The brain's vascular network dynamically affects its development and core functions. It rapidly responds to abnormal conditions by adjusting properties of the network, aiding stabilization and regulation of brain activities. Tracking prominent arterial changes has clear clinical and surgical advantages. However, the arterial network functions as a system; thus, local changes may imply global compensatory effects that could impact the dynamic progression of a disease. We developed automated personalized system-level analysis methods of the compensatory arterial changes and mean blood flow behavior from a patient's clinical images. By applying our approach to data from a patient with aggressive brain cancer compared with healthy individuals, we found unique spatiotemporal patterns of the arterial network that could assist in predicting the evolution of glioblastoma over time. Our personalized approach provides a valuable analysis tool that could augment current clinical assessments of the progression of glioblastoma and other neurological disorders affecting the brain.

A key aspect to controlling and reducing the effects invasive insect species have on agriculture is to obtain knowledge about the migration patterns of these species. Current state-of-the-art methods of studying these migration patterns involve a mark-release-recapture technique, in which insects are released after being marked and researchers attempt to recapture them later. However, this approach involves a human researcher manually searching for these insects in large fields and results in very low recapture rates. In this paper, we propose an automated system for detecting released insects using an unmanned aerial vehicle. This system utilizes ultraviolet lighting technology, digital cameras, and lightweight computer vision algorithms to more quickly and accurately detect insects compared to the current state of the art. The efficiency and accuracy that this system provides will allow for a more comprehensive understanding of invasive insect species migration patterns. Our experimental results demonstrate that our system can detect real target insects in field conditions with high precision and recall rates.

Large-scale annotation of image segmentation datasets is often prohibitively expensive, as it usually requires a huge number of worker hours to obtain high-quality results. Abundant and reliable data has been, however, crucial for the advances on image understanding tasks recently achieved by deep learning models. In this paper, we introduce FreeLabel, an intuitive open-source web interface that allows users to obtain high-quality segmentation masks with just a few freehand scribbles, in a matter of seconds. The efficacy of FreeLabel is quantitatively demonstrated by experimental results on the PASCAL dataset as well as on a dataset from the agricultural domain. Designed to benefit the computer vision community, FreeLabel can be used for both crowdsourced or private annotation and has a modular structure that can be easily adapted for any image dataset.

The segmentation of plant roots from soil and other growing media in X-ray computed tomography images is needed to effectively study the root system architecture without excavation. However, segmentation is a challenging problem in this context because the root and non-root regions share similar features. In this paper, we describe a method based on level sets and specifically adapted for this segmentation problem. In particular, we deal with the issues of using a level sets approach on large image volumes for root segmentation, and track active regions of the front using an occupancy grid. This method allows for straightforward modifications to a narrow-band algorithm such that excessive forward and backward movements of the front can be avoided, distance map computations in a narrow band context can be done in linear time through modification of Meijster et al.'s distance transform algorithm, and regions of the image volume are iteratively used to estimate distributions for root versus non-root classes. Results are shown of three plant species of different maturity levels, grown in three different media. Our method compares favorably to a state-of-the-art method for root segmentation in X-ray CT image volumes.

We consider the problem of extracting curve skeletons of three-dimensional, elongated objects given a noisy surface, which has applications in agricultural contexts such as extracting the branching structure of plants. We describe an efficient and robust method based on breadth-first search that can determine curve skeletons in these contexts. Our approach is capable of automatically detecting junction points as well as spurious segments and loops. All of that is accomplished with only one user-adjustable parameter. The run time of our method ranges from hundreds of milliseconds to less than four seconds on large, challenging datasets, which makes it appropriate for situations where real-time decision making is needed. Experiments on synthetic models as well as on data from real world objects, some of which were collected in challenging field conditions, show that our approach compares favorably to classical thinning algorithms as well as to recent contributions to the field.

