Correcting show-through effects on scanned color document images by multiscale analysis

This paper describes a new approach to restoring scanned color document images where the backside image shows through the paper sheet. A new framework is presented for correcting show-through components using digital image processing techniques. First, the foreground components on the front side are separated from the background and backside components through locally adaptive binarization for each color component and edge magnitude thresholding. Background colors are estimated locally through color thresholding to generate a restored image, and then corrected adaptively through multi-scale analysis along with comparison of edge distributions between the original and the restored image. The proposed method does not require specific input devices or the backside to be input; it is able to correct unneeded image components through analysis of the front side image alone. Experimental results are given to verify effectiveness of the proposed method.     

运动捕捉及其在动画制作中的应用

提出了基于运动捕捉技术的动画制作,介绍了运动捕捉技术的过程及其3个关键技术的实现。给出了运动捕捉系统的模型和具体的实现方法。对于图像捕捉的同步问题进行了分析和解决。论述了基于运动捕捉技术的动画制作过程中的动画合成和消除滑步等技术。     

Network traffic analysis using singular value decomposition and multiscale transforms

The present work integrates the multiscale transform provided by the wavelets and singular value decomposition (SVD) for the detection of anomaly in self-similar network data. The algorithm proposed in this paper uses the properties of singular value decomposition (SVD) of a matrix whose elements are local energies of wavelet coefficients at different scales. Unlike existing techniques, our method determines both the presence (i.e., the time intervals in which anomaly occurs) and the nature of anomaly (i.e., anomaly of bursty type, long or short duration, etc.) in network data. It uses the diagonal, left and right singular matrices obtained in SVD to determine the number of scales of self-similarity, location and scales of anomaly in data, respectively. Our simulation work on different data sets demonstrates that the method performs better than the existing anomaly detection methods proposed for self-similar data.     

Unified optical flow field approach to motion analysis from a sequence of stereo images

In this paper a new approach to motion analysis from stereo image sequences using unified temporal and spatial optical flow field (UOFF) is reported. That is, based on a four-frame rectangular model and the associated six UOFF field quantities, a set of equations is derived from which both position and velocity can be determined. It does not require feature extraction and correspondence establishment, which are known to be difficult, and only partial solutions suitable for simplistic situations have been developed. Furthermore, it is capable of detecting multiple moving objects even when partial occlusion occurs, and is potentially suitable for nonrigid motion analysis. Unlike the current existing techniques for motion analysis from stereo imagery, the recovered motion by using this new approach is for a whole continuous field instead of only for some features. It is a purely optical flow approach. Two experiments are presented to demonstrate the feasibility of the approach.     

Motion blur identification in noisy images using mathematical models and statistical measures

Motion blur is one of the most common blurs that degrades images. Restoration of such images is highly dependent on estimation of motion blur parameters. Since 1976, many researchers have developed algorithms to estimate linear motion blur parameters. These algorithms are different in their performance, time complexity, precision and robustness in noisy environments. In this paper, we have presented a novel algorithm to estimate linear motion blur parameters such as direction and length. We used Radon transform to find direction and bispectrum modeling to find the length of motion. Our algorithm is based on the combination of spatial and frequency domain analysis. The great benefit of our algorithm is its robustness and precision in noisy images. We used statistical measures to prove goodness of our model. Our method was tested on 80 standard images that were degraded with different directions and motion lengths, with additive Gaussian noise. The error tolerance average of the estimated parameters was 0.9° in direction and 0.95 pixel in length and the standard deviations were 0.69 and 0.85, respectively.     

Edges,transitions and criticality

In this article, various notions of edges encountered in digital image processing are reviewed in terms of compact representation (or completion). We show that critical exponents defined in Statistical Physics lead to a much more coherent definition of edges, consistent across the scales in acquisitions of natural phenomena, such as high resolution natural images or turbulent acquisitions. Edges belong to the multiscale hierarchy of an underlying dynamics, they are understood from a statistical perspective well adapted to fit the case of natural images. Numerical computation methods for the evaluation of critical exponents in the non-ergodic case are recalled, which apply for the vast majority of natural images. We study the framework of reconstructible systems in a microcanonical formulation, show how it redefines edge completion, and how it can be used to evaluate and assess quantitatively the adequation of edges as candidates for compact representations. We study with particular attention the case of turbulent data, in which edges in the classical sense are particularly challenged. Tests are conducted and evaluated on a standard database for natural images. We test the newly introduced compact representation as an ideal candidate for evaluating turbulent cascading properties of complex images, and we show better reconstruction performance than the classical tested methods.     

