Object Recognition Using Coloured Receptive Fields

Hall, Daniela; Verdière, Vincent Colin de; Crowley, James L.

doi:10.1007/3-540-45054-8_11

Cited by 32 publications

(35 citation statements)

References 9 publications

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“…Geusebroek et al [21] proposed a set of differential colour invariants that are invariant to illumination based on a reflectance model and the Gaussian colour model proposed by Koenderink. Hall et al [25] computed partial derivatives of colour-opponent channels, leading to an N-jet representation up to order one. Linde and Lindeberg [38] extended this idea by showing that highly discriminative image descriptors for object recognition can be obtained from histograms of spatio-chromatic differential invariants up to order two defined from colour-opponent channels.…”

Section: Related Workmentioning

confidence: 99%

“…invariants For colour images, we also define chromatic cues (c (1) , c (2) ) from RGB images by red/green and yellow/blue colour-opponent channels according to [25] …”

Section: Spatio-chromatic Derivatives and Differentialmentioning

confidence: 99%

“…• object recognition problems have been computed for all numbers 5 M ∈ {1, 2, 3, 4, 5,6,8,10,12,15,20,25, N } of principal components between 1 and the dimensionality N of the descriptor. The table also shows the influence of variance normalization, by comparing the results of the N -dimensional variance normalized descriptor to the performance without using PCA.…”

Section: Pca-reduced Histogramsmentioning

confidence: 99%

“…Three main groups of approaches have been developed, where receptive field responses are computed either (i) globally or regionally in terms of histograms (Swain and Ballard [56], Schiele and Crowley [52], Zelnik and Irani [64], Linde and Lindeberg [38], Dalal and Triggs [14], (ii) locally at sparse sets of interest points (Lowe [45], Bay et al [3], Csurka et al [13], Laptev and Lindeberg [33]), or (iii) locally at every point or at a dense grid of points (Hall et al [25], Jurie and Triggs [27], Lazebnik et al [34], Bosch et al [5]). …”

Section: Introductionmentioning

confidence: 99%

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Composed complex-cue histograms: An investigation of the information content in receptive field based image descriptors for object recognition

Linde

Lindeberg

2012

Computer Vision and Image Understanding

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Recent work has shown that effective methods for recognizing objects and spatio-temporal events can be constructed based on histograms of receptive field like image operations.This paper presents the results of an extensive study of the performance of different types of receptive field like image descriptors for histogram-based object recognition, based on different combinations of image cues in terms of Gaussian derivatives or differential invariants applied to either intensity information, colour-opponent channels or both. A rich set of composed complex-cue image descriptors is introduced and evaluated with respect to the problems of (i) recognizing previously seen object instances from previously unseen views, and (ii) classifying previously unseen objects into visual categories.It is shown that there exist novel histogram descriptors with significantly better recognition performance compared to previously used histogram features within the same class. Specifically, the experiments show that it is possible to obtain more discriminative features by combining lower-dimensional scale-space features into composed complexcue histograms. Furthermore, different types of image descriptors have different relative advantages with respect to the problems of object instance recognition vs. object category classification. These conclusions are obtained from extensive evaluations on two mutually independent data sets.For the task of recognizing specific object instances, combined histograms of spatial and spatio-chromatic derivatives are highly discriminative, and several image descriptors in terms rotationally invariant (intensity and spatiochromatic) differential invariants up to order two lead to very high recognition rates.For category classification, primary information is contained in both first-and second-order derivatives, where second-order partial derivatives constitute the most discriminative cue.Dimensionality reduction by principal component analysis and variance normalization prior to training and recognition can in many cases lead to a significant increase in recognition or classification performance. Surprisingly high recognition rates can even be obtained with binary histograms that reveal the polarity of local scale-space features, and which can be expected to be particularly robust to illumination variations. An overall conclusion from this study is that compared to previously used lower-dimensional histograms, the use of composed complex-cue histograms of higher dimensionality reveals the co-variation of multiple cues and enables much better recognition performance, both with regard to the problems of recognizing previously seen objects from novel views and for classifying previously unseen objects into visual categories.

