Relevant bibliographies by topics / Image segmentation tools

Academic literature on the topic 'Image segmentation tools'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Image segmentation tools"

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MEZARIS, VASILEIOS, IOANNIS KOMPATSIARIS, and MICHAEL G. STRINTZIS. "STILL IMAGE SEGMENTATION TOOLS FOR OBJECT-BASED MULTIMEDIA APPLICATIONS." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 04 (June 2004): 701–25. http://dx.doi.org/10.1142/s0218001404003393.

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In this paper, a color image segmentation algorithm and an approach to large-format image segmentation are presented, both focused on breaking down images to semantic objects for object-based multimedia applications. The proposed color image segmentation algorithm performs the segmentation in the combined intensity–texture–position feature space in order to produce connected regions that correspond to the real-life objects shown in the image. A preprocessing stage of conditional image filtering and a modified K-Means-with-connectivity-constraint pixel classification algorithm are used to allow for seamless integration of the different pixel features. Unsupervised operation of the segmentation algorithm is enabled by means of an initial clustering procedure. The large-format image segmentation scheme employs the aforementioned segmentation algorithm, providing an elegant framework for the fast segmentation of relatively large images. In this framework, the segmentation algorithm is applied to reduced versions of the original images, in order to speed-up the completion of the segmentation, resulting in a coarse-grained segmentation mask. The final fine-grained segmentation mask is produced with partial reclassification of the pixels of the original image to the already formed regions, using a Bayes classifier. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.
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Chandra De, Utpal, Madhabananda Das, Debashis Mishra, and Debashis Mishra. "Threshold based brain tumor image segmentation." International Journal of Engineering & Technology 7, no. 3 (August 22, 2018): 1801. http://dx.doi.org/10.14419/ijet.v7i3.12425.

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Image processing is most vital area of research and application in field of medical-imaging. Especially it is a major component in medical science. Starting from radiology to ultrasound (sonography), MRI, etc. in lots of area image is the only source of diagnosis process. Now-a-days, different types of devices are being introduced to capture the internal body parts in medical science to carry the diagnosis process correctly. However, due to various reasons, the captured images need to be tuned digitally to gain the more information. These processes involve noise reduction, segmentations, thresholding etc. . Image segmentation is a process to segment the target area of image to identify the area more prominently. There are different process are evolved to perform the segmentation process, one of which is Image thresholding. Moreover there are different tools are also introduce to perform this step of image thresholding. The recent introduced tool PSO is being used here to segment the MRI scans to identify the brain lesions using image thresholding technique.
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Tippner, Aleš. "Development of segmentation algorithm based region growing for software GIS GRASS." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 58, no. 1 (2010): 207–16. http://dx.doi.org/10.11118/actaun201058010207.

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Image segmentation is fundamental prerequisite for new satellite images interpretation methods. GIS GRASS provides segmentation tools enabling global image segmentation only. We designed procedure enabling local segmentation using existing GRASS tools and segmentation algorithm based on region growing that we developed with C++. This algorithm applies mathematical morphology operators to output segments, too. Principial aim of the project is creation of useful input for differentiation of base land cover classes in panchromatic high-resolution satellite image (or historical aerial photographs for example).
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Stevens, Courtney R., Josh Berenson, Michael Sledziona, Timothy P. Moore, Lynn Dong, and Jonathan Cheetham. "Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle." PLOS ONE 15, no. 12 (December 23, 2020): e0243163. http://dx.doi.org/10.1371/journal.pone.0243163.

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Currently available software tools for automated segmentation and analysis of muscle cross-section images often perform poorly in cases of weak or non-uniform staining conditions. To address these issues, our group has developed the MyoSAT (Myofiber Segmentation and Analysis Tool) image-processing pipeline. MyoSAT combines several unconventional approaches including advanced background leveling, Perona-Malik anisotropic diffusion filtering, and Steger’s line detection algorithm to aid in pre-processing and enhancement of the muscle image. Final segmentation is based upon marker-based watershed segmentation. Validation tests using collagen V labeled murine and canine muscle tissue demonstrate that MyoSAT can determine mean muscle fiber diameter with an average accuracy of ~92.4%. The software has been tested to work on full muscle cross-sections and works well even under non-optimal staining conditions. The MyoSAT software tool has been implemented as a macro for the freely available ImageJ software platform. This new segmentation tool allows scientists to efficiently analyze large muscle cross-sections for use in research studies and diagnostics.
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R, Asharani, and Naveen Kumar R. "Review on Brain Tumor Image Segmentation in Time-Frequency Domain." Journal of Image Processing and Artificial Intelligence 8, no. 3 (September 20, 2022): 1–6. http://dx.doi.org/10.46610/joipai.2022.v08i03.001.

