waashk/dblp huggingface dataset:
edge detection and ridge detection with automatic scale selection. when computing descriptors of image data, the type of information that can be extracted may be strongly dependent on the scales at which the image operators are applied. this article presents a systematic methodology for addressing this problem. a mechanism is presented for automatic selection of scale levels when detecting one-dimensional image features, such as edges and ridges.a novel concept of a scale-space edge is introduced, defined as a connected set of points in scale-space at which: (i) the gradient magnitude assumes a local maximum in the gradient direction, and (ii) a normalized measure of the strength of the edge response is locally maximal over scales. an important consequence of this definition is that it allows the scale levels to vary along the edge. two specific measures of edge strength are analyzed in detail, the gradient magnitude and a differential expression derived from the third-order derivative in the gradient direction. for a certain way of normalizing these differential descriptors, by expressing them in terms of so-called &gamma;-normalized derivatives, an immediate consequence of this definition is that the edge detector will adapt its scale levels to the local image structure. specifically, sharp edges will be detected at fine scales so as to reduce the shape distortions due to scale-space smoothing, whereas sufficiently coarse scales will be selected at diffuse edges, such that an edge model is a valid abstraction of the intensity profile across the edge.since the scale-space edge is defined from the intersection of two zero-crossing surfaces in scale-space, the edges will by definition form closed curves. this simplifies selection of salient edges, and a novel significance measure is proposed, by integrating the edge strength along the edge. moreover, the scale information associated with each edge provides useful clues to the physical nature of the edge.with just slight modifications, similar ideas can be used for formulating ridge detectors with automatic selection, having the characteristic property that the selected scales on a scale-space ridge instead reflect the width of the ridge.it is shown how the methodology can be implemented in terms of straightforward visual front-end operations, and the validity of the approach is supported by theoretical analysis as well as experiments on real-world and synthetic data.
viewpoint variation in the noise sensitivity of pose estimation. the paper presents an analysis of the stability of pose estimation. the investigated pose estimation technique is based on orientations of three edge segments and provides the rotation part of object pose.the specific emphasis of the analysis is on determining how the stability varies with view point relative to an object. the stability investigation propagates the uncertainty in edge segment orientations to the resulting effect on the pose parameters. it is shown that there is a very strong variation in noise sensitivity over the range of viewpoints and that exactly what viewpoints offer highest robustness towards noise can be determined in advance. experiments on real images verify the theoretical results and show that, dependent on viewpoint, pose parameter variance varies from 0.05 to 20 (degrees squared).
configuration based scene classification and image indexing. scene classification is a major open challenge in machine vision. most solutions proposed so far such as those based on color histograms and local texture statistics cannot capture a scene's global configuration, which is critical in perceptual judgments of scene similarity. we present a novel approach, "configural recognition", for encoding scene class structure. the approach's main feature is its use of qualitative spatial and photometric relationships within and across regions in low resolution images. the emphasis on qualitative measures leads to enhanced generalization abilities and the use of low-resolution images renders the scheme computationally efficient. we present results on a large database of natural scenes. we also describe how qualitative scene concepts may be learned from examples.
object tracking using affine structure for point correspondences. a new object tracking algorithm based on affine structure has been developed and it is shown that its performance is better than that of a kalman filter based correlation tracker. the algorithm is fast, reliable, viewpoint invariant, and insensitive to occlusion and/or individual corner disappearance or reappearance. detailed experimental analysis on a long real image sequence is also presented.
word spotting: a new approach to indexing handwriting. there are many historical manuscripts written in a single hand which it would be useful to index. examples include the early presidential papers at the library of congress and the collected works of w. b. dubois at the library of the university of massachusetts. the standard technique for indexing documents is to scan them in, convert them to machine readable form (ascii) using optical character recognition (ocr) and then index them using a text retrieval engine. however, ocr does not work well on handwriting. here an alternative scheme is proposed for indexing such texts. each page of the document is segmented into words. the images of the words are then matched against each other to create equivalence classes (each equivalence classes contains multiple instances of the same word). the user then provides ascii equivalents for say the top 2000 equivalence classes. the current paper deals with the matching aspects of this process. due to variations in even a single person''s handwriting, it is expected that the matching will be the most difficult step in the whole process. two different techniques for matching words are discussed. the first method, based on euclidean distance mapping, matches words assuming that the transformation between the words may be modelled by a translation (shift). the second method, based on an algorithm developed by scott and longuet higgins, matches words assuming that the transformation between the words may be modelled by an affine transform. experiments are shown demonstrating the feasibility of the approach for indexing handwriting.
