waashk/dblp huggingface dataset:
high accuracy handwritten chinese character recognition using quadratic classifiers with discriminative feature extraction. we aim to improve the accuracy of handwritten chinese character recognition using two advanced techniques: discriminative feature extraction (dfe) and discriminative learning quadratic discriminant function (dlqdf). both methods are based on the minimum classification error (mce) training method of juang et al. [7], and we propose to accelerate the training process on large category set using hierarchical classification. our experimental results on two large databases show that while the dfe improves the accuracy significantly, the dlqdf improves only slightly. compared to the modified quadratic discriminant function (mqdf) with fisher discriminant analysis, the error rates on two test sets were reduced by factors of 29.9% and 20.7%, respectively.
svm-based classifier design with controlled confidence. a new classification methodology with controlled error rates and a reject option is proposed in this paper. the proposed methodology is implemented using support vector machine's (svm's) posterior probability preserving property. a new nonparametric method is proposed to accurately estimate error rates from the output of a trained svm. the experimental results clearly demonstrate the efficacy of the suggested classifier design methodology.
cascading classifiers for consumer image indexing. in this paper, we propose a cascading framework of binary classifiers to extract and combine intra-image and inter-class semantics for image indexing and retrieval. support vector detectors are first trained on semantic support regions without image segmentation. the reconciled and aggregated detection-based indexes then serve as input for support vector learning of image classifiers to generate class-relative image indexes. during retrieval, similarities based on both indexes are combined to rank images. query-by-example experiments on2400 heterogeneous consumer photos with 16 semantic queries show that the combined matching approach is better than matching with single index. it also outperformed combined matching of color and texture features by 55% in average precision.
image tangent space for image retrieval. image tangent space is actually high-level semantic space learned from low-level feature space by modified local tangent space alignment which was originally proposed for nonlinear manifold learning. under the assumption that a data point in image space can be linearly approximated by some nearest neighbors in its local neighborhood, we develop a lazy learning method to locally approximate the optimal mapping function between image space and image tangent space. that is, the semantics of a new query image in image space can be inferred by the local approximation in its corresponding image tangent space. while euclidean distance induced by the ambient space is often used to represent the difference between images, clearly, their natural distance is possibly different from euclidean distance. here, we compare three distance metrics: chebyshev, manhattan and euclidean distances, and find that chebyshev distance outperforms the other two in measuring the semantic similarity during retrieval. experimental results show that our approach is effective in improving the performance of image retrieval systems.
image retrieval with relevance feedback based on graph-theoretic region correspondence estimation. this paper presents a graph-theoretic approach for region-based image retrieval. when dealing with image matching problem, we propose converting the region correspondence estimation into an attributed graph matching problem and measuring the image similarity in terms of both the region correspondence and the low-level features. in addition, during the relevance feedback, we propose using a maximum likelihood method to re-estimate region features and region importance while retaining its inherent spatial organization. experimental results show that the proposed graph-theoretic matching criterion outperforms other existing methods which include no spatial information in the matching criterion. the experiments also show that the performance can be further improved with our proposed relevance feedback scheme.
discovering operators and features for object detection. in this paper, we learn to discover composite operators and features that are evolved from combinations of primitive image processing operations to extract regions-of-interest (rois) in images. our approach is based on genetic programming (gp). the motivation for using gp is that there are a great many ways of combining these primitive operations and the human expert, limited by experience, knowledge and time, can only try a very small number of conventional ways of combination. genetic programming, on the other hand, attempts many unconventional ways of combination that may never be imagined by human experts. in some cases, these unconventional combinations yield exceptionally good results. our experimental results show that gp can find good composite operators to effectively extract the regions of interest in an image and the learned composite operators can be applied to extract rois in other similar images.
face recognition using patch-based spin images. this paper explores how spin images can be constructed using shape-from-shading information and used for the purposes of face recognition. we commence by extracting needle-maps from gray-scale images of faces, using a mean needle-map to enforce the correct pattern of facial convexity and concavity. spin images [4] are estimated from the needle maps using local spherical geometry to approximate the facial surface. our representation is based on the spin image histograms for an arrangement of image patches. we demonstrate how this representation can be used to perform face recognition across different subjects and illumination conditions. experiments show the method to be reliable and accurate.
