478 research outputs found
Automatic plant features recognition using stereo vision for crop monitoring
Machine vision and robotic technologies have potential to accurately monitor plant parameters which reflect plant stress and water requirements, for use in farm management decisions. However, autonomous identification of individual plant leaves on a growing plant under natural conditions is a challenging task for vision-guided agricultural robots, due to the complexity of data relating to various stage of growth and ambient environmental conditions. There are numerous machine vision studies that are concerned with describing the shape of leaves that are individually-presented to a camera. The purpose of these studies is to identify plant species, or for the autonomous detection of multiple leaves from small seedlings under greenhouse conditions. Machine vision-based detection of individual leaves and challenges presented by overlapping leaves on a developed plant canopy using depth perception properties under natural outdoor conditions is yet to be reported. Stereo vision has recently emerged for use in a variety of agricultural applications and is expected to provide an accurate method for plant segmentation and identification which can benefit from depth properties and robustness.
This thesis presents a plant leaf extraction algorithm using a stereo vision sensor. This algorithm is used on multiple leaf segmentation and overlapping leaves
separation using a combination of image features, specifically colour, shape and depth. The separation between the connected and the overlapping leaves relies on the measurement of the discontinuity in depth gradient for the disparity maps. Two techniques have been developed to implement this task based on global and local measurement. A geometrical plane from each segmented leaf can be extracted and used to parameterise a 3D model of the plant image and to measure the inclination angle of each individual leaf. The stem and branch segmentation and counting method was developed based on the vesselness measure and Hough transform technique. Furthermore, a method for reconstructing the segmented parts of hibiscus plants is presented and a 2.5D model is generated for the plant. Experimental tests were conducted with two different selected plants: cotton of different sizes, and hibiscus, in an outdoor environment under varying light conditions. The proposed algorithm was evaluated using 272 cotton and hibiscus plant images. The results show an observed enhancement in leaf detection when utilising depth features, where many leaves in various positions and shapes (single, touching and overlapping) were detected successfully.
Depth properties were more effective in separating between occluded and overlapping leaves with a high separation rate of 84% and these can be detected automatically without adding any artificial tags on the leaf boundaries. The results exhibit an acceptable segmentation rate of 78% for individual plant leaves thereby differentiating the leaves from their complex backgrounds and from each other. The results present almost identical performance for both species under various lighting and environmental conditions. For the stem and branch detection algorithm, experimental tests were conducted on 64 colour images of both species under different environmental conditions. The results show higher stem and branch segmentation rates for hibiscus indoor images (82%) compared to hibiscus outdoor images (49.5%) and cotton images (21%). The segmentation and counting of plant features could provide accurate estimation about plant growth parameters which can be beneficial for many agricultural tasks and applications
Efficient and Accurate Disparity Estimation from MLA-Based Plenoptic Cameras
This manuscript focuses on the processing images from microlens-array based plenoptic cameras. These cameras enable the capturing of the light field in a single shot, recording a greater amount of information with respect to conventional cameras, allowing to develop a whole new set of applications. However, the enhanced information introduces additional challenges and results in higher computational effort. For one, the image is composed of thousand of micro-lens images, making it an unusual case for standard image processing algorithms. Secondly, the disparity information has to be estimated from those micro-images to create a conventional image and a three-dimensional representation. Therefore, the work in thesis is devoted to analyse and propose methodologies to deal with plenoptic images. A full framework for plenoptic cameras has been built, including the contributions described in this thesis. A blur-aware calibration method to model a plenoptic camera, an optimization method to accurately select the best microlenses combination, an overview of the different types of plenoptic cameras and their representation. Datasets consisting of both real and synthetic images have been used to create a benchmark for different disparity estimation algorithm and to inspect the behaviour of disparity under different compression rates. A robust depth estimation approach has been developed for light field microscopy and image of biological samples
3D data fusion by depth refinement and pose recovery
Refining depth maps from different sources to obtain a refined depth map, and aligning
the rigid point clouds from different views, are two core techniques. Existing depth
fusion algorithms do not provide a general framework to obtain a highly accurate depth
map. Furthermore, existing rigid point cloud registration algorithms do not always align
noisy point clouds robustly and accurately, especially when there are many outliers and