controlled camera motions for scene reconstruction and exploration. this paper deals with the 3d structure estimation and exploration of a scene using active vision. our method is based on the structure from controlled motion approach which consists in constraining the camera motion in order to obtain a precise and robust estimation of the 3d structure of a geometrical primitive. since this approach involves to gaze on the considered primitive, we present a method for connecting up many estimations in order to recover the complete spatial structure of scenes composed of cylinders and segments. we have developed perceptual strategies able to perform a succession of robust estimations without any assumption on the number and on the localization of the different objects. furthermore, the proposed strategy ensures the completeness of the reconstruction. an exploration process centered on current visual features and on the structure of the previously studied primitives is presented. this leads to a gaze planning strategy that mainly uses a representation of known and unknown areas as a basis for selecting viewpoints. finally, experiments carried out on a robotic cell have proved the validity of our approach.
a class of probabilistic shape models. deformable models are related to other data representation methods. it was recently proposed a class of models based on a fuzzy energy function which includes many well known algorithms (snakes, elastic nets, fuzzy and hard c-means and kohonen maps). this paper describes a probabilistic extension of these algorithms in a bayesian framework, using gibbs-boltzman distributions. it is shown that the new class of models minimizes an energy function with an additional term: the log partition function. the role of the log partition function in probabilistic versions of snakes, c-means and elastic nets is studied and analytic expressions are derived in the case of probabilistic snakes. the log partition function produces an additional force field which improves the performance of these algorithms in some applications.
edge localization in surface reconstruction using optimal estimation theory. many relaxation based smoothing methods used in surface reconstruction algorithms filter out the effect of noise in image data, but result in the elimination of important discontinuity information as well. in this paper the inter-pixel interaction during relaxation is shown to be equivalent to a multiple measurement fusion problem which can be solved using optimal estimation theory. pixels in a given neighbourhood act as noisy information sources, combining their information to update the state of that neighbourhood. by formulating discontinuities as another ``noise'' source in the image, and by using the so-called curvature consistency reconstruction algorithm on range images, it is shown that optimal estimation theory offers a method for the automatic and adaptive localization of discontinuities while providing a smooth piece-wise continuous surface description.
physics-based segmentation: moving beyond color. we previously presented a framework for segmentation of complex scenes using multiple physical hypotheses for simple image regions. a consequence of that framework was a proposal for a new approach to the segmentation of complex scenes into regions corresponding to coherent surfaces rather than merely regions of similar color. herein we present an implementation of this new approach and show example segmentations for scenes containing multi-colored piece-wise uniform objects. using our approach we are able to intelligently segment scenes with objects of greater complexity than previous physics-based segmentation algorithms. the results show that by using general physical models we obtain segmentations that correspond more closely to coherent surfaces in the scene than segmentations found using only color.
a real-time computer vision system for measuring traffic parameters. for the problem of tracking vehicles on freeways using machine vision, existing systems work well in free-flowing traffic. traffic engineers, however, are more interested in monitoring freeways when there is congestion, and current systems break down for congested traffic due to the problem of partial occlusion. we have developed a feature-based tracking approach for the task of tracking vehicles under congestion. instead of tracking entire vehicles, vehicle sub-features are tracked to make the system robust to partial occlusion. in order to group together sub-features that come from the same vehicle, the constraint of common motion is used. here we describe the real-time implementation of the system using a network of dsp chips.
isotropic gradient estimatio. the vast majority of corner and edge detectors measure image intensity gradients in order to estimate the positions and strengths of features. however, many of the most popular intensity gradient estimators are inherently and significantly anisotropic. in spite of this, few algorithms take the anisotropy into account, and so the set of features uncovered is typically sensitive to rotations of the image, compromising recognition, matching (e.g. stereo), and tracking. we introduce an effective technique for removing unwanted anisotropies from analytical gradient estimates, by measuring local intensity gradients in four directions rather than the more traditional two. in experiments using real image data, our algorithm reduces the gradient anisotropy associated with conventional analytical gradient estimates by up to 85%, yeilding more consistent feature topologies.