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The progressive image segmentation is one of the necessary stages in image acquisition and recognition for an effective identification of brain tumor in advanced medical equipment’s, any image segmentation algorithms working effectively in distinguishing impaired and malignant information from tomographic images through various classification techniques. There is an ambiguity in segmentation for effective regeneration of disseminated information during investigation and extraction of features like shape, volume, and motions of organs from medical images is essential. Current research in medical imaging is aimed at developing automated image recognition and diagnostic systems, which require efficient image segmentation and quantification tools. This paper made an effort to realize the Time-frequency method of image segmentation and reviewing the findings of existing Medical segmentation techniques for medical images.
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He, Bing Song, Feng Zhu, and Yong Gang Shi. "Medical Image Segmentation." Advanced Materials Research 760-762 (September 2013): 1590–93. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1590.

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Medical image plays an important role in the assist doctors in the diagnosis and treatment of diseases. For the medical image, the further analysis and diagnosis of the target area is based on image segmentation. There are many different kinds of image segmentation algorithms. In this paper, image segmentation algorithms are divided into classical image segmentation algorithms and segmentation methods combined with certain mathematical tools, including threshold segmentation methods, image segmentation algorithms based on the edge, image segmentation algorithms based on the region, image segmentation algorithms based on artificial neural network technology, image segmentation algorithms based on contour model and image segmentation algorithm based on statistical major segmentation algorithm and so on. Finally, the development trend of medical image segmentation algorithms is discussed.
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Kang, Yan, Klaus Engelke, and Willi A. Kalender. "Interactive 3D editing tools for image segmentation." Medical Image Analysis 8, no. 1 (March 2004): 35–46. http://dx.doi.org/10.1016/j.media.2003.07.002.

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Atlasov, I. V., L. M. Bozhko, O. Ja Kravets, D. I. Mutin, and D. V. Popov. "Formation of a register of special technology objects based on integrating segmented images." Journal of Physics: Conference Series 2373, no. 2 (December 1, 2022): 022063. http://dx.doi.org/10.1088/1742-6596/2373/2/022063.

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Abstract The article considers aerospace technologies in terms of the details of the space survey of distributed objects. With discrete shooting of areas of interest, a set of images is formed, which are processed by segmentation tools. A set of overlapping or overlapping segments forms objects belonging to the register (areas of interest). The theoretical features of creating mathematical support for the process of forming areas of interest in which objects are present, based on a set of image segments obtained as a result of image processing by segmentation tools, are described. In particular, a mathematical model is presented for creating initial zones from multiple segments of sequential images. The process consists of determining the dynamic neighbourhood, determining the interaction weights and the rules for updating them, updating the spatial vectors of the segments of the processed images. The obtained results provide the creation of mathematical support for the process of forming areas of interest in which objects are present, based on a set of image segments obtained as a result of image processing by segmentation tools.
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Oyebode, Kazeem Oyeyemi. "Leveraging Deep Learning and Grab Cut for Automatic Segmentation of White Blood Cell Images." Journal of Biomimetics, Biomaterials and Biomedical Engineering 58 (August 19, 2022): 121–28. http://dx.doi.org/10.4028/p-oj4d78.

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White blood cell image segmentation provides the opportunity for medical experts to objectively diagnose the medical conditions of patients suffering from Leukemia, for example. Due to the rigorous nature of cell image acquisition (staining process and non-uniform illumination) efficient tools must be deployed to achieve the desired segmentation result. In this paper, a deep learning model is proposed together with a grab cut. The developed deep learning model provides an initial coarse segmentation of white blood cell images. However, the objective of this segmentation is to localize or identify regions of interest from white blood cell images. A bounding is generated from the localized cell image and then used to initiate an automatic cell image segmentation using grab cut. Results of the two publicly available datasets of white blood cell images are considered satisfactory on the proposed model.
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Ta, Vinh-Thong, Olivier Lézoray, Abderrahim Elmoataz, and Sophie Schüpp. "Graph-based tools for microscopic cellular image segmentation." Pattern Recognition 42, no. 6 (June 2009): 1113–25. http://dx.doi.org/10.1016/j.patcog.2008.10.029.