large occlusions. In this thesis, we present a general depth fusion framework based on
supervised, semi-supervised, and unsupervised adversarial network approaches. We
show that the refined depth maps are more accurate than the source depth maps by
depth fusion. We develop a new rigid point cloud registration algorithm by aligning two
uncertainty-based Gaussian mixture models, which represent the structures of the two
point clouds. We show that we can register rigid point clouds more accurately over a
larger range of perturbations. Subsequently, the new supervised depth fusion algorithm
and new rigid point cloud registration algorithm are integrated into the ROS system of a
real gardening robot (called TrimBot) for practical usage in real environments. All the
proposed algorithms have been evaluated on multiple existing datasets to show their
superiority compared to prior work in the field
A survey of visual preprocessing and shape representation techniques
Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)
Integration of a stereo vision system into an autonomous underwater vehicle for pipe manipulation tasks
Underwater object detection and recognition using computer vision are challenging tasks due to the poor light condition of submerged environments. For intervention missions requiring grasping and manipulation of submerged objects, a vision system must provide an Autonomous Underwater Vehicles (AUV) with object detection, localization and tracking capabilities. In this paper, we describe the integration of a vision system in the MARIS intervention AUV and its configuration for detecting cylindrical pipes, a typical artifact of interest in underwater operations. Pipe edges are tracked using an alpha-beta filter to achieve robustness and return a reliable pose estimation even in case of partial pipe visibility. Experiments in an outdoor water pool in different light conditions show that the adopted algorithmic approach allows detection of target pipes and provides a sufficiently accurate estimation of their pose even when they become partially visible, thereby supporting the AUV in several successful pipe grasping operations
Intraoperative Navigation Systems for Image-Guided Surgery
Recent technological advancements in medical imaging equipment have resulted in
a dramatic improvement of image accuracy, now capable of providing useful information
previously not available to clinicians. In the surgical context, intraoperative
imaging provides a crucial value for the success of the operation.
Many nontrivial scientific and technical problems need to be addressed in order to
efficiently exploit the different information sources nowadays available in advanced
operating rooms. In particular, it is necessary to provide: (i) accurate tracking of
surgical instruments, (ii) real-time matching of images from different modalities, and
(iii) reliable guidance toward the surgical target. Satisfying all of these requisites
is needed to realize effective intraoperative navigation systems for image-guided
surgery.
Various solutions have been proposed and successfully tested in the field of image
navigation systems in the last ten years; nevertheless several problems still arise in
most of the applications regarding precision, usability and capabilities of the existing
systems. Identifying and solving these issues represents an urgent scientific challenge.
This thesis investigates the current state of the art in the field of intraoperative
navigation systems, focusing in particular on the challenges related to efficient and
effective usage of ultrasound imaging during surgery.
The main contribution of this thesis to the state of the art are related to:
Techniques for automatic motion compensation and therapy monitoring applied
to a novel ultrasound-guided surgical robotic platform in the context of
abdominal tumor thermoablation.
Novel image-fusion based navigation systems for ultrasound-guided neurosurgery
in the context of brain tumor resection, highlighting their applicability
as off-line surgical training instruments.
The proposed systems, which were designed and developed in the framework of
two international research projects, have been tested in real or simulated surgical
scenarios, showing promising results toward their application in clinical practice
Change blindness: eradication of gestalt strategies
Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task
Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning
Growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is
fueled by their promise for enhanced safety, efficiency, and economic benefits.
While previous surveys have captured progress in this field, a comprehensive
and forward-looking summary is needed. Our work fills this gap through three
distinct articles. The first part, a "Survey of Surveys" (SoS), outlines the
history, surveys, ethics, and future directions of AD and IV technologies. The
second part, "Milestones in Autonomous Driving and Intelligent Vehicles Part I:
Control, Computing System Design, Communication, HD Map, Testing, and Human
Behaviors" delves into the development of control, computing system,
communication, HD map, testing, and human behaviors in IVs. This part, the
third part, reviews perception and planning in the context of IVs. Aiming to
provide a comprehensive overview of the latest advancements in AD and IVs, this
work caters to both newcomers and seasoned researchers. By integrating the SoS
and Part I, we offer unique insights and strive to serve as a bridge between
past achievements and future possibilities in this dynamic field.Comment: 17pages, 6figures. IEEE Transactions on Systems, Man, and
Cybernetics: System
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