waashk/dblp huggingface dataset:
improvements of volume computation from non-parallel cross-sections. we describe an algorithm for volume evaluation of a 3d object from area measurements done in non-parallel cross-sections. the algorithm is based on watanabe formula for volume computation and uses an interpolation by cubic splines. the same splines are applied also for computation of object area and centroid in every cross-section. it allowed us to derive an explicit formula for volume computation and to extend the algorithm described in [fast surface and volume estimation from non-parallel cross-sections for freehand 3-d ultrasound].
historical document image enhancement using background light intensity normalization. this paper presents a new background light intensity normalization algorithm suitable for historical document images. the algorithm uses an adaptive linear function to approximate the uneven background due to the uneven surface of the document paper, aged color and light source of the cameras for image lifting. our algorithm adaptively captures the background with a "best fit" linear function and normalized with respect to the approximation. the technique works for both gray scale and color images with significant improvement in readability.
fingerprint image enhancement based on skin profile approximation. the performance of the fingerprint identification and verification systems relies heavily on the quality of the input fingerprint images. in this paper, we propose an effective image enhancement algorithm especially suitable for low quality fingerprint images, which can improve the clarity and continuity of the ridge structures. unclean sensor plates, non-uniform and inconsistent contacts are among the major causes for poor samples and feature extraction artifacts during image processing. the proposed algorithm estimates the finger elasticity by approximating the uneven finger skin due to poor skin condition or imperfect acquisitions. then the fingerprint image is normalized with respect to the approximation. experimental results show that the enhanced image quality by using the proposed normalization algorithm is effective and much better than other existing methods for improving the minutiae detection.
critical vector learning to construct rbf classifiers. sensitivity is initially investigated for the construction of a network prior to its design. sensitivity analysis applied to network pruning seems particularly useful and valuable when network training involves a large amount of redundant data. this paper proposes a novel learning algorithm for the construction of radial basis function (rbf) classifiers using sensitive vectors (senv), to which the output is the most sensitive. in training, the number of hidden neurons and the centers of their radial basis functions are determined by the maximization of the output's sensitivity to the training data. in classification, the minimal number of such hidden neurons with the maximal sensitivity will be the most generalizable to unknown data. our experimental results suggests that our proposed methodology outperforms classical rbf classifiers constructed by clustering.
automatic grading prototype system for kanji dictation test. this paper presents an automatic grading prototype system developed as recognition engine for japanese kanji dictation test which aims to certificate the reading and writing ability of kanji characters. the system is designed to replace the conventional human-grading process. different from general handwritten character recognition systems which allow to read incorrect (misspelled) characters, the grading system is required to discriminate miswritten characters more strictly from correct ones. this paper introduces the core processing stages of the system. focused on the relationship between "error of judgement" and "rejection",several discrimination methods based on the character classification approach and the likelihood approach are comparatively evaluated. a grading accuracy of error rate less than 0.5% with rejection rate less than 50% is achieved in the performance evaluation test.
self-localization of a mobile robot using compressed image data of average and standard deviation. in this paper, an image-based self-localization method is proposed for a mobile robot. images are compressed for each column, and the average and standard deviation of the pixels in each column are used. environmental and observational data, which are the compressed image data at the registration and observational stages, are matched, and the position of the robot is obtained. the entire environment can be represented continuously with a small amount of data. a simple and robust matching method based on a voting process is introduced. the methods are evaluated through several experiments with omnidirectional images.
an error bound of relative image blur analysis. a lower bound of the relative image blur estimation error of a pair of images is derived analytically in this paper. this error analysis result shows that the optimal camera parameters for obtaining the most accurate depth from defocusing (dfd) results are strongly related to the object depth. therefore, dfd methods using only two images usually cannot achieve the most accurate depth estimates. instead of using only two images, a sequence of images focused at different depths should be provided in order to obtain accurate dfd results. with some prior knowledge of the object depth, the derived error bound is used to select optimal image pairs from the input image sequence for computing dfd. real experimental results show that the resulting dfd method outperforms both a dff method and a dfd method using two images.
