waashk/dblp huggingface dataset:
a higher-order active contour model for tree detection. we present a model of a 'gas of circles', the ensemble of regions in the image domain consisting of an unknown number of circles with approximately fixed radius and short range repulsive interactions, and apply it to the extraction of tree crowns from aerial images. the method uses the recently introduced 'higher order active contours' (hoacs), which incorporate long-range interactions between contour points, and thereby include prior geometric information without using a template shape. this makes them ideal when looking for multiple instances of an entity in an image. we study an existing hoac model for networks, and show via a stability calculation that circles stable to perturbations are possible for constrained parameter sets. combining this prior energy with a data term, we show results on aerial imagery that demonstrate the effectiveness of the method and the need for prior geometric knowledge. the model has many other potential applications.
support vector machine with local summation kernel for robust face recognition. this paper presents support vector machine (svm) with local summation kernel for robust face recognition.in recent years, the effectiveness of svm and local features is reported.however, conventional methods apply one kernel to global features.the effectiveness of local features is not used in those methods. in order to use the effectiveness of local features in svm, one kernel is applied to local features.it is necessary to compute one kernel value from local kernels in order to use the local kernels in svm. in this paper, the summation of local kernels is used because it is robust to occlusion.the robustness of the proposed method under partial occlusion is shown by the experiments using the occluded face images. in addition, the proposed method is compared with the global kernel based svm.the recognition rate of the proposed method is over 80% under large occlusion, while the recognition rate of the svm with global gaussian kernel decreases dramatically.
adaptive weighting of local classifiers by particle filter. this paper presents adaptive weighting method for combining local classifiers by particle filter. in recent years, the effectiveness of combination of local classifiers (features) is reported. however, those methods can not cope with partial occlusion or shadows by illumination direction changes, because the stable weight is used for combining local classifiers. to be robust to them, the weight should be changed adaptively. namely, we must select the good weight set given high likelihood from the weight space adaptively. for this purpose, particle filter is used. each particle corresponds to the weight set for combining local classifiers. by selecting the particle (weight set) given high likelihood in current situation, the proposed method can cope with partial occlusion. the proposed method is applied to face tracking problem. performance is evaluated by using the test sequence that the occluded area is changed dynamically. the proposed method decreases the weight for occluded region automatically, and it can track face under partial occlusion. effectiveness of the proposed method is shown by comparison with stable weight set used in conventional methods.
pattern recognition using average patterns of categorical k-nearest neighbors. the typical nonparametric method of pattern recognition "k-nearest neighbor rule (knn)" is carried out by counting the labels of k-nearest training samples to a test sample.this method collects the k-nearest neighbors without taking into account a class, and it outputs the class of the test sample by using only the labels of neighborhoods.this paper presents a classifier that outputs the class of a test sample by measuring the distance between the test sample and the average patterns, which are calculated using the k-nearest neighbors belonging to individual classes.a kernel method can be applied to this classifier for improving recognition rates.the performance of the proposed method is verified by experiments with benchmark data sets.
filter algorithm for 3d pose estimation of maneuvering target. this paper presents a filter algorithm for more accurate 3d pose estimation of a maneuvering target. first, by analyzing the error propagation from the 2d images to the target pose, linear measurement equations are derived. then, two filter schemes are proposed. the first filter uses the maneuver detection technique, in which two detectors fit for fast and slow maneuvers are deduced respectively, and limited memory filtering is adopted for maneuver correction. in the second filter, a robust dynamic model is constructed by the numerical differentiation technique, and an adaptively estimated fading factor is imported to restrain the filter divergence. finally, the two filter modules are combined for more accurate estimation. superior to previous approaches that were limited to the slowly and smoothly moving target, this algorithm is applicable for a maneuvering target that acts in an unknown manner. simulation results show the capacity of this algorithm.
estimation of the fundamental matrix based on ev mode. this thesis presents a nonlinear method to estimate the fundamental matrix, a key problem arising in projective motion estimation and reconstruction, based on a general errors-in-variables (ev) model. in this model, the method considers that all the measurements are corrupted by noises, and minimizes a cost function derived from a nonlinear criterion to estimate both the fundamental matrix and corrupted data, involving the rank-2 constraint with reasonably adjusted data, this method turns out to significantly increase the accuracy and robustness with a simple form of computation. the performance of the proposed approach is justified by theory and assessed by several experiments on real images.
