Segmentation of needle artifacts in real time 2D MR images

During percutaneous minimally invasive procedures a needle is used to access the region to be treated without need for an open surgery. Remarkable is the increasing use of percutaneous thermal ablation to treat neoplastic formation. The effectiveness of such procedures is highly dependent on the correct placement of the needle inside the region to be treated. Imaging monitoring provides the physician with the possibility to inspect the location of the device, which is responsible for a signal void in the MR images acquired referred to as needle artifact. The procedure can be performed under real time Magnetic Resonance Imaging guidance. In this work two algorithms for automatic needle detection in real time MR images were developed. The detection is anticipated to increase the accuracy of the device positionin

3D Automatic Target Recognition for Future LIDAR Missiles

We present a real-time three-dimensional automatic target recognition approach appropriate for future light detection and ranging-based missiles. Our technique extends the speeded-up robust features method into the third dimension by solving multiple two-dimensional problems and performs template matching based on the extreme case of a single pose per target. Evaluation on military targets shows higher recognition rates under various transformations and perturbations at lower processing time compared to state-of-the-art approaches

Object recognition using multi-view imaging

Single view imaging data has been used in most previous research in computer vision and image understanding and lots of techniques have been developed. Recently with the fast development and dropping cost of multiple cameras, it has become possible to have many more views to achieve image processing tasks. This thesis will consider how to use the obtained multiple images in the application of target object recognition. In this context, we present two algorithms for object recognition based on scale- invariant feature points. The first is single view object recognition method (SOR), which operates on single images and uses a chirality constraint to reduce the recognition errors that arise when only a small number of feature points are matched. The procedure is extended in the second multi-view object recognition algorithm (MOR) which operates on a multi-view image sequence and, by tracking feature points using a dynamic programming method in the plenoptic domain subject to the epipolar constraint, is able to fuse feature point matches from all the available images, resulting in more robust recognition. We evaluated these algorithms using a number of data sets of real images capturing both indoor and outdoor scenes. We demonstrate that MOR is better than SOR particularly for noisy and low resolution images, and it is also able to recognize objects that are partially occluded by combining it with some segmentation techniques

Registration and Recognition in 3D

The simplest Computer Vision algorithm can tell you what color it sees when you point it at an object, but asking that computer what it is looking at is a much harder problem. Camera and LiDAR (Light Detection And Ranging) sensors generally provide streams pixel of values and sophisticated algorithms must be engineered to recognize objects or the environment. There has been significant effort expended by the computer vision community on recognizing objects in color images; however, LiDAR sensors, which sense depth values for pixels instead of color, have been studied less. Recently we have seen a renewed interest in depth data with the democratization provided by consumer depth cameras. Detecting objects in depth data is more challenging in some ways because of the lack of texture and increased complexity of processing unordered point sets. We present three systems that contribute to solving the object recognition problem from the LiDAR perspective. They are: calibration, registration, and object recognition systems. We propose a novel calibration system that works with both line and raster based LiDAR sensors, and calibrates them with respect to image cameras. Our system can be extended to calibrate LiDAR sensors that do not give intensity information. We demonstrate a novel system that produces registrations between different LiDAR scans by transforming the input point cloud into a Constellation Extended Gaussian Image (CEGI) and then uses this CEGI to estimate the rotational alignment of the scans independently. Finally we present a method for object recognition which uses local (Spin Images) and global (CEGI) information to recognize cars in a large urban dataset. We present real world results from these three systems. Compelling experiments show that object recognition systems can gain much information using only 3D geometry. There are many object recognition and navigation algorithms that work on images; the work we propose in this thesis is more complimentary to those image based methods than competitive. This is an important step along the way to more intelligent robots

Ultra-fast Imaging of Two-Phase Flow in Structured Monolith Reactors; Techniques and Data Analysis

This thesis will address the use of nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) techniques to probe the “monolith reactor”, which consists of a structured catalyst over which reactions may occur. This reactor has emerged as a potential alternative to more traditional chemical engineering systems such as trickle bed and slurry reactors. However, being a relatively new design, its associated flow phenomena and design procedures are not rigorously understood, which is retarding its acceptance in industry. Traditional observations are unable to provide the necessary information for design since the systems are opaque and dynamic. Therefore, NMR is proposed as an ideal tool to probe these systems in detail. The theory of NMR is summarised and the development of novel NMR techniques is presented. Novel techniques are validated in simple systems, and tested in more complex systems to ascertain their quantitative nature, and to find their limitations. These techniques are improvements over existing techniques in that they either decrease the acquisition time (allowing the observation of dynamically-changing systems) or allow us to probe systems in different ways to extract useful information. The goal of this research is to better understand the flow phenomena present in such systems, and to use this information to design better, more efficient, more controllable industrial reactors. The analysis of the NMR data acquired is discussed in detail, and several novel image-processing techniques have been developed to aid in the quantification of features within the images, and also to measure quantities such as holdup and velocity. These novel techniques are validated, and then applied to the systems of interest. Various configurations of monolith reactor, ranging from low flow rate systems to more challenging (and more industrially relevant) turbulent systems, are probed using these methods, and the contrasting flow phenomena and performance of these systems are discussed, with a view to optimisation of the choice of design parameters

