Relevant bibliographies by topics / SEGMENTATION SYSTEM

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'SEGMENTATION SYSTEM'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "SEGMENTATION SYSTEM"

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Liu, Qiming, Qifan Lu, Yezi Chai, Zhengyu Tao, Qizhen Wu, Meng Jiang, and Jun Pu. "Radiomics-Based Quality Control System for Automatic Cardiac Segmentation: A Feasibility Study." Bioengineering 10, no. 7 (July 1, 2023): 791. http://dx.doi.org/10.3390/bioengineering10070791.

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Purpose: In the past decade, there has been a rapid increase in the development of automatic cardiac segmentation methods. However, the automatic quality control (QC) of these segmentation methods has received less attention. This study aims to address this gap by developing an automatic pipeline that incorporates DL-based cardiac segmentation and radiomics-based quality control. Methods: In the DL-based localization and segmentation part, the entire heart was first located and cropped. Then, the cropped images were further utilized for the segmentation of the right ventricle cavity (RVC), myocardium (MYO), and left ventricle cavity (LVC). As for the radiomics-based QC part, a training radiomics dataset was created with segmentation tasks of various quality. This dataset was used for feature extraction, selection, and QC model development. The model performance was then evaluated using both internal and external testing datasets. Results: In the internal testing dataset, the segmentation model demonstrated a great performance with a dice similarity coefficient (DSC) of 0.954 for whole heart segmentations. Images were then appropriately cropped to 160 × 160 pixels. The models also performed well for cardiac substructure segmentations. The DSC values were 0.863, 0.872, and 0.940 for RVC, MYO, and LVC for 2D masks and 0.928, 0.886, and 0.962 for RVC, MYO, and LVC for 3D masks with an attention-UNet. After feature selection with the radiomics dataset, we developed a series of models to predict the automatic segmentation quality and its DSC value for the RVC, MYO, and LVC structures. The mean absolute values for our best prediction models were 0.060, 0.032, and 0.021 for 2D segmentations and 0.027, 0.017, and 0.011 for 3D segmentations, respectively. Additionally, the radiomics-based classification models demonstrated a high negative detection rate of >0.85 in all 2D groups. In the external dataset, models showed similar results. Conclusions: We developed a pipeline including cardiac substructure segmentation and QC at both the slice (2D) and subject (3D) levels. Our results demonstrate that the radiomics method possesses great potential for the automatic QC of cardiac segmentation.
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Meenakshi BK, Meenakshi BK, and Prasad M. R. Prasad M R. "Survey on Segmentation to Iris Recognition System." International Journal of Scientific Research 3, no. 4 (June 1, 2012): 514–15. http://dx.doi.org/10.15373/22778179/apr2014/184.

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Gurari, Danna, Mehrnoosh Sameki, and Margrit Betke. "Investigating the Influence of Data Familiarity to Improve the Design of a Crowdsourcing Image Annotation System." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 4 (September 21, 2016): 59–68. http://dx.doi.org/10.1609/hcomp.v4i1.13294.

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Crowdsourced demarcations of object boundaries in images (segmentations) are important for many vision-based applications. A commonly reported challenge is that a large percentage of crowd results are discarded due to concerns about quality. We conducted three studies to examine (1) how does the quality of crowdsourced segmentations differ for familiar everyday images versus unfamiliar biomedical images?, (2) how does making familiar images less recognizable (rotating images upside down) influence crowd work with respect to the quality of results, segmentation time, and segmentation detail?, and (3) how does crowd workers’ judgments of the ambiguity of the segmentation task, collected by voting, differ for familiar everyday images and unfamiliar biomedical images? We analyzed a total of 2,525 segmentations collected from 121 crowd workers and 1,850 votes from 55 crowd workers. Our results illustrate the potential benefit of explicitly accounting for human familiarity with the data when designing computer interfaces for human interaction.
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Mendoza Garay, Juan Ignacio. "Segmentation boundaries in accelerometer data of arm motion induced by music: Online computation and perceptual assessment." Human Technology 18, no. 3 (December 28, 2022): 250–66. http://dx.doi.org/10.14254/1795-6889.2022.18-3.4.