dense nonrigid motion tracking from a sequence of velocity fields. we have addressed the problem of tracking the nonrigid motion of the heart using a sequence of velocity fields and a sequence of contours. the information from both the contours and the dense velocity fields is integrated into a deforming mesh that is placed over the myocardium at one time frame and then tracked over the entire cardiac cycle. the deformation is guided by a smoothing filter that provides a compromise between (i) believing the dense field velocity and the contour data when it is crisp and coherent in a local spatial and temporal sense and (ii) employing a temporally smooth cyclic model of cardiac motion when contour and velocity data are not trustworthy. the method has been carefully evaluated with simulated data and phantom data. experiments with in vivo data have also been conducted.
muse: robust surface fitting using unbiased scale estimates. despite many successful applications of robust statistics, they have yet to be completely adapted to many computer vision problems. range reconstruction, particularly in unstructured environments, requires a robust estimator that not only tolerates a large outlier percentage but also tolerates several discontinuities, extracting multiple surfaces in an image region. observing that random outliers and/or points from across discontinuities increase a hypothesized fit's scale estimate (standard deviation of the noise), our new operator, called muse (minimum unbiased scale estimator), evaluates a hypothesized fit over potential inlier sets via an objective function of unbiased scale estimates. muse extracts the single best fit from the data by minimizing its objective function over a set of hypothesized fits and can sequentially extract multiple surfaces from an image region. we show muse to be effective on synthetic data modelling small scale discontinuities and in preliminary experiments on complicated range data.
prediction intervals for surface growing range segmentation. the surface growing framework presented by besl and jain has served as the basis for many range segmentation techniques. it has been augmented with alternative fitting techniques, model selection criteria, and solid modelling components. all of these approaches, however, require global thresholds and large isolated seed regions. range scenes typically do not satisfy the global threshold assumption since it requires data noise characteristics to be constant throughout the scene. furthermore, as scene complexity increases, the number of surfaces, discontinuities, and outliers increase, hindering the identification of large seed regions.we present statistical criteria based on multivariate regression to replace the traditional decision criteria used in surface growing. we use local estimates and their uncertainties to construct criteria which capture the uncertainty in extrapolating estimated fits. we restrict surface expansion to very localized extrapolations, increasing the sensitivity to discontinuities and allowing regions to refine their estimates and uncertainties. our approach uses a small number of parameters which are either statistical thresholds or cardinality measures, i.e. we do not use thresholds defined by specific range distances or orientation angles.
interactive learning with a "society of models" . digital library access is driven by features, but the relevance of a feature for a query is not always obvious. this paper describes an approach for integrating a large number of context-dependent features into a semi-automated tool. instead of requiring universal similarity measures or manual selection of relevant features, the approach provides a learning algorithm for selecting and combining groupings of the data, where groupings can be induced by highly specialized features. the selection process is guided by positive and negative examples from the user. the inherent combinatorics of using multiple features is reduced by a multistage grouping generation, weighting, and collection process. the stages closest to the user are trained fastest and slowly propagate their adaptations back to earlier stages. the weighting stage adapts the collection stage's search space across uses, so that, in later interactions, good groupings are found given few examples from the user.
bayesian face recognition using deformable intensity surfaces. we describe a novel technique for face recognition based on deformable intensity surfaces which incorporates both the shape and texture components of the 2d image. the intensity surface of the facial image is modeled as a deformable 3d mesh in (z, y, i(x, y)) space. using an efficient technique for matching two surfaces (in terms of the analytic modes of vibration), we obtain a dense correspondence field (or 3d warp) between two images. the probability distributions of two classes of warps are then estimated from training data: interpersonal and extrapersonal variations. these densities are then used in a bayesian framework for image matching and recognition. experimental results with facial data from the us army feret database demonstrate an increased recognition rate over the previous best methods.
a hybrid framework for surface registration and deformable models. in computer vision, two complementary approaches have been widely used to perform object reconstruction and registration. the deformable model framework locally applies internal and external forces to fit 3d data. the non-rigid registration framework iteratively computes the best global transformation in order to minimize the distance between a template and the data. in this paper, we first show that applying a global transformation on a surface model, is equivalent to applying an external force on a deformable model without any regularizing force. second we propose a hybrid framework which combines the registration framework and the deformable models scheme. our hybrid deformation approach allows to control the scale at which the model is deformed. this is clearly beneficial for performing both reconstruction and registration tasks. we show many examples of this approach on active contours and deformable surfaces. furthermore, a global transformation based on axial symmetry is introduced.
