Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy.

We have developed a digital holographic microscope (DHM), in a transmission mode, especially dedicated to the quantitative visualization of phase objects such as living cells. The method is based on an original numerical algorithm presented in detail elsewhere [Cuche et al., Appl. Opt. 38, 6994 (1999)]. DHM images of living cells in culture are shown for what is to our knowledge the first time. They represent the distribution of the optical path length over the cell, which has been measured with subwavelength accuracy. These DHM images are compared with those obtained by use of the widely used phase contrast and Nomarski differential interference contrast techniques.

Stress intensity factor of wood from crack-tip displacement fields obtained from digital image processing

Abstract

Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky Images

A discussion is presented of daytime sky imaging and techniques that may be applied to the analysis of full-color sky images to infer cloud macrophysical properties. Descriptions of two different types of skyimaging systems developed by the authors are presented, one of which has been developed into a commercially available instrument. Retrievals of fractional sky cover from automated processing methods are compared to human retrievals, both from direct observations and visual analyses of sky images. Although some uncertainty exists in fractional sky cover retrievals from sky images, this uncertainty is no greater than that attached to human observations for the commercially available sky-imager retrievals. Thus, the application of automatic digital image processing techniques on sky images is a useful method to complement, or even replace, traditional human observations of sky cover and, potentially, cloud type. Additionally, the possibilities for inferring other cloud parameters such as cloud brokenness and solar obstruction further enhance the usefulness of sky imagers

Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images

In this paper, an advanced technique for the generation of deformation maps using synthetic aperture radar (SAR) data is presented. The algorithm estimates the linear and nonlinear components of the displacement, the error of the digital elevation model (DEM) used to cancel the topographic terms, and the atmospheric artifacts from a reduced set of low spatial resolution interferograms. The pixel candidates are selected from those presenting a good coherence level in the whole set of interferograms and the resulting nonuniform mesh tessellated with the Delauney triangulation to establish connections among them. The linear component of movement and DEM error are estimated adjusting a linear model to the data only on the connections. Later on, this information, once unwrapped to retrieve the absolute values, is used to calculate the nonlinear component of movement and atmospheric artifacts with alternate filtering techniques in both the temporal and spatial domains. The method presents high flexibility with respect to the required number of images and the baselines length. However, better results are obtained with large datasets of short baseline interferograms. The technique has been tested with European Remote Sensing SAR data from an area of Catalonia (Spain) and validated with on-field precise leveling measurements.

Statistical reference criteria for adaptive signal processing in digital communications

A general criterion for the design of adaptive systemsin digital communications called the statistical reference criterionis proposed. The criterion is based on imposition of the probabilitydensity function of the signal of interest at the outputof the adaptive system, with its application to the scenario ofhighly powerful interferers being the main focus of this paper.The knowledge of the pdf of the wanted signal is used as adiscriminator between signals so that interferers with differingdistributions are rejected by the algorithm. Its performance isstudied over a range of scenarios. Equations for gradient-basedcoefficient updates are derived, and the relationship with otherexisting algorithms like the minimum variance and the Wienercriterion are examined.

Embedding, Extraction and Detection of Digital Watermark in Spectral Images

Tässä diplomityössä tutkitaan tekniikoita, joillavesileima lisätään spektrikuvaan, ja menetelmiä, joilla vesileimat tunnistetaanja havaitaan spektrikuvista. PCA (Principal Component Analysis) -algoritmia käyttäen alkuperäisten kuvien spektriulottuvuutta vähennettiin. Vesileiman lisääminen spektrikuvaan suoritettiin muunnosavaruudessa. Ehdotetun mallin mukaisesti muunnosavaruuden komponentti korvattiin vesileiman ja toisen muunnosavaruuden komponentin lineaarikombinaatiolla. Lisäyksessä käytettävää parametrijoukkoa tutkittiin. Vesileimattujen kuvien laatu mitattiin ja analysoitiin. Suositukset vesileiman lisäykseen esitettiin. Useita menetelmiä käytettiin vesileimojen tunnistamiseen ja tunnistamisen tulokset analysoitiin. Vesileimojen kyky sietää erilaisia hyökkäyksiä tarkistettiin. Diplomityössä suoritettiin joukko havaitsemis-kokeita ottamalla huomioon vesileiman lisäyksessä käytetyt parametrit. ICA (Independent Component Analysis) -menetelmää pidetään yhtenä mahdollisena vaihtoehtona vesileiman havaitsemisessa.

