Rotation-invariant features for multi-oriented text detection in natural images.

Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes

A multi-scale, multi-wavelength source extraction method: getsources

We present a multi-scale, multi-wavelength source extraction algorithm called getsources. Although it has been designed primarily for use in the far-infrared surveys of Galactic star-forming regions with Herschel, the method can be applied to many other astronomical images. Instead of the traditional approach of extracting sources in the observed images, the new method analyzes fine spatial decompositions of original images across a wide range of scales and across all wavebands. It cleans those single-scale images of noise and background, and constructs wavelength-independent single-scale detection images that preserve information in both spatial and wavelength dimensions. Sources are detected in the combined detection images by following the evolution of their segmentation masks across all spatial scales. Measurements of the source properties are done in the original background-subtracted images at each wavelength; the background is estimated by interpolation under the source footprints and overlapping sources are deblended in an iterative procedure. In addition to the main catalog of sources, various catalogs and images are produced that aid scientific exploitation of the extraction results. We illustrate the performance of getsources on Herschel images by extracting sources in sub-fields of the Aquila and Rosette star-forming regions. The source extraction code and validation images with a reference extraction catalog are freely available.Comment: 31 pages, 27 figures, to be published in Astronomy & Astrophysic

Multi-scale gapped smoothing algorithm for robust baseline-free damage detection in optical infrared thermography

Flash thermography is a promising technique to perform rapid non-destructive testing of composite materials. However, it is well known that several difficulties are inherently paired with this approach, such as non-uniform heating, measurement noise and lateral heat diffusion effects. Hence, advanced signal-processing techniques are indispensable in order to analyze the recorded dataset. One such processing technique is Gapped Smoothing Algorithm, which predicts a gapped pixel’s value in its sound state from a measurement in the defected state by evaluating only its neighboring pixels. However, the standard Gapped Smoothing Algorithm uses a fixed spatial gap size, which induces issues to detect variable defect sizes in a noisy dataset. In this paper, a Multi-Scale Gapped Smoothing Algorithm (MSGSA) is introduced as a baseline-free image processing technique and an extension to the standard Gapped Smoothing Algorithm. The MSGSA makes use of the evaluation of a wide range of spatial gap sizes so that defects of highly different dimensions are identified. Moreover, it is shown that a weighted combination of all assessed spatial gap sizes significantly improves the detectability of defects and results in an (almost) zero-reference background. The technique thus effectively suppresses the measurement noise and excitation non-uniformity. The efficiency of the MSGSA technique is evaluated and confirmed through numerical simulation and an experimental procedure of flash thermography on carbon fiber reinforced polymers with various defect sizes

Hierarchical Graphical Models for Multigroup Shape Analysis using Expectation Maximization with Sampling in Kendall's Shape Space

This paper proposes a novel framework for multi-group shape analysis relying on a hierarchical graphical statistical model on shapes within a population.The framework represents individual shapes as point setsmodulo translation, rotation, and scale, following the notion in Kendall shape space.While individual shapes are derived from their group shape model, each group shape model is derived from a single population shape model. The hierarchical model follows the natural organization of population data and the top level in the hierarchy provides a common frame of reference for multigroup shape analysis, e.g. classification and hypothesis testing. Unlike typical shape-modeling approaches, the proposed model is a generative model that defines a joint distribution of object-boundary data and the shape-model variables. Furthermore, it naturally enforces optimal correspondences during the process of model fitting and thereby subsumes the so-called correspondence problem. The proposed inference scheme employs an expectation maximization (EM) algorithm that treats the individual and group shape variables as hidden random variables and integrates them out before estimating the parameters (population mean and variance and the group variances). The underpinning of the EM algorithm is the sampling of pointsets, in Kendall shape space, from their posterior distribution, for which we exploit a highly-efficient scheme based on Hamiltonian Monte Carlo simulation. Experiments in this paper use the fitted hierarchical model to perform (1) hypothesis testing for comparison between pairs of groups using permutation testing and (2) classification for image retrieval. The paper validates the proposed framework on simulated data and demonstrates results on real data.Comment: 9 pages, 7 figures, International Conference on Machine Learning 201

Large scale diffuse light in the Coma cluster: a multi-scale approach

We have obtained wide field images of the Coma cluster in the B, V, R and I bands with the CFH12K camera at CFHT. In order to search for large scale diffuse emission, we have applied to these images an iterative multi scale wavelet analysis and reconstruction technique which allowed to model all the sources (stars and galaxies) and subtract them from the original images. We found various concentrations of diffuse emission present in the central zone around the central galaxies NGC4874 and NGC4889. We characterize the positions, sizes and colors of these concentrations. Some sources do not seem to have strong star formation, while another one probably exhibits spiral-like color. One possible origin for the star forming diffuse emission sources is that in the region of the two main galaxies NGC4874 and NGC4889 spiral galaxies have recently been disrupted and star formation is still active in the dispersed material. We also use the characteristics of the sources of diffuse emission to trace the cluster dynamics. A scenario in which the group around NGC 4874 is moving north is consistent with our data.Comment: 11 pages, accepted in A&A, jpg figure