waashk/dblp huggingface dataset:
global-to-local non-rigid shape registration. non-rigid shape registration is an important issue in computer vision. in this paper we propose a novel globalto- local procedure for aligning non-rigid shapes. the global similarity transformation is obtained based on the corresponding pairs found by matching shape context descriptors. the local deformation is performed within an optimization formulation, in which the bending energy of thin plate spline transformation is incorporated as a regularization term to keep the structure of the model shape preserved under the shape deformation. the optimization procedure drives the initial global registration towards the target shape that results in the one-to-one correspondence between the model and target shape. experimental results demonstrate the effectiveness of the proposed approach.
invariant ridgelet-fourier descriptor for pattern recognition. in this paper, we present a novel descriptor for feature extraction by using a combination of ridgelets and fourier transform. we have successfully implemented ridgelets on the circular disk containing the pattern and applied fourier transform on the resulting ridgelet coefficients to extract rotation-invariant features for pattern recognition. the descriptor is very robust to gaussian noise even when the noise level is high. experimental results show that the new descriptor is a very good choice for pattern recognition.
discriminative distance measures for image matching. we propose a framework incorporating aspects of image classification to aid the matching of a reference image to target images. the framework involves an image representation based on a set of feature vectors, and a parametric distance measure on any two such vector sets. the distance measure may be optimized to provide maximum discrimination between the matching target images and the background images, when compared to the reference image. preliminary results indicate that the new distance measure performs substantially better than the traditional ssd and the bhattacharyya histogram measures in classification and tracking tasks.
improvement of bidirectional recurrent neural network for learning long-term dependencies. bidirectional recurrent neural network (brnn) is a non-causal generalization of recurrent neural networks (rnns). due to the problem of vanishing gradients, brnn cannot learn long-term dependencies efficiently with gradient descent. to tackle the long-term dependency problem, we propose segmented-memory recurrent neural network (smrnn) and develop a bidirectional segmented-memory recurrent neural network(bsmrnn). we test the performance of bsmrnn on the problem of information latching. our experimental results show that bsmrnn outperforms brnn on long-term dependency problems.
new efficient octree construction from multiple object silhouettes with construction quality control. in this paper, we propose an octree construction method with a new subdivision strategy which is governed by the degree of overlapping between each generated octant and the object. the specification of a maximum level of subdivision required in the conventional method is not needed. we introduce "grey-grey" and "grey-black" nodes based on a construction quality measure. the construction quality is measured by the maximum 2d projection error of the projected octant image with respective to the object silhouette in all views. only those octants which are "grey-grey" nodes will be subdivided. furthermore, we present a fast computation of the 2d projection and a new intersection test to reduce the computer processing time. computer simulations are conducted to show that the new method outperforms the conventional method in terms of memory space and computation time subject to the same level of construction quality.
a comparison of texture features based on svm and som. experimental results of texture features derived from gabor and other four wavelet transforms classified and clustered based on support vector machine (svms) and self-organizing maps (soms) are reported in this paper. a comparison of svm and som in texture classification is illustrated. the results show that these texture sets with appropriate classifiers perform reasonably well.
nighttime vehicle detection for driver assistance and autonomous vehicles. this study presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. the proposed method detects vehicles based on detecting and locating vehicle headlights and taillights using techniques of image segmentation and pattern analysis. first, to effectively extract bright objects of interest, a segmentation process based on automatic multilevel thresholding is applied on the grabbed road-scene images. then the extracted bright objects are processed by a rule-based procedure, to identify the vehicles by locating and analyzing their vehicle light patterns, and estimate their distances to the camera-assisted car. experimental results demonstrate the effectiveness of the proposed method on detecting vehicles at night.
resampling for face detection by self-adaptive genetic algorithm. over the past ten years, face detection has been thoroughly studied in computer vision research for its interesting applications. however, all of the state-of-the-art statistical methods suffer from the data collection for training a classifier. this paper presents a self-adaptive genetic algorithm (ga)-based method to swell face database through re-sampling from the existing faces. the basic idea is that a face is composed of a limited components set, and the ga can simulate the procedure of heredity. this simulation can also cover the variations of faces in different lighting conditions, poses, accessories, and quality conditions. to verify the generalization capability of the proposed method, we also use the expanded database to train an adaboost-based face detector and test it on the mit+cmu frontal face test set. the experimental results show that the data collection can be efficiently speeded up by the proposed methods.
