waashk/dblp huggingface dataset:
category-dependent feature extraction for recognition of degraded handwritten characters. conventional methods for recognizing multiple fonts and handwriting are generally robust against deformation but are weak against degradation. this paper proposes a category-dependent feature extraction method that resists both deformation and degradation. our proposed method compares an input pattern with the template of each category and estimates the degree of degradation of the input pattern. approximate stroke run-lengths without degradation are then obtained by compensating the inaccurate runs caused by degradation. recognition experiments using degraded handwritten characters show that the proposed feature is superior to conventional ones in resisting degradation.
gray-scale thinning by using a pseudo-distance map. in this paper, the algorithm for thinning of greyscale images is proposed that is based on a pseudodistance map (pdm). the pdm is a simplified distance map of gray-scale image and uses only that features of image and objects that are necessary to build a skeleton. the algorithm works fast for large gray-scale images and allows constructing a high quality skeleton.
early recognition and prediction of gestures. this paper is concerned with an early recognition and prediction algorithm of gestures. early recognition is the algorithm to provide recognition results before input gestures are completed. motion prediction is the algorithm to predict the subsequent posture of the performer by using early recognition. in addition to them, this paper considers a gesture network for improving the performance of these algorithms. the performance of the proposed algorithm was evaluated by experiments of real-time control of a humanoid by gestures.
multi-resolution template kernels. domains in which shapes of objects change rapidly and significantly are a challenge for existing representation techniques: sport is a good example of this. we present a texture-based approach that copes with these problems in addition to resolution variation. a set of exemplar poses are learned from subsampled example images of the target object, creating a set of multi-resolution template kernels which when convolved with the image respond suitably. this technique may then be used in established tracking algorithms (e.g. condensation [contour tracking by shochastic propagation of conditional density]). we demonstrate the technique in two domains, and suggest a markov approach using it to model behaviour.
a generalized k-means algorithm with semi-supervised weight coefficients. a new classification algorithm corresponding to a generalization of the k-means algorithm is proposed, whose algorithm is named as a weighted k-means algorithm. weight coefficients, which provide weighted distortions between data and cluster centers, are incorporated into the algorithm to realize reliable classification. a method determining the appropriate values of the weight coefficients from class labeled data is introduced. under the situations where statistical distributions of data are changing gradually with time, the weighted k-means algorithm for semi-supervised data composed from initial labeled data and succeeding unlabeled data is investigated.
an oriented-contour point based voting algorithm for vehicle type classification. this communication deals with an oriented-contour point based voting algorithm for multiclass vehicle type identification (make and model). the system obtains similar results for equivalent recognition frameworks with different feature selections [8]. results also show the method to be robust to partial occlusion.
tracking objects using recognition. tracking is frequently considered a frame-to-frame operation. as such, object recognition techniques are generally too slow to be used for tracking. there are domains, however, where the objects of interest do not move most of the time. in these domains, it is possible to watch for activity in the scene and then apply object recognition techniques to find the object''s new location. this makes tracking a discrete process of watching for object disappearances and reappearances. we have developed a memory assistance tool that uses this approach to help people with slight to moderate memory loss keep track of important objects around the house. the system is currently deployed in a prototype smart home.
three dimensional short-term memory image. we proposed the three dimensional short-term memory image related to human memory and human vision. three dimensional short-term memory image composed of the three dimensional features reconstructed from the binocular foveated vision. the next fixation point is determined statistically from the feature points in the image projected from the 3-d short-term memory image. we show the effectiveness by simulating the binocular eye movements for the virtual world generated computer graphics and for the real world using pan-tilt cameras. it is found that the viewing world does not change suddenly though the eye moves quickly by introducing the 3-d short-term memory.
real-time object tracking without feature extraction. in this paper we propose an appearance-based tracking method without any feature extraction from the images. at first, range-finder system or stereo cameras measure the shape of the incoming object to prepare a cg model of the object. then many images are generated from the model and compared with input images captured by the cameras. motion parameter of the object is modified to minimize the difference between generated and captured images, and the parameter tracks the motion of the real object precisely when minimization is converged. at the image generating stage, occlusion is also simulated and we can easily handle the visible region of the object.
