Results 1  10
of
58
Feature detection with automatic scale selection
 International Journal of Computer Vision
, 1998
"... The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works ..."
Abstract

Cited by 713 (34 self)
 Add to MetaCart
(Show Context)
The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works, Witkin (1983) and Koenderink (1984) proposed to approach this problem by representing image structures at different scales in a socalled scalespace representation. Traditional scalespace theory building on this work, however, does not address the problem of how to select local appropriate scales for further analysis. This article proposes a systematic methodology for dealing with this problem. A framework is proposed for generating hypotheses about interesting scale levels in image data, based on a general principle stating that local extrema over scales of different combinations of γnormalized derivatives are likely candidates to correspond to interesting structures. Specifically, it is shown how this idea can be used as a major mechanism in algorithms for automatic scale selection, which
Preattentive texture discrimination with early vision mechanisms
 Journal of the Optical Society of America A
, 1990
"... mechanisms ..."
Deformable Kernels for Early Vision
 IEEE Trans. Pattern Anal. Mach. Intell
, 1995
"... AbstractEarly vision algorithms often have a first stage of linearfiltering that ‘extracts ’ from the image information at multiple scales of resolution and multiple orientations. A common difficulty in the design and implementation of such schemes is that one feels compelled to discretize coarsel ..."
Abstract

Cited by 145 (11 self)
 Add to MetaCart
AbstractEarly vision algorithms often have a first stage of linearfiltering that ‘extracts ’ from the image information at multiple scales of resolution and multiple orientations. A common difficulty in the design and implementation of such schemes is that one feels compelled to discretize coarsely the space of scales and orientations in order to reduce computation and storage costs. This discretization produces anisotropies due to a loss of translation, rotation, and scalinginvariance that makes early vision algorithms less precise and more difficult to design. This need not be so: one can compute and store efficiently the response of families of linear filters defined on a continuum of orientations and scales. A technique is presented that allows 1) computing the best approximation of a given family using linear combinations of a small number of ‘basis ’ functions; 2) describing all finitedimensional families, i.e., the families of filters for which a finite dimensional representation is possible with no error. The technique is based on singular value decomposition and may be applied to generating filters in arbitrary dimensions and subject to arbitrary deformations; the relevant functional analysis results are reviewed and precise conditions for the decomposition to be feasible are stated. Experimental results are presented that demonstrate the applicability of the technique to generating multiorientation multiscale 2D edgedetection kernels. The implementation issues are also discussed. Index TermsSteerable filters, wavelets, early vision, multiresolution image analysis, multirate filtering, deformable filters, scalespace I.
An Active Vision Architecture based on Iconic Representations
 Artificial Intelligence
, 1995
"... Active vision systems have the capability of continuously interacting with the environment. The rapidly changing environment of such systems means that it is attractive to replace static representations with visual routines that compute information on demand. Such routines place a premium on image d ..."
Abstract

Cited by 143 (13 self)
 Add to MetaCart
(Show Context)
Active vision systems have the capability of continuously interacting with the environment. The rapidly changing environment of such systems means that it is attractive to replace static representations with visual routines that compute information on demand. Such routines place a premium on image data structures that are easily computed and used. The purpose of this paper is to propose a general active vision architecture based on efficiently computable iconic representations. This architecture employs two primary visual routines, one for identifying the visual image near the fovea (object identification), and another for locating a stored prototype on the retina (object location). This design allows complex visual behaviors to be obtained by composing these two routines with different parameters. The iconic representations are comprised of highdimensional feature vectors obtained from the responses of an ensemble of Gaussian derivative spatial filters at a number of orientations and...
Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex
 Neural Computation
, 1995
"... this paper, we describe a hierarchical network model of visual recognition that explains these experimental observations by using a form of the extended Kalman filter as given by the Minimum Description Length (MDL) principle. The model dynamically combines inputdriven bottomup signals with expec ..."
Abstract

Cited by 114 (21 self)
 Add to MetaCart
this paper, we describe a hierarchical network model of visual recognition that explains these experimental observations by using a form of the extended Kalman filter as given by the Minimum Description Length (MDL) principle. The model dynamically combines inputdriven bottomup signals with expectationdriven topdown signals to predict current recognition state. Synaptic weights in the model are adapted in a Hebbian manner according to a learning rule also derived from the MDL principle. The resulting prediction/learning scheme can be viewed as implementing a form of the ExpectationMaximization (EM) algorithm. The architecture of the model posits an active computational role for the reciprocal connections between adjoining visual cortical areas in determining neural response properties. In particular, the model demonstrates the possible role of feedback from higher cortical areas in mediating neurophysiological effects due to stimuli from beyond the classical receptive field. Si
Robust computation of optic flow in a multiscale differential framework
 International Journal of Computer Vision
, 1995
"... Abstract. We have developed a new algorithm for computing optical flow in a differential framework. The image sequence is first convolved with a set of linear, separable spatiotemporal filter kernels similar to those that have been used in other early vision problems such as texture and stereopsis. ..."
Abstract