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Dissertations / Theses on the topic "Image segmentation tools"

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Rackham, Thomas. "Ultrasound segmentation tools and their application to assess fetal nutritional health." Thesis, University of Oxford, 2016. http://ora.ox.ac.uk/objects/uuid:5d102b18-dd32-4004-8aa5-b04242139daa.

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Maternal diet can have a great impact on the health and development of the fetus. Poor fetal nutrition has been linked to the development of a set of conditions in later life, such as coronary heart disease, type 2 diabetes and hypertension, while restricted growth can result in hypogylcemia, hypocalcemia, hypothermia, polycythemia, hyperbilirubinemia and cerebral palsy. High alcohol consumption during pregnancy can result in Fetal Alcohol Syndrome, a condition that can cause growth retardation, lowered intelligence and craniofacial defects. Current biometric assessment of the fetus involves size-based measures which may not accurately portray the state of fetal development, since they cannot differentiate cases of small-but-healthy or large-but-unhealthy fetuses. This thesis aims to outline a set of more appropriate measures of accurately capturing the state of fetal development. Specifically, soft tissue area and liver volume measurement are examined, followed by facial shape characterisation. A number of tools are presented which aim to allow clinicians to achieve accurate segmentations of these landmark regions. These are modifications on the Live Wire algorithm, an interactive segmentation method in which the user places a number of anchor points and a minimum cost path is calculated between the previous anchor point and the cursor. This focuses on giving the clinician intuitive control over the exact position of the segmented contour. These modifications are FA-S Live Wire, which utilises Feature Asymmetry and a weak shape constraint, ASP Live Wire, which is a 3D expansion of Live Wire, and FA-O Live Wire, which uses Feature Asymmtery and Local Orientation to guide the segmentation process. These have been designed with each of the specific biometric landmarks in mind. Finally, a method of characterising fetal face shape is proposed, using a combination of the segmentation methods described here and a simple shape model with a parameterised b-spline meshing approach to facial surface representation.
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Skounakis, Emmanouil D. "Development of advanced 3D medical analysis tools for clinical training, diagnosis and treatment." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/7967.

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The objective of this PhD research was the development of novel 3D interactive medical platforms for medical image analysis, simulation and visualisation, with a focus on oncology images to support clinicians in managing the increasing amount of data provided by several medical image modalities. DoctorEye and Automatic Tumour Detector platforms were developed through constant interaction and feedback from expert clinicians, integrating a number of innovations in algorithms and methods, concerning image handling, segmentation, annotation, visualisation and plug-in technologies. DoctorEye is already being used in a related tumour modelling EC project (ContraCancrum) and offers several robust algorithms and tools for fast annotation, 3D visualisation and measurements to assist the clinician in better understanding the pathology of the brain area and define the treatment. It is free to use upon request and offers a user friendly environment for clinicians as it simplifies the implementation of complex algorithms and methods. It integrates a sophisticated, simple-to-use plug-in technology allowing researchers to add algorithms and methods (e.g. tumour growth and simulation algorithms for improving therapy planning) and interactively check the results. Apart from diagnostic and research purposes, it supports clinical training as it allows an expert clinician to evaluate a clinical delineation by different clinical users. The Automatic Tumour Detector focuses on abdominal images, which are more complex than those of the brain. It supports full automatic 3D detection of kidney pathology in real-time as well as 3D advanced visualisation and measurements. This is achieved through an innovative method implementing Templates. They contain rules and parameters for the Automatic Recognition Framework defined interactively by engineers based on clinicians’ 3D Golden Standard models. The Templates enable the automatic detection of kidneys and their possible abnormalities (tumours, stones and cysts). The system also supports the transmission of these Templates to another expert for a second opinion. Future versions of the proposed platforms could integrate even more sophisticated algorithms and tools and offer fully computer-aided identification of a variety of other organs and their dysfunctions.
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Ramme, Austin Jedidiah. "High throughput patient-specific orthopaedic analysis: development of interactive tools and application to graft placement in anterior cruciate ligament reconstruction." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/2966.

