Automated Classification of Galaxy Images

Calleja, Jorge de la; Fuentes, Olac

doi:10.1007/978-3-540-30134-9_55

Cited by 13 publications

(12 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3) Since other celestial bodies that are not of interest can eventually appear in the image, we clean the images by removing the objects containing less than 5000 pixels, this value being experimentally determined. 4) Aiming to increase the classifier efficiency, the rotation of the image such that the galaxies are brought into horizontal positions, is recommended [2]. We included this step in our implementation.…”

Section: A Data Preprocessingmentioning

confidence: 99%

“…For instance, in [2], data preprocessing steps, the projection of data in a lower dimension space and finally, their classification are presented.…”

Section: Based On Principal Componentsmentioning

confidence: 99%

“…The data preprocessing steps in our approach are quite different from [2] and they are briefly described in the following: 1) In order to remove the background noise by absorbing it in the darker surfaces in the image, we use a mask 5 × 5 to induce a blurring effect. 2) Determine the optimal threshold using the image histogram [9].…”

Section: A Data Preprocessingmentioning

confidence: 99%

“…The classification techniques proposed in [2] are Naive Bayes classifier, the rule-induction algorithm C4.5 and random forest (RF) method. We propose a model free approach based on the principal directions identified from data, we call them skeletons, the information contained by the skeletons corresponding to different classes being used in discriminating among the classes.…”

Section: Based On Principal Componentsmentioning

confidence: 99%

See 3 more Smart Citations

PCA Approach on Morphological Classification of Galaxies

State

Constantin

Sararu

2009

2009 16th International Conference on Systems, Signals and Image Processing

View full text Add to dashboard Cite

The paper presents some results of the attempt on applying a PCA-based classifier for solving problem of morphological classification of galaxies according to the Hubble scheme.A variant of the PCA based classification method [1] is adapted for solving the problem of galaxy image clustering. The performed tests pointed out that the first 24 principal components contain enough information to assure proper resizing for galaxy classification purposes [2]. The skeletons composed from the first principal components for each class can be computed either by a first order approximation technique or, in case there are few principal components, using an exact method.The final section presents a series of conclusions and experimental results confirming the performance of the proposed algorithm, together with a comparative analysis against the perceptron algorithm.

show abstract

Section: A Data Preprocessingmentioning

confidence: 99%

“…For instance, in [2], data preprocessing steps, the projection of data in a lower dimension space and finally, their classification are presented.…”

Section: Based On Principal Componentsmentioning

confidence: 99%

Section: A Data Preprocessingmentioning

confidence: 99%

Section: Based On Principal Componentsmentioning

confidence: 99%

See 2 more Smart Citations

PCA Approach on Morphological Classification of Galaxies

State

Constantin

Sararu

2009

2009 16th International Conference on Systems, Signals and Image Processing

View full text Add to dashboard Cite

show abstract

“…The morphology of galaxies is generally an important issue in the large scale study of the Universe [1]. Galaxy classification is considered as a staring point for astronomers towards a greater understanding of the origin and formation process of galaxies, better understanding of galaxy image properties, and accordingly the evolution processes of the Universe.…”

Section: Introductionmentioning

confidence: 99%

Principal Component Analysis Neural Network Hybrid Classification Approach for Galaxies Images

Elfattah

El-Bendary

Elsoud

et al. 2014

Innovations in Bio-Inspired Computing and Applications

View full text Add to dashboard Cite

Abstract. This article presents an automatic hybrid approach for galaxies images classification based on principal component analysis (PCA) neural network and moment-based features extraction algorithms. The proposed approach is consisted of four phases; namely image denoising, feature extraction, reduct generation, and classification phases. For the denoising phase, noise pixels are removed from input images, then input galaxy image is normalized to a uniform scale and Hu seven invariant moment algorithm is applied to reduce the dimensionality of the feature space during the feature extraction phase. Subsequently, for reduct generation phase, attributes in the information system table that is more important to the knowledge is generated as a subset of attributes. Rough set is used as feature reduction approach. The subset of attributed, which is called a reduct, is fully characterizing the knowledge in the database. Finally, during the classification phase, principal component analysis neural network algorithm is utilized for classifying the input galaxies

show abstract

Bibliography

2011

Analyzing the Large Number of Variables in Biomedical and Satellite Imagery

View full text Add to dashboard Cite

Automated Classification of Galaxy Images

Cited by 13 publications

References 8 publications

PCA Approach on Morphological Classification of Galaxies

PCA Approach on Morphological Classification of Galaxies

Principal Component Analysis Neural Network Hybrid Classification Approach for Galaxies Images

Bibliography

Contact Info

Product

Resources

About