waashk/dblp huggingface dataset:
combining generative and discriminative methods for pixel classification with multi-conditional learning. it is possible to broadly characterize two approaches to probabilistic modeling in terms of generative and discriminative methods. provided with sufficient training data the discriminative approach is expected to yield superior accuracy as compared to the analogous generative model since no modeling power is expended on the marginal distribution of the features. conversely, if the model is accurate the generative approach can perform better with less data. in general it is less vulnerable to overfitting and allows one to more easily specify meaningful priors on the model parameters. we investigate multi-conditional learning a method combining the merits of both approaches. through specifying a joint distribution over classes and features we derive a family of models with analogous parameters. parameter estimates are found by optimizing an objective function consisting of a weighted combination of conditional loglikelihoods. systematic experiments in the context of foreground/ background pixel classification with the microsoft- berkeley segmentation database using mixtures of factor analyzers illustrate tradeoffs between classifier complexity, the amount of training data and generalization accuracy. we show experimentally that this approach can lead to models with better generalization performance than purely generative or discriminative approaches.
3d scene reconstruction from reflection images in a spherical mirror. this paper proposes a method for reconstructing a 3d scene structure by using the images reflected in a spherical mirror. in our method, the mirror is moved freely within the field of view of a camera in order to observe a surrounding scene virtually from multiple viewpoints. the observation scheme, therefore, allows us to obtain the wide-angle multiviewpoint images of a wide area. in addition, the following characteristics of this observation enable multi-view stereo with simple calibration of the geometric configuration between the mirror and the camera; (1) the distance and direction from the camera to the mirror can be estimated directly from the position and size of the mirror in the captured image and (2) the directions of detected points from each position of the moving mirror can be also estimated based on reflection on a spherical surface. some experimental results show the effectiveness of our 3d reconstruction method.
an lvq-based automotive occupant classification system. this paper presents a description of a system designed to classify a passenger in an automobile into one of three classes: a) adult, b) children (6 years and younger) and infants in child seats and c) empty seat. the authors examine the application of neural networks (bp, lvq etc.) for this occupant classification system (ocs).
real-time estimation of light source environment for photorealistic augmented reality. this paper proposes a vision-based augmented reality system with correct representation of attached and cast shadows.to realize a seamless augmented reality system, we need to resolve a number of problems.especially, the geometric and photometric registration problems are important. these problems require to estimate the positions of light sources and user's viewpoint.the proposed system resolves the problems using a 3d marker which combines a 2d square marker and a mirror ball.the 2d marker and the mirror ball are used to estimate the relationship between the real and virtual worlds and the positions of light sources in the real world, respectively.
a fast template matching algorithm with adaptive skipping using inner-subtemplates' distances. this paper proposes a new fast template matching algorithm that skips comparison between a template and search windows neighboring an already compared dissimilar sub-window. comparison skipping is executed when a lower bound of distance between the template and a window exceeds a threshold. the lower bound of distance between the template and the window is determined by the triangular inequality in distances: the distance between a subtemplate and a subwindow and that between inter-subtemplates. experimental results demonstrate that the proposed method is up to five times faster than the conventional fast exhaustive search method (sequential similarity detection algorithm), strictly guaranteeing the same accuracy.
snake-based technique for plasmapause tracking. a new approach to tracking the boundary of the plasma-sphere (i.e., the plasmapause) in a time-series of satellite images of the earth is described. the approach is based on the active contour models (snakes) and exploits prior knowledge of the plasmasphere to automatically initialize and refine the snake's position in each image. it then uses a greedy minimization scheme to drive the snake toward the plasmapause in each image. a voronoi diagram is used to restrict the snake evolution to ensure that the snake does not loop over itself. correspondences between successive images are established using a radial alignment process. the approach aids in quantifying plasmasphere changes, which are key indicators of the impact of solar events on the earth's magnetosphere.
bayesian network structure learning and inference in indoor vs. outdoor image classification. bayesian network model selection techniques may be used to learn and elucidate conditional relationships between features in pattern recognition tasks. the learned bayesian network may then be used to infer unknown node-states, which may correspond to semantic tasks. one such application of this framework is scene categorization. in this paper, we employ low-level classification based on color and texture, semantic features, such as sky and grass detection, along with indoor vs. outdoor ground truth information, to create a feature set for bayesian network structure learning. indoor vs. outdoor inference may then be performed on a set of features derived from a testing set where node states are unknown. experimental results show that this technique provides classification rates of 97% correct, which is a significant improvement over previous work, where a bayesian network was constructed based on expert opinion.