Front detection on satellite images based on wavelet and evidence theory: Application to the sea breeze fronts

This paper is concerned with the detection of fronts in satellite images. We focus on some specific textured patterns of clouds that are visible on Meteosat Second Generation (MSG) images and generated at the so-called “sea breeze front”. This is the limit of the penetration of the sea breeze inland. The sea breeze circulation is a phenomenon that arises when land and sea surface temperatures reveal strong variations. This generates a landward wind that creates a cloud-free area starting from the coast line and ending at the sea breeze front. With the new geostationary meteorological sensors like MSG, this band of cloud-free area can clearly be seen. The automatic analysis of the sea breeze front with such image sensors (instead of using local measurements) is then of great importance. It has the precious advantage to extract huge amount of data and to get rid of the use of local sensors. Unfortunately, from an image processing point of view, this front appears as the limit of a very textured area. It is sometimes disturbed by clouds located in higher layers of the atmosphere. Due to this complexity, conventional detection methods issued from computer vision are not adapted. In this paper we propose an approach that automatically detects fronts in images and we apply this framework to the sea breeze fronts. The methodology is based on the well-known active contour method (or “snake”) issued from the computer vision community. The specific textures involved as well as the transparency phenomena are dealt with some properties of the wavelet decomposition of the images. This decomposition enables to compute several criteria related to the presence or not of a front that are combined using the Dempster-Shafer theory. The validation of our approach is done on synthetic and real data. It is important to outline that the presented theoretical framework is not only devoted to the detection of the sea breeze front but can also be used to detect any others textured patterns.     

Independent component analysis in the blind watermarking of digital images

We propose a new method for the blind robust watermarking of digital images based on independent component analysis (ICA). We apply ICA to compute some statistically independent transform coefficients where we embed the watermark. The main advantages of this approach are twofold. On the one hand, each user can define its own ICA-based transformation. These transformations behave as “private-keys” of the method. On the other hand, we will show that some of these transform coefficients have white noise-like spectral properties. We develop an orthogonal watermark to blindly detect it with a simple matched filter. We also address some relevant issues as the perceptual masking of the watermark and the estimation of the detection probability. Finally, some experiments have been included to illustrate the robustness of the method to common attacks and to compare its performance to other transform domain watermarking algorithms.     

A tutorial on the segmentation of metallographic images: Taxonomy,new MetalDAM dataset,deep learning-based ensemble model,experimental analysis and challenges

Image segmentation is an important issue in many industrial processes, with high potential to enhance the manufacturing process derived from raw material imaging. For example, metal phases contained in microstructures yield information on the physical properties of the steel. Existing prior literature has been devoted to develop specific computer vision techniques able to tackle a single problem involving a particular type of metallographic image. However, the field lacks a comprehensive tutorial on the different types of techniques, methodologies, their generalizations and the algorithms that can be applied in each scenario. This paper aims to fill this gap. First, the typologies of computer vision techniques to perform the segmentation of metallographic images are reviewed and categorized in a taxonomy. Second, the potential utilization of pixel similarity is discussed by introducing novel deep learning-based ensemble techniques that exploit this information. Third, a thorough comparison of the reviewed techniques is carried out in two openly available real-world datasets, one of them being a newly published dataset directly provided by ArcelorMittal, which opens up the discussion on the strengths and weaknesses of each technique and the appropriate application framework for each one. Finally, the open challenges in the topic are discussed, aiming to provide guidance in future research to cover the existing gaps.     

Joint analysis of temperature and ocean colour satellite images for mesoscale activities in the Gulf of Biscay

In the framework of the Ocean Colour European Archive Network (OCEAN) programme of the European Commission and the European Space Agency, a joint team composed of ACRI, GM-Images and Ecole des Mines de Paris proposed to analyse jointly Coastal Zone Color Scanner (CZCS) (chlorophyll) and Advanced Very High Resolution Radiometer (AVHRR) (temperature) data in order to study mesoscale dynamics features in the Gulf of Biscay. A large archive of AVHRR data, between 1978-1990, was analysed with the support of EPSHOM (Etablissement Principal du Service Hydrographique et Oceanographique de la Marine), together with about one hundred pairs of AVHRR and CZCS images. The method used to process AVHRR and CZCS data is presented in this paper. Interesting oceanographic results have been obtained. Mesoscale features such as eddies and plumes have been observed and detailed. Thestructures have been identified and classified according to an accurate typology. The appearance and evolution of mushroom-like structures have been noted, which may promote a high efficiency in the mixing of water masses. The benefit of the fusion of images is discussed. Although the locations of structures observed in both image types differ perceptibly, the increased number of observations leads to better mapping and monitoring of the features, both in space and time.