Structured Light-Based 3D Reconstruction System for Plants.

Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance

Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera.

This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images

MScMS-II: an innovative IR-based indoor coordinate measuring system for large-scale metrology applications

According to the current great interest concerning large-scale metrology applications in many different fields of manufacturing industry, technologies and techniques for dimensional measurement have recently shown a substantial improvement. Ease-of-use, logistic and economic issues, as well as metrological performance are assuming a more and more important role among system requirements. This paper describes the architecture and the working principles of a novel infrared (IR) optical-based system, designed to perform low-cost and easy indoor coordinate measurements of large-size objects. The system consists of a distributed network-based layout, whose modularity allows fitting differently sized and shaped working volumes by adequately increasing the number of sensing units. Differently from existing spatially distributed metrological instruments, the remote sensor devices are intended to provide embedded data elaboration capabilities, in order to share the overall computational load. The overall system functionalities, including distributed layout configuration, network self-calibration, 3D point localization, and measurement data elaboration, are discussed. A preliminary metrological characterization of system performance, based on experimental testing, is also presente

3D correlative single-cell imaging utilizing fluorescence and refractive index tomography

Cells alter the path of light, a fact that leads to well-known aberrations in single cell or tissue imaging. Optical diffraction tomography (ODT) measures the biophysical property that causes these aberrations, the refractive index (RI). ODT is complementary to fluorescence imaging and does not require any markers. The present study introduces RI and fluorescence tomography with optofluidic rotation (RAFTOR) of suspended cells, quantifying the intracellular RI distribution and colocalizing it with fluorescence in 3D. The technique is validated with cell phantoms and used to confirm a lower nuclear RI for HL60 cells. Furthermore, the nuclear inversion of adult mouse photoreceptor cells is observed in the RI distribution. The applications shown confirm predictions of previous studies and illustrate the potential of RAFTOR to improve our understanding of cells and tissues.Comment: 15 pages, 5 figure

3D Scanning System for Automatic High-Resolution Plant Phenotyping

Thin leaves, fine stems, self-occlusion, non-rigid and slowly changing structures make plants difficult for three-dimensional (3D) scanning and reconstruction -- two critical steps in automated visual phenotyping. Many current solutions such as laser scanning, structured light, and multiview stereo can struggle to acquire usable 3D models because of limitations in scanning resolution and calibration accuracy. In response, we have developed a fast, low-cost, 3D scanning platform to image plants on a rotating stage with two tilting DSLR cameras centred on the plant. This uses new methods of camera calibration and background removal to achieve high-accuracy 3D reconstruction. We assessed the system's accuracy using a 3D visual hull reconstruction algorithm applied on 2 plastic models of dicotyledonous plants, 2 sorghum plants and 2 wheat plants across different sets of tilt angles. Scan times ranged from 3 minutes (to capture 72 images using 2 tilt angles), to 30 minutes (to capture 360 images using 10 tilt angles). The leaf lengths, widths, areas and perimeters of the plastic models were measured manually and compared to measurements from the scanning system: results were within 3-4% of each other. The 3D reconstructions obtained with the scanning system show excellent geometric agreement with all six plant specimens, even plants with thin leaves and fine stems.Comment: 8 papes, DICTA 201