RootAnalyzer: A Cross-Section image analysis tool for automated characterization of root cells and tissues

The morphology of plant root anatomical features is a key factor in effective water and nutrient uptake. Existing techniques for phenotyping root anatomical traits are often based on manual or semi-automatic segmentation and annotation of microscopic images of root cross sections. In this article, we propose a fully automated tool, hereinafter referred to as RootAnalyzer, for efficiently extracting and analyzing anatomical traits from root-cross section images. Using a range of image processing techniques such as local thresholding and nearest neighbor identification, RootAnalyzer segments the plant root from the image’s background, classifies and characterizes the cortex, stele, endodermis and epidermis, and subsequently produces statistics about the morphological properties of the root cells and tissues. We use RootAnalyzer to analyze 15 images of wheat plants and one maize plant image and evaluate its performance against manually-obtained ground truth data. The comparison shows that RootAnalyzer can fully characterize most root tissue regions with over 90% accuracy

ITErRoot: High Throughput Segmentation of 2-Dimensional Root System Architecture

Root system architecture (RSA) analysis is a form of high-throughput plant phenotyping which has recently benefited from the application of various deep learning techniques. A typical RSA pipeline includes a segmentation step, where the root system is extracted from 2D images. The segmented image is then passed to subsequent steps for processing, which result in some representation of the architectural properties of the root system. This representation is then used for trait computation, which can be used to identify various desirable properties of a plant’s RSA. Errors which arise at the segmentation stage can propagate themselves throughout the remainder of the pipeline and impact results of trait analysis. This work aims to design an iterative neural network architecture, called ITErRoot, which is particularly well suited to the segmentation of root structure from 2D images in the presence of non-root objects. A novel 2D root image dataset is created along with a ground truth annotation tool designed to facilitate consistent manual annotation of RSA. The proposed architecture is able to take advantage of the root structure to obtain a high quality segmentation and is generalizable to root systems with thin roots, showing improved quality over recent approaches to RSA segmentation. We provide rigorous analysis designed to identify the strengths and weaknesses of the proposed model as well as to validate the effectiveness of the approach for producing high-quality segmentations

Enhanced detection of point correspondences in single-shot structured light

The crucial role of point correspondences in the process of stereo vision and camera projector calibration is to determine the relationship between the camera view(s) and the projector view(s). Consequently, acquiring accurate and robust point correspondences can result in a very accurate 3D point cloud of a scene. Designing a method that can detect pixel correspondences quickly and accurately and be robust to factors such as object motions and color is an important subject of study. The information that lies in the point correspondences determines the geometry of the scene in which depth plays a very important role, if not the most important. However, point correspondences will include some outliers. Outlier removal is another important aspect of obtaining correspondences that can have substantial impact on the reconstructed point cloud of an object. During the Single-Shot Structured Light (SSSL) calibration process, a pattern consisting of tags with differently shaped symbols inside and separated by grids are projected onto the object. The intersections of these grid lines are considered to be potential pixel correspondences between a camera image and the projector pattern. The purpose of this thesis is to study the robustness and accuracy of pixel correspondences and to enhance their quality. In this thesis we propose a detection method that uses the model of the pattern, specifically the grid lines, which are the largest and brightest feature of the pattern. The input image is partitioned into smaller patches and then the optimization process is executed on each patch. Eventually, the grid lines will be detected and fitted to the grid, and the intersections of those lines are taken as potential corresponding pixels between the views. In order to remove incorrect pixel correspondences, or in other words, outliers, Connected Component Analysis is used to find the closest detected point to the top left corner of each tag. The points remaining after this step are the correct pixel correspondences. Experimental results show the improvement of using a locally adaptive thresholding method against the baseline in detecting tags. The proposed thresholding method showed a maintained accuracy compared to the baseline method while automatically tune all the parameters whereas in the baseline method some parameters need fine tuning. Introduced model-based grid intersection detection yields an approximately 50 times improvement in speed. Inaccuracy in point correspondences are compared with state-of-the-art method based on the generated final reconstructed point clouds using both methods against the CAD model as ground truth. Results show an average of 3 pixels higher error in distance, between the reconstructed point clouds and the CAD model, in the proposed method compared to the baseline