local context in non-linear deformation models for handwritten character recognition. we evaluate different two-dimensional non-linear deformation models for handwritten character recognition. starting from a true two-dimensional model, we derive pseudo-two-dimensional and zero-order deformation models. experiments show that it is most important to include suitable representations of the local image context of each pixel to increase performance. with these methods, we achieve very competitive results across five different tasks, in particular 0.5% error rate on the mnist task.
linear discriminant analysis and discriminative log-linear modeling. we discuss the relationship between the discriminative training of gaussian models and the maximum entropy framework for log-linear models. observing that linear transforms leave the distributions resulting from the log-linear model unchanged, we derive a discriminative linear feature reduction technique from the maximum entropy approach and compare it to the well-known linear discriminant analysis. from experiments on different corpora we observe that the new technique performs better than linear discriminant analysis if the dimensionality of the feature space is large with respect to the number of classes.
on-line signature verification by exploiting inter-feature dependencies. the traditional on-line signature verification process involves use of various dynamic features such as velocity, pressure, acceleration, angles, etc. the idea is to device a composite vector structure combining more than one feature where each feature is treated independently. our proposed research work is an attempt to exploit the interfeature dependencies by employing a higher dimensional vector approach. the strategy adopted here is to obtain pressure strokes with respect to various velocity bands. the strokes thus obtained are found to portray a reasonably accurate basis for discriminating genuine vs forgery class. the simulation results validate our assumptions and show improvements in the discriminating index.
using constraint inequality on estimated correlation for rapid image search. a rapid algorithm of searching for similar images to an object image in a set of registered images is proposed. provided that correlations of all the pairs of registered images in advance, we can derive a constraint inequality for similarity of the object. by use of the upper bounds defined by the inequality, three conditions of neglect can be derived, which enable to quicken computation of searching. efficiency rate of eight times in average could be obtained for searching 204 images in the set of 1,200 registered images.
parallel pattern recognition computations within a wireless sensor network. the computational properties of a wireless sensor network (wsn) have been investigated by implementing a fully distributed pattern recognition algorithm within the network. it is shown that the set up allows a physical object to develop a capability, which to some extent may be considered similar to our sense of touch, with the wsn acting as an artificial nervous system in this regard. the effectiveness of the algorithm is inspected by comparing the outputs from the sensors with the stress patterns generated through a simple finite element model and then stored within the network. it is shown that the test object could successfully differentiate between its internal stress states resulting from the changes to its external loading conditions. suitability of the algorithm is discussed with respect to the data storage requirement per node of the wsn.
combining visual features with semantics for a more effective image retrieval. we present a new framework which tries to improve the effectiveness of cbir by integrating semantic concepts extracted from text. our model is inspired from the vsm model developed in information retrieval. we represent each image in our collection with a vector of probabilities linking it to the different keywords. in addition to the semantic content of images, these probabilities capture the user's preference in each step of relevance feedback. the obtained features are then combined with visual ones in retrieval phase. evaluation carried out on more than 10,000 images shows that this considerably improves retrieval effectiveness.
object reacquisition using invariant appearance model. we present an approach for reacquisition of detected moving objects. we address the tracking problem by modeling the appearance of the moving region using stochastic models. the appearance of the object is described by multiple models representing spatial distributions of objects' colors and edges. this representation is invariant to 2d rigid and scale transformation. it provides a good description of the object being tracked, and produces an efficient blob similarity measure for tracking. three different similarity measures are proposed, and compared to show the performance of each model. the proposed appearance model allows to track a large number of moving people with partial and total occlusions and permits to reacquire objects that have been previously tracked. we demonstrate the performance of the system on several real video surveillance sequences.
