Intrinsic Image Transfer for Illumination Manipulation

This paper presents a novel intrinsic image transfer (IIT) algorithm for illumination manipulation, which creates a local image translation between two illumination surfaces. This model is built on an optimization-based framework consisting of three photo-realistic losses defined on the sub-layers factorized by an intrinsic image decomposition. We illustrate that all losses can be reduced without the necessity of taking an intrinsic image decomposition under the well-known spatial-varying illumination illumination-invariant reflectance prior knowledge. Moreover, with a series of relaxations, all of them can be directly defined on images, giving a closed-form solution for image illumination manipulation. This new paradigm differs from the prevailing Retinex-based algorithms, as it provides an implicit way to deal with the per-pixel image illumination. We finally demonstrate its versatility and benefits to the illumination-related tasks such as illumination compensation, image enhancement, and high dynamic range (HDR) image compression, and show the high-quality results on natural image datasets

Motion Segmentation Aided Super Resolution Image Reconstruction

This dissertation addresses Super Resolution (SR) Image Reconstruction focusing on motion segmentation. The main thrust is Information Complexity guided Gaussian Mixture Models (GMMs) for Statistical Background Modeling. In the process of developing our framework we also focus on two other topics; motion trajectories estimation toward global and local scene change detections and image reconstruction to have high resolution (HR) representations of the moving regions. Such a framework is used for dynamic scene understanding and recognition of individuals and threats with the help of the image sequences recorded with either stationary or non-stationary camera systems. We introduce a new technique called Information Complexity guided Statistical Background Modeling. Thus, we successfully employ GMMs, which are optimal with respect to information complexity criteria. Moving objects are segmented out through background subtraction which utilizes the computed background model. This technique produces superior results to competing background modeling strategies. The state-of-the-art SR Image Reconstruction studies combine the information from a set of unremarkably different low resolution (LR) images of static scene to construct an HR representation. The crucial challenge not handled in these studies is accumulating the corresponding information from highly displaced moving objects. In this aspect, a framework of SR Image Reconstruction of the moving objects with such high level of displacements is developed. Our assumption is that LR images are different from each other due to local motion of the objects and the global motion of the scene imposed by non-stationary imaging system. Contrary to traditional SR approaches, we employed several steps. These steps are; the suppression of the global motion, motion segmentation accompanied by background subtraction to extract moving objects, suppression of the local motion of the segmented out regions, and super-resolving accumulated information coming from moving objects rather than the whole scene. This results in a reliable offline SR Image Reconstruction tool which handles several types of dynamic scene changes, compensates the impacts of camera systems, and provides data redundancy through removing the background. The framework proved to be superior to the state-of-the-art algorithms which put no significant effort toward dynamic scene representation of non-stationary camera systems

Photonic crystal slabs for low-cost biosensors

Biosensors are devices that utilize biological recognition elements to selectively detect and analyze specific biological and chemical analyte substances. In this work a technology platform for label-free optical biosensors based on surface-functionalized photonic crystal slabs is proposed. Using this technology platform, low-cost solutions for three biotechnical questions are presented

Reconstructing Geometry from Its Latent Structures

Our world is full of objects with complex shapes and structures. Through extensive experience humans quickly develop an intuition about how objects are shaped, and what their material properties are simply by analyzing their appearance. We engage this intuitive understanding of geometry in nearly everything we do.It is not surprising then, that a careful treatment of geometry stands to give machines a powerful advantage in the many tasks of visual perception. To that end, this thesis focuses on geometry recovery in a wide range of real-world problems. First, we describe a new approach to image registration. We observe that the structure of the imaged subject becomes embedded in the image intensities. By minimizing the change in shape of these intensity structures we ensure a physically realizable deformation. We then describe a method for reassembling fragmented, thin-shelled objects from range-images of their fragments using only the geometric and photometric structure embedded in the boundary of each fragment. Third, we describe a method for recovering and representing the shape of a geometric texture (such as bark, or sandpaper) by studying the characteristic properties of texture---self similarity and scale variability. Finally, we describe two methods for recovering the 3D geometry and reflectance properties of an object from images taken under natural illumination. We note that the structure of the surrounding environment, modulated by the reflectance, becomes embedded in the appearance of the object giving strong clues about the object's shape.Though these domains are quite diverse, an essential premise---that observations of objects contain within them salient clues about the object's structure---enables new and powerful approaches. For each problem we begin by investigating what these clues are.We then derive models and methods to canonically represent these clues and enable their full exploitation. The wide-ranging success of each method shows the importance of our carefully formulated observations about geometry, and the fundamental role geometry plays in visual perception.Ph.D., Computer Science -- Drexel University, 201

