814 research outputs found

    Framework for extracting and solving combination puzzles

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    Selles töös uuritakse, kuidas arvuti nĂ€gemisega seotud algoritme on vĂ”imalik rakendada objektide tuvastuse probleemile. TĂ€psemalt, kas arvuti nĂ€gemist on vĂ”imalik kasutada pĂ€ris maailma kombinatoorsete probleemide lahendamiseks. Idee kasutada arvuti rakendust probleemide lahendamiseks, tulenes tĂ€helepanekust, et probleemide lahenduse protsessid on kĂ”ik enamasti algoritmid. Sellest vĂ”ib jĂ€reldada, et arvutid sobivad algoritmiliste probleemide lahendamiseks paremini kui inimesed, kellel vĂ”ib sama ĂŒlesande peale kuluda kordades kauem. Siiski ei vaatle arvutid probleeme samamoodi nagu inimesed ehk nad ei saa probleeme analĂŒĂŒsida. Niisiis selle töö panuseks saab olema erinevate arvuti nĂ€gemise algoritmide uurimine, mille eesmĂ€rgiks on pĂ€ris maailma kombinatoorsete probleemide tĂ”lgendamine abstraktseteks struktuurideks, mida arvuti on vĂ”imeline mĂ”istma ning lahendama.Praegu on antud valdkonnas vĂ€he materiali, mis annab hea vĂ”imaluse panustada sellesse valdkonda. Seda saavutatakse lĂ€bi empiirilise uurimise testide kogumiku kujul selleks, et veenduda millised lĂ€henemised on kĂ”ige paremad. Nende eesmĂ€rkide saavutamiseks töötati lĂ€bi suur hulk arvuti nĂ€gemisega seotud materjale ning teooriat. Lisaks vĂ”eti ka arvesse reaalaja toimingute tĂ€htsus, mida vĂ”ib nĂ€ha erinevate liikumisest struktuuri eraldavate algoritmide(SLAM, PTAM) Ă”pingutest, mida hiljem edukalt kasutati navigatsiooni ja liitreaalsuse probleemide lahendamiseks. Siiski tuleb mainida, et neid algoritme ei kasutatud objektide omaduste tuvastamiseks.See töö uurib, kuidas saab erinevaid lĂ€henemisi kasutada selleks, et aidata vĂ€hekogenud kasutajaid kombinatoorsete pĂ€ris maailma probleemide lahendamisel. Lisaks tekib selle töö tulemusena vĂ”imalus tuvastada objektide liikumist (translatsioon, pöörlemine), mida saab kasutada koos virutaalse probleemi mudeliga, et parandada kasutaja kogemust.This thesis describes and investigates how computer vision algorithms and stereo vision algorithms may be applied to the problem of object detection. In particular, if computer vision can aid on puzzle solving. The idea to use computer application for puzzle solving came from the fact that all solution techniques are algorithms in the end. This fact leads to the conclusion that algorithms are well solved by machines, for instance, a machine requires milliseconds to compute the solution while a human can handle this in minutes or hours. Unfortunately, machines cannot see puzzles from human perspective thus cannot analyze them. Hence, the contribution of this thesis is to study different computer vision approaches from non-related solutions applied to the problem of translating the physical puzzle model into the abstract structure that can be understood and solved by a machine.Currently, there is a little written on this subject, therefore, there is a great chance to contribute. This is achieved through empirical research represented as a set of experiments in order to ensure which approaches are suitable. To accomplish these goals huge amount of computer vision theory has been studied. In addition, the relevance of real-time operations was taken into account. This was manifested through the Different real-time Structure from Motion algorithms (SLAM, PTAM) studies that were successfully applied for navigation or augmented reality problems; however, none of them for object characteristics extraction.This thesis examines how these different approaches can be applied to the given problem to help inexperienced users solve the combination puzzles. Moreover, it produces a side effect which is a possibility to track objects movement (rotation, translation) that can be used for manipulating a rendered game puzzle and increase interactivity and engagement of the user

    Contour extraction from HVEM image of microvessel using active contour models

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    This thesis reports the research results on automatic contour extraction from high voltage electron microscope (HVEM) image of thick cross section montages of small blood vessels. The previous work on this subject, which was based on the conventional edge detection operations combined with edge linking, has proven inadequate to describe the inner structural compartments of microvessels. In this thesis, an active contour model (commonly referred to as Snakes ) has been applied to advance the previous work. Active contour models have proven themselves to be a powerful and flexible paradigm for many problems in image understanding, especially in contour extraction from medical images. With the developed energy functions, the active contour is attracted towards the edges under the action of internal forces (describing some elasticity properties of the contour), image forces and external forces by means of minimization of the energy functions. Based on this active model, an effective algorithm is implemented as a powerful tool for 2-D contour extraction in our problem for the first time. The results thus obtained turn out to be encouraging