non-iterative approach to multiple 2d motion estimation. we present an innovative method estimating multiple 2d motions from uncalibrated images. our approach robustly and non-iteratively estimates multiple 2d parametric motions, affine or homography, from noisy initial matches without pre-specifying the number of motions this approach is based on: (1) a parametric motion model to detect and extract 2d affine or homography motions; (2) the representation of matching points in decoupled joint image spaces; (3) the characterization of the property associated with affine transformation in the defined spaces; (4) a non-iterative process to extract multiple 2d motions simultaneously based on tensor-voting; (5) local affine to global homography estimation. the major contribution of our work is the extension to our existing affine motion estimation method for homography estimation. the robustness of the approach is demonstrated with several results.
product approximation by minimizing the upper bound of bayes error rate for bayesian combination of classifiers. in combining multiple classifiers using a bayesian formalism, a high dimensional probability distribution is composed of a class and decisions of classifiers. in order to do product approximation of the probability distribution, the upper bound of bayes error rate, bounded by the conditional entropy of a class and decisions, should be minimized. a second-order dependency-based product approximation is proposed in this paper by considering the second-order dependency between the class and decisions. the proposed method is evaluated by combining the classifiers recognizing unconstrained handwritten numerals.
predicting the benefit of sample size extension in multiclass k-nn classification. in industrial quality inspection obtaining the training data needed for classification problems is still a very costly task. nevertheless, the classifier quality is crucial for economic success. thus, the question whether the influence of the training data on the classification error has been fully exploited and enough data has been obtained is very important. this paper introduces a method to answer this question for a specific problem. to be able to make a concrete statement and not only general recommendations, we focus on the k-nn classifier, since it is widely used in industrial implementations. the method is tested on four different multiclass problems: original data from an optical media inspection problem, the mnist database, and two artificial problems with known probability densities.
real-time multiple people tracking using competitive condensation. the condensation algorithm has attracted as it has robust tracking performance and potential of real-time implementation. however the condensation tracker has difficulty with real-time implementation for multiple people tracking since it requires complicated shape model and large number of samples for precise tracking performance. this paper presents two improvements for real-time multiple object tracking: the discrete shape model with a small search space and the competition rule which requires a small number of samples to track multiple people. we show that they achieve robust and real-time tracking for image sequences of a crowd of people.
hough transform in log-polar image including foveal and peripheral information. line equation in cartesian space can be mapped conformally to log-polar. but many the applications of log-polar mapping are using only the peripheral region. this paper presents a method of a hough transform for a log-polar mapped edge image that includes both foveal and peripheral visual information. it is shown that this method achieves a good performance for line detection on the log-polar space.
scene text extraction in natural scene images using hierarchical feature combining and verification. we propose a method that extracts text regions in natural scene images using low-level image features and that verifies the extracted regions through a high-level text stroke feature. then the two level features are combined hierarchically. the low-level features are color continuity, gray-level variation and color variance. the color continuity is used since most of the characters in a text region have the same color, and the gray-level variation is used since the text strokes are distinctive to the background in their gray-level values. also, the color variance is used since the text strokes are distinctive in their colors to the background, and this value is more sensitive than the gray-level variations. as a high level feature, text stroke is examined using multi-resolution wavelet transforms on local image areas and the feature vector is input to a svm(support vector machine) for verification. we tested the proposed method with various kinds of the natural scene images and confirmed that extraction rates are high even in complex images.
evaluation on selection criteria of multiple numeral recognizers with the fixed number of recognizers. the combination of multiple recognizers shows various performances according to the selection of recognizers and the combination method of them. the selection of recognizers on how to select them or how many to select them still remains one of important research issues. in this paper, information-theoretic criteria for selecting the multiple numeral recognizers are evaluated together with the simple selection criteria in terms of recognition rates by the handwritten numerals from concordia university and uci, on the supposition that the number of selected recognizers is fixed in advance. the selection criteria are applied to the pool of numeral recognizers and examine the possible recognizer sets from the pool, and then select one of the recognizer sets as a multiple recognizer system candidate.
visual keywords labeling in soccer video. in this paper, we propose a framework to label soccer video shots with pre-defined visual keywords to bridge the semantic gap between low-level features and semantic understanding. based on these visual keywords, further structure analysis and event detection will be facilitated. in our system, a mpeg-1 soccer video stream is partitioned into shots, from which each p-frame is converted into color-based and edge-based binary maps. then, we detect the playing field and segment the regions of interest (rois) inside the playing field. finally two support vector machine classifiers and some decision rules are applied to the properties of the rois such as size, position, texture ratio, etc and the position of the playing field to label the video shot with visual keyword. we have applied the proposed method to 3495 soccer video shots and achieved 94.3% and 97.2% average precision and recall respectively.