combining adaptive pde and wavelet shrinkage in image denoising with edge enhancing property. partial differential equation (pde) and wavelet shrinkage are two kinds of feature preserving regularized image recovery methods. in this paper, the equivalence of the two methods is discussed according to function space theory, and a new hybrid model combining adaptive pde and wavelet shrinkage is proposed. in the new hybrid model, we use adaptive pde with edge enhancing property. the regular coefficient of adaptive model is locally adjusted according to directional derivatives of image. compared with traditional smoothing models, the new hybrid model has no gibbs phenomena, smoothes image without staircasing, and enhances edge to preserve feature and texture of image. both theoretical analysis and experiments have verified the validity o f the new model proposed in this paper.
efficient non-maximum suppression. in this work we scrutinize a low level computer vision task - non-maximum suppression (nms) - which is a crucial preprocessing step in many computer vision applications. especially in real time scenarios, efficient algorithms for such preprocessing algorithms, which operate on the full image resolution, are important. in the case of nms, it seems that merely the straightforward implementation or slight improvements are known. we show that these are far from being optimal, and derive several algorithms ranging from easy-to-implement to highly-efficient.
eigen nodule: view-based recognition of lung nodule in chest x-ray ct images using subspace method. we previously proposed a recognition method of lung nodules based on experimentally selected feature values (such as contrast, circularities, etc.) of pathological candidate regions detected by our quoit filter. in this paper, we propose a new recognition method of lung nodule using each ct value itself in roi (region of interest) area as a feature value. in the clustering stage, first, the pathological candidate regions are classified into some clusters using principal component(pc) theories. a set of ct values in each roi is regarded as a feature vector, and then eigen vectors and eigen values are calculated for each cluster by applying principal component analysis(pca). the eigen vectors (we call them eigen images) corresponding to the 10 largest eigen values, are utilized as base vectors for subspaces of the clusters in the feature space. in the discrimination stage, correlations are measured between the testing feature vector and the subspace which is spanned by the eigen images. if the correlation with the abnormal subspace is large, the pathological candidate region is determined to be abnormal. otherwise, it is determined to be normal. by applying our new method, good results have been acquired.
objects velocity estimation on images sequences by hough transform with projection (htp). we propose to extend the hough transform (ht) to 3d lines in a particular case: for each 3d point, we search for the line passing through this point and fitting the other points. the method uses projection of data on different planes and the combination of these results using least square algorithm. we call this algorithm "hough transform with projection (htp)". the robustness of this method, compared to the classical ht will be displayed. we use later this algorithm to compute the speed of moving regions in images sequences, without any tracking.
direct mapping of visual input to motor torques. most methods for visual control of robots formulate the robot command in joint or cartesian space. to move the robot these commands are remapped to motor torques usually requiring a dynamic model of the robot. in this paper we present a method for parameterizing joint torques and learning to map visual input directly to them. the system is implemented and used to control a crs 465 robot. the results of the implementation demonstrate that the parameterization of the torques allows both the motion and position of the robot's end effectors to be controlled. moreover, it is shown that it is possible to map visual input directly to joint torques.
pattern recognition in interrelated data: the problem, fundamental assumptions, recognition algorithms. as an adjunct to the classical pattern recognition theory dealing with single objects, a new approach to supervised pattern recognition is proposed for a variety of practical problems in which the class-memberships of several inter-related objects making an entire data array are to be estimated jointly. it is assumed, first, that the known structure of the array has the form of an undirected graph of immediate pair-wise adjacency of objects represented by their feature vectors, and, second, that the a priori knowledge on expected combinations of classes is expressed as a hidden markov random field on that graph. the presence of pronounced a priori information on interdependence of class-memberships of immediately adjacent objects allows for drawing much more reliable decisions from relatively unreliable features than in the classical case when the classes of single object are a priori considered as independent.
a probabilistic approach to learning costs for graph edit distance. graph edit distance provides an error-tolerant way to measure distances between attributed graphs. the effectiveness of edit distance based graph classification algorithms relies on the adequate definition of edit operation costs. we propose a cost inference method that is based on a distribution estimation of edit operations. for this purpose we employ an expectation maximization algorithm to learn mixture densities from a labeled sample of graphs and derive edit costs that are subsequently applied in the context of a graph edit distance computation framework. we evaluate the performance of the proposed distance model in comparison to another recently introduced learning model for edit costs.
a convolution edit kernel for error-tolerant graph matching. general graph matching methods often suffer from the lack of mathematical structure in the space of graphs. using kernel functions to evaluate structural graph similarity allows us to formulate the graph matching problem in an implicitly existing vector space and to apply well-known methods for pattern analysis. in this paper we propose a novel convolution graph kernel. our kernel function differs from other graph kernels mainly in that it is closely related to error-tolerant graph edit distance and can therefore be applied to attributed graphs of various kinds. the proposed kernel function is evaluated on two graph datasets. it turns out that our method is generally more accurate than a standard edit distance based nearest-neighbor classifier, an edit distance based kernel variant, and a random walk graph kernel.