closest point search in high dimensions. the problem of finding the closest point in high-dimensional spaces is common in computational vision. unfortunately, the complexity of most existing search algorithms, such as k-d tree and r-tree, grows exponentially with dimension, making them impractical for dimensionality above 15. in nearly all applications, the closest point is of interest only if it lies within a user specified distance \epsilon. we present a simple and practical algorithm to efficiently search for the nearest neighbor within euclidean distance \epsilon. our algorithm uses a projection search technique along with a novel data structure to dramatically improve performance in high dimensions. a complexity analysis is presented which can help determine \epsilon in structured problems. benchmarks clearly show the superiority of the proposed algorithm for high dimensional search problems frequently encountered in machine vision, such as real-time object recognition.
fast 3d stabilization and mosaic construction. we present a fast electronic image stabilization system that compensates for 3d rotation. the extended kalman filter framework is employed to estimate the rotation between frames, which is represented using unit quaternions. a small set of automatically selected and tracked feature points are used as measurements. the effectiveness of this technique is also demonstrated by constructing mosaic images from the motion estimates, and comparing them to mosaics built from 2d stabilization algorithms. two different stabilization schemes are presented. the first, implemented in a real-time platform based on a datacube mv200 board, estimates the motion between two consecutive frames and is able to process gray level images of resolution 128x120 at 10 hz. the second scheme estimates the motion between the current frame and an inverse mosaic; this allows better estimation without the need for indexing the new image frames. experimental results for both schemes using real and synthetic image sequences are presented.
registering multiple cartographic models with the hierarchical mixture of experts algorithm. this paper describes an application of the hierarchical mixture of experts algorithm to the registration of multiple cartographic models to noisy radar data according to the hme algorithm each model is represented by a set of maximum likelihood registration parameters together with a set of matching probabilities. this architecture can be viewed as providing simultaneous registration and hypothesis verification. the maps in the cartographic data-base compete to account for radar data through the imposed probability normalization. the resulting matching algorithm can be regarded as a generic tool for model retrieval from a data-base. our evaluation on radar images illustrates some of the characteristics of the algorithm. our main conclusions are that the method is both robust to added image noise and poor initialization.
parametric feature detection. we propose an algorithm to automatically construct feature detectors for arbitrary parametric features. to obtain a high level of robustness we advocate the use of realistic multi-parameter feature models and incorporate optical and sensing effects. each feature is represented as a densely sampled parametric manifold in a low dimensional subspace of a hilbert space. during detection, the brightness distribution around each image pixel is projected into the subspace. if the projection lies sufficiently close to the feature manifold, the feature is detected and the location of the closest manifold point yields the feature parameters. the concepts of parameter reduction by normalization, dimension reduction, pattern rejection, and heuristic search are all employed to achieve the required efficiency. by applying the algorithm to appropriate parametric feature models, detectors have been constructed for five features, namely, step edge, roof edge, line, corner, and circular disc. detailed experiments are reported on the robustness of detection and the accuracy of parameter estimation.
eigen-texture method: appearance compression based on 3d model. image-based and model-based methods are two representative rendering methods for generating virtual images of objects from their real images. extensive research on these two methods has been made in cv and cg communities. however, both methods still have several drawbacks when it comes to applying them to the mixed reality where we integrate such virtual images with real background images. to overcome these difficulties, we propose a new method, which we refer to as the eigen-texture method. the proposed method samples appearances of a real object under various illumination and viewing conditions, and compresses them in the 2d coordinate system defined on the 3d model surface. the 3d model is generated from a sequence of range images. the eigen-texture method is practical because it does not require any detailed reflectance analysis of the object surface, and has great advantages due to the accurate 3d geometric models. this paper describes the method, and reports on its implementation.
3-d object pose estimation by shading and edge data fusion ----simulating virtual manipulation on mental images. human beings seem to recognize objects based on a kind of model-matching, i.e., a virtual manipulation on mental images. this paper presents a 3-d object pose estimation method simulating the human recognition scheme. computer synthesizes not only an edge image but also a shading image from an object model. then, it matches the two kinds of synthesized images with the inputted images individually by using a non-linear least-squares method, and estimates the pose parameter values. finally, it chooses the better of the individually estimated poses. thus, the fusion of the shading and the edge information is achieved. since the two pieces of information complement each other, this method has the advantage of much higher robustness and accuracy of pose estimation than ordinary model-matching techniques which rely only on geometrical features such as vertices or edges.