A novel imaging and software platform was developed for the high-throughput phenotyping of three-dimensional root traits during seedling development. To demonstrate the platform’s capacity, plants of two rice ( Oryza sativa ) genotypes, Azucena and IR64, were grown in a transparent gellan gum system and imaged daily for 10 d. Rotational image sequences consisting of 40 two-dimensional images were captured using an optically corrected digital imaging system. Three-dimensional root reconstructions were generated and analyzed using a custom-designed software, RootReader3D. Using the automated and interactive capabilities of RootReader3D, five rice root types were classified and 27 phenotypic root traits were measured to characterize these two genotypes. Where possible, measurements from the three-dimensional platform were validated and were highly correlated with conventional two-dimensional measurements. When comparing gellan gum-grown plants with those grown under hydroponic and sand culture, significant differences were detected in morphological root traits ( P

Three-Dimensional Root Phenotyping with a Novel Imaging and Software Platform

A novel imaging and software platform was developed for the high-throughput phenotyping of three-dimensional root traits during seedling development. To demonstrate the platform’s capacity, plants of two rice (Oryza sativa) genotypes, Azucena and IR64, were grown in a transparent gellan gum system and imaged daily for 10 d. Rotational image sequences consisting of 40 two-dimensional images were captured using an optically corrected digital imaging system. Three-dimensional root reconstructions were generated and analyzed using a custom-designed software, RootReader3D. Using the automated and interactive capabilities of RootReader3D, five rice root types were classified and 27 phenotypic root traits were measured to characterize these two genotypes. Where possible, measurements from the three-dimensional platform were validated and were highly correlated with conventional two-dimensional measurements. When comparing gellan gum-grown plants with those grown under hydroponic and sand culture, significant differences were detected in morphological root traits (P , 0.05). This highly flexible platform provides the capacity to measure root traits with a high degree of spatial and temporal resolution and will facilitate novel investigations into the development of entire root systems or selected components of root systems. In combination with the extensive genetic resources that are now available, this platform will be a powerful resource to further explore the molecular and genetic determinants of root system architecture.

Three-Dimensional Root Phenotyping with a Novel Imaging and Software Platform 1(C)(W)(OA)

Varying scene illumination poses many challenging problems for machine vision systems. One such issue is developing global enhancement methods that work effectively across the varying illumination. In this paper, we introduce two novel image enhancement algorithms: edge-preserving contrast enhancement, which is able to better preserve edge details while enhancing contrast in images with varying illumination, and a novel multihistogram equalization method which utilizes the human visual system (HVS) to segment the image, allowing a fast and efficient correction of nonuniform illumination. We then extend this HVS-based multihistogram equalization approach to create a general enhancement method that can utilize any combination of enhancement algorithms for an improved performance. Additionally, we propose new quantitative measures of image enhancement, called the logarithmic Michelson contrast measure (AME) and the logarithmic AME by entropy. Many image enhancement methods require selection of operating parameters, which are typically chosen using subjective methods, but these new measures allow for automated selection. We present experimental results for these methods and make a comparison against other leading algorithms.

Human Visual System-Based Image Enhancement and Logarithmic Contrast Measure

Methods and arrangements involving portable user devices such smartphones and wearable electronic devices are disclosed, as well as other devices and sensors distributed within an ambient environment. Some arrangements enable a user to perform an object recognition process in a computationally- and time-efficient manner. Other arrangements enable users and other entities to, either individually or cooperatively, register or enroll physical objects into one or more object registries on which an object recognition process can be performed. Still other arrangements enable users and other entities to, either individually or cooperatively, associate registered or enrolled objects with one or more items of metadata. A great variety of other features and arrangements are also detailed.

Methods for object recognition and related arrangements

IEEE transactions on systems, man and cybernetics. Part B, Cybernetics

The goal of this study is to investigate the reconstruction of three-dimensional (3-D) graphical models of real objects in a controlled imaging environment and present the work done in our group based on silhouette-based reconstruction. Although many parts of the whole system have been well-known in the literature and in practice, the main contribution of the paper is that it describes a complete, end-to-end system explained in detail. Based on a multi-image calibration method, an algorithm to extract the rotation axis of a turn-table has been developed. Furthermore, this can be extended to estimate robustly the initial bounding volume of the object to be modeled. The disadvantages of the silhouette-based reconstruction can be removed by an algorithm using photoconsistency. This algorithm has a simpler visibility check, and it eliminates the selection of threshold existing in similar algorithms. Besides, in order to construct the appearance, we use the concept of particles. The reconstruction results are shown both on real world and synthetic objects.

Silhouette-based 3-D model reconstruction from multiple images

An image based model reconstruction system is described. Real images of a rigid object acquired under a simple but controlled environment are used to recover the three dimensional geometry and the surface appearance. Based on a multi-image calibration method, an algorithm to extract the rotation axis of a turn-table has been developed. Furthermore, this can be extended to estimate robustly the initial bounding volume of the object to be modeled The coarse volume obtained, is then carved using a stereo correction method which removes the disadvantages of silhouette based reconstruction by photoconsistency. The concept of surface particles is adapted in order to extract a texture map for the model. Some metrics are defined to measure the quality of the reconstructed models.

Reconstruction of three dimensional models from real images

In this study, an appearance reconstruction method based on extraction of material reflectance properties of a three-dimensional (3D) object from its two-dimensional (2D) images is explained. One of the main advantages of this system is that the reconstructed object can be rendered in real-time with photorealistic quality in varying illumination conditions. The reflectance of the object is decomposed into diffuse and specular components. White the diffuse component is stored in a global texture, the specular component is represented with a bi-directional reflectance distribution function (BRDF). While estimating the diffuse components, illumination-invariant images of the object are computed from the input images, and a global texture of the object is extracted from these images by using surface particles. The specular reflectance data are collected from the residual images obtained by taking the difference between the input images and corresponding illumination-invariant images, and a BRDF model is fitted to these data. At the rendering phase, the diffuse and specular components are blended into each other to achieve a photorealistic appearance of the reconstructed object.

U. Yilmaz

Papers

Silhouette-based 3-D model reconstruction from multiple images

Reconstruction of three dimensional models from real images

Image-based extraction of material reflectance properties of a 3D rigid object