Brain surface non-rigid registration in auditory processing

Résumé: Le développement rapide de nouvelles technologies comme l'imagerie médicale a permis l'expansion des études sur les fonctions cérébrales. Le rôle principal des études fonctionnelles cérébrales est de comparer l'activation neuronale entre différents individus. Dans ce contexte, la variabilité anatomique de la taille et de la forme du cerveau pose un problème majeur. Les méthodes actuelles permettent les comparaisons interindividuelles par la normalisation des cerveaux en utilisant un cerveau standard. Les cerveaux standards les plus utilisés actuellement sont le cerveau de Talairach et le cerveau de l'Institut Neurologique de Montréal (MNI) (SPM99). Les méthodes de recalage qui utilisent le cerveau de Talairach, ou celui de MNI, ne sont pas suffisamment précises pour superposer les parties plus variables d'un cortex cérébral (p.ex., le néocortex ou la zone perisylvienne), ainsi que les régions qui ont une asymétrie très importante entre les deux hémisphères. Le but de ce projet est d'évaluer une nouvelle technique de traitement d'images basée sur le recalage non-rigide et utilisant les repères anatomiques. Tout d'abord, nous devons identifier et extraire les structures anatomiques (les repères anatomiques) dans le cerveau à déformer et celui de référence. La correspondance entre ces deux jeux de repères nous permet de déterminer en 3D la déformation appropriée. Pour les repères anatomiques, nous utilisons six points de contrôle qui sont situés : un sur le gyrus de Heschl, un sur la zone motrice de la main et le dernier sur la fissure sylvienne, bilatéralement. Evaluation de notre programme de recalage est accomplie sur les images d'IRM et d'IRMf de neuf sujets parmi dix-huit qui ont participés dans une étude précédente de Maeder et al. Le résultat sur les images anatomiques, IRM, montre le déplacement des repères anatomiques du cerveau à déformer à la position des repères anatomiques de cerveau de référence. La distance du cerveau à déformer par rapport au cerveau de référence diminue après le recalage. Le recalage des images fonctionnelles, IRMf, ne montre pas de variation significative. Le petit nombre de repères, six points de contrôle, n'est pas suffisant pour produire les modifications des cartes statistiques. Cette thèse ouvre la voie à une nouvelle technique de recalage du cortex cérébral dont la direction principale est le recalage de plusieurs points représentant un sillon cérébral. Abstract : The fast development of new technologies such as digital medical imaging brought to the expansion of brain functional studies. One of the methodolgical key issue in brain functional studies is to compare neuronal activation between individuals. In this context, the great variability of brain size and shape is a major problem. Current methods allow inter-individual comparisions by means of normalisation of subjects' brains in relation to a standard brain. A largerly used standard brains are the proportional grid of Talairach and Tournoux and the Montreal Neurological Insititute standard brain (SPM99). However, there is a lack of more precise methods for the superposition of more variable portions of the cerebral cortex (e.g, neocrotex and perisyvlian zone) and in brain regions highly asymmetric between the two cerebral hemipsheres (e.g. planum termporale). The aim of this thesis is to evaluate a new image processing technique based on non-linear model-based registration. Contrary to the intensity-based, model-based registration uses spatial and not intensitiy information to fit one image to another. We extract identifiable anatomical features (point landmarks) in both deforming and target images and by their correspondence we determine the appropriate deformation in 3D. As landmarks, we use six control points that are situated: one on the Heschl'y Gyrus, one on the motor hand area, and one on the sylvian fissure, bilaterally. The evaluation of this model-based approach is performed on MRI and fMRI images of nine of eighteen subjects participating in the Maeder et al. study. Results on anatomical, i.e. MRI, images, show the mouvement of the deforming brain control points to the location of the reference brain control points. The distance of the deforming brain to the reference brain is smallest after the registration compared to the distance before the registration. Registration of functional images, i.e fMRI, doesn't show a significant variation. The small number of registration landmarks, i.e. six, is obvious not sufficient to produce significant modification on the fMRI statistical maps. This thesis opens the way to a new computation technique for cortex registration in which the main directions will be improvement of the registation algorithm, using not only one point as landmark, but many points, representing one particular sulcus.

Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine

Psychophysical studies suggest that humans preferentially use a narrow band of low spatial frequencies for face recognition. Here we asked whether artificial face recognition systems have an improved recognition performance at the same spatial frequencies as humans. To this end, we estimated recognition performance over a large database of face images by computing three discriminability measures: Fisher Linear Discriminant Analysis, Non-Parametric Discriminant Analysis, and Mutual Information. In order to address frequency dependence, discriminabilities were measured as a function of (filtered) image size. All three measures revealed a maximum at the same image sizes, where the spatial frequency content corresponds to the psychophysical found frequencies. Our results therefore support the notion that the critical band of spatial frequencies for face recognition in humans and machines follows from inherent properties of face images, and that the use of these frequencies is associated with optimal face recognition performance.

