waashk/dblp huggingface dataset:
differential epipolar constraint in mobile robot egomotion estimation. the estimation of camera egomotion is a well established problem in computer vision. many approaches have been proposed based on both the discrete and the differential epipolar constraint. the discrete case is mainly used in self-calibrated stereoscopic systems, whereas the differential case deals with an unique moving camera. this article surveys several methods for mobile robot egomotion estimation covering more than 0.5 million samples using synthetic data. results from real data are also given. the surveyed algorithms have been programmed and are available on the internet:http://eia.udg.es/-armangue/research.
modeling shape and topology of 3d images of biological specimens. this paper presents an efficient way of representing the geometry and topology of 3d images of biological specimens derived from electron microscopy. a vector quantization algorithm is used in order to select a reduced set of interior points that best approximates the probability density function of the original volumetric data. object's geometry and topology are obtained from the point set by the use of the alpha shapes theory.
3d modeling from turntable sequences using dense stereo carving and multi-view consistency. this paper addresses the problem of reconstructing a 3-d model, with texture, from a sequence of images of the object taken from a turntable. previous approaches have successfully solved the problem either using active interaction techniques, like the laser pen in immersion's lightscribe commercial system or directly relying on objects texture (not implemented commercially). in contrast, we present a general methodology, which combines silhouette carving with dense stereo and multi-view consistency. it functions even in the presence of highlights or lack of texture. results are presented on real objects with the above-mentioned characteristics.
compressed spatio-temporal descriptors for video matching and retrieval. the contents of a video can be described in terms of appearance and motion of the scenes. in this paper, we propose a compressed spatio-temporal descriptor that is suitable for video matching and retrieval tasks. we use a modified wavelet based compression technique that exploits the temporal redundancy of the data using optical flow. in order to achieve a compact flow representation, a spline based technique is used. the optical flow field gives the directions along which the gray levels have regular variations in time. wavelet decomposition along these directions results in fewer coefficients and thus higher compression. we demonstrate that the wavelet coefficients and flow parameters can be efficiently used for 1) video retrieval and matching, and 2) calculating spatio-temporal similarity between articulated objects. the results are demonstrated on several sequences.
trains of keypoints for 3d object recognition. this paper presents a 3d object recognition method that exploits the spatio-temporal coherence of image sequences to capture the object most relevant features. we start from an image sequence that describes the object's visual appearance from different view points. we extract local features (sift) and track them over the sequence. the tracked interest points form trains of features that are used to build a vocabulary for the object. training images are represented with respect to that vocabulary and an svm classifier is trained to recognize the object. we present very promising results on a dataset of 11 objects. tests are performed under varying illumination, scale, and scene clutter.
perception planning for an exploration task of a 3d environment. the incremental recovery of the 3d model of a closed environment is a complex task as it involves several functions: 3d acquisition, data registration and fusion, construction of a 3d triangular mesh or other representations. the sensor must be placed in several positions so that the resulting model is complete (no unseen areas) and has the required resolution. this paper presents a method devoted to the selection of the next best view for a sensor used for 3d modelling of an environment; the objective is to minimise the number of acquisitions and optimise the quality of the final model. from the current sensor position, an optimal next view is selected by the use of utility functions computed for some possible solutions; the method combines exhaustive search and hill climbing optimisation. thanks to a simulation, results are presented for a synthetic environment, using a virtual 3d sensor on a 5 degrees of freedom robot.
estimating fibre twist and aspect ratios in 3d voxel volumes. aspect ratios and twist measures can help us characterise paper fibre properties. such measures, that need volume data, are presented here. to test the developed methods, simulated voxel volumes of fibr es were created by defining each fibr e as a spline curve with an elliptical cross sectional shape and a constant twist per length unit. this allows us to directly compare the measurements from the voxel volume to the properties of the original spline fibres. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
analysis of ramp discontinuity model for multiscale image segmentation. this paper presents an algorithm for multiscale image segmentation. towards this, it proposes a new region model, that of a homogenous region surrounded by ramp discontinuities (a scenario usually encountered in real images). this paper presents the analysis of this model, leading to a robust algorithm for detection of ramp discontinuities in the image, and finally segmentation of the image at different photometric scales. the algorithm is further specialized for detecting very thin regions. the final segmentation algorithm can detect regions in the image, with varying photometric scales, sizes and arbitrary geometric properties. the properties of the algorithm are experimentally verified on synthetic and real images.