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Segmentation is a cognitive process involved in the understanding of information perceived through the senses. Likewise, the automatic segmentation of data captured by sensors may be used for the identification of patterns. This study is concerned with the segmentation of dancing motion captured by accelerometry and its possible applications, such as pattern learning and recognition, or gestural control of devices. To that effect, an automatic segmentation system was formulated and tested. Two participants were asked to ‘dance with one arm’ while their motion was measured by an accelerometer. The performances were recorded on video, and manually segmented by six annotators later. The annotations were used to optimize the automatic segmentation system, maximizing a novel similarity score between computed and annotated segmentations. The computed segmentations with highest similarity to each annotation were then manually assessed by the annotators, resulting in Precision between 0.71 and 0.89, and Recall between 0.82 to 1.
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Sunoqrot, Mohammed R. S., Kirsten M. Selnæs, Elise Sandsmark, Gabriel A. Nketiah, Olmo Zavala-Romero, Radka Stoyanova, Tone F. Bathen, and Mattijs Elschot. "A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI." Diagnostics 10, no. 9 (September 18, 2020): 714. http://dx.doi.org/10.3390/diagnostics10090714.

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Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations.
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Joseph, E., A. M. Aibinu, B. A. Sadiq, H. Bello Salau, and M. J. E. Salami. "Scorpion image segmentation system." IOP Conference Series: Materials Science and Engineering 53 (December 20, 2013): 012055. http://dx.doi.org/10.1088/1757-899x/53/1/012055.

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Mauricaite, Radvile, Ella Mi, Jiarong Chen, Andrew Ho, Lillie Pakzad-Shahabi, and Matthew Williams. "Fully automated deep learning system for detecting sarcopenia on brain MRI in glioblastoma." Neuro-Oncology 23, Supplement_4 (October 1, 2021): iv13. http://dx.doi.org/10.1093/neuonc/noab195.031.

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Abstract Aims Glioblastoma multiforme (GBM) is an aggressive brain malignancy. Performance status is an important prognostic factor but is subjectively evaluated, resulting in inaccuracy. Objective markers of frailty/physical condition, such as measures of skeletal muscle mass can be evaluated on cross-sectional imaging and is associated with cancer survival. In GBM, temporalis muscle has been identified as a skeletal muscle mass surrogate and a prognostic factor. However, current manual muscle quantification is time consuming, limiting clinical adoption. We previously developed a deep learning system for automated temporalis muscle quantification, with high accuracy (Dice coefficient 0.912), and showed muscle cross-sectional area is independently significantly associated with survival in GBM (HR 0.380). However, it required manual selection of the temporalis muscle-containing MRI slice. Thus, in this work we aimed to develop a fully automatic deep-learning system, using the eyeball as an anatomic landmark for automatic slice selection, to quantify temporalis and validate on independent datasets. Method 3D brain MRI scans were obtained from four datasets: our in-house glioblastoma patient dataset, TCGA-GBM, IVY-GAP and REMBRANDT. Manual eyeball and temporalis segmentations were performed on 2D MRI images by two experienced readers. Two neural networks (2D U-Nets) were trained, one to automatically segment the eyeball and the other to segment the temporalis muscle on 2D MRI images using Dice loss function. The cross sectional area of eyeball segmentations were quantified and thresholded, to select the superior orbital MRI slice from each scan. This slice underwent temporalis segmentation, whose cross sectional area was then quantified. Accuracy of automatically predicted eyeball and temporalis segmentations were compared to manual ground truth segmentations on metrics of Dice coefficient, precision, recall and Hausdorff distance. Accuracy of MRI slice selection (by the eyeball segmentation model) for temporalis segmentation was determined by comparing automatically selected slices to slices selected manually by a trained neuro-oncologist. Results 398 images from 185 patients and 366 images from 145 patients were used for the eyeball and temporalis segmentation models, respectively. 61 independent TCGA-GBM scans formed a validation cohort to assess the performance of the full pipeline. The model achieved high accuracy in eyeball segmentation, with test set Dice coefficient of 0.9029 ± 0.0894, precision of 0.8842 ± 0.0992, recall of 0.9297 ± 0.6020 and Hausdorff distance of 2.8847 ± 0.6020. High segmentation accuracy was also achieved by the temporalis segmentation model, with Dice coefficient of 0.8968 ± 0.0375, precision of 0.8877 ± 0.0679, recall of 0.9118 ± 0.0505 and Hausdorff distance of 1.8232 ± 0.3263 in the test set. 96.1% of automatically selected slices for temporalis segmentation were within 2 slices of the manually selected slice. Conclusion Temporalis muscle cross-sectional area can be rapidly and accurately assessed from 3D MRI brain scans using a deep learning-based system in a fully automated pipeline. Combined with our and others’ previous results that demonstrate the prognostic significance of temporalis cross-sectional area and muscle width, our findings suggest a role for deep learning in muscle mass and sarcopenia screening in GBM, with the potential to add significant value to routine imaging. Possible clinical applications include risk profiling, treatment stratification and informing interventions for muscle preservation. Further work will be to validate the prognostic value of temporalis muscle cross sectional area measurements generated by our fully automatic deep learning system in the multiple in-house and external datasets.
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Wang, Zhengfei, Lai Wang, Fu'an Xiao, Qingsong Chen, Liming Lu, and Jiaming Hong. "A Traditional Chinese Medicine Traceability System Based on Lightweight Blockchain." Journal of Medical Internet Research 23, no. 6 (June 21, 2021): e25946. http://dx.doi.org/10.2196/25946.