elastic transformation of the image pixel grid for similarity based face identification. nonlinear transformation of one image plane relative to another by spatially constrained elastic matching of two pixel grids is proposedas a technique of measuring image similarity for the purpose of featureless face identification. the elastic matching algorithm is devised as a combination of two dynamic programming procedures applied independently to each row and then to each column of the pixel grid. in contrast to the commonly adopted method of measuring face image similarity based on the dynamic link architecture, the proposed method is non-iterative and it avoids image segmentation. most importantly the method provides the linear computational complexity with respect to the number of pixels without application of parallel computers.
automatic segmentation of muscles of mastication from magnetic resonance images using prior knowledge. we propose a knowledge-based, fully automatic methodology for segmenting muscles of mastication from 2-d magnetic resonance (mr) images. to the best of our knowledge, there is currently no methodology which automatically segment muscles of mastication. in our approach, mr images with muscles of interest that have been manually segmented by medical experts are used to train the system to identify a relationship between the region of interest (roi) of the head and roi of the muscle. anisotropic diffusion is used to smooth the roi of the latter. neighboring regions of the muscle are removed by thresholding. a template of the muscle, from the manual tracings, is used to obtain an initial segmentation of the muscle. small unwanted regions in the roi are removed via connected components labeling. a gradient vector flow (gvf) snake, using the initial segmentation as initialization, is used to refine the initial segmentation. we performed 2-d segmentation of the medial and lateral pterygoids on a total of 50 mr images, in the mid-facial region through the mandible with accuracy ranging from 85% to 98%.
adjacent orientation vector based fingerprint minutiae matching system. minutia matching is the most popular approach to fingerprint recognition. in this paper, we analyzed a novel fingerprint feature named adjacent orientation vector, or aov, for fingerprint matching. in the first stage, aov is used to find possible minutiae pairs. then one minutiae set is rotated and translated. this is followed by a preliminary matching to ensure reliability as well as a fine matching to overcome possible distortion. such method has been deployed to a payroll and security access information system and its workability is encouraging. the information system aims to offer a highly secured and automated identification system for payroll tracking as well as authorized access to working areas.
a unified strategy to deal with different natures of reject. the interest of reject for classifier optimization has been shown many times. the diversity of the applications requiring this concept makes us to distinguish two main natures of reject with distinct goals: the confusion reject and the distance reject. after the description of this two kinds of reject, we present a unified formalism to define them using reliability functions and reject thresholds. then we present a generic algorithm dedicated to the automatic learning of these thresholds. finally, we compare various possibilities of reject to achieve application goals.
fingerprint image enhancement using a parallel ridge filter. fingerprint image enhancement is an important process for improving the matching performance of low quality fingerprints. in order to eliminate high contrast noise lines such as deep wrinkles, which are prevalent in the low quality fingerprint images, we focus on the fact that true ridges have multiple paralleling neighbors. in this report, we propose the parallel ridge filtering method which can strongly suppress non-parallel noise lines by utilizing the parallelism of ridges. we also show the efficiency of our method by experimental results.
separating subsurface scattering from photometric image. while subsurface scattering is common in many real objects, almost all separation algorithms focus on extracting specular and diffuse components from real images. in this paper, we present a model-less approach derived from the bi-directional surface scattering reflectance distribution function (bssrdf). in our approach, we show that an illumination image is composed by the lambertian diffuse and subsurface scattering images. by converting the separation problem into one of two-layer separation in the illumination domain, a bayesian framework is used to solve the optimization problem which incorporates spatial and illumination constraints, the latter of which are captured as a set of diffuse priors. we present the detailed mathematical formulation and experimental results.
monocular vision based slam for mobile robots. this paper describes a new vision based method for the simultaneous localization and mapping of mobile robots. the only data used is a video input from a moving calibrated monocular camera. from the detection and matching of interest points in images at video rate, robust estimates of the camera poses are computed in real-time and a 3d map of the environment is reconstructed. the computed 3d structure is constantly refined thanks to the introduction of a fast and local bundle adjustment method that makes this approach particularly accurate and reliable. actually, this method can be seen as a new visual tool that may be used in conjunction with usual systems (gps, inertia sensors, etc) in slam applications.