spatial-variant image filtering based on bidimensional empirical mode decomposition. this paper presents a fully automatic spatialvariant approach for image filtering and representation based on bidimensional empirical mode decomposition (bemd). unlike traditional filtering strategies which demonstrate poor performance for multicomponent, non-stationary images, the proposed method adaptively tracks the local characteristics of image intensities. in this paper, we first describe our own bemd algorithm and use it to decompose gray level images into a finite number of spatial frequency components, called intrinsic mode functions (imf). then based on the statistical properties of the imfs, features can be extracted. the idea is to group certain adjacent modes together to realize image filtering. experiments on natural multipartite images have indicated the effectiveness of our approach.
new experiments on icp-based 3d face recognition and authentication. in this paper, we discuss new experiments on face recognition and authentication based on dimensional surface matching. while most of existing methods use facial intensity images, a newest ones focus on introducing depth information to surmount some of classical face recognition problems such as pose, illumination, and facial expression variations. the presented matching algorithm is based on icp (iterative closest point) that provides perfectly the posture of presented probe. in addition, the similarity metric is given by spatial deviation between the overlapped parts in matched surfaces. the general paradigm consists in building a full 3d face gallery using a laser-based scanner (the off-line phase). at the on-line phase, identification or verification, only one captured 2.5d face model is performed with the whole set of 3d faces from the gallery or compared to the 3d face model of the genuine, respectively. this probe model can be acquired from arbitrary viewpoint, with arbitrary facial expressions, and under arbitrary lighting conditions.a new multi-view registered 3d face database, including these variations, is developed within biosecure workshop 2005 in order to perform significant experiments.
curvature scale space corner detector with adaptive threshold and dynamic region of support. corners play an important role in object identification methods used in machine vision and image processing systems. single-scale feature detection finds it hard to detect both fine and coarse features at the same time. on the other hand, multi-scale feature detection is inherently able to solve this problem. this paper proposes an improved multi-scale corner detector with dynamic region of support, which is based on curvature scale space (css) technique. the proposed detector first uses an adaptive local curvature threshold instead of a single global threshold as in the original and enhanced css methods. second, the angles of corner candidates are checked in a dynamic region of support for eliminating falsely detected corners. the proposed method has been evaluated over a number of images and compared with some popular corner detectors. the results showed that the proposed method offers a robust and effective solution to images containing widely different size features.
monnet: monitoring pedestrians with a network of loosely-coupled cameras. monnet is a visual surveillance system for tracking pedestrians over extended premises. the monnet system is composed of intelligent nodes, which exchange information on the individually tracked pedestrians in an asynchronous manner. each node in monnet builds an appearance model for every observed pedestrian and compares it with models received from other nodes. the compact appearance models based on colour cues and face biometrics are stored locally on each node. the system is dynamically reconfigurable since its design allows for adding/removing nodes in a simple manner, comparable to the 'plug and play' technology. monnet also contains an optional 'observer' node for interactive data visualization. this node displays a user interface which allows a human operator to observe and to interact in real-time with the distributed tracking process. monnet was extensively tested with and without user input, and it is able to function correctly in both modes.
fast support vector machine classification using linear svms. we propose a classification method based on a decision tree whose nodes consist of linear support vector machines (svms). each node defines a decision hyperplane that classifies part of the feature space. for large classification problems (with many support vectors (svs)) it has the advantage that the classification time does not depend on the number of svs. here, the classification of a new sample can be calculated by the dot product with the orthogonal vector of each hyperplane. the number of nodes in the tree has shown to be much smaller than the number of svs in a non-linear svm, thus, a significant speedup in classification time can be achieved. for non-linear separable problems, the trivial solution (zero vector) of a linear svm is analyzed and a new formulation of the optimization problem is given to avoid it.