a bi-directional visual stereo interface for accessing stereo matching results from a human brain. in this paper, a novel interface, termed bvsi, allowing people to input stereo correspondences more efficiently is proposed. the bvsi consists of a stereo display for providing a stereo view of a 3-d scene and a binocular gaze tracker for recording the binocular fixation points of an operator. a calibration method of the bvsi is presented in this paper. the horizontal gaze tracking error of the calibrated bvsi is about ± 10 pixels. real experiments have been conducted and the results showed that the proposed system is very promising.
face detection using discriminating feature analysis and support vector machine in video. this paper presents a novel face detection method in video by using discriminating feature analysis (dfa) and support vector machine (svm). our method first incorporates temporal and skin color information to locate the field of interests. then the face class is modelled using a small training set and the nonface class is defined by choosing nonface images that lie close to the face class. finally, the svm classifier together with bayesian statistical analysis procedure applies the efficient features defined by dfa for face and nonface classification. experiments using both still images and video streams show the feasibility of our new face detection method. in particular, when using 92 images (containing 282 faces) from the mit-cmu test sets, our method achieves 98.2% correct face detection accuracy with 2 false detections. when using video streams, our method detects faces reliably with computational efficiency of more than 20 frames per second.
simultaneous optimization of class configuration and feature space for object recognition. a new algorithm for object classification based on an extension of the fisher's discriminant analysis is presented. object recognition algorithms using the standard fisher's algorithm, such as the fisherface, train the classifier using sample-class pairs, where, for the classes, object categories determined in the application systems are used directly. in contrast, the new algorithm automatically produces sub-classes, within each predetermined category, that are actually used for classification, via unsupervised learning. in order to perform this, we combine the fisher's discriminant analysis with the akaike information criterion, optimizing the class configuration, i.e. sample-subclass correspondences, and the feature extraction function simultaneously, thereby improving the potential of linear separability. by applying this new method to face recognition, we show how it outperforms the traditional fisher-based method.
constituting origami models from sketches. this paper proposes an approach to constituting craese patterns, the unfolded origami models, by using skeletons obtained from 2-d images such as handwriting sketches. firstly, we describe a method for constructing a data structure which represents all the parts of an origami model and their relationships based on an extracted skeleton, and then give an algorithm for constituting a crease pattern using this data structure. to show the validity of proposed method and algorithm, we finally demonstrate how a crease pattern is generated and how a origami model is actually realized using some real illustrations taken from a origami drill book.
analysis of overlapping faces for constructing paper-made objects from sketches. this paper describes an approach to constructing a 3- d paper-made object from hand-written sketches. this approach consists of two phases. one is the phase that constitutes a crease pattern based on a sketch. another is the phase that constructs a virtual origami model from the obtained crease pattern. the crease pattern is a set of line segments in an unfolded sheet of paper and often designed by origami design methods. when an origami model is folded from a crease pattern, faces in the crease pattern may be transformed into the same plane and some inconsistent objects may be represented. in order to construct feasible (fold-able) objects, we need to dispose the faces on the same plane consistently. therefore, in this paper, a method for analyzing overlap order of faces based on simulated annealing is proposed. furthermore, we show some examples of 3-d objects constructed by our method. the proposed method is useful for packaging and architectural modeling.
estimation of ball route under overlapping with players and lines in soccer video image sequence. this paper deals with the analysis of broadcast soccer video. to recognize interesting events such as a goal, estimation of ball movements is necessary. it is, however, sometimes difficult to detect a ball by a simple color and shapebased method when it overlaps with players and lines. we therefore develop a method of estimating a ball route during such overlaps by considering spatio-temporal relationships between players, lines, and the ball. the method can deal with difficult cases such as the one where a ball disappears at a player and re-appears from another player. experimental results show the effectiveness of the method.
super-resolution under image deformation. this paper proposes a new method to obtain precise projective parameters of image deformation simultaneously with non-iterative calculation by extending area-based matching and sub-pixel estimation. the method requires no "a priori" knowledge of images at all. the proposed method is based on a practical similarity model in 8-dparameter space. using similarity measures obtained at discrete positions in the parameter space, our method provides a highly accurate maximum position of similarity in sub-sampling resolution; that position corresponds to image deformation parameters. the estimated parameters can be used for direct multi-image super-resolution, which can directly reconstruct a high-resolution full-color image from a set of low-resolution bayer cfa images. experiments on the super-resolution processing were performed using real image sequences to verify the proposed method.