a robust algorithm of principal curve detection. in this paper, we present a new method for the detection of principal curves in complicated feature images. based on the criteria of the shortest path of curves and directional deviation of paths, principal curve detection is carried out in graph domain. dfs searching scheme is adopted in exploration of a graph network. the motivation of this research is to find road boundaries and house contours from printed map images. by extensive experiments, the algorithm has shown good efficiency and robustness with real map images. the technique described in this paper can also be used in other applications, such as in character recognition, to separate characters from other unwanted document components that overlap with the characters.
yuv correction for multi-view video compression. a luminance and color correction algorithm for multi-view video compression is proposed in this paper. due to the dissimilar radiometric characteristics of different cameras and the variation of lighting conditions, significant luminance and color discrepancies among different camera views often exist in multi-view video sequences. these variations may cause the wrong disparity matching and result in the degradation of coding efficiency. to elevate the multiview video coding efficiency, a discrepancy model of different views based on modulation and translation parameters is proposed, and the discrepancies among different views are compensated before disparitymatching. the optimization of compensation parameters is discussed, and histogram matching method is used to mitigate the computational load. experimental results on both visual quality and coding efficiency have verified the validity and robustness of the proposed method.
automatic segmentation of lung fields from radiographic images of sars patients using a new graph cuts algorithm. this paper proposes an approach to the segmentation of lung fields in the severe acute respiratory syndrome (sars) infected radiographic images, which is the first step towards a computer-aided diagnosis system. to overcome the segmentation difficulty of highly atypical property of sars in the lung images, our algorithm first uses morphological operations to obtain the initial estimation of the regions where the lung boundaries lie in, and then applies a new graphbased optimization method to find the interested regions. the theoretical analysis shows that our approach is resistant to boundary discontinuity, noise, and large patches that affect the boundary search. experimental results are given to demonstrate the good performance of our algorithm.
a moving object tracked by a mobile robot with real-time obstacles avoidance capacity. this paper describes a robotic application that tracks a moving object by utilizing a mobile robot with multiple sensors. the robotic platform uses a visual camera to sense the movement of the desired object and a range sensor to help the robot detect and then avoid obstacles in real time while continuing to track and follow the desired object. in terms of real-time obstacle avoidance capacity, this paper also presents a modified potential field algorithm called dynamic goal potential field algorithm (dgpf) for this robotic application specifically. experimental results show that the robotic and intelligent system can fulfill the requirements of tracking an object and avoiding obstacles simultaneously when the object is moving.
lbt based low complexity image compression method. the lapped biorthogonal transform (lbt) based low complexity and low memory image compression method is proposed in this paper. lbt costs 46% computation as the discrete wavelet based (dwt), and only buffers 1/15 data that dwt does. by analyzing the distribution of the coefficients of lbt, the quantized set partition codec is used in our method. in coding process no context information is needed, and no complicated data structure, such as list, is used, which is low memory and low computation. the compression results on many standard test images show that, using the same coding method, lbt based one performs better than dwt based, with higher psnr and less distortion. we also find that lbt based method is especially fit to images with more details, such as high resolution satellite images. after all, our method is suitable to portable devices and real time processing.
a universal method for single character type recognition. character image contains not only the character information, but also the type information. the character's type information denotes the various category of the character, such as the font families, handwritten or printed, to what language the character belongs, etc. most of the existent methods for character type recognition are carried out on a group of characters belonging to the same category, and all these methods concentrate on their own specific field. in this paper, a novel universal method for character type recognition is proposed, which is based on a single unknown character and can be used in various fields. we employ a wavelet transform on the character image and extract wavelet features from the transformed image, which are used by a mqdf classifier. compared with existent methods, our method is much more flexible, robust and effective.
boundary correction for total variation regularized l^1 function with applications to image decomposition and segmentation. the total variation model with l^1 norm fidelity term (tvl^1) has been proposed to serve as an effective cartoontexture image decomposition tool because of its unique scale-dependent decomposition ability. nevertheless, one of its largely overlooked limitations is its inability to perfectly retain the original contours of the selected patterns when the fidelity term is not sufficiently weighted. in this paper, we propose a boundary correction method to refine the contours of extracted patterns under such circumstances. a scale-driven image segmentation algorithm extended from the boundary correction method is presented as an application. experimental results demonstrate that our works overcome the drawbacks of existing tv-l^1 model and provide an alternative segmentation method.