sketched symbol recognition using zernike moments. in this paper, we present an on-line recognition method for hand-sketched symbols. the method is independent of stroke-order, -number, and -direction, as well as invariant to scaling, translation, rotation and reflection of symbols. zernike moment descriptors are used to represent symbols and three different classification techniques are compared: support vector machines (svm), minimum mean distance (mmd), and nearest neighbor (nn). we have obtained a 97% recognition accuracy rate on a dataset consisting of 7,410 sketched symbols using zernike moment features and a svm classifier.
morphology-based license plate detection from complex scenes. this paper presents a morphology-based method for detecting license plates from cluttered images. the proposed system consists of three major components. at the first, a morphology-based method is proposed to extract important contrast features as guides to search the desired license plates. the contrast feature is robust to lighting changes and invariant to several transformations like scaling, translation, and skewing. then, a recovery algorithm is applied for reconstructing a license plate if the plate is fragmented into several parts. the last step is to do license plate verification. the morphology-based method can significantly reduce the number of candidates extracted from the cluttered images and thusspeeds up the subsequent plate recognition. under the experimental database, 128 examples got from 130 images were successfully detected. the average accuracy of license plate detection is 98%. experimental results show that the proposed method improves the state-of-the-art work in terms of effectiveness and robustness of license plate detection.
a shadow elimination method for vehicle analysis. this paper proposes a novel shadow elimination method for solving the shadow occlusion problems of vehicle analysis. different from traditional methods which only consider intensity properties in shadow modeling, this method introduces a new important feature to eliminate all unwanted shadows, i.e., lane line geometries. in this approach, a set of moving vehicles are first segmented from backgrounds by using a background subtraction technique. at this moment, each extracted vehicle may contain shadows which will cause the failure of further vehicle analysis. to remove these unwanted shadows, a histogram-based method is then proposed for detecting different lane dividing lines from video sequence. according to these lines, a line-based shadow modeling process is then applied for shadow elimination. two kinds of lines are used here for shadow elimination, i.e., the ones parallel and vertical to lane directions, respectively. different type of lines has different capabilities to eliminate different kinds of shadows. experiments demonstrate that approximately 92% of shadows can be successfully eliminated from moving vehicles.
improving retrieval performance by global analysis. in this paper, we propose a global analysis approach for query expansion in information retrieval. this approach, which concentrates on extracting associated expansion terms for a given query so as to improve the retrieval performance, can be divided into three stages. the first stage, called association calculation, is to measure associations between term-term pairs. the second stage focuses on how to select suitable terms for expansion. in the last stage, a reweighting scheme is introduced in order to clarify the impact of each expansion term. experimental results on the trec collection demonstrate the effectiveness of our approach.
tracking people through occlusions. in people tracking, one of the most challenging issues is occlusion handling. to cope with it, we introduce a bayesian network which involves human 2d ellipse models and an extra hidden process for occlusion relation. the 2d ellipse model combines color and spatial information simultaneously by creating color histograms for serval sub-regions. the extra hidden process for occlusion indicates the depth information of people through occlusions. the tracking process is performed using a condensation algorithm. experiments show the effectiveness of the proposed method.
robust projective reconstruction with missing information. this paper presents a robust approach based on evolutionary agents for projective reconstruction in the presence of missing data and unknown depths. agents denote possible submatrices for rank constraints, and carry out some evolutionary behavior to exploit a vast solution space. our approach combines the benefits of excellent searching ability of evolutionary agents for getting a good solution, with a proper treatment of missing information with linear fitting. experimental results demonstrate better performance of our approach than other typical methods in terms of accuracy and robustness to noise and missing data.
an interweaved hmm/dtw approach to robust time series clustering. we introduce an approach for model-based sequence clustering that addresses several drawbacks of existing algorithms. the approach uses a combination of hidden markov models (hmms) for sequence estimation and dynamic time warping (dtw) for hierarchical clustering, with interlocking steps of model selection, estimation and sequence grouping. we demonstrate experimentally that the algorithm can effectively handle sequences of widely varying lengths, unbalanced cluster sizes, as well as outliers.
normalization of functional magnetic resonance images by classified cerebrospinal fluid cluster. for functional magnetic resonance imaging (fmri) time series data, traditional intensity normalization techniques may introduce negative correlation with the neurological stimulation in non-activated voxels, and hence may cause incorrect identification of the activated/deactivited region. in this study, we present a modified proportional scaling method for intensity normalization using segmented specific tissue. in particular, the mean intensity across the classified cerebrospinal fluid (csf) cluster, instead of the one across the entire intracerebral voxels, is used for the rescaling of all voxel intensity of a particular image frame. the usefulness of the method is demonstrated on block design fmri data, which shows that the approach can avoid the negative shift in z statistics quite well. in addition, this strategy can also be applicable to the analysis of positron emission tomography (pet), single photon emission computed tomography (spect) and other functional imaging modalities.