vision based fire detection. vision based fire detection is potentially a useful technique. with the increase in the number of surveillance cameras being installed, a vision based fire detection capability can be incorporated in existing surveillance systems at relatively low additional cost. vision based fire detection offers advantages over the traditional methods. it will thus complement the existing devices. in this paper, we present spectral, spatial and temporal models of fire regions in visual image sequences. the spectral model is represented in terms of the color probability density of fire pixels. the spatial model captures the spatial structure within a fire region. the shape of a fire region is represented in terms of the spatial frequency content of the region contour using its fourier coefficients. the temporal changes in these coefficients are used as the temporal signatures of the fire region. specifically, an autoregressive model of the fourier coefficient series is used. experiments with a large number of scenes show that our method is capable of detecting fire reliably.
influence of language models and candidate set size on contextual post-processing for chinese script recognition. in the chinese language, a word consisting of one or more characters is a basic syntax-meaningful unit, however, each character in the word also has a definite meaning in itself. in this paper, we compare the perplexities of four n-gram language models (character-based bigram, character-based trigram, word-based bigram and class-based bigram) and their influence on the performance of contextual post-processing of chinese scripts in an offline handwritten chinese character recognition system. we also demonstrate the influence of the candidate set size on the performance of contextual post-processing in detail, and indicate that the number of candidates should vary with each script.
depth recovery from motion blurred images. this paper presents a novel method to obtain the depth information from motion blurred images. under the assumption of uniform linear motion between the camera and the scene during finite exposure time, both the pinhole model and the camera with a finite aperture are considered. it is shown that the image blur produced by lateral motion of the camera is inversely proportional to the distance of the object. furthermore, if the speed of the relative motion is known, the depth of the object can be acquired by identifying the blur parameters. an image blur model for uniform linear motion is formulated based on geometric optics. the proposed method has been verified experimentally using edge images.
an off-line chinese writer retrieval system based on text-sensitive writer identification. in this paper an off-line chinese writer retrieval system based on text-sensitive writer identification is practised. ocr technique is used to mark the handwritten script content. the used text-sensitive writer identification algorithm extracts directional element features(defs), reduces the dimensions using pca and lda, and adopts the simple euclidean classifier. by introducing negative samples, the similarity measure is more comprehensive. however the writer identification algorithm is text-sensitive, a novel method for retrieving in an amount of writers who has different handwriting script content is proposed in this paper, by combining and sorting the confidence results of writing identification. an experiment, which is carried out in a handwriting script database, demonstrates the effectiveness of the proposed method.
a real-time multiple-vehicle detection and tracking system with prior occlusion detection and resolution, and prior queue detection and resolution. the proposed multiple-vehicle detection and tracking (mvdt) system utilizes a color background to segment moving objects and exploits relations among the moving objects and existed trajectories to track vehicles. initially, the background is extracted by classification. then, it is regularly updated by previous moving objects to guarantee robust segmentation in luminance-change circumstance. for partial wrong converged background due to roadside parking vehicles, it will be corrected later by checking fed back trajectories to avoid false detection after the vehicles moving away. in tracking processing, the relations of distances or distances and angles are applied to determine whether to create, extend, and delete a trajectory. if occlusion detected after trajectory creation, it will be resolved by rule-based tracking reasoning. otherwise, lane information will be used. in addition, to further resolve occlusion occurred in lane, a queue detection and resolution technique will be utilized prior to tracking processing. finally, for easy setup, parameter automation for the system is proposed.
a novel blind watermarking algorithm in contourlet domain. a novel watermarking algorithm based on contourlet transform is proposed in this paper. the watermark composed of pseudo-random sequence is embedded in the selected contourlet transform coefficients by means of multiplicative method. the contourlet coefficients are modeled with generalized gaussian distribution with zero mean, and then watermark detection method is proposed based on maximum likelihood detection. furthermore the decision rule is optimized via neyman-pearson criterion. experimental results show that the fidelity of the watermarked image is good and robust to signal processing and small geometrical attacks.