fast semi-local alignment for dn sequence database search. given a query dn sequence, our goal is to find in the dn sequence database all the sequence segments that are similar to the query. in this paper we present a string-to-signal transform technique that can transform a dn sequence into a four-channel signal. without considering gaps, the edit distance between two dn sequences can be calculated as the sum of absolute difference (sad) between their corresponding four-channel signals. the algorithm proposed in this paper can then be applied to speed up the process of searching for the desired sequence segments that yield small sads. in addition to efficiency, this algorithmguarantees the optimal search. that is, all the sequence segments that are similar enough to the query can be found without any miss.
segmentation of human body parts using deformable triangulation. this paper presents a new segmentation algorithm to segment a body posture into different body parts using the technique of triangulation. for well analyzing each posture, we first propose a triangulation-based method to triangulate it to different triangle meshes. then, we use a depth-first search scheme to find a spanning tree as its skeleton feature from the set of triangulation meshes. the triangulation-based scheme to extract important skeleton features has more robustness and effectiveness than other silhouette-based approaches. then, different body parts can be roughly extracted by removing all the branching points from the spanning tree. a model-driven technique is then proposed for more accurately segmenting a human body into semantic parts. this technique uses the concept of gaussian mixture model (gmm) to model different visual properties of different body parts. then, a suitable segmentation scheme can be driven by classifying these models using their skeletons. experimental results have proved that the proposed method is robust, accurate, and powerful in body part segmentation.
the application of a convolution neural network on face and license plate detection. in this paper, two detectors, one for face and the other for license plates, are proposed, both based on a modified convolutional neural network(cnn) verifier. in our proposed verifier, a single feature map and a fully connected mlp were trained by examples to classify the possible candidates. pyramid-based localization techniques were applied to fuse the candidates and to identify the regions of faces or license plates. in addition, geometrical rules filtered out false alarms in license plate detection. some experimental results are given to show the effectiveness of the approach. keywords: face detection, license plate detection, convolution neural network, feature map.
planar metric rectification by algebraically estimating the image of the absolute conic. a new metric rectification method for planar homography is proposed based on a closed form algebraic solution of the image of the absolute conic on the image plane.our solution allows shape measurement to be made directly on the image plane without explicitly computing the homography matrix or recoreringing the rectified image.we show that the invariance property of the relationship between the circular points and the absolute conic under projective transformation can effectively do planar metric rectification.in this approach, the image of the absolute conic is solved algebraically to achieve metric rectification based only on the vanishing line and the image of one arbitrary circle on the world plane extracted automatically from the image plane.the process of conic solving introduces no errors and the performance of the method is mainly dependent on the robustness of the straight line and ellipse fitting processes.the fitting scheme suggested in the paper is robust and give good results in most cases.
tooth contour extraction for matching dental radiographs. in dental biometrics [dental biometrics: human identification using dental radiographs] [matching of dental x-ray images for human identification], the contours of teeth in dental radiographs are utilized for human identification. however, in many images, the contours of teeth are fuzzy and only partially visible. to extract the contours from these images, we propose a method based on the active contour models [snakes: active contour models]. a new dynamic energy is proposed for the directional snake to discriminate boundaries of adjacent teeth. matching result shows the contours extracted with this method perform better than traditional methods.
robust nonlinear dimensionality reduction for manifold learning. this paper proposes an effective preprocessing procedure for current manifold learning algorithms, such as lle and isomap, in order to make the reconstruction more robust to noise and outliers. given a set of noisy data sampled from an underlying manifold, we first detect outliers by histogram analysis of the neighborhood distances of data points. the linear error-in-variables (eiv) model is then applied in each region to compute the locally smoothed values of data. finally a number of locally smoothed values of each sample are combined together to obtain the global estimate of its noise-free coordinates. the fusion process is weighted by the fitness of eiv model in each region to account for the variation of curvatures of the manifold. experimental results demonstrate that our preprocessing procedure enables the current manifold learning algorithms to achieve more robust and accurate reconstruction of nonlinear manifolds.
