waashk/dblp huggingface dataset:
the using of thermal images of palm-dorsa vein-patterns for biometric verification. a novel personal verification method using the thermal images of palm-dorsa vein-patterns is presented in this paper. the characteristics of the proposed method are that no prior knowledge about the objects is necessary and the parameters can be set automatically. in our work, an infrared (ir) camera is adopted as the input device to capture the thermal images of palm-dorsa. according to the heat conduction law (the fourier law), multiple features can be extracted from each feature points of the vein-patterns (fpvps). multiresolution representations of images with fpvps are obtained using multiple multiresolution filters (mrfs) that extract the dominant points by filtering miscellaneous features for each fpvp. a hierarchical integrating function is then applied to integrate multiple features and multiresolution representations. we also introduce a logical and reasonable method to select a trained threshold for verification. the experimental results demonstrate that our proposed approach is valid and effective for vein-pattern verification.
combining null space-based gabor features for face recognition. we propose a novel face recognition strategy combining various discriminating gabor features in multi-scales and multi-orientations. a bank of well-chosen gabor filters is applied on the image to construct a group of feature vectors, and then the null space-based lda (nlda) is performed simultaneously on each orientation channel and the original image to give 5 component classifier outputs, which are then combined to increase the final recognition rate. experimental results on the feret database demonstrate the effectiveness and flexibility of our proposed method.
laser stripe peak detector for 3d scanners. a fir filter approach. the accuracy of a 3d reconstruction using laser scanners is significantly determined by the detection of the laser stripe. since the energy pattern of such a stripe corresponds to a gaussian profile, it makes sense to detect the point of maximum light intensity (or peak) by computing the zero-crossing point of the first derivative of such gaussian profile. however, because noise is present in every physical process, such as electronic image formation, it is not sensitive to perform the derivative of the image of the stripe in almost any situation, unless a previous filtering stage is done. considering that stripe scanning is an inherently row-parallel process, every row of a given image must be processed independently in order to compute its corresponding peak position in the row. this paper reports on the use of digital filtering techniques in order to cope with the scanning of different surfaces with different optical properties and different noise levels, leading to the proposal of a more accurate numerical peak detector, even at very low signal-to-noise ratios.
using statistical shape priors in geodesic active contours for robust object detection. a novel statistical shape prior model based on level set representations is proposed in this paper for robust object detection by geodesic active contours. this prior model is able to accommodate multiple shape states of objects. the level set representations (signed distance map) of the shapes are considered to form distinct clusters in a low dimensional feature subspace and a gaussian mixture model (gmm) is employed to fit the feature distribution in the subspace. a bayesian classifier is used to assign the currently detected object to the most similar shape cluster. a shape prior is then constructed from the statistical properties of that cluster and is used to drive the geodesic active contour curve towards it in the subsequent evolution. experiments demonstrate the effectiveness of our shape prior model.
improvement of ica based probability density estimation for pattern recognition. probability density function (pdf) estimation is a fundamentally important problem for statistical pattern recognition. independent component analysis (ica) can be applied to the feature vectors so that the pdf estimation of a high dimensional vector can be converted to the pdf estimation of several 1-dimensional variables. but in practice we find that this pdf is in poor generalization ability for pattern classification because of the implied noise. so this paper proposes an improvement of ica based pdf estimation method. a latent variable model is built to separate the noise from the feature vector so that the pattern information and the noise can be dealt with respectively. based on the latent variable model, a modified ica based pdf is deduced. the validity of our proposed method is demonstrated by the experiments of off-line handwritten numeral recognition.
event classification for automatic visual-based surveillance of parking lots. in this paper, a visual-based surveillance system for real-time event detection and classification in parking lots is presented. the focus is on the high-level part of the system, i.e., the event recognition (er) module, which is able to analyze two kinds of events (i.e., simple and composite events) that occur in the observed scene. simple events are represented by single moving objects, e.g., vehicles, pedestrians, etc. while a composite event is represented by a set of temporally consecutive simple events, e.g., people exiting a car just entered in the parking area. an adaptive high order neural tree (ahnt) is applied for recognizing both objects and complex events.
