The study of phenotypic variation in plant pathogenesis provides fundamental information about the nature of disease resistance. Cellular mechanisms that alter pathogenesis can be elucidated with confocal microscopy; however, systematic phenotyping platforms-from sample processing to image analysis-to investigate this do not exist. We have developed a platform for 3D phenotyping of cellular features underlying variation in disease development by fluorescence-specific resolution of host and pathogen interactions across time (4D). A confocal microscopy phenotyping platform compatible with different maize-fungal pathosystems (fungi: Setosphaeria turcica, Cochliobolus heterostrophus, and Cercospora zeae-maydis) was developed. Protocols and techniques were standardized for sample fixation, optical clearing, species-specific combinatorial fluorescence staining, multisample imaging, and image processing for investigation at the macroscale. The sample preparation methods presented here overcome challenges to fluorescence imaging such as specimen thickness and topography as well as physiological characteristics of the samples such as tissue autofluorescence and presence of cuticle. The resulting imaging techniques provide interesting qualitative and quantitative information not possible with conventional light or electron 2D imaging. Microsc. Res. Tech., 81:141-152, 2018. © 2016 Wiley Periodicals, Inc.

Automating the extraction and quantification of features from three-dimensional (3-D) image stacks is a critical task for advancing many applications, such as the analysis of biological structures. The quantification of biological resistance of a plant tissue to fungal infection through the analysis of attributes such as fungal penetration depth, fungal mass, and branching of the fungal network at large scales requires the union of 3D image acquisition and analysis. From an image processing perspective, these tasks reduce to segmentation of vessel-like structures and the extraction of features from their skeletonization. In order to sample multiple infection events for analysis, we have developed an approach we refer to as macroscopic microscopy. However, macroscopic microscopy produces high-resolution image stacks that pose challenges to routine approaches and are difficult for a human to annotate to obtain ground truth data. We present a synthetic hyphal network generator, a comparison of several vessel segmentation methods, and a minimum spanning tree method for connecting small gaps resulting from imperfections in imaging or incomplete skeletonization of hyphal networks. Qualitative results are shown for real microscopic data. We believe the comparison of vessel detectors on macroscopic microscopy data, the synthetic vessel generator, and the gap closing technique are beneficial to the image processing community.

Reflective and specular surfaces are problematic for traditional reconstruction techniques. Light projects non-linearly in scenes with these surfaces, and existing techniques to model this are poorly suited for real world applications. Accurately modeling the reflective surface is difficult without complete knowledge of the scene. To overcome this problem, we propose using different modalities of stereo vision to capture both the reflecting surface and the reflected scene. Using a four camera system consisting of a pair of visible wavelength cameras and a pair of long wave infrared cameras, we accurately reconstruct the reflective surface and ray trace reflected correspondences in the complementary modality. This approach allows for 3D reconstruction in the presence of a reflection, and does not require complete knowledge of the scene.

Calibration of stereo cameras is important for accurate 3D reconstruction. For standard color cameras there are many available tools and algorithms for accurate calibration, such as detecting corners of chessboard patterns on planar calibration boards. When viewed in thermal imagery, these chessboard patterns are difficult to detect due to uniform temperature between the white and black squares. Previous techniques involve creating a custom calibration board using multiple materials. We propose improvements to a method that does not require a custom calibration board. Our method is made more reliable by using an iterative pre-processing technique to enhance contrast and a ceramic tile backing to retain heat longer. We present results which show our calibration board retains heat to reliably detect corners for over 10 minutes; our method performs well in real calibration trials.

Techniques based on the well-studied approaches of structure from motion and bundle adjustment are very robust for scenes with texture. In scenes with little texture information these approaches can fail. Shape from shading determines the shape of an object up to a scale from a single image, and performs better than structure from motion methods in textureless regions. We propose using Gradient Constrained Interpolation to estimate a dense point cloud where holes are caused by regions of low texture during structure from motion reconstruction. Our technique is demonstrated to show good results in both synthetic and real data and outperforms methods which do not use image information.

Textureless regions, though error prone in stereo, may contain shading information that may be exploited. Shape from shading (SFS) results relate to world coordinates by arbitrary scaling factors which are difficult to estimate. We propose a method for estimating dense disparities from sparse correspondences using SFS cues. We show that SFS can impose constraints on the gradient of disparity in textureless regions with constant albedo. Gradient Constrained Interpolation (GCI), which solves the estimation problem in one dimension, is presented. We efficiently generate paths between correspondences that cover the image and then use GCI to fill the pixels in between. Results are presented on real and synthetic images, and provide quantitative evaluations to show that the method outperforms baseline methods.

With image capturing technology growing ubiquitous in consumer products and scientific studies, there is a corresponding growth in the applications that utilize scene structure for deriving information. This trend has also been reflected in the plethora of recent studies on reconstruction using robust structure from motion, bundle adjustment, and related techniques. Most of these studies, however, have concentrated on unstructured collections of images. In this paper, we propose a feature tracking and reconstruction framework for structured image collections using heterogenous features. This is motivated by the observation that images contain a small number of features that are fast/easy to track and a large number of features that are difficult/slow to track. By tracking these separately, we show that we can not only improve the tracking speed, but also improve the tracking accuracy by using a camera geometry based descriptor. We demonstrate this on a new challenging dataset which contains images of Arctic sea ice. The reconstruction pipeline constructed using the proposed method provides near real time reconstruction of the scene, enabling the user to parse vast amounts of data rapidly. Quantitative comparisons with baseline SFM techniques show that reconstruction accuracy does not suffer.