    An Analytical Framework for Assessing the Efficacy of Small Satellites in Performing Novel Imaging Missions

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    In the last two decades, small satellites have opened up the use of space to groups other than governments and large corporations, allowing for increased participation and experimentation. This democratization of space was primarily enabled by two factors: improved technology and reduced launch costs. Improved technology allowed the miniaturization of components and reduced overall cost meaning many of the capabilities of larger satellites could be replicated at a fraction of the cost. In addition, new launcher systems that could host many small satellites as ride-shares on manifested vehicles lowered launch costs and simplified the process of getting a satellite into orbit. The potential of these smaller satellites to replace or augment existing systems has led to a flood of potential satellite and mission concepts, often with little rigorous study of whether the proposed satellite or mission is achievable or necessary. This work proposes an analytical framework to aid system designers in evaluating the ability of an existing concept or small satellite to perform a particular imaging mission, either replacing or augmenting existing capabilities. This framework was developed and then refined by application to the problem of using small satellites to perform a wide area search mission – a mission not possible with existing imaging satellites, but one that would add to current capabilities. Requirements for a wide area search mission were developed, along with a list of factors that would affect image quality and system performance. Two existing small satellite concepts were evaluated for use by examining image quality from the systems, selecting an algorithm to perform the search function automatically, and then assessing mission feasibility by applying the algorithm to simulated imagery. Finally, a notional constellation design was developed to assess the number of satellites required to perform the mission. It was found that a constellation of 480 CubeSats producing 4 m spatial resolution panchromatic imagery and employing an on-board processing algorithm would be sufficient to perform a wide area search mission

    Automatic reconstruction of three-dimensional building models from dense image matching datasets

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    PhD ThesisThe generation of three-dimensional (3D) building models without roof geometry is currently easily automated using a building footprint and single height value. The automatic reconstruction of roof structures, however, remains challenging because of the complexity and variability in building geometry. Attempts from imagery have utilised high spatial resolution but have only reconstructed simple geometry. This research addresses the complexity of roof geometry reconstruction by developing an approach, which focuses on the extraction of corners to reconstruct 3D buildings as boundary representation models, to try overcome the limitations of planar fitting procedures, which are currently favoured. Roof geometry information was extracted from surface models, true orthophotos and photogrammetric point clouds; reconstructed at the same spatial resolution of the captured aerial imagery, with developments in pixel-to-pixel matching. Edges of roof planes were extracted by the Canny edge detector, and then refined with a workflow based on the principles of scan-line segmentation to remove false positive detection. Line tracing procedures defined the corner positions of the extracted edges. A connectivity ruleset was developed, which searches around the endpoints of unconnected lines, testing for potential connecting corners. All unconnected lines were then removed reconstruct 3D models as a closed network of connecting roof corners. Building models have been reconstructed both as block models and also with roof structures. The methodology was tested on data of Newcastle upon Tyne, United Kingdom, with results showing corner extraction success at 75% and to within a planimetric accuracy of ±0.5 m. The methodology was then tested on data of Vaihingen, Germany, which forms part of the ISPRS 3D reconstruction benchmark. This allowed direct comparisons to be made with other methods. The results from both study areas showed similar planimetric accuracy of extracted corners. However, both sites were not as successful in the reconstruction of roof planes.Ordnance Surve

    Color Image Edge Detection and Segmentation: A Comparison of the Vector Angle and the Euclidean Distance Color Similarity Measures

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    This work is based on Shafer's Dichromatic Reflection Model as applied to color image formation. The color spaces RGB, XYZ, CIELAB, CIELUV, rgb, l1l2l3, and the new h1h2h3 color space are discussed from this perspective. Two color similarity measures are studied: the Euclidean distance and the vector angle. The work in this thesis is motivated from a practical point of view by several shortcomings of current methods. The first problem is the inability of all known methods to properly segment objects from the background without interference from object shadows and highlights. The second shortcoming is the non-examination of the vector angle as a distance measure that is capable of directly evaluating hue similarity without considering intensity especially in RGB. Finally, there is inadequate research on the combination of hue- and intensity-based similarity measures to improve color similarity calculations given the advantages of each color distance measure. These distance measures were used for two image understanding tasks: edge detection, and one strategy for color image segmentation, namely color clustering. Edge detection algorithms using Euclidean distance and vector angle similarity measures as well as their combinations were examined. The list of algorithms is comprised of the modified Roberts operator, the Sobel operator, the Canny operator, the vector gradient operator, and the 3x3 difference vector operator. Pratt's Figure of Merit is used for a quantitative comparison of edge detection results. Color clustering was examined using the k-means (based on the Euclidean distance) and Mixture of Principal Components (based on the vector angle) algorithms. A new quantitative image segmentation evaluation procedure is introduced to assess the performance of both algorithms. Quantitative and qualitative results on many color images (artificial, staged scenes and natural scene images) indicate good edge detection performance using a vector version of the Sobel operator on the h1h2h3 color space. The results using combined hue- and intensity-based difference measures show a slight improvement qualitatively and over using each measure independently in RGB. Quantitative and qualitative results for image segmentation on the same set of images suggest that the best image segmentation results are obtained using the Mixture of Principal Components algorithm on the RGB, XYZ and rgb color spaces. Finally, poor color clustering results in the h1h2h3 color space suggest that some assumptions in deriving a simplified version of the Dichromatic Reflectance Model might have been violated

