Rotation invariant descriptors for galaxy morphological classification
نویسندگان
چکیده
منابع مشابه
Rotation Invariant Local Shape Descriptors for Classification of Archaeological 3D Models
We introduce a method for estimation of rotation invariant local shape descriptors for 3D models. This method follows a successful idea commonly used to obtain rotation invariant descriptors in 2D images, and improves it by tackling the difficulty of the 3 degrees of freedom that exists in 3D models. Our method is simple, yet it achieves high levels of invariance after rotation transformations,...
متن کاملRotation invariant texture descriptors based on Gaussian Markov random fields for classification
Local Parameter Histograms (LPH) based on Gaussian Markov random fields (GMRFs) have been successfully used in effective texture discrimination. LPH features represent the normalized histograms of locally estimated GMRF parameters via local linear regression. However, these features are not rotation invariant. In this paper two techniques to design rotation invariant LPH texture descriptors are...
متن کاملSHOG - Spherical HOG Descriptors for Rotation Invariant 3D Object Detection
We present a method for densely computing local spherical histograms of oriented gradients (SHOG) in volumetric images. The descriptors are based on the continuous representation of the orientation histograms in the harmonic domain, which we compute very efficiently via spherical tensor products and the fast Fourier transformation. Building upon these local spherical histogram representations, ...
متن کاملGabor Filters for Rotation Invariant Texture Classification
A Gabor filter based feature extraction scheme for texture classification is proposed. By using a novel set of circularly symmetric filters, rotation invariance is achieved. The scheme offers a high classification performance on textures at any orientation using both fewer features and a smaller area of analysis than most existing schemes. The performance of the scheme on noisy images is also i...
متن کاملRobust rotation invariant texture classification
The importance of texture analysis and classification in image processing is well known. However, many existing texture classification schemes suffer from a number of drawbacks. A large number of features are commonly used to represent each texture and an excessively large image area is often required for the texture analysis, both leading to high computational complexity. Furthermore, most exi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Machine Learning and Cybernetics
سال: 2020
ISSN: 1868-8071,1868-808X
DOI: 10.1007/s13042-020-01075-w