moving object detection with mobile stereo omni-directional system (sos) based on motion compensatory inter-frame depth subtraction. moving object detection with a mobile image sensor is an important task when considering mobile robots for use in human environments. in this paper, we propose a novel method for effectively solving the problem of detecting moving objects for mobile robots by using the stereo omni-directional system (sos) which has a complete spherical fov. we first predict the depth image for the present time from the self-motion of the sos and the depth image obtained at the previous time, and then detect the moving objects by comparing the predicted depth image with the actual one obtained at the present time. experiments in the real world show the effectiveness of the proposed method.
efficient search and verification for function based classification from real range images. in this work we propose a probabilistic model for generic object classification from raw range images. our approach supports a validation process in which classes are verified using a functional class graph in which functional parts and their realization hypotheses are explored. the validation tree is efficiently searched. some functional requirements are validated in a final procedure for more efficient separation of objects from non-objects. the search employs a knowledge repository mechanism that monotonically adds knowledge during the search and speeds up the classification process. finally, we describe our implementation and present results of experiments on a database that comprises about one-hundred-and-fifty real raw range images of object instances from ten classes.
triangular mesh generation of octrees of non-convex 3d objects. a general surface-generating algorithm, the marching cube, produces triangular meshes from octants where the vertices of octants are clearly classified into either inside or outside the object. however, the algorithm is ambiguous for octrees corresponding to nonconvex objects generated using a shape from silhouette technique. this paper presents a methodology which involves delaunay triangulation to generate surface meshes for such octrees. since the general 3d delaunay triangulation creates 3d convex hull which consists of tetrahedron meshes, we propose a method which applies the delaunay algorithm locally in order to deal with non-convex objects. the proposed method first slices an octree and detects the clusters in each slice. all clusters between adjacent slices are linked based on a 3d probability density cube. the delaunay algorithm is then applied to locally-linked clusters. finally the accumulation of triangular meshes forms a final non-convex surface mesh.
correction of intensity of a color image using a range intensity image. this paper proposes a method of correcting intensity of a color image, which is used for the texture of a 3d model, using a range intensity image. a range intensity image has an effective characteristic that it is obtained under controlled illumination. this enables correction of the intensity of the color image without estimating illumination for the color image. experiments show the effectiveness of the proposed method and a 3d model with proper color information generated by the method.
an efficient text capture method for moving robots using dct feature and text tracking. when a moving robot tries to find text in the surrounding scene by an onboard video camera, the same text strings appear in many image frames. since it is a waste of time to recognize the same text strings repeatedly, it is necessary to decrease text candidate regions for recognition. this paper presents a text capture system that can look around the environment by an active camera, reducing the number of text strings to be recognized. the text candidate regions are extracted from the images by an improved dct feature. the text regions are tracked in a video sequence to reduce the text candidate strings. in experiments, we tested 55 images of corridor with seven text strings. the text candidate regions are reduced by 86.8% by our method.
multi-biometrics fusion for identity verification. in this paper, we accomplish matching score level fusion of multi-biometrics. in order to solve the incomparability among different classifiers' outputs, adaptive confidence transform (act) is introduced to convert the raw outputs of different classifiers to the estimates of posteriori probabilities conforming to different users. these posteriori probabilities are then combined using several fusion methods. experiments conducted on a database (including face, iris, online signature and offline signature traits) of about 100 users indicate that for the same fusion method, act based normalization generally results in better verification performance and is more robust compared to other normalization methods. effects of different normalization and fusion methods on combination of "strong" and "weak" classifiers are also examined.
moving shadow detection with support vector domain description in the color ratios space. moving shadow detection is a fundamental step in video-surveillance applications since it is generally confused with foreground. in this paper, we propose a novel statistical non-parametric method to detect moving shadow in a road traffic image sequences. we consider a diagonal model to describe the shadow distortion in the rgb color space. a support vector domain description (svdd) algorithm is applied in the color ratios space in order to discriminate shaded pixels from foreground.
real-time multi-frame analysis of dominant translation. this paper describes an ultratfast method ,for estimation of translational motion in video sequences. the method is based on the princkle that 2 0 image translation results in translation of imageprojections. to obtain superior robustness and accuracy we developed a multitfranie e-xtension, which robustly integrates motion information over time. our methodgives reliable results even when video images are blurred by significant and rapid motion and in the presence of independent local motions. the estimation process is very fast: on a standard desktop computer without using any hardwareor assembler-based optimization it works at a rate of 1400 fps (vga frame size), exceeding real-time requirements and is typically 500 times faster than some prior-art approaches, including 2 0 phase correlation. our method also provides a ve ry compact description of a video,frame, applicable to motion analysis and other video processing tasks in low-cost dsp systems.