OPTIMISATION OF SPECTRAL PROPERTIES OF NANOPHOTONIC STRUCTURES

The optimisation of the spectral properties of nanophotonic structures is of great importance as it enables precise control over light-matter interactions, leading to improved performance in various applications such as radiative cooling, visible colour generation, spectral filtering, photovoltaics, and sensing, imaging, and light-emitting devices. This thesis focuses on optimising the spectral properties to achieve two distinct functionalities: designing a broadband selective radiative cooler and a narrowband selective visible appearance coloured structure. Additionally, the thermal regulation capability of an aramid fabric was explored to demonstrate its potential for personal thermal management. The thesis begins with an introduction and a review of recent developments in plasmonic and dielectric-coloured structures that manipulate optically generated resonances to produce vivid colours. It initially demonstrates that spectra with a Lorentzian profile can achieve high-performance colours with high purity and a broad colour gamut. Then, a combination of a symmetry-broken structure and an index-matched anti-reflective layer, supporting high-Q resonance, was demonstrated to enhance the colour impression while suppressing the contribution of higher-order modes outside the main resonance peak. The thesis then explores nanophotonic spectral control, specifically focusing on designing a passive radiative cooler. It reviews recent progress in daytime radiative-cooling technology and identifies the constraints hindering emitter performance enhancement. To address these constraints, the thesis demonstrates the design of a broadband thermal emitter that satisfies the stringent requirements of passive radiative cooling. The design parameters are optimised using both conventional optimisation algorithm and deep-learning models. The theoretical analysis shows that the conventional method is computationally expensive and resource-intensive, requiring multiple iterations to achieve the desired response. To overcome the limitations, an auto ML-based convolutional neural network was identified as a more robust method for predicting optimal design than other networks. Finally, the thesis experimentally investigates a wearable fabric’s optical and thermal properties to assess its suitability for providing local thermal comfort. The thermal-regulation ability of the fabric was validated based on a heat-transfer model. A performance analysis shows that the fabric is suitable for use in round-the-clock thermal management, trapping body heat inside during colder nights and releasing excess heat during intense sunny days. The findings of this thesis have the potential to pave the way for further developments in the spectral control of nanophotonic structures

Color balance in LASER scanner point clouds

Color balancing is an important domain in the field of photography and imaging. Its use is necessitated because of the color inconsistencies that arise due to a number of factors before and after capturing an image. Any deviation from the original color of a scene is an irregularity which is dealt with color balancing techniques. Images may deviate from their accurate representation because of different illuminant ambient conditions, non-linear behavior of the camera sensors, the conversion of file format from a wider color gamut of raw camera format to a file format with a narrower color gamut and so on. Many approaches exist to correct the color inconsistencies. One of the basic techniques is to do a histogram equalization to increase the contrast in an image by utilizing the whole dynamic range of the brightness values. To remove color casts introduced due to false illuminant selection at the time of image capture many white balancing techniques exist. The white balancing can be employed before image capture right in the camera using hardware filters with dials to set illuminant conditions in the scene. A lot of research has been done regarding the effectiveness of white balancing after image capture. The choice of color space and the file format is quite important to consider before white balancing. Another side to color balancing is color transfer whereby the image statistics of one image are transferred to another image. Histogram matching is quite widely used to match the histogram of a source image to that of a target. Other statistics for color transfer are to match the mean and standard deviation of a source image to a target image. These two approaches for color transfer are analyzed and tested in this thesis on images displaying the same scene but with different color casts. Color transfer matching the means and standard deviations is selected because of its superior color balancing and ease of implementation. While a lot of color balancing work has been done in 2D images, no significant work is done in the 3D domain. There exist 3D scanners which scan a scene to build its 3D model. The 3D equivalent of the 2D pixel is a scan point which is obtained by reflecting a laser beam from a point in a scene. Hundreds of thousands of such points make up a single scan which displays the scene that was in the view of the 3D scanner. Because a single scan cannot capture scene behind obstructions or the scenes out of the scanners’ range, more than one scans are undertaken from different positions and time. More than one scans grouped together make up a data structure called a point cloud. Due to these changes in position and time, luminance conditions alter. As a result the scan points from different scans representing the same scene show a considerably different color cast. Color balancing by matching the means and standard deviation is applied on the point cloud. The color inconsistencies such as sharp color gradients between points of different scans, the presence of stray color streaks from one scan into another are greatly reduced. The results are quite appealing as the resulting point clouds show a smooth gradient between different scans

Engineering Data Compendium. Human Perception and Performance, Volume 1

The concept underlying the Engineering Data Compendium was the product an R and D program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design of military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by system designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is Volume 1, which contains sections on Visual Acquisition of Information, Auditory Acquisition of Information, and Acquisition of Information by Other Senses