moving obstacles extraction with stereo global motion model. extraction of moving obstacles from a moving observer is vital for many its applications like forward vehicle collision mitigation (fvcm) and adaptive cruise control (acc) systems. single camera based global motion model (gmm) like optical flow is commonly used in the past decade. this paper addresses a relatively new problem in the literature: gmm based on binocular stereovision which involves the depth (disparity) information in the model, thus global motion can be analyzed in 3d space rather than the traditional 2d image plane. an efficient algorithm is presented which parameterizes the gmm based on the 3d camera motion analysis within u-v-disparity domain. combining the geometric and motion information, regions that do not match the gmm will be extracted as moving obstacles.
a new kernel based on weighted cross-correlation coefficient for svms and its application on prediction of t-cell epitopes. t-cell epitopes play vital roles in immune response. its recognition by t-cell receptors is a precondition for the activation of t-cell clone. this recognition is antigen-specific. therefore, identifying the pattern of a mhc restricted t-cell epitopes is of great importance for immunotherapy and vaccine design. in this paper, we designed a new kernel based on weighted crosscorrelation coefficients for support vector machine and applied it to the direct prediction of t-cell epitopes. the experiment was carried on an mhc type i restricted t-cell clone lau203-1.5. the results showed that this approach is efficient and promising.
automatic pose recovery for high-quality textures generation. this paper proposes new techniques to generate high quality textures for urban building models by automatic camera calibration and pose recovery. the camera pose is decomposed into an orientation and a translation, an edge error model and knowledge-based filters are used to estimate correct vanishing points with heavy trees occlusion, and the vanishing points are used for the camera calibration and orientation estimation. we propose new techniques to estimate the camera orientation with infinite vanishing points and translation with under-constraints. the final textures are generated using color calibration and blending with the recovered pose. a number of textures for outdoor buildings are automatically generated, which shows the effectiveness of our algorithms.
boosting nested cascade detector for multi-view face detection. in this paper, a novel nested cascade detector for multi-view face detection is presented. this nested cascade is learned by schapire and singer's improved boosting algorithms that use real-valued confidence-rated weak classifiers [improved boosting algorithms using confidence-rated predictions], wherewe use confidence-rated look-up-table (lut) weak classifiers based on haar features. experiments show the system performance is significantly improved compared with previous methods.
a pixel-wise object tracking algorithm with target and background sample. in this paper, we present a clustering-based tracking algorithm for non-rigid object. non-rigid object tracking is a challenging task because the target often appears as a concave shape or an object with apertures. in such cases, many background areas will be mixed into the tracking target, which are difficult to be removed by modifying the shape of the search area. our algorithm realizes robust tracking for such objects by classifying the pixels in the search area into "target" and "background" with k-means clustering algorithm that uses both the "positive" and "negative" samples. the contributions of this research are: 1) using a 5d feature vector to describe both the geometric feature "(x, y)' and color feature "(y,u, v )" of an object (or a pixel) uniformly. this description enables the simultaneous adaptation of both the geometric and color variance during tracking; 2) using a variable ellipse model (a) to describe the search area; (b) to model the surrounding background. this guarantees the stable tracking of objects with various geometric transformations. through extensive experiments in various environments and conditions, the effectiveness and the efficiency of the proposed algorithm is confirmed.
segmentation of medical images with regional inhomogeneities. this paper presents a novel deformable model for accurate delineation of regions of interest in medical images that contain regional inhomogeneities. such images are common in various medical imaging domains including endoscopy and radiology. the proposed model improves the active contour without edges (acwe) model by excluding sparse regional inhomogeneities from both the foreground and the background of the images to be segmented. the proposed model is tolerant to noise and allows for the delineation of multiple objects. experiments were performed on both endoscopic and ultrasonic images from different organs. the results show that the proposed model can be effectively utilized for the delineation of abnormal tissue findings, and in presence of regional inhomogeneities it can be more accurate compared with the acwe model.
contrast context histogram - a discriminating local descriptor for image matching. this paper presents a new invariant local descriptor, contrast context histogram, for image matching. it represents the contrast distributions of a local region, and serves as a local distinctive descriptor of this region. object recognition can be considered as matching salient corners with similar contrast context histograms on two or more images in our work. our experimental results show that the developed descriptor is accurate and efficient for matching.