fault detection in distributed systems by representative subspace mapping. the high dimensionality of system observation, together with the frequent changes of system normal behavior resulting from workload variations, makes fault detection very difficult in distributed computing systems. this paper addresses these issues by proposing a novel statistical technique, the principal canonical correlation analysis (pcca), and applying it to monitor the system in a supervised manner. given a set of input variables u and system measurements x, pcca extracts a subspace x(1) from x that is not only highly correlated with the input u, but also a significant representative of the whole distribution of x. such property of pcca, which combines the strengths of both pca and cca, is beneficial to the fault detection task. experimental results from a real e-commerce system based on the multi-tiered j2ee architecture demonstrate the effectiveness of pcca.
albedo recovery using a photometric stereo approach. this paper describes a method for the estimation of surface reflectance values using a photometric stereo approach. evaluations show that surfaces rendered with reflectance values calculated by the proposed method have more realistic appearances than those with constant albedo.
illumination and expression invariant face recognition with one sample image. most face recognition approaches either assume constant lighting condition or standard facial expressions, thus cannot deal with both kinds of variations simultaneously. this problem becomes more serious in applications when only one sample images per class is available. in this paper, we present a linear pattern classification algorithm, adaptive principal component analysis (apca), which first applies pca to construct a subspace for image representation; then warps the subspace according to the within-class co-variance and between-class covariance of samples to improve class separability. this technique performed well under variations in lighting conditions. to produce insensitivity to expressions, we rotate the subspace before warping in order to enhance the representativeness of features. this method is evaluated on the asian face image database. experiments show that apca outperforms pca and other methods in terms of accuracy, robustness and generalization ability.
in this paper, an approach for deaf-people. this paper presents the design and implementation of a fingertip writing interface which recognizes the moving trajectory of the user's fingertip into alphabets and numerals. the processes are divided into tracking and recognition. for the fingertip tracking process, the interface employees techniques including background subtraction, skincolor modeling, finger extraction, fingertip positioning and kalman filter prediction. to recognize the fingertip trajectories, four types of features are defined for recognition with hidden markov models. according to our performance evaluation, the writing interface achieves an accuracy rate of 98% for fingertip tracking and reaches a recognition accuracy as high as 93% for alphabets and numerals, demonstrating its potential to serve as a feasible human-machine interface of natural modality.
probabilistic tracking with adaptive feature selection. we propose a color-based tracking framework that infers alternately an object's configuration and good color features via particle filtering. the tracker adaptively selects discriminative color features that well distinguish foregrounds from backgrounds. the effectiveness of a feature is weighted by the kullback-leibler observation model, which measures dissimilarities between the color histograms of foregrounds and backgrounds. experimental results show that the probabilistic tracker with adaptive feature selection is resilient to lighting changes and background distractions.
video scene extraction using mosaic technique. scene extraction is the first step toward semantic understanding of the video. this paper presents an effective approach to video scene extraction based on the analysis of background images. our approach exploits the fact that shots belonging to one particular scene often have similar background. although part of the video frame is covered by foreground objects, background scene still can be reconstructed by mosaic technique. the proposed scene extraction algorithm consists of two main components: the determination of shot similarity measure and shot grouping process. in our approach, several low-level visual features are integrated to compute the similarity measure between two shots. on the other hand, highlevel knowledge in cinematography is used to guide the shot grouping process. experimental results show that our approach is promising and outperforms some existing technique.
integration of gibbs prior models and deformable models for 3d medical image segmentation. this paper proposes a new methodology for 3d medical image segmentation based on the integration of 3d deformable and markov random field models. our method makes use of markov random field theory to build gibbs prior models for the 3d medical image with arbitrary initial parameters to estimate the organ boundary. then we use a 3d deformable model to fit the estimated boundary under the influence of gradient information in the initial 3d image and the balloon force. the result of the deformable model fit is used to update the gibbs prior model parameters, such as the gradient threshold of a boundary. based on the updated parameters we restart the gibbs prior models. by integrating these processes recursively we achieve an automated segmentation of the initial 3d images. our segmentation solution greatly reduces the time for 3d segmentation process and is capable of getting out of local minim. results of the method are presented for several examples, including som mri images with significant amount of noise.