Abstract: WND-CHARM quantitatively analyzes morphologies of galaxy mergers and associate galaxies by their morphology. It computes a large set (up to ~2700) of image features for each image based on the WND-CHARM algorithm. It can then split the images into training and test sets and classify them. The software extracts the image content descriptor from raw images, image transforms, and compound image transforms. The most informative features are then selected, and the feature vector of each image is used for classification and similarity measurement using Fisher discriminant scores and a variation of Weighted Nearest Neighbor analysis. WND-CHARM's results comparable favorably to the performance of task-specific algorithms developed for tested datasets. The simple user interface allows researchers who are not knowledgeable in computer vision methods and have no background in computer programming to apply image analysis to their data.
Credit: Lior Shamir, Nikita Orlov, D. Mark Eckley, Tomasz Macura, Josiah Johnston, Ilya G. Goldberg