A 3-D wavelet analysis of substructure in the Coma cluster: statistics and morphology

Evidence for clustering within the Coma cluster is found by means of a multiscale analysis of the combined angular-redshift distribution. We have compiled a catalogue of 798 galaxy redshifts from published surveys from the region of the Coma cluster. We examine the presence of substructure and of voids at different scales ranging from $\sim 1 to \sim 16 h^{-1}$ Mpc, using subsamples of the catalogue, ranging from $cz=3000$ km/s to $cz=28000$ km/s. Our substructure detection method is based on the wavelet transform and on the segmentation analysis. The wavelet transform allows us to find out structures at different scales and the segmentation method allows us a quantitative statistical and morphological analysis of the sample. From the whole catalogue we select a subset of 320 galaxies, with redshifts between cz=5858 km/s and cz=8168 km/s that we identify as belonging to the central region of Coma and on which we have performed a deeper analysis, on scales ranging from $180 h^{-1}$ kpc to $1.44 h^{-1}$ Mpc. Our results are expressed in terms of the number of structures or voids and their sphericity for different values of the threshold detection and at all the scales investigated. According to our analysis, there is strong evidence for multiple hierarchical substructure, on scales ranging from a few hundreds of kpc to about $4 h^{-1}$ Mpc. The morphology of these substructures is rather spherical. On the scale of $720 h^{-1}$ kpc we find two main subclusters which where also found before, but our wavelet analysis shows even more substructures, whose redshift position is approximatively marked by these bright galaxies: NGC 4934 & 4840, 4889, 4898 & 4864, 4874 & 4839, 4927, 4875.Comment: 24 pages, 6 figures. ApJ (Main Journal), accepted for publication. Added one section on statistical tests and slightly modified text and abstrac

Empirical Study of Car License Plates Recognition

The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card for a vehicle. It can map to the owner and further information about vehicle. License plate information is useful to help traffic management systems. For example, traffic management systems can check for vehicles moving at speeds not permitted by law and can also be installed in parking areas to se-cure the entrance or exit way for vehicles. License plate recognition algorithms have been proposed by many researchers. License plate recognition requires license plate detection, segmentation, and charac-ters recognition. The algorithm detects the position of a license plate and extracts the characters. Various license plate recognition algorithms have been implemented, and each algorithm has its strengths and weaknesses. In this research, I implement three algorithms for detecting license plates, three algorithms for segmenting license plates, and two algorithms for recognizing license plate characters. I evaluate each of these algorithms on the same two datasets, one from Greece and one from Thailand. For detecting li-cense plates, the best result is obtained by a Haar cascade algorithm. After the best result of license plate detection is obtained, for the segmentation part a Laplacian based method has the highest accuracy. Last, the license plate recognition experiment shows that a neural network has better accuracy than other algo-rithm. I summarize and analyze the overall performance of each method for comparison

3D medical volume segmentation using hybrid multiresolution statistical approaches

This article is available through the Brunel Open Access Publishing Fund. Copyright © 2010 S AlZu’bi and A Amira.3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations

Interactive volumetric segmentation for textile micro-tomography data using wavelets and nonlocal means

This work addresses segmentation of volumetric images of woven carbon fiber textiles from micro-tomography data. We propose a semi-supervised algorithm to classify carbon fibers that requires sparse input as opposed to completely labeled images. The main contributions are: (a) design of effective discriminative classifiers, for three-dimensional textile samples, trained on wavelet features for segmentation; (b) coupling of previous step with nonlocal means as simple, efficient alternative to the Potts model; and (c) demonstration of reuse of classifier to diverse samples containing similar content. We evaluate our work by curating test sets of voxels in the absence of a complete ground truth mask. The algorithm obtains an average 0.95 F1 score on test sets and average F1 score of 0.93 on new samples. We conclude with discussion of failure cases and propose future directions toward analysis of spatiotemporal high-resolution micro-tomography images