A Low-cost Depth Imaging Mobile Platform for Canola Phenotyping

To meet the high demand for supporting and accelerating progress in the breeding of novel traits, plant scientists and breeders have to measure a large number of plants and their characteristics accurately. A variety of imaging methodologies are being deployed to acquire data for quantitative studies of complex traits. When applied to a large number of plants such as canola plants, however, a complete three-dimensional (3D) model is time-consuming and expensive for high-throughput phenotyping with an enormous amount of data. In some contexts, a full rebuild of entire plants may not be necessary. In recent years, many 3D plan phenotyping techniques with high cost and large-scale facilities have been introduced to extract plant phenotypic traits, but these applications may be affected by limited research budgets and cross environments. This thesis proposed a low-cost depth and high-throughput phenotyping mobile platform to measure canola plant traits in cross environments. Methods included detecting and counting canola branches and seedpods, monitoring canola growth stages, and fusing color images to improve images resolution and achieve higher accuracy. Canola plant traits were examined in both controlled environment and field scenarios. These methodologies were enhanced by different imaging techniques. Results revealed that this phenotyping mobile platform can be used to investigate canola plant traits in cross environments with high accuracy. The results also show that algorithms for counting canola branches and seedpods enable crop researchers to analyze the relationship between canola genotypes and phenotypes and estimate crop yields. In addition to counting algorithms, fusing techniques can be helpful for plant breeders with more comfortable access plant characteristics by improving the definition and resolution of color images. These findings add value to the automation, low-cost depth and high-throughput phenotyping for canola plants. These findings also contribute a novel multi-focus image fusion that exhibits a competitive performance with outperforms some other state-of-the-art methods based on the visual saliency maps and gradient domain fast guided filter. This proposed platform and counting algorithms can be applied to not only canola plants but also other closely related species. The proposed fusing technique can be extended to other fields, such as remote sensing and medical image fusion

Methods for Real-time Visualization and Interaction with Landforms

This thesis presents methods to enrich data modeling and analysis in the geoscience domain with a particular focus on geomorphological applications. First, a short overview of the relevant characteristics of the used remote sensing data and basics of its processing and visualization are provided. Then, two new methods for the visualization of vector-based maps on digital elevation models (DEMs) are presented. The first method uses a texture-based approach that generates a texture from the input maps at runtime taking into account the current viewpoint. In contrast to that, the second method utilizes the stencil buffer to create a mask in image space that is then used to render the map on top of the DEM. A particular challenge in this context is posed by the view-dependent level-of-detail representation of the terrain geometry. After suitable visualization methods for vector-based maps have been investigated, two landform mapping tools for the interactive generation of such maps are presented. The user can carry out the mapping directly on the textured digital elevation model and thus benefit from the 3D visualization of the relief. Additionally, semi-automatic image segmentation techniques are applied in order to reduce the amount of user interaction required and thus make the mapping process more efficient and convenient. The challenge in the adaption of the methods lies in the transfer of the algorithms to the quadtree representation of the data and in the application of out-of-core and hierarchical methods to ensure interactive performance. Although high-resolution remote sensing data are often available today, their effective resolution at steep slopes is rather low due to the oblique acquisition angle. For this reason, remote sensing data are suitable to only a limited extent for visualization as well as landform mapping purposes. To provide an easy way to supply additional imagery, an algorithm for registering uncalibrated photos to a textured digital elevation model is presented. A particular challenge in registering the images is posed by large variations in the photos concerning resolution, lighting conditions, seasonal changes, etc. The registered photos can be used to increase the visual quality of the textured DEM, in particular at steep slopes. To this end, a method is presented that combines several georegistered photos to textures for the DEM. The difficulty in this compositing process is to create a consistent appearance and avoid visible seams between the photos. In addition to that, the photos also provide valuable means to improve landform mapping. To this end, an extension of the landform mapping methods is presented that allows the utilization of the registered photos during mapping. This way, a detailed and exact mapping becomes feasible even at steep slopes

Advancements in multi-view processing for reconstruction, registration and visualization.

The ever-increasing diffusion of digital cameras and the advancements in computer vision, image processing and storage capabilities have lead, in the latest years, to the wide diffusion of digital image collections. A set of digital images is usually referred as a multi-view images set when the pictures cover different views of the same physical object or location. In multi-view datasets, correlations between images are exploited in many different ways to increase our capability to gather enhanced understanding and information on a scene. For example, a collection can be enhanced leveraging on the camera position and orientation, or with information about the 3D structure of the scene. The range of applications of multi-view data is really wide, encompassing diverse fields such as image-based reconstruction, image-based localization, navigation of virtual environments, collective photographic retouching, computational photography, object recognition, etc. For all these reasons, the development of new algorithms to effectively create, process, and visualize this type of data is an active research trend. The thesis will present four different advancements related to different aspects of the multi-view data processing: - Image-based 3D reconstruction: we present a pre-processing algorithm, that is a special color-to-gray conversion. This was developed with the aim to improve the accuracy of image-based reconstruction algorithms. In particular, we show how different dense stereo matching results can be enhanced by application of a domain separation approach that pre-computes a single optimized numerical value for each image location. - Image-based appearance reconstruction: we present a multi-view processing algorithm, this can enhance the quality of the color transfer from multi-view images to a geo-referenced 3D model of a location of interest. The proposed approach computes virtual shadows and allows to automatically segment shadowed regions from the input images preventing to use those pixels in subsequent texture synthesis. - 2D to 3D registration: we present an unsupervised localization and registration system. This system can recognize a site that has been framed in a multi-view data and calibrate it on a pre-existing 3D representation. The system has a very high accuracy and it can validate the result in a completely unsupervised manner. The system accuracy is enough to seamlessly view input images correctly super-imposed on the 3D location of interest. - Visualization: we present PhotoCloud, a real-time client-server system for interactive exploration of high resolution 3D models and up to several thousand photographs aligned over this 3D data. PhotoCloud supports any 3D models that can be rendered in a depth-coherent way and arbitrary multi-view image collections. Moreover, it tolerates 2D-to-2D and 2D-to-3D misalignments, and it provides scalable visualization of generic integrated 2D and 3D datasets by exploiting data duality. A set of effective 3D navigation controls, tightly integrated with innovative thumbnail bars, enhances the user navigation. These advancements have been developed in tourism and cultural heritage application contexts, but they are not limited to these

Mission Control Center (MCC) System Specification for the Shuttle Orbital Flight Test (OFT) Timeframe

System specifications to be used by the mission control center (MCC) for the shuttle orbital flight test (OFT) time frame were described. The three support systems discussed are the communication interface system (CIS), the data computation complex (DCC), and the display and control system (DCS), all of which may interfere with, and share processing facilities with other applications processing supporting current MCC programs. The MCC shall provide centralized control of the space shuttle OFT from launch through orbital flight, entry, and landing until the Orbiter comes to a stop on the runway. This control shall include the functions of vehicle management in the area of hardware configuration (verification), flight planning, communication and instrumentation configuration management, trajectory, software and consumables, payloads management, flight safety, and verification of test conditions/environment