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Book ChapterDOI

Affine/ Photometric Invariants for Planar Intensity Patterns

TLDR
This paper uses moments as the basic features, but extends the literature in two respects: deliberate mixes of different types of moments to keep the order of the moments low and yet have a sufficiently large number to safeguard discriminant power; and invariance with respect to photometric changes is incorporated in order to find the simplest moment invariants that can cope with changing lighting conditions.
Abstract
The paper contributes to the viewpoint invariant recognition of planar patterns, especially labels and signs under affine deformations. By their nature, the information of such ‘eye-catchers’ is not contained in the outline or frame — they often are affinely equivalent like parallelograms and ellipses — but in the intensity content within. Moment invariants are well suited for their recognition. They need a closed bounding contour, but this is comparatively easy to provide for the simple shapes considered. On the other hand, they characterize the intensity patterns without the need for error prone feature extraction. This paper uses moments as the basic features, but extends the literature in two respects: (1) deliberate mixes of different types of moments to keep the order of the moments (and hence also the sensitivity to noise) low and yet have a sufficiently large number to safeguard discriminant power; and (2) invariance with respect to photometric changes is incorporated in order to find the simplest moment invariants that can cope with changing lighting conditions which can hardly be avoided when changing viewpoint. The paper gives complete classifications of such affine / photometric moment invariants. Experiments are described that illustrate the use of some of them.

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Citations
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Journal ArticleDOI

A performance evaluation of local descriptors

TL;DR: It is observed that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best and Moments and steerable filters show the best performance among the low dimensional descriptors.
Proceedings ArticleDOI

A performance evaluation of local descriptors

TL;DR: It is observed that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best and Moments and steerable filters show the best performance among the low dimensional descriptors.
Proceedings ArticleDOI

PCA-SIFT: a more distinctive representation for local image descriptors

TL;DR: This paper examines (and improves upon) the local image descriptor used by SIFT, and demonstrates that the PCA-based local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation.
Proceedings ArticleDOI

Bag-of-visual-words and spatial extensions for land-use classification

TL;DR: This work considers a standard non-spatial representation in which the frequencies but not the locations of quantized image features are used to discriminate between classes analogous to how words are used for text document classification without regard to their order of occurrence, and considers two spatial extensions.
Journal ArticleDOI

ASIFT: A New Framework for Fully Affine Invariant Image Comparison

TL;DR: The proposed affine-SIFT (ASIFT), simulates all image views obtainable by varying the two camera axis orientation parameters, namely, the latitude and the longitude angles, left over by the SIFT method, and will be mathematically proved to be fully affine invariant.
References
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Journal ArticleDOI

Visual pattern recognition by moment invariants

TL;DR: It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be accomplished and it is indicated that generalization is possible to include invariance with parallel projection.
Journal ArticleDOI

On image analysis by the methods of moments

TL;DR: Various types of moments have been used to recognize image patterns in a number of applications and some fundamental questions are addressed, such as image-representation ability, noise sensitivity, and information redundancy.
Book

Geometric invariance in computer vision

TL;DR: In this paper, Abhyankar et al. proposed a geometric interpretation of joint conic invariants, and presented an experimental evaluation of projective invariants for curves in two and three dimensions.
Journal ArticleDOI

Pattern recognition by affine moment invariants

TL;DR: The invariants from second- and third-order moments are derived and shown to be complete and to be invariant under general affine transformation and used for recognition of affine-deformed objects.
Journal ArticleDOI

A survey of moment-based techniques for unoccluded object representation and recognition

TL;DR: Basic Cartesian moment theory is reviewed and its application to object recognition and image analysis is presented and the geometric properties of low-order moments are discussed along with the definition of several moment-space linear geometric transforms.
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