3d model acquisition based on projections of level curves. a novel method for 3d model acquisition based on projections of level curves is presented. the idea is similar to surface from parallel contours. however, different from ct or laser range scanning for range data acquisition, our approach is implemented on a low-cost passive camera system. the object is placed in a container and the level curves are generated by raising the water level. the 3d surface is recovered by multiple 2d projections of parallel level curves with camera pose estimation. furthermore, the complete 3d model is reconstructed by registration and integration of multi-view 3d shape acquisition. experimental results are presented for real image sequences. the average error is less than 3 mm in the working range of about 1 meter from the camera.
reliable video clock time recognition. we propose a novel approach to read the video clock in real time by recognizing the clock digits using a few techniques relative to the transition patterns of the clock. with these techniques, the clock digits are located without recognizing all the text characters overlaid together with the clock on the video, the sample digit patterns are automatically extracted and labeled in real time. the clock digits are recognized while the video is playing back without any offline training. the recognition result is further verified against the digit transition sequence to improve the performance. experimental results show that this approach is reliable and the result is accurate regardless of the variation of font, size and color of the digit characters in different videos.
normalized sampling for color clustering in medical diagnosis. the classical approach of using minimum cut criterion for clustering is often ineffective due to the existence of outliers in the data. this paper presents a novel normalized graph sampling algorithm for clustering that improves the solution of clustering via the incorporation of a priori constraint in a stochastic graph sampling procedure. the quality of the proposed algorithm is empirically evaluated on two synthetic datasets and a color medical image database.
an embedded watermark technique in video for copyright protection. as the internet and storage equipment become more and more populous, the digital data can easy to distribution. the pirate can easy and limitless to copy digital multimedia. therefore people are more and more attentive the copyright protection for digital multimedia. digital watermarking is an important emerging technique for copyright protection and authentication. this paper presents a novel videowatermarking algorithm based on block matching algorithm to achieve copyright protection of owner. the watermark is mainly embedded in the uncompressed domain and is detected without the use of the original video information. our proposed method is easy to perform and can provide an adjustable threshold to obtain the imperceptibility and robustness performance. the obtained results show the robustness of this approach.
a real-time vehicle detection and tracking system in outdoor traffic scenes. this paper presents a moving vehicle detection and tracking system, mvdt, for real-time operation in outdoor scenes. mvdt consists of three major components: road detection, vehicle detection, and vehicle tracking. the road detection algorithm utilizes a plane-fitting feature, the vehicle detection uses both segmented blob and snakes blob features in a neural network classifier, and a fast vehicle tracking algorithm is designed to locate vehicles in consecutive image frames. we show through experiments that mvdt is effective in detecting moving vehicles in various outdoor scenes and can indeed reach real-time operation requirements.
switching auxiliary chains for speech recognition based on dynamic bayesian networks. this paper investigates the problem of incorporating auxiliary information (e.g. pitch) for speech recognition using dynamic bayesian networks (dbns). previous works usually model acoustic features conditional on the pitch auxiliary variable for both voiced and unvoiced phonetic states, and therefore ignore the fact that pitch (frequency) information is meaningful only for voiced states.. in this paper we propose a switching two auxiliary chain model tailored to voiced/unvoiced states for exploiting pitch information, which is essentially built on the switching parent functionality of bayesian multinets. experiments on the ogi numbers database show that significant performance improvements are achieved from switching auxiliary chain modeling, compared with regular auxiliary chain modeling and the standard hmm.
fingerprint indexing based on symmetrical measurement. due to the small number of classes and uneven distribution, the henry's fingerprint classification approach is not able to provide an efficient way to narrow down the search when identifying a person in a large database. in this paper, three symmetrical filters are used to measure different orientation structures. we proposed a new fingerprint indexing approach based on these three symmetrical measurements. experiments conducted on the nist database 4 show the effectiveness of the proposed approach.
an adaptive data hiding technique for binary images. a simple data hiding technique for binary images is proposed. the proposed method embeds secure data at the edge portion of host binary image. we find the best changeable pixels in a block by changing distance matrix dynamically and compute its changeable score by weighting mechanism. the proposed method uses the pseudo random number generator based on rabin public key cryptography system to embed secret data into a binary image. according to the pseudo random number generator, we can distribute secret data into the binary image to make binary image quality better and get high security.
image segmentation based on inscribed circle. we present an image segmentation algorithm based on inscribed circle (icseg) in this paper. the algorithm includes multi-step. it examines edges at first, and then produces a binary image with edges as foreground. after that, inscribed circles are created to cover the background of the binary image. in the first partition, the image is subdivided into separated regions by combining inscribed circles. finally, the regions are merged by computing their shape and gray level features. this approach integrates the boundarybased and region-based techniques. it is a simple and efficient way for full image segmentation.