incremental pca or on-line visual learning and recognition. the methods for visual learning that compute a space of eigenvectors by principal component analysis (pca) traditionally require a batch computation step. since this leads to potential problems when dealing with large sets of images, several incremental methods for the computation of the eigenvectors have been introduced. however, such learning cannot be considered as an on-line process, since all the images are retained until the final step of computation of space of eigenvectors, when their coefficients in this subspace are computed. in this paper we propose a method that allows for simultaneous learning and recognition. we show that we can keep only the coefficients of the learned images and discard the actual images and still are able to build a model of appearance that is fast to compute and open-ended. we performed extensive experimental testing which showed that the recognition rate and reconstruction accuracy are comparable to those obtained by the batch method.
segmentation and typography extraction in document images using geodesic active regions. this paper is addressed to the problem of typography extraction in document images. for that, we propose the use of robust textural image processing methods (gabor filtering and the geodesic active regions model) instead of the classical document image processing techniques (physical segmentation and logical labeling) which work at a pixel level and are very sensitive to a lot of parameters such as: noise, skewing... we show, on a few examples, that our method is generic enough to cope with a lot of recurrent problems in the field of document processing.
morse homology descriptor for shape characterization. in this paper, we propose a new topological method for shape description that is suitable for any multi-dimensional data set that can be modelled as a manifold.the description is obtained for all pairs (m, f), where m is a closed smooth manifold and f a morse function defined on m.more precisely, we characterize the topology of all pairs of lower level sets (m_y, m_x) of f, where m_a = f^{-1} ((-¿, a]), for all a ¿ r.classical morse theory is used to establish a link between the topology of a pair of lower level sets of f and its critical points lying between the two levels.
light: local invariant generalized hough transform. in this paper, we present a novel method for 2d shape extraction based on the hough transform. the method is applicable under similarity transformations while maintaining the dimensionality of the problem as that of the original ght. this is possible due to the use of a set of fourier based descriptors which remain invariant under translation, scale and rotation. in contrast with other invariants used in the same context, the descriptors we present here are local, and therefore our method is specially tolerant to noise and occlusion. experimental results are presented demonstrating performance on highly occluded scenes and showing significantly superior performance of our method, related to the ght, in the presence of global unmodelled deformations.
image-based rendering of synthetic diffuse objects in natural scenes. we present a method for solving the global illumination problem for synthetic diffuse objects. the approach generates realistic shading for applications where a synthetic object is to be inserted in a natural scene. using only a few images of the surroundings of the object, we first build an environment map for representing the ambient light. we then model the global illumination integral using chebyshev polynomials. we show that due to the orthogonality of 2d chebyshev moments, the global illumination integral can reduce to the inner product of two vectors, representing the irradiance and the bidirectional reflectance distribution function (brdf). the chebyshev moments of these two functions are computed off-line and stored in the memory. the rendering of the object in the scene then becomes a simple problem of computing the inner product of the two vectors for each point.
image classification from generalized image distance features: application to detection of interstitial disease in chest radiographs. one of the most important tasks in medical image analysis is to detect the absence or presence of disease in an image, without having precise delineations of pathology available for training. a novel method is proposed to solve such a classification task, based on a generalized representation of an image derived from local per-pixel features. from this representation, differences between images can be computed, and these can be used to classify the image requiring knowledge of only global image labels for training. it is shown how to construct multiple representations of one image to get multiple classification opinions and combine them to smooth over errors of individual classifiers. the performance of the method is evaluated on the detection of interstitial lung disease on standard chest radiographs. the best result is obtained for the combining classification scheme yielding an area under the roc curve of 0.955.
approximating high dimensional probability distributions. we present an approach to estimating high dimensional discrete probability distributions with decomposable graphical models. starting with the independence assumption we add edges and thus gradually increase the complexity of our model. bounded by the minimum description length principle we are able to produce highly accurate models without overfitting. we discuss the properties and benefits of this approach in an experimental evaluation and compare it to the well studied chow-liu algorithm.
finding rule groups to classify high dimensional gene expression datasets. microarray data provides quantitative information about the transcription profile of cells. to analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. some approaches of machine learning are widely used to classify and mine biological datasets. however, many gene expression datasets are extremely high dimensionality, traditional machine learning methods can not be applied effectively and efficiently. this paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes), which are most discriminative to classify samples in different classes, to form rule groups for the classification of expression datasets. our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches.