a novel approach to automatically extracting basic units from chinese sign language. in sign language recognition, using subwords instead of whole signs as basic units will scale well with increasing vocabulary size. however, there are no subwords defined in the signs' lexical forms. how to automatically extract subwords is a challenging issue. in this paper, a novel approach is proposed to automatically extract these subwords from chinese sign language(csl). signs can be broken down into several segments using hidden markov models in which each state represents one segment. temporal clustering algorithm is presented to extract subwords from these segments. the 238 subwords are automatically extracted from 5113 signs, and they can be used as the basic units for large vocabulary csl recognition with good performance.
real-time range imaging for dynamic scenes using colour-edge based structured light. a novel real time 3d-data acquisition system for highly dynamic scenes is presented. it is based on colour coded structured light using a single projection pattern. existing systems utilising this approach are usually limited to scenes with neural surfaces or not very robust. the presented system overcomes these shortcomings by using an innovative approach for recognising the projected pattern based on colour edges. the low-cost system is integrated using off-the-shelf components only. experimental results are presented including a report on the successful use of the system with a 3d gesture recognition application.
learning sample subspace with application to face detection. in this paper, we present a novel maximum correlation sample subspace method and apply it to human face detection [detecting face in images: a survey] in still images. the algorithm starts by projecting all the training samples onto each sample and selects the sample with the largest accumulated projection as the first subspace base vector. after a base vector is selected, all other samples are made orthogonal to the current base vector and which is in turn used to form the training samples for learning the next base vector. each subspace base is created by a one-pass process and therefore the method is computationally very efficient. these bases form a transform and we use it to derive discriminative features for face detection by training a support vector machine classifier. we perform testing on both cmu and mit face detection image data sets. extensive experiments demonstrate that our results are comparable to those published in state of the art literature.
ica filters for lighting invariant face recognition. the use of ica (independent component analysis) for the construction of filters for lighting invariant face recognition will be investigated. ica is used to provide filters which are applied as a pre-processing step to a low dimensional pca subspace representation of the databases. test faces imaged under varying illumination from a face database are classified using a support vector classifier. the ica pre-filter recognition results are compared against those using log (laplacian of gaussian) filter of various spatial resolutions and no pre-filtering. the ica pre-filters are shown to be very effective at selectively reducing the effect of illumination variance in object and face recognition without the need for tuning the filters to the orientations and spatial resolutions present in the images.
car/non-car classification in an informative sample subspace. in this paper, we present a method for data classification with application to car/non-car objects. we first developed a sample based car/non-car maximal mutual information low dimensional subspace. we then trained a support vector machine (svm) in this subspace for the detection of cars. using publicly available standard training and testing data sets, we demonstrated that our car detector gave very competitive performances.
a comparison of pca and ica for object recognition under varying illumination. an experiment is performed to evaluate the ability of two different subspace methods to recognize objects under different illumination conditions. principal component analysis (pca) and independent component analysis (ica) are compared for classifying 25 different objects with varying degrees of specularity under different illumination. each object was sampled under three widely different lighting conditions to form a set of training images used to createsubspaces with dimensions ranging from 10 to 30 basis vectors. the efficacy of ica and pca to correctly classify the objects was tested using two test images for each object under unique lighting conditions not included in the training set. the results were also determined when the images were pre-filtered with a laplacian of gaussian (log) filter. results show that ica techniques show promise for object recognition under varying illumination conditions.
combinatorial surface integration. graph-spectral surface integration techniques construct an integration path assuming that the surface contains a path along which the integration error is minimal. this paper presents a generalisation that uses minimum spanning trees of the weighted grid graph of surface normals, which scales with no need for surface segmentation. the problem of choosing an integration path is reduced to defining a local weight function. the method is assessed at weighting human face surface normals with geometric and information-theoretic functions of local support.
detection and recognition of lung abnormalities using deformable templates. automatic detection and recognition of lung cancer during mass screening of spiral computer tomographic (ct) chest scans is one of most important problems of todays medical image analysis. we propose an algorithm for isolating lung abnormalities (nodules) from arteries, veins, bronchi, and bronchioles after all these objects have been already separated from the surrounding anatomical structures. the separation is presented elsewhere, and this paper focuses on nodule detection using deformable 3d and 2d templates describing typical geometry and gray level distribution within the nodules of the same type. the detection combines normalized cross-correlation template matching by genetic optimization and bayesian post-classification. experiments with 200 spiral low dose ct (ldct) scans confirm the accuracy of our approach.
