NeatVision: a development environment for machine vision engineers

This Chapter will detail a free image analysis development environment for machine vision engineers. The environment provides high-level access to a wide range of image manipulation, processing and analysis algorithms (over 300 to date) through a well-defined and easy to use graphical interface. Users can extend the core library using the developer’s interface, a plug-in, which features, automatic source code generation, compilation with full error feedback and dynamic algorithm updates. The Chapter will also discuss key issues associated with the environment and outline the advantages in adopting such a system for machine vision application developmen

Controlling the Fluorescence Properties of Diarylethene-based Photochromic Systems

Diarylethene (DAE) photoswitches are one of the most promising families of photochromic molecules because of their outstanding photophysical/photochemical properties. This class of compounds, which can photoisomerize between an open colorless and a closed colored form, has been applied in various fields in this thesis work, spanning one-color fluorescence intensity modulation, all-photonic full-color reproduction, light-induced color changes for molecular logic gates and information processing. Particularly, all systems presented can be all-photonically controlled, which is extremely beneficial as light is a sustainable resource from nature that is non-invasive, clean, and waste free that also allows for remote operation.The first part of the thesis deals with introducing the light-induced isomerization process of the diarylethene derivatives. Through the isomerization of DAEs, intrinsic one-color “on-off” fluorescent intensity modulation as well as dynamic multicolor changes can be realized in the designed systems. In paper I, the diarylethene derivative Dasy is applied as a fluorescent probe aiming at phase-sensitive (lock-in) detection for high-contrast cell studies using fluorescence microscopy. The rapid switching fluorescence signal of Dasy can be successfully discriminated from strong fluorescence background using amplitude modulated red light. In paper II, a photoswitch cocktail mixture is designed where the color of the system can be tuned dynamically only by light-controlled isomerizations of the two monomer photoswitches.The second part of the thesis focuses on discussing F\uf6rster Resonance Energy Transfer (FRET) based photoswitching systems where the emission is controlled through FRET processes by harnessing the different absorption and emission properties of the open and closed isomers of the DAE derivatives. In paper III, the FRET process can be orthogonally controlled by selective isomerization of two individual DAE acceptors, which results in an all-photonic full color red-green-blue (RGB) emissive system. In paper IV, a photoswitch triad is used as a sequential molecular logic gate where the emission output can be controlled by two mechanisms, both inherent and FRET controlled intensity change

Automated Extraction of Fire Line Parameters from Multispectral Infrared Images

Remotely sensed infrared images are often used to assess wildland ¯re conditions. Separately, ¯re propagation models are in use to forecast future conditions. In the Dynamic Data Driven Application System (DDDAS) concept, the ¯re propagation model will react to the image data, which should produce more accurate predictions of ¯re propagation. In this study we describe a series of image processing tools that can be used to extract ¯re propagation parameters from multispectral infrared images so that the parameters can be used to drive a ¯re propagation model built upon the DDDAS concept. The method is capable of automatically determining the ¯re perimeter, active ¯re line, and ¯re propagation direction. A multi-band image gradient calculation, the Normalized Di®erence Vegetation Index, and the Normalized Di®erence Burn Ratio along with several standard image processing techniques are used to identify and constrain the ¯re propagation parameters. These ¯re propagation parameters can potentially be used within the DDDAS modeling framework for model update and adjustment

Linear And Nonlinear Arabesques: A Study Of Closed Chains Of Negative 2-Element Circuits

In this paper we consider a family of dynamical systems that we call "arabesques", defined as closed chains of 2-element negative circuits. An $n$-dimensional arabesque system has $n$ 2-element circuits, but in addition, it displays by construction, two $n$-element circuits which are both positive vs one positive and one negative, depending on the parity (even or odd) of the dimension $n$. In view of the absence of diagonal terms in their Jacobian matrices, all these dynamical systems are conservative and consequently, they can not possess any attractor. First, we analyze a linear variant of them which we call "arabesque 0" or for short "A0". For increasing dimensions, the trajectories are increasingly complex open tori. Next, we inserted a single cubic nonlinearity that does not affect the signs of its circuits (that we call "arabesque 1" or for short "A1"). These systems have three steady states, whatever the dimension is, in agreement with the order of the nonlinearity. All three are unstable, as there can not be any attractor in their state-space. The 3D variant (that we call for short "A1\_3D") has been analyzed in some detail and found to display a complex mixed set of quasi-periodic and chaotic trajectories. Inserting $n$ cubic nonlinearities (one per equation) in the same way as above, we generate systems "A2\_$n$D". A2\_3D behaves essentially as A1\_3D, in agreement with the fact that the signs of the circuits remain identical. A2\_4D, as well as other arabesque systems with even dimension, has two positive $n$-circuits and nine steady states. Finally, we investigate and compare the complex dynamics of this family of systems in terms of their symmetries.Comment: 22 pages, 12 figures, accepted for publication at Int. J. Bif. Chao

Accurate detection of dysmorphic nuclei using dynamic programming and supervised classification

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows

Incremental refinement of image salient-point detection

Low-level image analysis systems typically detect "points of interest", i.e., areas of natural images that contain corners or edges. Most of the robust and computationally efficient detectors proposed for this task use the autocorrelation matrix of the localized image derivatives. Although the performance of such detectors and their suitability for particular applications has been studied in relevant literature, their behavior under limited input source (image) precision or limited computational or energy resources is largely unknown. All existing frameworks assume that the input image is readily available for processing and that sufficient computational and energy resources exist for the completion of the result. Nevertheless, recent advances in incremental image sensors or compressed sensing, as well as the demand for low-complexity scene analysis in sensor networks now challenge these assumptions. In this paper, we investigate an approach to compute salient points of images incrementally, i.e., the salient point detector can operate with a coarsely quantized input image representation and successively refine the result (the derived salient points) as the image precision is successively refined by the sensor. This has the advantage that the image sensing and the salient point detection can be terminated at any input image precision (e.g., bound set by the sensory equipment or by computation, or by the salient point accuracy required by the application) and the obtained salient points under this precision are readily available. We focus on the popular detector proposed by Harris and Stephens and demonstrate how such an approach can operate when the image samples are refined in a bitwise manner, i.e., the image bitplanes are received one-by-one from the image sensor. We estimate the required energy for image sensing as well as the computation required for the salient point detection based on stochastic source modeling. The computation and energy required by the proposed incremental refinement approach is compared against the conventional salient-point detector realization that operates directly on each source precision and cannot refine the result. Our experiments demonstrate the feasibility of incremental approaches for salient point detection in various classes of natural images. In addition, a first comparison between the results obtained by the intermediate detectors is presented and a novel application for adaptive low-energy image sensing based on points of saliency is presented

Multi-Dimensional Wave Front Sensing Algorithms for Embedded Tracking and Adaptive Optics Applications

Current tracking and adaptive optics techniques cannot compensate for fast-moving extended objects, which is important for ground-based telescopes providing space situational awareness. To fill this need, a vector-projection maximum-likelihood wave-front sensing algorithm development and testing follows for this application. A derivation and simplification of the Cramer-Rao Lower Bound for wavefront sensing using a laser guide star bounds the performance of these systems and guides implementation of a vastly optimized maximum-likelihood search algorithm. A complete analysis of the bias, mean square error, and variance of the algorithm demonstrates exceptional performance of the new sensor. A proof of concept implementation shows feasibility of deployment in modern adaptive optics systems. The vector-projection maximum-likelihood sensor satisfies the need for tracking and wave-front sensing of extended objects using current adaptive optics hardware designs