Cited by 112 (4 self)
 Add to MetaCart
(Show Context)
Abstract. We have developed a new algorithm for computing optical flow in a differential framework. The image sequence is first convolved with a set of linear, separable spatiotemporal filter kernels similar to those that have been used in other early vision problems such as texture and stereopsis. The brightness constancy constraint can then be applied to each of the resulting images, giving us, in general, an overdetermined system of equations for the optical flow at each pixel. There are three principal sources of error: (a) stochastic error due to sensor noise (b) systematic errors in the presence of large displacements and (c) errors due to failure of the brightness constancy model. Our analysis of these errors leads us to develop an algorithm based on a robust version of total least squares. Each optical flow vector computed has an associated reliability measure which can be used in subsequent processing. The performance of the algorithm on the data set used by Barron et al. (IJCV 1994) compares favorably with other techniques. In addition to being separable, the filters used are also causal, incorporating only past time frames. The algorithm is fully parallel and has been implemented on a multiple processor machine. 1
Object indexing using an iconic sparse distributed memory
, 1995
"... A generalpurpose object indexing technique is described that combines the virtues of principal component analysis with the favorable matching properties of highdimensional spaces to achieve high precision recognition. An object is represented by a set of highdimensional iconic feature vectors com ..."
Abstract

Cited by 65 (9 self)
 Add to MetaCart
(Show Context)
A generalpurpose object indexing technique is described that combines the virtues of principal component analysis with the favorable matching properties of highdimensional spaces to achieve high precision recognition. An object is represented by a set of highdimensional iconic feature vectors comprised of the responses of derivative of Gaussian filters at a range of orientations and scales. Since these filters can be shown to form the eigenvectors of arbitrary images containing both natural and manmade structures, they are wellsuited for indexing in disparate domains. The indexing algorithm uses an active vision system in conjunction with a modified form of Kanerva’s sparse distributed memory which facilitates interpolation between views and provides a convenient platform for learning the association between an object’s appearance and its identity. The robustness of the indexing method was experimentally confirmed by subjecting the method to a range of viewing conditions and the accuracy was verified using a wellknown model database containing a number of complex 3D objects under varying pose. 1
Multiresolution Histograms and their Use for Recognition
 IEEE Trans. on PAMI
, 2004
"... ..."
(Show Context)
Logical/Linear Operators for Image Curves
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1995
"... We propose a language for designing image measurement operators suitable for early vision. We refer to them as logical/linear (L/L) operators, since they unify aspects of linear operator theory and boolean logic. A family of these operators appropriate for measuring the loworder differential struct ..."
Abstract

Cited by 55 (7 self)
 Add to MetaCart
(Show Context)
We propose a language for designing image measurement operators suitable for early vision. We refer to them as logical/linear (L/L) operators, since they unify aspects of linear operator theory and boolean logic. A family of these operators appropriate for measuring the loworder differential structure of image curves is developed. These L/L operators are derived by decomposing a linear model into logical components to ensure that certain structural preconditions for the existence of an image curve are upheld. Tangential conditions guarantee continuity, while normal conditions select and categorize contrast profiles. The resulting operators allow for coarse measurement of curvilinear differential structure (orientation and curvature) while successfully segregating edge and linelike features. By thus reducing the incidence of falsepositive responses, these operators are a substantial improvement over (thresholded) linear operators which attempt to resolve the same class of features. ...
Natural basis functions and topographic memory for face recognition
 In Proc. of IJCAI
, 1995
"... Recent work regarding the statistics of natural images has revealed that the dominant eigenvectors of arbitrary natural images closely approximate various oriented derivativeofGaussian functions; these functions have also been shown to provide the best fit to the receptive field profiles of cells ..."
Abstract

Cited by 39 (5 self)
 Add to MetaCart
Recent work regarding the statistics of natural images has revealed that the dominant eigenvectors of arbitrary natural images closely approximate various oriented derivativeofGaussian functions; these functions have also been shown to provide the best fit to the receptive field profiles of cells in the primate striate cortex. We propose a scheme for expressioninvariant face recognition that employs a fixed set of these "natural " basis functions to generate multiscale iconic representations of human faces. Using a fixed set of basis functions obviates the need for recomputing eigenvectors (a step that was necessary in some previous approaches employing principal component analysis (PCA) for recognition) while at the same time retaining the redundancyreducing properties of PCA. A face is represented by a set of iconic representations automatically extracted from an input image. The description thus obtained is stored in a topographicallyorganized sparse distributed memory that is based on a model of human longterm memory first proposed by Kanerva. We describe experimental results for an implementation of the method on a pipeline image processor that is capable of achieving near realtime recognition by exploiting the processor's framerate convolution capability for indexing purposes. 1