motion features from lip movement for person authentication. this paper describes a new motion based feature extraction technique for speaker identification using orientation estimation in 2d manifolds. the motion is estimated by computing the components of the structure tensor from which normal flows are extracted. by projecting the 3d spatiotemporal data to 2-d planes we obtain projection coefficients which we use to evaluate the 3-d orientations of brightness patterns in tv like image sequences. this corresponds to the solutions of simple matrix eigenvalue problems in 2d, affording increased computational efficiency. an implementation based on joint lip movements and speech is presented along with experiments which confirm the theory, exhibiting a recognition rate of 98% on the publicly available xm2vts database.
a low-dimensional illumination space representation of human faces for arbitrary lighting conditions. in this paper, a method for low-dimensional illumination space representation (ldisr) of human faces is proposed. the method can not only synthesize a virtual face image when given lighting condition but also estimate lighting conditions when given a face image. the ldisr is based on the observation that 9 basis point light resources can represent almost arbitrary lighting conditions for face recognition application and different human faces have the similar ldisr. the advantage of ldisr is that it can be trained on images of one human face and can be used for all human faces. the experiments on image reconstruction indicated the efficiency of ldisr.
a similarity measure based on hausdorff distance for human face recognition. a similarity measure based on hausdorff distance (smbhd) for face recognition is proposed in this paper. different from the conventional hausdorff distance based measures, the proposed measure can provide not only the dissimilarity information but also the similarity information of two objects to compare them. the added similarity information can especially better the discriminating capability of an object recognition system for similar objects such as faces with variant lighting condition and facial expression. in order to evaluate the performance of a face recognition system using the proposed similarity measure based on hausdorff distance (smbhd), the face images included in the ar, orl, and yale face databases have been used. the experimental results show that the system has a better performance than the systems based on conventional hausdorff distance measures and the eigenfaces approaches.
rigorous accuracy bounds for calibrated stereo reconstruction. we deal with the problem of obtaining rigorous bounds to the position of 3-d points computed by stereo triangulation when both the camera matrix and the image points are affected by uncertainty. by "rigorous bounds" we mean that the true unknown 3-d points are guaranteed to lie within the given intervals. to this end we first model the calibration process by assuming a bounded error in the localization of the reference points in the image, then we narrow the entries of the camera matrix. finally, we apply triangulation and obtain cuboids that bound points coordinates. we concentrated two state-of-the art methods for the solution of linear system of equations, namely intlab's and shary's methods. empirical comparison shows that the latter always provides sharper error bounds, in this application.
large scale feature selection using modified random mutation hill climbing. feature selection is a critical component of many pattern recognition applications. there are two distinct mechanisms for feature selection, namely the wrapper methods and the filter methods. the filter methods are generally considered inferior to the wrapper method, however wrapper methods are computationally more demanding than filter methods. one of the popular methods for wrapper-based feature selection is random mutation hill climbing. it performs a random search over the feature space to derive the optimal set of features. we will describe two enhancements to this algorithm, one that will improve its convergence time, and the other that will allow us to bias the results towards either higher accuracy or lower final feature space dimensionality. we will apply the algorithm to a real-world massive-scale feature selection problem involving the image classification problem associated with suppressing automobile airbags for children. we will provide classification results on an image database of nearly 4,000 images that indicate the advantages of the proposed method.
a new approach for relevance feedback through positive and negative samples. relevance feedback has recently emerged as a solution to the problem of providing an effective response to a similarity query in an images retrieval system based on low-level information such as color, texture and shape features. this paper describes an approach for learning an optimal similarity metric based on the analysis of relevant and non-relevant information given by the user during the feedback process. a positive and a negative space are determined as an approximation of the examples given by the user. the relevant region is represented by a kl subspace of positive examples and is iteratively updated at each feedback iteration. the non-relevant region is modeled by a mkl space, which better characterizes the variety of negative examples, which very likely could belong to more than one class. the search process is, then, formulated as a classification problem, based on the calculation of the minimal distance to the relevant or non-relevant region.