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Section: Related Workmentioning

confidence: 99%

“…invariants For colour images, we also define chromatic cues (c (1) , c (2) ) from RGB images by red/green and yellow/blue colour-opponent channels according to [25] …”

Section: Spatio-chromatic Derivatives and Differentialmentioning

confidence: 99%

Section: Pca-reduced Histogramsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Composed complex-cue histograms: An investigation of the information content in receptive field based image descriptors for object recognition

Linde

Lindeberg

2012

Computer Vision and Image Understanding

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show abstract

“…Geusebroek et al [49] proposed a set of differential colour invariants that are invariant to illumination based on a reflectance model and the Gaussian colour model proposed by Koenderink. Hall et al [54] computed partial derivatives of colour-opponent channels, leading to an N-jet representation up to order one. Linde and Lindeberg [87,88] extended this idea by showing that highly discriminative image descriptors for object recognition can be obtained from histograms of spatio-chromatic differential invariants up to order two defined from colour-opponent channels.…”

Section: Related Workmentioning

confidence: 99%

Image Matching Using Generalized Scale-Space Interest Points

Lindeberg

2013

Lecture Notes in Computer Science

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The performance of matching and object recognition methods based on interest points depends on both the properties of the underlying interest points and the choice of associated image descriptors. This paper demonstrates advantages of using generalized scale-space interest point detectors in this context for selecting a sparse set of points for computing image descriptors for image-based matching. For detecting interest points at any given scale, we make use of the Laplacian ∇ 2 norm L, the determinant of the Hessian det H norm L and four new unsigned or signed Hessian feature strength measures D 1,norm L,D 1,norm L, D 2,norm L and D 2,norm L, which are defined by generalizing the definitions of the Harris and Shi-and-Tomasi operators from the second moment matrix to the Hessian matrix. Then, feature selection over different scales is performed either by scale selection from local extrema over scale of scale-normalized derivates or by linking features over scale into feature trajectories and computing a significance measure from an integrated measure of normalized feature strength over scale. A theoretical analysis is presented of the robustness of the differential entities underlying these interest points under image deformations, in terms of invariance properties under affine image deformations or approximations thereof. Disregarding the effect of the rotationally symmetric scale-space smoothing operation, the determinant of the Hessian det H norm L is a truly affine covariant differential entity and the Hessian feature strength measures D 1,norm L andD 1,norm L have a major contribution from the affine covariant determinant of the Hessian, implying that local extrema of these differen- It is shown how these generalized scale-space interest points allow for a higher ratio of correct matches and a lower ratio of false matches compared to previously known interest point detectors within the same class. The best results are obtained using interest points computed with scale linking and with the new Hessian feature strength measures D 1,norm L,D 1,norm L and the determinant of the Hessian det H norm L being the differential entities that lead to the best matching performance under perspective image transformations with significant foreshortening, and better than the more commonly used Laplacian operator, its difference-of-Gaussians approximation or the Harris-Laplace operator. We propose that these generalized scale-space interest points, when accompanied by associated local scale-invariant image descriptors, should allow for better performance of interest point based methods for image-based matching, object recognition and related visual tasks.

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Scale‐Space

Lindeberg¹

2008

Wiley Encyclopedia of Computer Science and Engineering

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Scale‐space theory is a framework for multiscale image representation, which has been developed by the computer vision community with complementary motivations from physics and biologic vision. The idea is to handle the multiscale nature of real‐world objects, which implies that object may be perceived in different ways depending on the scale of observation. If one aims to develop automatic algorithms for interpreting images of unknown scenes, no way exists to know a priori what scales are relevant. Hence, the only reasonable approach is to consider representations at all scales simultaneously. From axiomatic derivations is has been shown that given the requirement that coarse‐scale representations should correspond to true simplifications of fine scale structures, convolution with Gaussian kernels and Gaussian derivatives is singled out as a canonical class of image operators forthe earliest stages of visual processing. These image operators can be used as basis to solve a large variety of visual tasks, including feature detection, feature classification, stereo matching, motion descriptors, shape cues, and image‐based recognition. By complementing scale‐space representation with a module for automatic scale selection based on the maximization of normalized derivatives over scales, early visual modules can be made scale invariant. In this way, visual modules canadapt automatically to the unknown scale variations that may occur because of objects and substructures of varying physical size as well as objects with varying distances to the camera. An interesting similarity to biologic vision is that the scale‐space operators resemble closely receptive field profiles registered in neurophysiologic studies of the mammalian retina and visual cortex.

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Object Recognition Using Coloured Receptive Fields

Cited by 32 publications

References 9 publications

Composed complex-cue histograms: An investigation of the information content in receptive field based image descriptors for object recognition

Composed complex-cue histograms: An investigation of the information content in receptive field based image descriptors for object recognition

Image Matching Using Generalized Scale-Space Interest Points

Scale‐Space

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