Automatic Pipeline Surveillance Air-Vehicle

This thesis presents the developments of a vision-based system for aerial pipeline Right-of-Way surveillance using optical/Infrared sensors mounted on Unmanned Aerial Vehicles (UAV). The aim of research is to develop a highly automated, on-board system for detecting and following the pipelines; while simultaneously detecting any third-party interference. The proposed approach of using a UAV platform could potentially reduce the cost of monitoring and surveying pipelines when compared to manned aircraft. The main contributions of this thesis are the development of the image-analysis algorithms, the overall system architecture and validation of in hardware based on scaled down Test environment. To evaluate the performance of the system, the algorithms were coded using Python programming language. A small-scale test-rig of the pipeline structure, as well as expected third-party interference, was setup to simulate the operational environment and capture/record data for the algorithm testing and validation. The pipeline endpoints are identified by transforming the 16-bits depth data of the explored environment into 3D point clouds world coordinates. Then, using the Random Sample Consensus (RANSAC) approach, the foreground and background are separated based on the transformed 3D point cloud to extract the plane that corresponds to the ground. Simultaneously, the boundaries of the explored environment are detected based on the 16-bit depth data using a canny detector. Following that, these boundaries were filtered out, after being transformed into a 3D point cloud, based on the real height of the pipeline for fast and accurate measurements using a Euclidean distance of each boundary point, relative to the plane of the ground extracted previously. The filtered boundaries were used to detect the straight lines of the object boundary (Hough lines), once transformed into 16-bit depth data, using a Hough transform method. The pipeline is verified by estimating a centre line segment, using a 3D point cloud of each pair of the Hough line segments, (transformed into 3D). Then, the corresponding linearity of the pipeline points cloud is filtered within the width of the pipeline using Euclidean distance in the foreground point cloud. Then, the segment length of the detected centre line is enhanced to match the exact pipeline segment by extending it along the filtered point cloud of the pipeline. The third-party interference is detected based on four parameters, namely: foreground depth data; pipeline depth data; pipeline endpoints location in the 3D point cloud; and Right-of-Way distance. The techniques include detection, classification, and localization algorithms. Finally, a waypoints-based navigation system was implemented for the air- vehicle to fly over the course waypoints that were generated online by a heading angle demand to follow the pipeline structure in real-time based on the online identification of the pipeline endpoints relative to a camera frame

The application of range imaging for improved local feature representations

This thesis presents an investigation into the integration of information extracted from co-aligned range and intensity images to achieve pose invariant object recognition. Local feature matching is a fundamental technique in image analysis that underpins many computer vision-based applications; the approach comprises identifying a collection of interest points in an image, characterising the local image region surrounding the interest point by means of a descriptor, and matching these descriptors between example images. Such local feature descriptors are formed from a measure of the local image statistics in the region surrounding the interest point. The interest point locations and the means of measuring local image statistics should be chosen such that resultant descriptor remains stable across a range of common image transformations. Recently the availability of low cost, high quality range imaging devices has motivated an interest in local feature extraction from range images. It has been widely assumed in the vision community that the range imaging domain has properties which remain quasi-invariant through a wide range of changes in illumination and pose. Accordingly, it has been suggested that local feature extraction in the range domain should allow the calculation of local feature descriptors that are potentially more robust than those calculated from the intensity imaging domain alone. However, range images represent differing characteristics from those represented within intensity images which are frequently used, independently from range images, to create robust local features. Therefore, this work attempts to establish the best means of combining information from these two imaging modalities to further increase the reliability of matching local features. Local feature extraction comprises a series of processes applied to an image location such that a collection of repeatable descriptors can be established. By using co-aligned range and intensity images this work investigates the choice of modality and method for each step in the extraction process as an approach to optimising the resulting descriptor. Additionally, multimodal features are formed by combining information from both domains in a single stage in the extraction process. To further improve the quality of feature descriptors, a calculation of the surface normals and a use of the 3D structure from the range image are applied to correct the 3D appearance of a local sample patch, thereby increasing the similarity between observations. The matching performance of local features is evaluated using an experimental setup comprising a turntable and stereo pair of cameras. This experimental setup is used to create a database of intensity and range images for 5 objects imaged at 72 calibrated viewpoints, creating a database of 360 object observations. The use of a calibrated turntable in combination with the 3D object surface coordiantes, supplied by the range image allow location correspondences between object observations to be established; and therefore descriptor matches to be labelled as either true positive or false positive. Applying this methodology to the formulated local features show that two approaches demonstrate state-of-the-art performance, with a ~40% increase in area under ROC curve at a False Positive Rate of 10% when compared with standard SIFT. These approaches are range affine corrected intensity SIFT and element corrected surface gradients SIFT. Furthermore,this work uses the 3D structure encoded in the range image to organise collections of interest points from a series of observations into a collection of canonical views in a new model local feature. The canonical views for a interest point are stored in a view compartmentalised structure which allows the appearance of a local interest point to be characterised across the view sphere. Each canonical view is assigned a confidence measure based on the 3D pose of the interest point at observation, this confidence measure is then used to match similar canonical views of model and query interest points thereby achieving a pose invariant interest point description. This approach does not produce a statistically significant performance increase. However, does contribute a validated methodology for combining multiple descriptors with differing confidence weightings into a single keypoint