the existing image processing applications, in particular the construction of super-resolution videos, require an accurate high-speed motion tracking algorithm. this paper proposes an efficient recursive approach that recovers 3-d motion from a stereo image sequence with high precision based on the trifocal tensor. in the computation process, neither the 3-d structure of the scene nor its reconstruction is necessary. the validity of the proposed algorithm is demonstrated by applying it to upgrade the resolution of real stereo images. empirical comparisons show that our novel approach outperformed traditional work in terms of both the accuracy of motion estimation and the quality of super-resolved images.
a robust algorithm for generalized orthonormal discriminant vectors. in this paper, we propose a robust and efficient algorithm for generalized orthonormal discriminant vectors (godv). the major advantage of the proposed method is the use of the rank-one update technique, rather than the lagrange multipliers method, to iteratively derive the formula of computing the discriminant vectors of godv. by contrast with the previous algorithms of godv, the proposed algorithm has the computational efficiency and the numerical stability because of the avoidance of solving the inverse computation of matrices. moreover, the proposed algorithm can be easily extended to tackle the nonlinear problem via kernel trick. the performance of the proposed algorithm is tested on the yale face database and orl face database, respectively.
effective classification image space which can solve small sample size problem. linear discriminant analysis (lda) is one of the most popular methods in feature extraction and dimension reduction. however, in many real applications, particularly in image recognition applications such as face recognition, conventional lda algorithm will often encounter small sample size problem. in this paper, an effective classification image space is defined and optimal features are extracted from this space. with the proposed method, an effective classification image space of each original image is first obtained. then, optimal features are extracted from this space. the small sample size problem is solved effectively with the proposed method. experimental results on xm2vts face database demonstrate the effectiveness of the proposed method.
adaptive discriminant projection for content-based image retrieval. content-based image retrieval (cbir) is a computer vision application that aims at automatically retrieving images based on their visual content. linear discriminat analysis and its variants have been widely used in cbir applications because of their effectiveness in finding a projection that maps the original highdimensional space to a low-dimensional one and preserves the most discriminant features. those techniques assume images from certain class(es) are all visually similar and try to cluster them in the projected space. in this paper we show that the human high-level concept of semantic similarity between images may not arise only from the low-level visual similarity and consequently that assumption is inappropriate in many cases. we propose an adaptive discrimant projection (adp) framework which could model different data distributions based on the clustering of different classes. to learn the best model fitting the real scenario, boosted adaptive discriminant projection is further proposed. extensive experiments are designed to evaluate our methods and compare them to the state-of-the-art techniques on benchmark data set and real image retrieval applications. the results show the superior performance of our proposed methods.
multiscale feature extraction of finger-vein patterns based on curvelets and local interconnection structure neural network. in this paper, we originally propose a multiscale feature extraction method of finger-vein patterns based on curvelets and local interconnection structure neural networks. the curvelets is used to perform the multiscale self-adaptive enhancement transform on the finger-vein image and a neural network with local interconnection structure is designed to extract the features of the finger-vein pattern. this method has the following features: firstly, the feature of finger-vein is line feature, or anisotropy, which is more suitable to be processed by curvelets than wavelets, especially when dealing with the obscure anisotropic features. secondly, when the multiscale self-adaptive enhancement transform is applied to the finger-vein image, the finger-vein pattern is emphasized and noises are refrained greatly. thirdly, a local interconnection neural network with linear receptive field is designed to deal with finger-vein patterns of different thickness and capture the patterns. fourthly, the method is very fast by using the integral image method. the experimental results show the proposed method is superior to other methods in finger-vein feature extraction and solve the problem of how to extract features from obscure images efficiently. the eer of the proposed method is 0.128%.
a framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. gait recognition has gained increasing interest from researchers, but there is still no standard evaluation method to compare the performance of different gait recognition algorithms. in this paper, a framework is proposed in an attempt to tackle this problem. the framework consists of a large gait database, a large set of well designed experiments and some evaluation metrics. there are 124 subjects in the database, and the gait data was captured from 11 views. three variations, namely view angle, clothing and carrying condition changes, are separately considered in the database. the database is one of the largest database among the existing databases. three sets of experiments, including a total of 363 experiments, are designed in the framework. some metrics are proposed to evaluate gait recognition algorithms.