ethiopic character recognition using direction field tensor. many languages in ethiopia use a unique alphabet called ethiopic for writing. however, there is no ocr system developed to date. in an effort to develop automatic recognition of ethiopic script, a novel system is designed by applying structural and syntactic techniques. the recognition system is developed by extracting primitive structural features and their spatial relationships. a special tree structure is used to represent the spatial relationship of primitive structures. for each character, a unique string pattern is generated from the tree and recognition is achieved by matching the string against a stored knowledge base of the alphabet. to implement the recognition system, we use direction field tensor as a tool for character segmentation, and extraction of structural features and their spatial relationships. experimental results are reported.
image interpolation by high dimensional projection based on subspace method. this paper presents a new image interpolation technique for image cracking such as telop character occlusion or picture deterioration. the landscape picture and texture pattern or other natural scenes contain the behavior of the autocorrelation pattern, and a method of image interpolation based on the autocorrelation pattern has been established. this method extracts many local regions from a piece of image and generates a subspace by the image vectors. the subspace limits the freedom of the image description at the local region by the hyperplane restriction in the image vector space. this paper explains for an image interpolation method based on eigen-space and explains the expansion using kernel non-linear projection.
a computational algebraic topology approach for optical flow. this paper proposes an alternative to partial differential equations (pdes) for the solution of the optical flow problem. the problem is modeled using the heat transfer process. instead of using pdes, we propose to use the global equation of heat conservation. we use a computational algebraic topology-based image model which allows us to encode some underlying physical laws by linking a global value on a domain with values on its boundary. the numerical scheme is derived in a straightforward way from the problem modeled and provides a physical explanation of each solving step. experimental results are presented.
anomaly detection for video surveillance applications. we investigate the problem of anomaly detection for video surveillance applications. in our approach, we use a compression-based similarity measure to determine similarity between images in a video sequence. images that are sufficiently dissimilar are deemed anomalous and stored to be compared against subsequent images in the sequence. the goal of our research is two-fold; in addition to detecting anomalous images, the issue of heavy computational and storage resource demands is addressed.
lie algebra template tracking. visual cues are often very difficult to track. we use an effective least squares estimation of the lie algebra parameters to find the affine transformation involved in a visual region tracking. these parameters represent the geodesics of the optimal transformation orbit. our experiments validate the effectiveness of the method.
improving seismic horizon matching by ordinal measures. many processing and interpretation tasks of seismic images are based on cross-correlation (cc) as a measure of similarity of reflection patterns. although cc has several advantages over other correlation methods, it is very sensitive to nonlinear intensity variations or single outliers, which are however characteristic for seismic data. we show for the example of horizon matching across faults, that more robust ordinal measures can perform better, especially in case of poor data quality.
geometric neurocomputing for pattern recognition and pose estimation. this paper presents a geometric neurocomputing approach for 3d pose recognition using the framework of the geometric clifford algebras. the type of geometric problems like pattern recognition and 3d pose recognition can be very efficiently handled using geometric neural networks. our experimental part shows the application of generalized clifford moments for pattern recognition and 3d pose estimation of rigid objects using visual information captured by a trinocular head.
online learning of discriminative patterns from unlimited sequences of candidates. recent research in object recognition has demonstrated the advantages of representing objects and scenes through localized patterns such as small image templates. in this paper we study the selection of patterns in the framework of extended supervised online learning, where not only new examples but also new candidate patterns become available over time. we propose an algorithm that maintains a pool of discriminative patterns and improves the quality of the pool in a disciplined manner over time. the proposed algorithm is not tied to any specific pattern type or data domain. we evaluate the method on several object detection tasks.
visual recognition of similar gestures. naturalness and effectiveness of gesture-based communication strongly depend on the success of gesture recognition. however, confusion in classification increases when considering gestures with similar evolutions. given that neither typical motion-based features, nor hidden markov models are capable to distinguish accurately among them, it is common to consider only gestures that require different forms of execution. in this paper, we present empirical evidence showing that, in addition to motion, posture information significantly increases classification rates, even with similar gestures. moreover, for recognition, we propose dynamic naive bayesian classifiers. in comparison to hidden markov models, these models require less iterations of the em algorithm for training, while keeping competitive classification rates. the proposed system was evaluated considering 9 classes of similar gestures, showing a significant increase in performance by integrating motion and posture attributes.