one dimensional fractal coder for online signature recognition. fractal theory has been used for computer graphics, image compression and different fields of pattern recognition. in this paper we simplified a general purpose two-dimensional fractal coder used for image compression. since in the case of on-line signature recognition, we loose gray levels, contrast and luminosity information, we do not employ these parameters in the fractal coder. instead, we focused on geometrical relationship between the range block and its best domain block. then, some features were extracted directly by the proposed one dimensional fractal coder. we will show their usefulness in the application of persian on-line signature recognition.
feature comparison between fractal codes and wavelet transform in handwritten alphanumeric recognition using svm classifier. in this paper we proposed a new method for isolated handwritten farsi/arabic characters and numerals recognition using fractal codes and haar wavelet transform. fractal codes represent affine transformations which when iteratively applied to the range-domain pairs in an arbitrary initial image, the result is close to the given image. each fractal code consists of six parameters such as corresponding domain coordinates for each range block, brightness offset and an affine transformation. in this system, the support vector machine (svm) whih is based on statistical learning theory, with good generalization ability is used as the classifier. this method is robust to scale and frame size changes. 32 farsi's characters are categorized to 8 different classes in which the characters are very similar to each others. there are ten digits in farsi/arabic language and since two of them are not used in the postal codes in iran, therefore 8 more classes are needed for digits. according to experimental results, classification rates of 92.71% and 92% were obtained for digits and characters respectively on the test sets gathered from various people with different educational background and different ages.
traffic prediction using ying-yang fuzzy cerebellar model articulation controller. traffic prediction is a critical element in traffic control today. with the increase of transportation, an effective traffic prediction allows to prevent traffic problems. this research aims to propose a novel approach to traffic prediction using ying-yang fuzzy cerebellar model articulation controller (yyfcmac). the model is motivated from the famous chinese ancient ying-yang philosophy, which views everything as a product of conflict-harmony process between ying and yang. that principle is applied to find the optimal number of clusters and fuzzy sets in the fuzzification phase of the hybrid fuzzy-neural yyfcmac network. the analyzed experiment on a set of real traffic data flow of the east-bound pan island expressway (pie) in singapore shows the effectiveness of the yy-fcmac in universal approximation and prediction.
hierarchical monitoring of people's behaviors in complex environments using multiple cameras. we present a distributed, surveillance system that works in large and complex indoor environments. to track and recognize behaviors of people, we propose the use of the abstract hidden markov model (ahmm), which can be considered as an extension of the hidden markov model (hmm), where the single markov chain in the hmm is replaced by a hierarchy of markov policies. in this policy hierarchy, each behavior can be represented as a policy at the corresponding level of abstraction. the noisy observations are handled in the same way as an hmm and an efficient rao-blackwellised particle filter method is used to compute the probabilities of the current policy at different levels of the hierarchy. the novelty of the paper lies in the implementation of a scalable framework in the context of both the scale of behaviors and the size of the environment, making it ideal for distributed surveillance. the results of the system demonstrate the ability to answer queries about people's behaviors at different levels of details using multiple cameras in a large and complex indoor environment.
a markovian approach for handwritten document segmentation. we address in this paper the problem of segmenting complex handritten pages such as novelist drafts or authorial manuscripts. we propose to use stochastic and contextual models in order to cope with local spatial variability, and to take into account some prior knowledge about the global structure of the document image. the models we propose to use are markov random field models.
extending the linear interpolating condition to advanced synthetic discriminant function variants. in previous work, we demonstrated that a linearity condition was sufficient for us to estimate distortion from the output of the synthetic discriminant function (sdf). however, many variants of the sdf have been developed. we extend the linear interpolating property to a class of sdf variants and use this result to analyse the properties of two of these variants.
4-d voting for matching, densification and segmentation into motion layers. we present a novel approach for grouping from motion, based on a 4-d tensor voting computational framework. from sparse point tokens in two frames we recover the dense velocity field, motion boundaries and regions, in a non-iterative process that does not involveinitialization or search in a parametric space, and therefore does not suffer from local optima or poor convergence problems. we encode the image position and potential velocity for each token into a 4-d tensor. a voting process then enforces the smoothness of motion while preserving motion discontinuities, selecting the correct velocity for each input point, as the most salient token. by performing an additional dense voting step we infer velocities at every pixel location, which are then used to determine motion boundaries and regions. we demonstrate our contribution with synthetic and real images, by analyzing several difficult cases ¿ opaque and transparent motion, rigid and non-rigid motion.