Classification and segmentation of rotated and scaled textured images using texture “tuned” masks

You, Jane; Cohen, Harvey A.

doi:10.1016/0031-3203(93)90033-s

Cited by 86 publications

(39 citation statements)

References 25 publications

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“…A steerable oriented pyramid was used to extract rotation invariant features by Greenspan et al (10) and a covariance-based representation to transform neighborhood about each pixel into a set of invariant descriptors was proposed by Madiraju and Liu (11) . You and Cohen extended Laws' masks for rotation-invariant texture characterization in their "tuned" mask scheme (12) .…”

Section: Introductionmentioning

confidence: 99%

Rotation-invariant texture classification using feature distributions

Pietikäinen

Ojala

2000

Pattern Recognition

399

170

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Section: Introductionmentioning

confidence: 99%

Rotation-invariant texture classification using feature distributions

Pietikäinen

Ojala

2000

Pattern Recognition

399

170

View full text Add to dashboard Cite

“…Even at moderate scale distortion, classification error percentage (CEP) is scarcely ever zero [1], [2], [4]. At maximal scale distortion, CEP rises up to a few percent [12].…”

Section: Related Workmentioning

confidence: 99%

“…Thus, they are treated as scaled ones regarding the average dimensions. Generally, this is a problem of image recognition [1], [2]. Objects appear closer and farther in front of the cam.…”

Section: Introductionmentioning

confidence: 99%

Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem

Romanuke

2015

Information Technology and Management Science

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-The research is focused on optimising two-layer perceptron for generalised scaled object classification problem. The optimisation criterion is minimisation of inaccuracy. The inaccuracy depends on training parameters and hidden layer neuron number. After its statistics is accumulated, minimisation is executed by a numerical search. Perceptron is optimised additionally by extra training. As it is done, the classification error percentage does not exceed 3 % in case of the worst scale distortion.

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“…However, real world textures can occur at arbitrary spatial resolutions and rotations and they may be subjected to varying illumination conditions. This has inspired a collection of studies, which generally incorporate invariance with respect to one or at most two of the properties spatial scale, orientation and gray scale, among others [1,2,3,4,5,6,7,8,10,11,13,14].…”

Section: Introductionmentioning

confidence: 99%

A Generalized Local Binary Pattern Operator for Multiresolution Gray Scale and Rotation Invariant Texture Classification

Ojala

Pietikäinen

Mäenpää

2001

Lecture Notes in Computer Science

234

113

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Abstract. This paper presents generalizations to the gray scale and rotation invariant texture classification method based on local binary patterns that we have recently introduced. We derive a generalized presentation that allows for realizing a gray scale and rotation invariant LBP operator for any quantization of the angular space and for any spatial resolution, and present a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray scale variations, since the operator is by definition invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity, as the operator can be realized with a few operations in a small neighborhood and a lookup table. Excellent experimental results obtained in a true problem of rotation invariance, where the classifier is trained at one particular rotation angle and tested with samples from other rotation angles, demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns. These operators characterize the spatial configuration of local image texture and the performance can be further improved by combining them with rotation invariant variance measures that characterize the contrast of local image texture. The joint distributions of these orthogonal measures are shown to be very powerful tools for rotation invariant texture analysis. nonparametric texture analysis distribution histogram Brodatz contrast

show abstract

Classification and segmentation of rotated and scaled textured images using texture “tuned” masks

Cited by 86 publications

References 25 publications

Rotation-invariant texture classification using feature distributions

Rotation-invariant texture classification using feature distributions

Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem

A Generalized Local Binary Pattern Operator for Multiresolution Gray Scale and Rotation Invariant Texture Classification

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