The field of self-assembled monolayers (SAMs) has witnessed tremendous growth in synthetic sophistication and depth of characterization over the past 15 years.1 However, it is interesting to comment on the modest beginning and on important milestones. The field really began much earlier than is now recognized. In 1946 Zisman published the preparation of a monomolecular layer by adsorption (self-assembly) of a surfactant onto a clean metal surface.2 At that time, the potential of self-assembly was not recognized, and this publication initiated only a limited level of interest. Early work initiated in Kuhn’s laboratory at Gottingen, applying many years of experience in using chlorosilane derivative to hydrophobize glass, was followed by the more recent discovery, when Nuzzo and Allara showed that SAMs of alkanethiolates on gold can be prepared by adsorption of di-n-alkyl disulfides from dilute solutions.3 Getting away from the moisture-sensitive alkyl trichlorosilanes, as well as working with crystalline gold surfaces, were two important reasons for the success of these SAMs. Many self-assembly systems have since been investigated, but monolayers of alkanethiolates on gold are probably the most studied SAMs to date. The formation of monolayers by self-assembly of surfactant molecules at surfaces is one example of the general phenomena of self-assembly. In nature, self-assembly results in supermolecular hierarchical organizations of interlocking components that provides very complex systems.4 SAMs offer unique opportunities to increase fundamental understanding of self-organization, structure-property relationships, and interfacial phenomena. The ability to tailor both head and tail groups of the constituent molecules makes SAMs excellent systems for a more fundamental understanding of phenomena affected by competing intermolecular, molecular-substrates and molecule-solvent interactions like ordering and growth, wetting, adhesion, lubrication, and corrosion. That SAMs are well-defined and accessible makes them good model systems for studies of physical chemistry and statistical physics in two dimensions, and the crossover to three dimensions. SAMs provide the needed design flexibility, both at the individual molecular and at the material levels, and offer a vehicle for investigation of specific interactions at interfaces, and of the effect of increasing molecular complexity on the structure and stability of two-dimensional assemblies. These studies may eventually produce the design capabilities needed for assemblies of three-dimensional structures.5 However, this will require studies of more complex systems and the combination of what has been learned from SAMs with macromolecular science. The exponential growth in SAM research is a demonstration of the changes chemistry as a disciAbraham Ulman was born in Haifa, Israel, in 1946. He studied chemistry in the Bar-Ilan University in Ramat-Gan, Israel, and received his B.Sc. in 1969. He received his M.Sc. in phosphorus chemistry from Bar-Ilan University in 1971. After a brief period in industry, he moved to the Weizmann Institute in Rehovot, Israel, and received his Ph.D. in 1978 for work on heterosubstituted porphyrins. He then spent two years at Northwestern University in Evanston, IL, where his main interest was onedimensional organic conductors. In 1985 he joined the Corporate Research Laboratories of Eastman Kodak Company, in Rochester, NY, where his research interests were molecular design of materials for nonlinear optics and self-assembled monolayers. In 1994 he moved to Polytechnic University where he is the Alstadt-Lord-Mark Professor of Chemistry. His interests encompass self-assembled monolayers, surface engineering, polymers at interface, and surfaces phenomena. 1533 Chem. Rev. 1996, 96, 1533−1554

/pdf/formation-and-structure-of-self-assembled-monolayers-5eorfomxfu.pdf

Formation and Structure of Self-Assembled Monolayers.

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

How to ... be a reflective practitioner.

We present a method of recovering high dynamic range radiance maps from photographs taken with conventional imaging equipment. In our method, multiple photographs of the scene are taken with different amounts of exposure. Our algorithm uses these differently exposed photographs to recover the response function of the imaging process, up to factor of scale, using the assumption of reciprocity. With the known response function, the algorithm can fuse the multiple photographs into a single, high dynamic range radiance map whose pixel values are proportional to the true radiance values in the scene. We demonstrate our method on images acquired with both photochemical and digital imaging processes. We discuss how this work is applicable in many areas of computer graphics involving digitized photographs, including image-based modeling, image compositing, and image processing. Lastly, we demonstrate a few applications of having high dynamic range radiance maps, such as synthesizing realistic motion blur and simulating the response of the human visual system.