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Medical imaging technologies have allowed for in vivo evaluation of the human musculoskeletal system. With advances in both medical imaging and computing, patient-specific model development of anatomic structures is becoming a reality. Three-dimensional surface models are useful for patient-specific measurements and finite element studies. Orthopaedics is closely tied to engineering in the analysis of injury mechanisms, design of implantable medical devices, and potentially in the prediction of injury. However, a disconnection exists between medical imaging and orthopaedic analysis; whereby, the ability to generate three-dimensional models from an imaging dataset is difficult, which has restricted its application to large patient populations. We have compiled image processing, image segmentation, and surface generation tools in a single software package catered specifically to image-based orthopaedic analysis. We have also optimized an automated segmentation technique to allow for high-throughput bone segmentation and developed algorithms that help to automate the cumbersome process of mesh generation in finite element analysis. We apply these tools to evaluate graft placement in anterior cruciate ligament reconstruction in a multicenter study that aims to improve the patient outcomes of those that undergo this procedure.
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Bishop, Courtney Alexandra. "Development and application of image analysis techniques to study structural and metabolic neurodegeneration in the human hippocampus using MRI and PET." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:2549bad2-432f-4d0e-8878-be9cce6ae0d2.

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Despite the association between hippocampal atrophy and a vast array of highly debilitating neurological diseases, such as Alzheimer’s disease and frontotemporal lobar degeneration, tools to accurately and robustly quantify the degeneration of this structure still largely elude us. In this thesis, we firstly evaluate previously-developed hippocampal segmentation methods (FMRIB’s Integrated Registration and Segmentation Tool (FIRST), Freesurfer (FS), and three versions of a Classifier Fusion (CF) technique) on two clinical MR datasets, to gain a better understanding of the modes of success and failure of these techniques, and to use this acquired knowledge for subsequent method improvement (e.g., FIRSTv3). Secondly, a fully automated, novel hippocampal segmentation method is developed, termed Fast Marching for Automated Segmentation of the Hippocampus (FMASH). This combined region-growing and atlas-based approach uses a 3D Sethian Fast Marching (FM) technique to propagate a hippocampal region from an automatically-defined seed point in the MR image. Region growth is dictated by both subject-specific intensity features and a probabilistic shape prior (or atlas). Following method development, FMASH is thoroughly validated on an independent clinical dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), with an investigation of the dependency of such atlas-based approaches on their prior information. In response to our findings, we subsequently present a novel label-warping approach to effectively account for the detrimental effects of using cross-dataset priors in atlas-based segmentation. Finally, a clinical application of MR hippocampal segmentation is presented, with a combined MR-PET analysis of wholefield and subfield hippocampal changes in Alzheimer’s disease and frontotemporal lobar degeneration. This thesis therefore contributes both novel computational tools and valuable knowledge for further neurological investigations in both the academic and the clinical field.
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Bardera, i. Reig Antoni. "New medical registration and segmentation techniques using information-theoretic tools." Doctoral thesis, Universitat de Girona, 2008. http://hdl.handle.net/10803/7891.

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El processament d'imatges mèdiques és una important àrea de recerca. El desenvolupament de noves tècniques que assisteixin i millorin la interpretació visual de les imatges de manera ràpida i precisa és fonamental en entorns clínics reals.
La majoria de contribucions d'aquesta tesi són basades en Teoria de la Informació. Aquesta teoria tracta de la transmissió, l'emmagatzemament i el processament d'informació i és usada en camps tals com física, informàtica, matemàtica, estadística, biologia, gràfics per computador, etc.
En aquesta tesi, es presenten nombroses eines basades en la Teoria de la Informació que milloren els mètodes existents en l'àrea del processament d'imatges, en particular en els camps del registre i la segmentació d'imatges.
Finalment es presenten dues aplicacions especialitzades per l'assessorament mèdic que han estat desenvolupades en el marc d'aquesta tesi.
Medical imaging is an important focus of research. The development of new techniques that assist and enhance visual image interpretation in a timely and accurate manner is fundamental in real clinical environments.
Most of the contributions of this thesis are based on Information Theory. This theory deals with the transmission, storage and processing of information and it is used in fields such as physics, computer science, mathematics, statistics, economics, biology, computer graphics, etc.
In this thesis, several information theoretic tools have been presented in order to improve the existing methods in the image processing area, in particular in the image registration and segmentation fields.
Finally, two of the specialized applications for medical assessment which have been developed in the scope of this thesis are presented.
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Mahmood, Muhammad Habib. "Motion annotation in complex video datasets." Doctoral thesis, Universitat de Girona, 2018. http://hdl.handle.net/10803/667583.