ica-based clustering for resolving permutation ambiguity in frequency-domain convolutive source separation. permutation ambiguity is an inherent limitation in independent component analysis, which is a bottleneck in frequency-domain methods of convolutive source separation. in this paper we present a method for resolving this permutation ambiguity, where we group vectors of estimated frequency responses into clusters in such a way that each cluster contains frequency responses associated with the same source. the clustering is carried out, applying independent component analysis to estimated frequency responses. in contrast to existing methods, the proposed method does not require any prior information such as the geometric configuration of microphone arrays or distances between sources and microphones. experimental results confirm the validity of our method.
automatic text location using cluster-based template matching. this paper proposes a method for automatic text location in images including multi-segment characters. accordingly, we use shape information in addition to size and location information.this shape information is represented by cluster-based templates obtained from target characters using k -means clustering algorithm. experimental results show the effectiveness of the proposed method.
a generic camera calibration method for fish-eye lenses. fish-eye lenses are convenient in such computer vision applications where a very wide angle of view is needed. however, their use for measurement purposes is limited by the lack of an accurate, generic, and easy-to-use calibration procedure. we hence propose a generic camera model for cameras equipped with fish-eye lenses and a method for calibration of such cameras. the calibration is possible by using only one view of a planar calibration object but more views should be used for better results. the proposed calibration method was evaluated with real images and the obtained results are promising. the calibration software will become commonly available at the author's web page.
robust feature matching across widely separated color images. we present a novel method for feature matching across widely separated color images. the proposed approach is robust and can support various correspondence based algorithms e.g. the recovery of epipolar geometry. our algorithm extends an existing gray-scale corner detector to color. the feature matching algorithm robustly segments the area around the feature into significant color regions using the mean shift mode estimator. the recovered data structures are matched under all possible rotations and the best rotation and its corresponding matches are selected. the results of the matching algorithm are used for recovery of the epipolar geometry from wide base line stereo image pairs. the algorithm has been tested extensively yielding good results over a wide range of scenes and viewpoints. a small subset of these results are presented in the paper.
post-processing scheme for improving recognition performance of touching handwritten numeral strings. this paper describes post-processing scheme to improve recognition performance of touchinghandwritten numeral strings. verification factors are defined to rectify recognition results of digits that are segmented by the highest reliability values. three kinds of verification factors from structural features and recognition probability are used to determine missegmented digits. additional segment digit set is included in valid digit sets when it satisfies the reconsideration condition. final optimal digit set is selected as the highest ranked segment digit set among all candidate segmented digit sets. introduction of reconsideration condition and additional segment digit set improves the reliability of recognition result as well as recognition performance. experiments are carried out with touching handwritten numeral strings of nist sd19 database and an encouraging recognition result has been obtained.
text extraction from web images based on a split-and-merge segmentation method using colour perception. this paper describes a complete approach to the segmentation and extraction of text from web images for subsequent recognition, to ultimately achieve both effective indexing and presentation by non-visual means (e.g., audio). the method described here (the first in the authorsý systematic approach to exploit human colour perception) enables the extraction of text in complex situations such as in the presence of varying colour (characters and background). more precisely, in addition to using structural features, the segmentation follows a split-and-merge strategy based on the hue-lightness-saturation (hls) representation of colour as a first approximation of an anthropocentric expression of the differences in chromaticity and lightness. character-like components are then extracted as forming textlines in a number of orientations and along curves.
variational multigrid for fast 3d interpretation of image sequences. we propose a variational multigrid method for fast 3d interpretation of image sequences, in which a dense depth map and 3d motion are directly recovered from spatiotemporal change of intensity images without prior matching and estimation. in this paper, we adopt the multigrid methods to efficiently reduce the computational complexity of the variational method, and suggest a new variational formulation to reliably perform the 3d interpretation. we show the efficiency and effectiveness of our method through experimental results with synthetic and real images.