6,662 research outputs found
Rotation and Scale Invariant Texture Classification
Texture classification is very important in image analysis. Content based image retrieval, inspection of surfaces, object recognition by texture, document segmentation are few examples where texture classification plays a major role. Classification of texture images, especially those with different orientation and scale changes, is a challenging and important
problem in image analysis and classification. This thesis proposes an effective scheme for rotation and scale invariant texture classification. The rotation and scale invariant feature extraction for a given image involves applying a log-polar transform to eliminate the rotation and scale effects, but at same time produce a row shifted log-polar image, which is then
passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects. So, the output wavelet coefficients are rotation and scale invariant. The adaptive row shift invariant wavelet packet transform is quite efficient with only O (n*log n) complexity. The experimental results, based on different testing data sets for images from Brodatz album
with different orientations and scales, show that the implemented classification scheme outperforms other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 87.09 percent
Rotation and scale invariant texture classification using log polar wavelet energy signatures
Classification of texture images, especially those with different orientation and scale changes, is a challenging and important problem in image analysis and classification. This thesis proposes an effective scheme for rotation and scale invariant texture classification using log-polar wavelet signatures.The rotation and scale invariant feature extraction for a given image involves applying a log-polar transform to eliminate the rotation and scale effects, but at same time produce a row shifted log-polar image, which is then passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects. So,the output wavelet coefficients are rotation and scale invariant. The adaptive row shift invariant wavelet packet transform is quite efficient with only O(n*log n) complexity. A feature vector of the most dominant log-polar wavelet energy signatures extracted from each subband of wavelet coefficients is constructed for rotation and scale invariant texture classification. In the experiments, we employed a modified Mahalanobis classifier to classify a set of 12 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing data sets for images with different orientations and scales, show that the implemented classification scheme using log- polar wavelet signatures outperforms other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 87.59 percent, demonstrating that the extracted energy signatures are effective rotation and scale invariant features
Invertible Orientation Scores of 3D Images
The enhancement and detection of elongated structures in noisy image data is
relevant for many biomedical applications. To handle complex crossing
structures in 2D images, 2D orientation scores were introduced, which already
showed their use in a variety of applications. Here we extend this work to 3D
orientation scores. First, we construct the orientation score from a given
dataset, which is achieved by an invertible coherent state type of transform.
For this transformation we introduce 3D versions of the 2D cake-wavelets, which
are complex wavelets that can simultaneously detect oriented structures and
oriented edges. For efficient implementation of the different steps in the
wavelet creation we use a spherical harmonic transform. Finally, we show some
first results of practical applications of 3D orientation scores.Comment: ssvm 2015 published version in LNCS contains a mistake (a switch
notation spherical angles) that is corrected in this arxiv versio
Fingerprint Recognition Using Translation Invariant Scattering Network
Fingerprint recognition has drawn a lot of attention during last decades.
Different features and algorithms have been used for fingerprint recognition in
the past. In this paper, a powerful image representation called scattering
transform/network, is used for recognition. Scattering network is a
convolutional network where its architecture and filters are predefined wavelet
transforms. The first layer of scattering representation is similar to sift
descriptors and the higher layers capture higher frequency content of the
signal. After extraction of scattering features, their dimensionality is
reduced by applying principal component analysis (PCA). At the end, multi-class
SVM is used to perform template matching for the recognition task. The proposed
scheme is tested on a well-known fingerprint database and has shown promising
results with the best accuracy rate of 98\%.Comment: IEEE Signal Processing in Medicine and Biology Symposium, 201
A Comparative study of Arabic handwritten characters invariant feature
This paper is practically interested in the unchangeable feature of Arabic
handwritten character. It presents results of comparative study achieved on
certain features extraction techniques of handwritten character, based on Hough
transform, Fourier transform, Wavelet transform and Gabor Filter. Obtained
results show that Hough Transform and Gabor filter are insensible to the
rotation and translation, Fourier Transform is sensible to the rotation but
insensible to the translation, in contrast to Hough Transform and Gabor filter,
Wavelets Transform is sensitive to the rotation as well as to the translation
Left-invariant evolutions of wavelet transforms on the Similitude Group
Enhancement of multiple-scale elongated structures in noisy image data is
relevant for many biomedical applications but commonly used PDE-based
enhancement techniques often fail at crossings in an image. To get an overview
of how an image is composed of local multiple-scale elongated structures we
construct a multiple scale orientation score, which is a continuous wavelet
transform on the similitude group, SIM(2). Our unitary transform maps the space
of images onto a reproducing kernel space defined on SIM(2), allowing us to
robustly relate Euclidean (and scaling) invariant operators on images to
left-invariant operators on the corresponding continuous wavelet transform.
Rather than often used wavelet (soft-)thresholding techniques, we employ the
group structure in the wavelet domain to arrive at left-invariant evolutions
and flows (diffusion), for contextual crossing preserving enhancement of
multiple scale elongated structures in noisy images. We present experiments
that display benefits of our work compared to recent PDE techniques acting
directly on the images and to our previous work on left-invariant diffusions on
orientation scores defined on Euclidean motion group.Comment: 40 page
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