Computational visual quality of digitally printed images

The problem of understanding how humans perceive the quality of a reproduced image is of interest to researchers of many fields related to vision science and engineering: optics and material physics, image processing (compression and transfer), printing and media technology, and psychology. A measure for visual quality cannot be defined without ambiguity because it is ultimately the subjective opinion of an “end-user” observing the product. The purpose of this thesis is to devise computational methods to estimate the overall visual quality of prints, i.e. a numerical value that combines all the relevant attributes of the perceived image quality. The problem is limited to consider the perceived quality of printed photographs from the viewpoint of a consumer, and moreover, the study focuses only on digital printing methods, such as inkjet and electrophotography. The main contributions of this thesis are two novel methods to estimate the overall visual quality of prints. In the first method, the quality is computed as a visible difference between the reproduced image and the original digital (reference) image, which is assumed to have an ideal quality. The second method utilises instrumental print quality measures, such as colour densities, measured from printed technical test fields, and connects the instrumental measures to the overall quality via subjective attributes, i.e. attributes that directly contribute to the perceived quality, using a Bayesian network. Both approaches were evaluated and verified with real data, and shown to predict well the subjective evaluation results.

A Technique for Digital Color Image Watermarking Using ICA

With the increase of use of digital media the need for the methods of multimedia protection becomes extremely important. The number of the solutions to the problem from encryption to watermarking is large and is growing every year. In this work digital image watermarking is considered, specifically a novel method of digital watermarking of color and spectral images. An overview of existing methods watermarking of color and grayscale images is given in the paper. Methods using independent component analysis (ICA) for detection and the ones using discrete wavelet transform (DWT) and discrete cosine transform (DCT) are considered in more detail. A novel method of watermarking proposed in this paper allows embedding of a color or spectral watermark image into color or spectral image consequently and successful extraction of the watermark out of the resultant watermarked image. A number of experiments have been performed on the quality of extraction depending on the parameters of the embedding procedure. Another set of experiments included the test of the robustness of the algorithm proposed. Three techniques have been chosen for that purpose: median filter, low-pass filter (LPF) and discrete cosine transform (DCT), which are a part of a widely known StirMark - Image Watermarking Robustness Test. The study shows that the proposed watermarking technique is fragile, i.e. watermark is altered by simple image processing operations. Moreover, we have found that the contents of the image to be watermarked do not affect the quality of the extraction. Mixing coefficients, that determine the amount of the key and watermark image in the result, should not exceed 1% of the original. The algorithm proposed has proven to be successful in the task of watermark embedding and extraction.

Local Features in Image and Video Processing - Object Class Matching and Video Shot Detection

The usage of digital content, such as video clips and images, has increased dramatically during the last decade. Local image features have been applied increasingly in various image and video retrieval applications. This thesis evaluates local features and applies them to image and video processing tasks. The results of the study show that 1) the performance of different local feature detector and descriptor methods vary significantly in object class matching, 2) local features can be applied in image alignment with superior results against the state-of-the-art, 3) the local feature based shot boundary detection method produces promising results, and 4) the local feature based hierarchical video summarization method shows promising new new research direction. In conclusion, this thesis presents the local features as a powerful tool in many applications and the imminent future work should concentrate on improving the quality of the local features.

Neural Network based studies on Spectroscopic Analysis and Image Processing

International School of Photonics, Cochin University of Science and Technology

Development of Shape Descriptors Based on Legendre Polynomials and Content-Based Retrieval of Scoliosis Images

The wealth of information available freely on the web and medical image databases poses a major problem for the end users: how to find the information needed? Content –Based Image Retrieval is the obvious solution.A standard called MPEG-7 was evolved to address the interoperability issues of content-based search.The work presented in this thesis mainly concentrates on developing new shape descriptors and a framework for content – based retrieval of scoliosis images.New region-based and contour based shape descriptor is developed based on orthogonal Legendre polymomials.A novel system for indexing and retrieval of digital spine radiographs with scoliosis is presented.

classification and content based retrieval of digital mammograms and placental sonograms

Image processing has been a challenging and multidisciplinary research area since decades with continuing improvements in its various branches especially Medical Imaging. The healthcare industry was very much benefited with the advances in Image Processing techniques for the efficient management of large volumes of clinical data. The popularity and growth of Image Processing field attracts researchers from many disciplines including Computer Science and Medical Science due to its applicability to the real world. In the meantime, Computer Science is becoming an important driving force for the further development of Medical Sciences. The objective of this study is to make use of the basic concepts in Medical Image Processing and develop methods and tools for clinicians’ assistance. This work is motivated from clinical applications of digital mammograms and placental sonograms, and uses real medical images for proposing a method intended to assist radiologists in the diagnostic process. The study consists of two domains of Pattern recognition, Classification and Content Based Retrieval. Mammogram images of breast cancer patients and placental images are used for this study. Cancer is a disaster to human race. The accuracy in characterizing images using simplified user friendly Computer Aided Diagnosis techniques helps radiologists in detecting cancers at an early stage. Breast cancer which accounts for the major cause of cancer death in women can be fully cured if detected at an early stage. Studies relating to placental characteristics and abnormalities are important in foetal monitoring. The diagnostic variability in sonographic examination of placenta can be overlooked by detailed placental texture analysis by focusing on placental grading. The work aims on early breast cancer detection and placental maturity analysis. This dissertation is a stepping stone in combing various application domains of healthcare and technology.