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Motion segmentation refers to the process of separating regions and trajectories from a video sequence into coherent subsets of space and time. In this thesis, we created a new multifaceted motion segmentation dataset enclosing real-life long and short sequences, with different numbers of motions and frames per sequence, and real distortions with missing data. Trajectory- and region-based ground-truth is provided on all the frames of all the sequences. We also proposed a new semi-automatic tool for delineating the trajectories in complex videos, even in videos captured from moving cameras. With a minimal manual annotation of an object mask, the algorithm is able to propagate the label mask in all the frames. Object label correction based on static and moving occluder is performed by applying occluder mask tracking for a given depth ordering. The results show that our cascaded-naive approach provides successful results in a variety of video sequences.
La segmentació del moviment es refereix al procés de separar regions i trajectòries d'una seqüència de vídeo en subconjunts coherents d'espai i de temps. En aquesta tesi hem creat un nou i multifacètic dataset amb seqüències de la vida real que inclou diferent número de moviments i fotogrames per seqüència i distorsions amb dades incomplertes. A més, inclou ground-truth en tots els fotogrames basat en mesures de trajectòria i regió. Hem proposat també una nova eina semiautomàtica per delinear les trajectòries en vídeos complexos, fins i tot en vídeos capturats amb càmeres mòbils. Amb una mínima anotació manual dels objectes, l'algoritme és capaç de propagar-la en tots els fotogrames. Durant les oclusions, la correcció de les etiquetes es realitza aplicant el seguiment de la màscara per a cada ordre de profunditat. Els resultats obtinguts mostren que el nostre enfocament ofereix resultats reeixits en una àmplia varietat de seqüències de vídeo.
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Kinkar, Shilpa N. "Development and Application of Semi-automated ITK Tools Development and Application of Semi-automated ITK Tools for the Segmentation of Brain MR Images." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-050505-103641/.

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Narayanan, Priya Lakshmi. "Development of a tool for automatic segmentation of the cerebellum in MR images of children." Doctoral thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/20262.

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The human cerebellar cortex is a highly foliated structure that supports both motor and complex cognitive functions in humans. Magnetic Resonance Imaging (MRI) is commonly used to explore structural alterations in patients with psychiatric and neurological diseases. The ability to detect regional structural differences in cerebellar lobules may provide valuable insights into disease biology, progression and response to treatment, but has been hampered by the lack of appropriate tools for performing automated structural cerebellar segmentation and morphometry. In this thesis, time intensive manual tracings by an expert neuroanatomist of 16 cerebellar regions on high-resolution T1-weighted MR images of 18 children aged 9-13 years were used to generate the Cape Town Pediatric Cerebellar Atlas (CAPCA18) in the age-appropriate National Institute of Health Pediatric Database (NIHPD) asymmetric template space. An automated pipeline was developed to process the MR images and generate lobule-wise segmentations, as well as a measure of the uncertainty of the label assignments. Validation in an independent group of children with ages similar to those of the children used in the construction of the atlas, yielded spatial overlaps with manual segmentations greater than 70% in all lobules, except lobules VIIb and X. Average spatial overlap of the whole cerebellar cortex was 86%, compared to 78% using the alternative Spatially Unbiased Infra-tentorial Template (SUIT), which was developed using adult images.
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Hallenberg, Johan. "Robot Tool Center Point Calibration using Computer Vision." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-9520.

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Today, tool center point calibration is mostly done by a manual procedure. The method is very time consuming and the result may vary due to how skilled the operators are.

This thesis proposes a new automated iterative method for tool center point calibration of industrial robots, by making use of computer vision and image processing techniques. The new method has several advantages over the manual calibration method. Experimental verifications have shown that the proposed method is much faster, still delivering a comparable or even better accuracy. The setup of the proposed method is very easy, only one USB camera connected to a laptop computer is needed and no contact with the robot tool is necessary during the calibration procedure.

The method can be split into three different parts. Initially, the transformation between the robot wrist and the tool is determined by solving a closed loop of homogeneous transformations. Second an image segmentation procedure is described for finding point correspondences on a rotation symmetric robot tool. The image segmentation part is necessary for performing a measurement with six degrees of freedom of the camera to tool transformation. The last part of the proposed method is an iterative procedure which automates an ordinary four point tool center point calibration algorithm. The iterative procedure ensures that the accuracy of the tool center point calibration only depends on the accuracy of the camera when registering a movement between two positions.

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Ndlovu, Joram. "Branding as a strategic tool to reposition a destination a survey of key tourism stakeholders in Zimbabwe /." Thesis, Pretoria : [s.n.], 2009. http://upetd.up.ac.za/thesis/available/etd-09242009-225847/.