off-line handwritten chinese character stroke extraction. stroke extraction is of great significance for an offline character recognition system. in this paper, we present an efficient stroke extraction method based on a combination of a simple feature point detection scheme and a novel stroke segment connecting method. the algorithm can fast and accurately extract the strokes from the thinned chinese character images. experimental results on a large data set with over eighteen thousand character strokes achieve over 99% accuracy.
detecting text lines in handwritten documents. although detecting text lines in machine printed documents is typically considered a solved problem, it is still a challenge to segment handwritten text lines in the general sense given no prior knowledge of script. this paper models text line detection as an image segmentation problem by enhancing text line structures using a gaussian window and adopting the level set method to evolve text line boundaries. experiments show that the method, which is script independent, achieves high accuracy for detecting text lines in heterogeneous handwritten documents.
articulate hand motion capturing based on a monte carlo nelder-mead simplex tracker. this paper presents an algorithm for tracking the articulate hand motion in monocular video sequences. the task is challenging due to the high degrees of freedom involved in the hand motion. the complexity can be reduced by considering the natural motion constraints. to take advantage of the constraints, we propose to use a nonparametric representation of the feasible configuration space and employ a monte carlo nelder-mead simplex search algorithm. the tracker combines the strengths of both sequential monte carlo and direct search algorithms. first, its multiple hypotheses nature increases the chance of the simplex method to identify the global maximum. second, the direct search algorithm produces a set of more representative particles. experiment results show that this hybrid approach is robust for tracking the hand motion.
a new strategy for selecting working sets applied in smo. at present sequential minimal optimization (smo) is one of the most popular and efficient training algorithms for support vector machines (svm), especially for large-scale problems. a novel strategy for selecting working sets applied in smo is presented in the paper. based onthe original feasible direction method, the new strategy also takes the efficiency of kernel cache maintained in smo into consideration. it is shown in the experiments on the well-known data sets that computation of the kernel function and training time is reduced greatly,especially for the problems with many samples and support vectors.
continuous optimization based-on boosting gaussian mixture model. a new estimation of distribution algorithm(eda) based-on gaussian mixture model (gmm) is proposed, in which boosting, an efficient ensemble learning method, is adopted to estimate gmm. by boosting simple gmm with two components, it has the ability of learning the model structure and parameters automatically without any requirement for prior knowledge. moreover, since boosting can be viewed as a gradient search for a good fit of some objective in function space, the new eda is time efficient. a set of experiments is implemented to evaluate the efficiency and performance of the new algorithm. the results show that, with a relatively smaller population and less number of generations, the new algorithm can perform as well as compared edas in optimizing multimodal functions.
bagging based efficient kernel fisher discriminant analysis for face recognition. kernel fisher discriminant analysis (kfda) has achieved great success in pattern recognition recently. however, the training process of kfda is too time consuming (even intractable) for a large training set, because, for a training set with n examples, both its between-class and within-class scatter matrices are of n×nand the time complexity of the kfda training process is ofo(n^3). aiming at this problem, this paper employs bagging technique to decrease the time-space cost of kfda training process. in addition, this paper is more than just a simple application of bagging. we have made an important adaptation which can further guarantee the performance of kfda. our experimental results demonstrate that the proposed method can not only greatly reduce the cost of time of the training process, but also achieve higher recognition accuracy than traditional kfda and the simple application of bagging.
object tracking using incremental fisher discriminant analysis. this paper presents a novel object tracking algorithm using incremental fisher linear discriminant (fld) algorithm. the sample distribution of the target class is modeled by a single gaussian and the non-target background class is modeled by a mixture of gaussians. to a facilitate a multiclass classification problem, we recast the classic fld algorithm in which the number of classes does not need to be pre-determined. the most discriminant projection matrix that best separates the samples in the projected space is computed using fld at each frame. based on the current target location, an efficient sampling algorithm is used to predict the possible locations in the next frame. using the current projection matrix computed by fld, the most likely candidate which is closed to the center of the target class in the projected space is selected. since the fld is repeatedly computed at each frame, we develop an incremental and efficient method to compute the projection matrix based on the previous results. experimental results show that our tracker is able to follow the target with large lighting, pose and expression variation.