There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

/pdf/computer-vision-algorithms-and-applications-25dn6wu83j.pdf

Computer Vision: Algorithms and Applications

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

OBJECTIVE. In this article, we present the basic principles of diffusion-weighted imaging (DWI) that can aid radiologists in the qualitative and quantitative interpretation of DW images. However, a detailed discussion of the physics of DWI is beyond the scope of this article. A short discussion ensues on the technical aspects of performing DWI in the body. The emerging applications of DWI for tumor detection, tumor characterization, distinguishing tumor tissue from nontumor tissue, and monitoring and predicting treatment response are highlighted. The challenges to widespread adoption of the technique for cancer imaging in the body are discussed. CONCLUSION. DWI derives its image contrast from differences in the motion of water molecules between tissues. Such imaging can be performed quickly without the need for the administration of exogenous contrast medium. The technique yields qualitative and quantitative information that reflects changes at a cellular level and provides unique insights about tumor cellularity and the integrity of cell membranes. Recent advances enable the technique to be widely applied for tumor evaluation in the abdomen and pelvis and have led to the development of whole-body DWI.

https://www.ajronline.org/doi/pdf/10.2214/AJR.06.1403

Diffusion-weighted MRI in the body: applications and challenges in oncology.

In this work we propose the use of a modified version of the correlation coefficient as a performance criterion for the image alignment problem. The proposed modification has the desirable characteristic of being invariant with respect to photometric distortions. Since the resulting similarity measure is a nonlinear function of the warp parameters, we develop two iterative schemes for its maximization, one based on the forward additive approach and the second on the inverse compositional method. As it is customary in iterative optimization, in each iteration the nonlinear objective function is approximated by an alternative expression for which the corresponding optimization is simple. In our case we propose an efficient approximation that leads to a closed form solution (per iteration) which is of low computational complexity, the latter property being particularly strong in our inverse version. The proposed schemes are tested against the forward additive Lucas-Kanade and the simultaneous inverse compositional algorithm through simulations. Under noisy conditions and photometric distortions our forward version achieves more accurate alignments and exhibits faster convergence whereas our inverse version has similar performance as the simultaneous inverse compositional algorithm but at a lower computational complexity.

/pdf/parametric-image-alignment-using-enhanced-correlation-32wwqhred3.pdf

Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization

This paper describes a probabilistic generative model and its associated algorithm to jointly register multiple point sets. The vast majority of state-of-the-art registration techniques select one of the sets as the “model” and perform pairwise alignments between the other sets and this set. The main drawback of this mode of operation is that there is no guarantee that the model-set is free of noise and outliers, which contaminates the estimation of the registration parameters. Unlike previous work, the proposed method treats all the point sets on an equal footing: they are realizations of a Gaussian mixture (GMM) and the registration is cast into a clustering problem. We formally derive an EM algorithm that estimates both the GMM parameters and the rotations and translations that map each individual set onto the “central” model. The mixture means play the role of the registered set of points while the variances provide rich information about the quality of the registration. We thoroughly validate the proposed method with challenging datasets, we compare it with several state-of-the-art methods, and we show its potential for fusing real depth data.

/pdf/a-generative-model-for-the-joint-registration-of-multiple-2kbsvsvk0g.pdf

A Generative Model for the Joint Registration of Multiple Point Sets

The invariance of the similarity measure in photometric distortions as well as its capability in producing subpixel accuracy are two desired and often required features in most stereo vision applications. In this paper we propose a new correlation-based measure which incorporates both mentioned requirements. Specifically, by using an appropriate interpolation scheme in the candidate windows of the matching image, and using the classical zero mean normalized cross correlation function, we introduce a suitable measure. Although the proposed measure is a nonlinear function of the sub-pixel displacement parameter, its maximization results in a closed form solution, resulting in reduced complexity for its use in matching techniques. Application of the proposed measure in a number of benchmark stereo pair images reveals its superiority over existing correlation-based techniques used for subpixel accuracy.

/pdf/an-enhanced-correlation-based-method-for-stereo-4kxslmusg3.pdf

An enhanced correlation-based method for stereo correspondence with subpixel accuracy

The task of objective perimetry is to scan the visual field and find an answer about the function of the visual system. Flicker-burst stimulation-a physiological sensible combination of transient and steady-state stimulation-is used to generate deterministic sinusoidal responses or visually evoked potentials (VEPs) at the visual cortex, which are derived from the electroencephalogram by a suitable electrode array. Here, the authors develop a new method for the detection of VEPs. Based on the periodogram of a time-series, they test the data for the presence of hidden periodic components, which correspond to steady-state VEPs. The method is applied successfully to real data.

/pdf/a-periodogram-based-method-for-the-detection-of-steady-state-4zery3zrri.pdf

A periodogram-based method for the detection of steady-state visually evoked potentials

The authors present a method for the design of 2-D zero-phase finite-impulse-response (FIR) fan filters with quadrantal symmetry using the McClellan transform. They give conditions that the coefficients of the McClellan transform must satisfy in order to avoid the scaling of the transform. The proposed design method satisfies these conditions. The resulting cut-off isopotential of the design method is shown to have a very small relative absolute deviation from the ideal one. The method is extended to the design of 2-D zero-phase FIR fan filters of general shape. >

Emmanouil Z. Psarakis

Papers

Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization

A Generative Model for the Joint Registration of Multiple Point Sets

An enhanced correlation-based method for stereo correspondence with subpixel accuracy

A periodogram-based method for the detection of steady-state visually evoked potentials

Design of two-dimensional zero phase FIR fan filters via the McClellan transform