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Book chapters on the topic "Image segmentation tools"

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Heckel, Frank, Momchil I. Ivanov, Jan H. Moltz, and Horst K. Hahn. "Toward Automated Validation of Sketch-Based 3D Segmentation Editing Tools." In Image Analysis, 256–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38886-6_25.

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Li, Hang, Dan Li, Kailiang Zhang, and Chuangeng Tian. "Research on Image Segmentation of Complex Environment Based on Variational Level Set." In Simulation Tools and Techniques, 691–701. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72795-6_55.

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Wielgosz, Maciej, Ernest Jamro, Dominik Żurek, and Kazimierz Wiatr. "FPGA Implementation of the Selected Parts of the Fast Image Segmentation." In Intelligent Tools for Building a Scientific Information Platform, 203–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24809-2_12.

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Tang, Peng, and Weidong Jin. "Automatic Image Semantic Segmentation by MRF with Transformation-Invariant Shape Priors." In Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems, 216–26. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2663-8_23.

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Cao, Chunhong, Chengyao Zhou, Jie Yu, Kai Hu, and Fen Xiao. "A Novel Active Contour Model Using Oriented Smoothness and Infinite Laplacian for Medical Image Segmentation." In Cognitive Internet of Things: Frameworks, Tools and Applications, 311–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04946-1_31.

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Piemonte, Andrea, and Gabriella Caroti. "Photogrammetric Techniques and Image Segmentation via Machine Learning as Supporting Tools in Paving Asphalt Mixtures Studies." In Communications in Computer and Information Science, 283–97. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94426-1_21.

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Rubtsova, Julia. "Approach to Image-Based Segmentation of Complex Surfaces Using Machine Learning Tools During Motion of Mobile Robots." In Electromechanics and Robotics, 191–200. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2814-6_17.

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Klepac, Goran. "REFII Model and Fuzzy Logic as a Tool for Image Classification Based on Image Example." In Hybrid Soft Computing for Image Segmentation, 87–108. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47223-2_4.

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Lerousseau, Marvin, Marion Classe, Enzo Battistella, Théo Estienne, Théophraste Henry, Amaury Leroy, Roger Sun, et al. "Weakly Supervised Pan-Cancer Segmentation Tool." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 248–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87237-3_24.

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Noor, Norliza Mohd, Omar Mohd Rijal, Joel Chia Ming Than, Rosminah M. Kassim, and Ashari Yunus. "Regression as a Tool to Measure Segmentation Quality and Preliminary Indicator of Diseased Lungs." In Image and Video Technology, 502–11. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29451-3_40.

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Conference papers on the topic "Image segmentation tools"

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"Image segmentation." In 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2015. http://dx.doi.org/10.1109/ipta.2015.7367108.

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Padfield, Dirk, and James Ross. "Validation tools for image segmentation." In SPIE Medical Imaging, edited by Josien P. W. Pluim and Benoit M. Dawant. SPIE, 2009. http://dx.doi.org/10.1117/12.812470.

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Jackowski, Marcel P., Ardeshir Goshtasby, and Martin Satter. "Interactive tools for image segmentation." In Medical Imaging '99, edited by Kenneth M. Hanson. SPIE, 1999. http://dx.doi.org/10.1117/12.348501.

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"Medical image segmentation." In 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2015. http://dx.doi.org/10.1109/ipta.2015.7367116.

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"Image segmentation and detection." In 2010 2nd International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2010. http://dx.doi.org/10.1109/ipta.2010.5586832.

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"Image segmentation and detection." In 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2012. http://dx.doi.org/10.1109/ipta.2012.6469490.

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"Image segmentation and detection." In 2014 4th International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2014. http://dx.doi.org/10.1109/ipta.2014.7001932.

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Wang Chongjun, Li Wu jun, Ding Lin, Tian Juan, and Chen Shifu. "Image segmentation using spectral clustering." In 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05). IEEE, 2005. http://dx.doi.org/10.1109/ictai.2005.74.

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Alvarez Padilla, Francisco Javier, Eloise Grossiord, Barbara Romaniuk, Benoit Naegel, Camille Kurtz, Hugues Talbot, Laurent Najman, Romain Guillemot, Dimitri Papathanassiou, and Nicolas Passat. "Multicriteria 3D PET image segmentation." In 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2015. http://dx.doi.org/10.1109/ipta.2015.7367162.

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"Medical image segmentation and detection." In 2010 2nd International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2010. http://dx.doi.org/10.1109/ipta.2010.5586827.

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