/pdf/recovering-high-dynamic-range-radiance-maps-from-photographs-zk2yr4fcgd.pdf

Recovering high dynamic range radiance maps from photographs

We show that surface reconstruction from oriented points can be cast as a spatial Poisson problem. This Poisson formulation considers all the points at once, without resorting to heuristic spatial partitioning or blending, and is therefore highly resilient to data noise. Unlike radial basis function schemes, our Poisson approach allows a hierarchy of locally supported basis functions, and therefore the solution reduces to a well conditioned sparse linear system. We describe a spatially adaptive multiscale algorithm whose time and space complexities are proportional to the size of the reconstructed model. Experimenting with publicly available scan data, we demonstrate reconstruction of surfaces with greater detail than previously achievable.

Poisson surface reconstruction

The Ball-Pivoting Algorithm (BPA) computes a triangle mesh interpolating a given point cloud. Typically, the points are surface samples acquired with multiple range scans of an object. The principle of the BPA is very simple: Three points form a triangle if a ball of a user-specified radius p touches them without containing any other point. Starting with a seed triangle, the ball pivots around an edge (i.e., it revolves around the edge while keeping in contact with the edge's endpoints) until it touches another point, forming another triangle. The process continues until all reachable edges have been tried, and then starts from another seed triangle, until all points have been considered. The process can then be repeated with a ball of larger radius to handle uneven sampling densities. We applied the BPA to datasets of millions of points representing actual scans of complex 3D objects. The relatively small amount of memory required by the BPA, its time efficiency, and the quality of the results obtained compare favorably with existing techniques.

/pdf/the-ball-pivoting-algorithm-for-surface-reconstruction-12jv6xu280.pdf

The ball-pivoting algorithm for surface reconstruction

We present a tone reproduction operator that preserves visibility in high dynamic range scenes. Our method introduces a new histogram adjustment technique, based on the population of local adaptation luminances in a scene. To match subjective viewing experience, the method incorporates models for human contrast sensitivity, glare, spatial acuity, and color sensitivity. We compare our results to previous work and present examples of our techniques applied to lighting simulation and electronic photography.

/pdf/a-visibility-matching-tone-reproduction-operator-for-high-1ewtsx359n.pdf

A visibility matching tone reproduction operator for high dynamic range scenes

Radiosity and other global illumination methods for image synthesis calculate the real world radiance values of a scene instead of the display radiance values that will represent them. Though radiosity and ray tracing methods can compute extremely accurate and wide-ranging scene radiances, modern display devices emit light only in a tiny fixed range. The radiances must be converted, but ad-hoc conversions cause serious errors and give little assurance that the evoked visual sensations are truly equivalent. Sensation-preserving conversions for display, already known in photography, printing, and television as tone reproduction methods, are discussed. Computer graphics workers can apply the existing photographic methods, but may also extend them to include more complex and subtle effects of human vision using the published findings of vision researchers. Ways of constructing a sensation-preserving display converter, or tone reproduction operator, for monochrome images are demonstrated. >

Tone reproduction for realistic images

Three-dimensional (3D) image acquisition systems are rapidly becoming more affordable, especially systems based on commodity electronic cameras. At the same time, personal computers with graphics hardware capable of displaying complex 3D models are also becoming inexpensive enough to be available to a large population. As a result, there is potentially an opportunity to consider new virtual reality applications as diverse as cultural heritage and retail sales that will allow people to view realistic 3D objects on home computers. Although there are many physical techniques for acquiring 3D data—including laser scanners, structured light and time-of-flight—there is a basic pipeline of operations for taking the acquired data and producing a usable numerical model. We look at the fundamental problems of range image registration, line-of-sight errors, mesh integration, surface detail and color, and texture mapping. In the area of registration we consider both the problems of finding an initial global alignment using manual and automatic means, and refining this alignment with variations of the Iterative Closest Point methods. To account for scanner line-of-sight errors we compare several averaging approaches. In the area of mesh integration, that is finding a single mesh joining the data from all scans, we compare various methods for computing interpolating and approximating surfaces. We then look at various ways in which surface properties such as color (more properly, spectral reflectance) can be extracted from acquired imagery. Finally, we examine techniques for producing a final model representation that can be efficiently rendered using graphics hardware.

/pdf/the-3d-model-acquisition-pipeline-4c4s96rxbu.pdf

Holly Rushmeier

Papers

The ball-pivoting algorithm for surface reconstruction

A visibility matching tone reproduction operator for high dynamic range scenes

Tone reproduction for realistic images

The 3D Model Acquisition Pipeline

The 3D Model Acquisition Pipeline.