real time limb tracking with adaptive model selection. we describe an efficient and robust method of tracking human forearms as skin colored regions. of special consideration in the design of this system are real-time and robustness issues. we approach this as 2d tracking problem using skin color and edge information. multiple 2d limb models are used to enhance tracking of the underlying 3d structure. this includes models for lateral forearm views (waving) as well as for pointing gestures. experiments on test sequences demonstrate the efficacy of this approach.
detecting human motion with support vector machines. this paper presents a method for detection of humans in video sequences. the intended application of the method is outdoor surveillance. in such an uncontrolled environment, the appearance of humans varies hugely due to clothing, identity, weather and amount and direction of light. the idea is therefore to detect patterns of human motion, which to a large extent is independent of the differences in appearance. to this end, a support vector machine is trained with dense optical flow patterns originating from humans. the subjects are moving in different angles to the camera plane, on different image scales. this trained svm is the core of a human detection algorithm which searches optical flow images for human-like motion patterns.
improvement of histogram-based image retrieval and classification. histogram-based techniques are commonly used in image retrieval ranging from basic color histograms to sophisticated histograms of various local feature vectors. our approach considers multidimensional histograms of locally invariant features. in order to get rid of the discontinuous behavior of traditional histograms we develop a fuzzy histogram which does no sharp assignment of a feature vector to one bin only but performs a weighted assignment to allneighboring bins. this improvement is not restricted to the kind of feature histograms considered here but could generally improve histogram-based retrieval or classification techniques.
a multi-agent based interactive system towards child's emotion performances quantified through affective body gestures. studies in cognitive science have found that interactive robots, interactive games, etc., may help the emotional development of children with mental-health problems (e.g. autistic children). it is very important with these children to show empathic behaviors and to take into account their emotion when relating to them. current technology cannot do this because it cannot recognize the emotion of the children. few studies are directed toward this goal by categorizing affective behavior of the child into a set of discrete categories. but still two problems exist: gesture is not yet a concern as channel of affective communication in interactive technology, and existing systems only model discrete categories, but not affective dimensions, e.g., intensity. in this work, we propose a multi-agent based interactive system that can quantify child's performance (emotion with intensity of emotion) in real time.
optimization of neural classifiers based on bayesian decision boundaries and idle neurons pruning. in this article we describe a feature extraction algorithm for pattern classification based on bayesian decision boundaries and pruning techniques. the proposed method is capable of optimizing mlp neural classifiers by retaining those neurons in the hidden layer that reallycontribute to correct classification. also in this article we proposed a method which defines a plausible number of neurons in the hidden layer based on the stem-and-ieaf graphics of training samples. experimentai investigation reveals the efficiency of the proposed method.
locating characters in scene images using frequency features. this paper presents a {language-independent) method of locating rectangular text regions in natural scene images. the method consists in two steps that can be applied in succession or independently: the frequency of edge pixels across vertical and horizontal scan lines, and thefundamental frequency in the fourier domain. the frequency feature of text images is highly intuitive, and thus appealing as much. this is the focus of the research. also addressed is the detection of rectangles using rough transform. since texts that is meaningful to many viewersusually appear in rectangles of colours of high contrast to the background. hence it is natural to assume the detection rectangles may be helpful for locating desired texts correctly in natural outdoor scene images.
robust klt tracking with gaussian and laplacian of gaussian weighting functions. object tracking algorithms extensively found in literature are either constrained with assumptions or are overly sensitive to noise. we propose and successfully test two new weighting functions for a feature-based object tracker to achieve superior tracking performance and noise immunity. also presented is a mechanism for image based optimal weighting function determination.
colour image texture analysis: dependence on colour spaces. in this paper we investigate the role of colour spaces on texture analysis. we extract a range of correlogram and colour moment features for the vistex colour texture benchmark in different colour spaces and find the average probabilistic distance of separation across different objects for different features and suggest the colour spaces that are best suited for the classification process. we also show the results of knn classification for different features and their combined set.