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Studying 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, but 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, multi-sample imaging, and image processing for investigation at the macro scale. 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 quantification information not possible with conventional light or electron 2D imaging.

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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.

Traditional stereo approaches assume a lambertian scene, an assumption which is violated in the presence of specular reflections. A variety of techniques have been developed to detect and reconstruct these surfaces [1, 4] using a variety of constraints, however in this work we attempt to reconstruct a reflecting surface and a reflected scene using different imaging modalities. Using a four camera system shown in Fig. 1, operating as two stereo pairs we reconstruct a reflecting surface as well as a reflected scene. The camera system consists of a pair of visible band cameras and a pair of Long Wave InfraRed (LWIR) cameras. We leverage these different imaging modalities by taking advantage of the fact that reflectivity is wavelength dependent and reflective materials in one modality can appear non reflective in complementing modalities.We calibrate the system by extending the work of [3] to handle cross modality calibration. This gives us a common coordinate system. To reconstruct a reflected scene we first extract the reflecting surface. This is done by adding texture to the surface in only one modality, and reconstructing correspondences, and fitting a plane to these points using principle component analysis. The implicit representation of this planecan be used to intersect rays for reconstruction. Where P is the origin, n is the plane normal and d is a constant. Reconstructing the scene is accomplished by stereo matching the reflected images using uncalibrated rectification and disparity matching using [2]. Each point in the disparity map can be viewed as a ray going from the camera center through the image plane defined by,where C 0 is the camera center, andwhere A is the camera matrix, and R is the camera rotation matrix, and [x i , y i ] is the pixel location. We intersect these rays with the reflecting surface by substituting P from equation 1 with V i from equation 2, and the reflected ray direction is defined as,