The autonomous measurement of tree traits, such as branching structure, branch diameters, branch lengths, and branch angles, is required for tasks such as robotic pruning of trees as well as structural phenotyping. We propose a robotic vision system called the Robotic System for Tree Shape Estimation (RoTSE) to determine tree traits in field settings. The process is composed of the following stages: image acquisition with a mobile robot unit, segmentation, reconstruction, curve skeletonization, conversion to a graph representation, and then computation of traits. Quantitative and qualitative results on apple trees are shown in terms of accuracy, computation time, and robustness. Compared to ground truth measurements, the RoTSE produced the following estimates: branch diameter (mean-squared error 0.99 mm), branch length (mean-squared error 45.64 mm), and branch angle (mean-squared error 10.36 degrees). The average run time was 8.47 minutes when the voxel resolution was 3 mm3.

The use of shape from silhouette for reconstruction tasks is plagued by two types of real-world errors: camera calibration error and silhouette segmentation error. When either error is present, we call the problem the Shape from Inconsistent Silhouette (SfIS) problem. In this paper, we show how small camera calibration error can be corrected when using a previously-published SfIS technique to generate a reconstruction, by using an Iterative Closest Point (ICP) approach. We give formulations under two scenarios: the first of which is only external camera calibration parameters rotation and translation need to be corrected for each camera and the second of which is that both internal and external parameters need to be corrected. We formulate the problem as a 2D-3D ICP problem and find approximate solutions using a nonlinear minimization algorithm, the Levenberg-Marquadt method. We demonstrate the ability of our algorithm to create more representative reconstructions of both synthetic and real datasets of thin objects as compared to uncorrected datasets.

Accurate robot-world, hand-eye calibration is crucial to automation tasks. In this paper, we discuss the robot-world, hand-eye calibration problem which has been modeled as the linear relationship AX = ZB, where X and Z are the unknown calibration matrices composed of rotation and translation components. While there are many different approaches to determining X and Z, including linear and iterative methods, we parameterize the rotation components using Euler angles and find a solution using Levenberg-Marquadt iterative approach. We also offer a method to determine A, X, and Z, by formulating the robot-world, hand-eye calibration problem in terms of camera reprojection error. We compare both of these approaches to the state-of-the-art and conclude that our approaches yield lower values of camera reprojection error. In addition, we demonstrate the improved reconstruction accuracy when using the robot-world, hand-eye calibrations produced from our methods.

This paper considers the problem of reconstructing the shape of thin, texture-less objects such as leafless trees when there is noise or deterministic error in the silhouette extraction step or there are small errors in camera calibration. Traditional intersection-based techniques such as the visual hull are not robust to error because they penalize false negative and false positive error unequally. We provide a voxel-based formalism that penalizes false negative and positive error equally, by casting the reconstruction problem as a pseudo-Boolean minimization problem, where voxels are the variables of a pseudo-Boolean function and are labeled occupied or empty. Since the pseudo-Boolean minimization problem is NP-Hard for nonsubmodular functions, we developed an algorithm for an approximate solution using local minimum search. Our algorithm treats input binary probability maps (in other words, silhouettes) or continuously-valued probability maps identically, and places no constraints on camera placement. The algorithm was tested on three different leafless trees and one metal object where the number of voxels is 54.4 million (voxel sides measure 3.6 mm). Results show that our approach reconstructs the complicated branching structure of thin, texture-less objects in the presence of error where intersection-based approaches currently fail.

This paper seeks to increase the efficiency of background subtraction algorithms for motion detection. Our method uses a quadtree-base hierarchical framework that samples a small portion of the pixels in each image and yet produces motion detection results that are very similar compared to the conventional methods that raster scan entire images. The hierarchical data structure presented in this paper can be used with any background subtraction algorithm that employs background modeling and motion detection on a per-pixel basis. We have tested our method using two common background subtraction algorithms: running average and mixture of Gaussian. Our experimental results show that the application of the hierarchical data structure significantly increases the processing speed for accurate motion detection. For example, the mixture of Gaussian method with our hierarchical data structure is able to process 1600 by 1200 images at 11~12 frames per second compared to 2~3 frames per second without using the hierarchical data structure.