a complete and rapid feature extraction method for face recognition. feature extraction is one of the key steps in face recognition. in this paper, common vector is used to extract features from null space of within-class scatter matrix, which is independent of the sample index in the same class and accelerates the speed of feature extraction. furthermore, effective features in regular space are extracted to enhance the performance of face recognition. the proposed method not only solves the small sample size problem, but also extracts more effective features from face images. experimental results on two popular databases demonstrate the effectiveness of the proposed method.
gca: a real-time grid-based clustering algorithm for large data set. few of the current existing methods for unsupervised learning (clustering) algorithms consider clustering the data points in a low-dimensional subspace in real time. in this paper, we present a grid based clustering algorithm (gca) with time complexity (o(n)). unlike previous clustering algorithm, gca pays more attention to the running time of the algorithm. gca achieves low running time by (i) determining the number of the clusters according to the point density of the grid cell and (ii) computing the distances between the centers of the clusters and the grid cells, not the data points. in order to make gca more efficient, principal component analysis(pca) is introduced to transform the data points from high dimension to low dimension. finally, we analyze the performance of gca and show that it outperforms most of the current state-of-the-art methods in terms of efficiency. in particular, it outperforms k-means algorithm by several orders in the running time.
surface tortuosity and its application to analyzing cracks in concrete. previous studies of tortuosity were restricted to a curve in 2d or 3d. we propose several measures of surface tortuosity based on surface normals and principal curvatures, and apply these measures to analyze the tortuosity of three-dimensional crack surfaces of concrete. we show that all crack surfaces are similar in tortuosity despite their different sizes and locations, while distinctive from various geometric surfaces.
genetic-based k-means algorithm for selection of feature variables. this paper proposes a genetic-based k-means(gk) algorithm for selection of the k value and selection of feature variables by minimizing an associated objective function. the algorithm combines the advantage of genetic algorithm(ga) and k-means to search the subspace thoroughly. therefore, our algorithm converges globally. a weighting function is then introduced to initialize the parameters of the algorithm. the experiments on a synthetic dataset and a real dataset shows that (i) gk outperforms kmeans since gk achieves the minimal value of the objective function and (ii) gk with the weighting function performs better than gk.
improved temporal correspondences in stereo-vision by ransac. determining the image correspondences of scene points in two or more views remain elusive especially for large baselines and (or) rotations. assuming a calibrated stereo rig, we propose two novel ransac-based algorithms to establish temporal correspondences of triplet and quadruplet matches in two stereo pairs robustly. we devise an efficient technique for the absolute orientation problem with minimum three matches [closed-form solution of absolute orientation using orthonormal matrices]. this drastically reduces the number of random samplings, for a given probability of selecting no outlier in at least one set, compared to the application of the 7-/8-point algorithms [a robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry, the developement and comparison of robust methods for estimating the fundamental matrix]. within the general ransac paradigm, a low-cost selective random sampling scheme is incorporated to further enhance the robustness of the algorithm that utilizes quadruplet matches.
mining uncertain data in low-dimensional subspace. mining for clusters in a database with uncertain data is a hot topic in many application areas, such as sensor database, location database, face recognition system and so on. since it is commonly assumed that most of the objects which are contained in a high-dimensional dataset are located in a low-dimensional subspace, mining clusters in a subspace in an uncertain database is a new task. in this paper, we adopt and combine fractal correlation dimension with fuzzy distance function to find out the clusters in a low-dimensional subspace in an uncertain database. we also propose the fuzzy kth nn algorithm to retrieve the kth nearest neighbor which can accelerate the process of mining. the experiments show that the new algorithm works well in an uncertain database.
style quantification of scanned multi-source digits. the co-occurring patterns in a group carrying the traits of common origin are statistically dependent via an underlying style context. exploiting style consistency in groups of patterns from multiple sources can increase ocr accuracy. the accuracy gains obtained by a style consistent classifier depend on the amount of style in isogenous (same-source) fields. we present mathematical models to quantify the amount of single-class and multi-class style using entropy, correlation and mutual information. we also demonstrate a method for style homogenization that allows testing our metrics on real data.