reward-punishment editing. in this work a novel editing technique is proposed. the basic idea of the algorithm is to reward patterns that contribute to a correct classification and to punish those that provide a wrong one. reward-punishment is performed according to two criteria: the former operates at very local level while the latter analyses the training set at coarser scales in a multi-resolution fashion. a score is calculated for each pattern according to the two criteria and patterns whose score is lower than a predefined threshold are edited out. experiments carried out on two difficult classification problems show the superiority of this method with respect to other well known approaches.
a wrapper-based approach to image segmentation and classification. the traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest. this is extremely difficult without any prior knowledge about the object that is being extracted from the scene. we propose a method of segmentation that uses the classification subsystem as an integral part of the segmentation, which will provide contextual information regarding the objects to be segmented. we note that traditional segmentation can then be viewed as a filter operating on the image independently of the classifier, much like the filter methods for feature selection. our motivation for integrating segmentation and classification follows the wrapper methods of feature selection. in the wrapper methods for feature selection, the classifier is an integral part of the selection process and serves as the metric to decide the best feature set. in the same way, we wrap the segmentation and classification together, and use the classification accuracy as the metric to determine the best segmentation. we show the performance of wrapper-based segmentation on real-world and complex images of automotive vehicle occupants.
rotation-invariant neoperceptron. approaches based on local features and descriptors are increasingly used for the task of object recognition due to their robustness with regard to occlusions and geometrical deformations of objects. in this paper we present a local feature based, rotation-invariant neoperceptron. by extending the weight-sharing properties of convolutional neural networks to orientations, we obtain a neural network that is inherently robust to object rotations, while still being capable to learn optimally discriminant features from training data. the performance of the network is evaluated on a facial expression database and compared to a standard neoperceptron as well as to the scale invariant feature transform (sift), a-state-of-the-art local descriptor. the results confirm the validity of our approach.
natural, salient image patches for robot localization. this paper addresses a major problem in mobile robot localization.in most approaches to simultaneous localization and mapping (slam) only line and point features are used which easily lead to ambiguities in loop-closing and global relocalization.in this work we propose to use natural, salient image patches as additional high discriminative landmarks to aid in unclear localization scenarios which might occur in modern highly symmetric indoor environments.a new method of landmark extraction is presented as well as a matching method for storing and retrieving the landmarks in a location database.
content analysis in document images: a scale space approach. with the growing interest in automatic transformation of paper document to its electronic version, geometrical and logical structures have become an active research area for a decade. nowadays, kernel scale space has been widely adopted as the most promising multi-scale image document analysis method. yet still, traditional methods using scale space approach has its limitations: they are useful mostly on character extraction and they carry alarge computational load. in view of these limitations, this paper proposes a new approach using scale space in order to analyse the composite document content. in the proposed method, scale space transform is used to decompose an image into different scaled objects where the scale value is used for detecting progressively finer objects: text, line drawing, logo, and image, with encouraging results on real-life data.
learning pairwise similarity for data clustering. each clustering algorithm induces a similarity between given data points, according to the underlying clustering criteria. given the large number of available clustering techniques, one is faced with the following questions: (a) which measure of similarity should be used in a given clustering problem? (b) should the same similarity measure be used throughout the d-dimensional feature space? in other words, are the underlying clusters in given data of similar shape? our goal is to learn the pairwise similarity between points in order to facilitate a proper partitioning of the data without the a priori knowledge of k, the number of clusters, and of the shape of these clusters. we explore a clustering ensemble approach combined with cluster stability criteria to selectively learn the similarity from a collection of different clustering algorithms with various parameter configurations.
coarse detection and fine color description for region-based image queries. in content-based image retrieval systems, region-based queries allow more precise search than global ones. the user can retrieve similar regions of interest regardless their background in images.the definition of regions in thousands of generic images is a difficult key point, since it should not need user interaction for each image, and nevertheless be as close as possibleto regions of interest (to the user).in this paper we fir st present a technique of unsupervised coarse detection of regions which improves their visual specificity . the segmentation scheme is based on the classification of local distributions of quantized colors (ldqc). the competitive agglomeration (ca) classification algorithm, which has the advantage to automatically determine the optimal number of classes, is used.the second key point is the region description which must be finer for regions than for images. we present a region descriptor of fine color variability: the adaptive distribution of color shades (adcs). this color description is finer and more accurate than existing region color descrip-tors.