Surface Reflectance Estimation and Natural Illumination Statistics

Humans recognize optical reflectance properties of surfaces such as metal, plastic, or paper from a single image without knowledge of illumination. We develop a machine vision system to perform similar recognition tasks automatically. Reflectance estimation under unknown, arbitrary illumination proves highly underconstrained due to the variety of potential illumination distributions and surface reflectance properties. We have found that the spatial structure of real-world illumination possesses some of the statistical regularities observed in the natural image statistics literature. A human or computer vision system may be able to exploit this prior information to determine the most likely surface reflectance given an observed image. We develop an algorithm for reflectance classification under unknown real-world illumination, which learns relationships between surface reflectance and certain features (statistics) computed from a single observed image. We also develop an automatic feature selection method.

How do Humans Determine Reflectance Properties under Unknown Illumination?

Under normal viewing conditions, humans find it easy to distinguish between objects made out of different materials such as plastic, metal, or paper. Untextured materials such as these have different surface reflectance properties, including lightness and gloss. With single isolated images and unknown illumination conditions, the task of estimating surface reflectance is highly underconstrained, because many combinations of reflection and illumination are consistent with a given image. In order to work out how humans estimate surface reflectance properties, we asked subjects to match the appearance of isolated spheres taken out of their original contexts. We found that subjects were able to perform the task accurately and reliably without contextual information to specify the illumination. The spheres were rendered under a variety of artificial illuminations, such as a single point light source, and a number of photographically-captured real-world illuminations from both indoor and outdoor scenes. Subjects performed more accurately for stimuli viewed under real-world patterns of illumination than under artificial illuminations, suggesting that subjects use stored assumptions about the regularities of real-world illuminations to solve the ill-posed problem.

Recognition of Surface Reflectance Properties from a Single Image under Unknown Real-World Illumination

This paper describes a machine vision system that classifies reflectance properties of surfaces such as metal, plastic, or paper, under unknown real-world illumination. We demonstrate performance of our algorithm for surfaces of arbitrary geometry. Reflectance estimation under arbitrary omnidirectional illumination proves highly underconstrained. Our reflectance estimation algorithm succeeds by learning relationships between surface reflectance and certain statistics computed from an observed image, which depend on statistical regularities in the spatial structure of real-world illumination. Although the algorithm assumes known geometry, its statistical nature makes it robust to inaccurate geometry estimates.

Surface Reflectance Recognition and Real-World Illumination Statistics

Humans distinguish materials such as metal, plastic, and paper effortlessly at a glance. Traditional computer vision systems cannot solve this problem at all. Recognizing surface reflectance properties from a single photograph is difficult because the observed image depends heavily on the amount of light incident from every direction. A mirrored sphere, for example, produces a different image in every environment. To make matters worse, two surfaces with different reflectance properties could produce identical images. The mirrored sphere simply reflects its surroundings, so in the right artificial setting, it could mimic the appearance of a matte ping-pong ball. Yet, humans possess an intuitive sense of what materials typically "look like" in the real world. This thesis develops computational algorithms with a similar ability to recognize reflectance properties from photographs under unknown, real-world illumination conditions. Real-world illumination is complex, with light typically incident on a surface from every direction. We find, however, that real-world illumination patterns are not arbitrary. They exhibit highly predictable spatial structure, which we describe largely in the wavelet domain. Although they differ in several respects from the typical photographs, illumination patterns share much of the regularity described in the natural image statistics literature. These properties of real-world illumination lead to predictable image statistics for a surface with given reflectance properties. We construct a system that classifies a surface according to its reflectance from a single photograph under unknown illuminination. Our algorithm learns relationships between surface reflectance and certain statistics computed from the observed image. Like the human visual system, we solve the otherwise underconstrained inverse problem of reflectance estimation by taking advantage of the statistical regularity of illumination. For surfaces with homogeneous reflectance properties and known geometry, our system rivals human performance.