    Automatic plant features recognition using stereo vision for crop monitoring

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    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

    Implementation of a real time Hough transform using FPGA technology

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    This thesis is concerned with the modelling, design and implementation of efficient architectures for performing the Hough Transform (HT) on mega-pixel resolution real-time images using Field Programmable Gate Array (FPGA) technology. Although the HT has been around for many years and a number of algorithms have been developed it still remains a significant bottleneck in many image processing applications. Even though, the basic idea of the HT is to locate curves in an image that can be parameterized: e.g. straight lines, polynomials or circles, in a suitable parameter space, the research presented in this thesis will focus only on location of straight lines on binary images. The HT algorithm uses an accumulator array (accumulator bins) to detect the existence of a straight line on an image. As the image needs to be binarized, a novel generic synchronization circuit for windowing operations was designed to perform edge detection. An edge detection method of special interest, the canny method, is used and the design and implementation of it in hardware is achieved in this thesis. As each image pixel can be implemented independently, parallel processing can be performed. However, the main disadvantage of the HT is the large storage and computational requirements. This thesis presents new and state-of-the-art hardware implementations for the minimization of the computational cost, using the Hybrid-Logarithmic Number System (Hybrid-LNS) for calculating the HT for fixed bit-width architectures. It is shown that using the Hybrid-LNS the computational cost is minimized, while the precision of the HT algorithm is maintained. Advances in FPGA technology now make it possible to implement functions as the HT in reconfigurable fabrics. Methods for storing large arrays on FPGA’s are presented, where data from a 1024 x 1024 pixel camera at a rate of up to 25 frames per second are processed

    Lidar In Coastal Storm Surge Modeling: Modeling Linear Raised Features

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    A method for extracting linear raised features from laser scanned altimetry (LiDAR) datasets is presented. The objective is to automate the method so that elements in a coastal storm surge simulation finite element mesh might have their edges aligned along vertical terrain features. Terrain features of interest are those that are high and long enough to form a hydrodynamic impediment while being narrow enough that the features might be straddled and not modeled if element edges are not purposely aligned. These features are commonly raised roadbeds but may occur due to other manmade alterations to the terrain or natural terrain. The implementation uses the TauDEM watershed delineation software included in the MapWindow open source Geographic Information System to initially extract watershed boundaries. The watershed boundaries are then examined computationally to determine which sections warrant inclusion in the storm surge mesh. Introductory work towards applying image analysis techniques as an alternate means of vertical feature extraction is presented as well. Vertical feature lines extracted from a LiDAR dataset for Manatee County, Florida are included in a limited storm surge finite element mesh for the county and Tampa Bay. Storm surge simulations using the ADCIRC-2DDI model with two meshes, one which includes linear raised features as element edges and one which does not, verify the usefulness of the method

    A novel automated approach of multi-modality retinal image registration and fusion

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    Biomedical image registration and fusion are usually scene dependent, and require intensive computational effort. A novel automated approach of feature-based control point detection and area-based registration and fusion of retinal images has been successfully designed and developed. The new algorithm, which is reliable and time-efficient, has an automatic adaptation from frame to frame with few tunable threshold parameters. The reference and the to-be-registered images are from two different modalities, i.e. angiogram grayscale images and fundus color images. The relative study of retinal images enhances the information on the fundus image by superimposing information contained in the angiogram image. Through the thesis research, two new contributions have been made to the biomedical image registration and fusion area. The first contribution is the automatic control point detection at the global direction change pixels using adaptive exploratory algorithm. Shape similarity criteria are employed to match the control points. The second contribution is the heuristic optimization algorithm that maximizes Mutual-Pixel-Count (MPC) objective function. The initially selected control points are adjusted during the optimization at the sub-pixel level. A global maxima equivalent result is achieved by calculating MPC local maxima with an efficient computation cost. The iteration stops either when MPC reaches the maximum value, or when the maximum allowable loop count is reached. To our knowledge, it is the first time that the MPC concept has been introduced into biomedical image fusion area as the measurement criteria for fusion accuracy. The fusion image is generated based on the current control point coordinates when the iteration stops. The comparative study of the presented automatic registration and fusion scheme against Centerline Control Point Detection Algorithm, Genetic Algorithm, RMSE objective function, and other existing data fusion approaches has shown the advantage of the new approach in terms of accuracy, efficiency, and novelty
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