a wrapper for feature selection based on mutual information. this paper adopts a wrapper method to find a subset of features that are most relevant to the classification task. the approach utilizes an improved estimation of the conditional mutual information which is used as an independent measure for feature ranking in the local search operations. meanwhile, the mutual information between the predictive labels of a trained classifier and the true classes is used as the fitness function in the global search for the best subset of features. thus, the local and global searches consist of a hybrid genetic algorithm for feature selection. experimental results demonstrate both parsimonious feature selection and excellent classification accuracy of the method on a range of benchmark data sets.
road sign interpretation using matching pursuit method. this paper describes an automatic a new road sign interpretation method using matching pursuit (mp) filters. there are two processes. the detection process finds the relative position of road sign in the original distant image and then extract the internal content of road sign from the closer view image. the recognition process consists of two stages: training and testing. in the first stage, it finds a set of best mp filter bases for each road sign. in the second stage, it projects the input unknown road sign to different set of the mp filter bases to find the best match.
model-based human body tracking. visual tracking of human body movement is a key technology in a number of areas. in this paper we present a 2-d model-based method ofhuman body trackingfrom a monocular video sequence. morris & rehg put forward a 2-d scaled prismatic model (spm) for figure registration which has far fewer singularity problems than 3-d models. here we extend it in a 2-d cardboard human body model with additional one dof ofwidth change. we set up a mixture motion model for body movements and then solve body motion parameters using em in a statistical framework, where the model-based kinematic constraints are incorporated in a linear form. tracking results from real video sequences are encouraging.
a region-based method for model-free object tracking. we propose a region-based methodfor model-free object tracking. in our method the object information of temporal motion and spatial luminance are fully utilized. we first compute dominant motion of the tracked object. using this result we warp the object template to generate a prediction template. static segmentation is incorporated to modify this prediction, where the warping error of each watershed segment and its rate of overlapping with warped template are utilized to help classification of some possible watershed segments near the object bordel: applications of facial expression tracking and two-handed gesture tracking demonstrate its performance.
improving mmi with enhanced-fcm for the fusion of brain mr and spect images. recently, maximization mutual information (mmi) of image intensities has been proposed as a new matching criterion for automated multimodality image registration. however, the success of the mmi relies on the similarity of the histogram distribution between the images to be fused. this condition is usually hard to be achieved in practical application. besides, mmi is time consuming because it needs to find an optimal solution about six parameters (three for shifts and three for rotations) during the registration process. to overcome these drawbacks of using traditional mmi, a novel scheme, named improved mmi, which is based on fuzzy c-means (fcm) and mmi, is proposed. the experimental results, using mr and spect images, to confirm the superior performance of the proposed method in comparison with the traditional mmi method are also included.
a vision-based vehicle identification system. this paper presents a vision-based vehicle identification system which consists of object extraction, object tracking, occlusion detection and segmentation, and vehicle classification. since the vehicles on the freeway may occlude each other, their trajectories may merge or split. to separate the occluded objects, we develop three processed: occlusion detection, motion vector calibration, and motion field clustering. finally, the segmented objects are classified into seven different categorized vehicles.
a secret image sharing method using integer-to-integer wavelet transform. we present a new image sharing method based on the integer-to-integer (iti) wavelet transform and shamir's (r, m) threshold scheme that provide highly compact shadows for real time progressive transmission. our approach, working in the wavelet domain, processes the transform coefficients in each subband, divides each of the resulting combination coefficient into m shadows for sharing, and recovers the complete secret image using any r or more shadows(r \leqslant m). we take advantage of the properties of the wavelet multi-resolution representation of an image, such as coefficient magnitude decay and excellent energy compaction, to design combination procedures for transform coefficients and processing sequences in wavelet decomposition subbands such that compact shadows for real time progressive transmission are obtained.
solving the small sample size problem of lda. the small sample size problem is often encountered in pattern recognition. it results in the singularity of the within-class scatter matrix sw in linear discriminant analysis (lda). different methods have been proposed to solve this problem in face recognition literature. some methods reduce the dimension of the original sample space and hence unavoidably remove the null space of sw, which has been demonstrated to contain considerable discriminative information; whereas other methods suffer from the computational problem. in this paper, we propose a new method to make use of the null space of sw effectively and solve the small sample size problem of lda. we compare our method with several well-known methods, and demonstrate the efficiency of our method.