Fluorescence Assay for Polymerase Arrival Rates

To engineer complex synthetic biological systems will require modular design, assembly, and characterization strategies. The RNA polymerase arrival rate (PAR) is defined to be the rate that RNA polymerases arrive at a specified location on the DNA. Designing and characterizing biological modules in terms of RNA polymerase arrival rates provides for many advantages in the construction and modeling of biological systems. PARMESAN is an in vitro method for measuring polymerase arrival rates using pyrrolo-dC, a fluorescent DNA base that can substitute for cytosine. Pyrrolo-dC shows a detectable fluorescence difference when in single-stranded versus double-stranded DNA. During transcription, RNA polymerase separates the two strands of DNA, leading to a change in the fluorescence of pyrrolo-dC. By incorporating pyrrolo-dC at specific locations in the DNA, fluorescence changes can be taken as a direct measurement of the polymerase arrival rate.

Perceiving Illumination Inconsistencies in Scenes

The human visual system is adept at detecting and encoding statistical regularities in its spatio-temporal environment. Here we report an unexpected failure of this ability in the context of perceiving inconsistencies in illumination distributions across a scene. Contrary to predictions from previous studies [Enns and Rensink, 1990; Sun and Perona, 1996a, 1996b, 1997], we find that the visual system displays a remarkable lack of sensitivity to illumination inconsistencies, both in experimental stimuli and in images of real scenes. Our results allow us to draw inferences regarding how the visual system encodes illumination distributions across scenes. Specifically, they suggest that the visual system does not verify the global consistency of locally derived estimates of illumination direction.

Synthesis, Optical properties and Applications of Water Soluble Conjugated PPPs for Biosensors

In recent years, application of fluorescent conjugated polymers to sense chemical and biological analytes has received much attention owing to its technological significance. Water soluble conjugated polymers are interesting towards the developing sensors for biomolecules. In this present contribution, we describe the syntheses and characterization of a series of water soluble conjugated polymers with sulfonic acid groups in the side chain. Such anionic conjugated polymers are designed to interact with biomolecules such as cytochrome-C. All polymers are water soluble and showed strong blue emission. Significant quenching of the fluorescence from our functionalized PPP was observed upon addition of viologen derivatives or cytochrome -C.

Targeted Stimuli-Responsive Dextran Conjugates for Doxorubicin Delivery to Hepatocytes

A targeted, stimuli-responsive, polymeric drug delivery vehicle is being developed in our lab to help alleviate severe side-effects caused by narrow therapeutic window drugs. Targeting specific cell types or organs via proteins, specifically, lectin-mediated targeting holds potential due to the high specificity and affinity of receptor-ligand interactions, rapid internalization, and relative ease of processing. Dextran, a commercially available, biodegradable polymer has been conjugated to doxorubicin and galactosamine to target hepatocytes in a three-step, one-pot synthesis. The loading of doxorubicin and galactose on the conjugates was determined by absorbance at 485 nm and elemental analysis, respectively. Conjugation efficiency based on the amount loaded of each reactant varies from 20% to 50% for doxorubicin and from 2% to 20% for galactosamine. Doxorubicin has also been attached to dextran through an acid-labile hydrazide bond. Doxorubicin acts by intercalating with DNA in the nuclei of cells. The fluorescence of doxorubicin is quenched when it binds to DNA. This allows a fluorescence-based cell-free assay to evaluate the efficacy of the polymer conjugates where we measure the fluorescence of doxorubicin and the conjugates in increasing concentrations of calf thymus DNA. Fluorescence quenching indicates that our conjugates can bind to DNA. The degree of binding increases with polymer molecular weight and substitution of doxorubicin. In cell culture experiments with hepatocytes, the relative uptake of polymer conjugates was evaluated using flow cytometry, and the killing efficiency was determined using the MTT cell proliferation assay. We have found that conjugate uptake is much lower than that of free doxorubicin. Lower uptake of conjugates may increase the maximum dose of drug tolerated by the body. Also, non-galactosylated conjugate uptake is lower than that of the galactosylated conjugate. Microscopy indicates that doxorubicin localizes almost exclusively at the nucleus, whereas the conjugates are present throughout the cell. Doxorubicin linked to dextran through a hydrazide bond was used to achieve improved killing efficiency. Following uptake, the doxorubicin dissociates from the polymer in an endosomal compartment and diffuses to the nucleus. The LC₅₀ of covalently linked doxorubicin is 7.4 μg/mL, whereas that of hydrazide linked doxorubicin is 4.4 μg/mL.