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Background Recently, the problem of traditional Chinese medicine (TCM) safety has attracted attention worldwide. To prevent the spread of counterfeit drugs, it is necessary to establish a drug traceability system. A traditional drug traceability system can record the whole circulation process of drugs, from planting, production, processing, and warehousing to use by hospitals and patients. Once counterfeit drugs are found, they can be traced back to the source. However, traditional drug traceability systems have some drawbacks, such as failure to prevent tampering and facilitation of sensitive disclosure. Blockchain (including Bitcoin and Ethernet Square) is an effective technology to address the problems of traditional drug traceability systems. However, some risks impact the reliability of blockchain, such as information explosion, sensitive information leakage, and poor scalability. Objective To avoid the risks associated with the application of blockchain, we propose a lightweight block chain framework. Methods In this framework, both horizontal and vertical segmentations are performed when designing the blocks, and effective strategies are provided for both segmentations. For horizontal segmentation operations, the header and body of the blockchain are separated and stored in the blockchain, and the body is stored in the InterPlanetary File System. For vertical segmentation operations, the blockchain is cut off according to time or size. For the addition of new blocks, miners only need to copy the latest part of the blockchain and append the tail and vertical segmentation of the block through the consensus mechanism. Results Our framework could greatly reduce the size of the blockchain and improve the verification efficiency. Conclusions Experimental results have shown that the efficiency improves compared with ethernet when a new block is added to the blockchain and a search is conducted.
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Allioui, Hanane, Mohamed Sadgal, and Aziz El Fazziki. "An Improved Image Segmentation System." Journal of communications software and systems 16, no. 2 (April 27, 2020): 143–55. http://dx.doi.org/10.24138/jcomss.v16i2.830.

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In this paper, we present a solution-based cooperation approach for strengthening the image segmentation.This paper proposes a cooperative method relying on Multi-Agent System. The main contribution of this work is to highlight the importance of cooperation between the contour and region growing based on Multi-Agent System (MAS). Consequently, agents’ interactions form the main part of the whole process for image segmentation. Similar works were proposed to evaluate the effectiveness of the proposed solution. The main difference is that our Multi-Agent System can perform the segmentation process ensuring efficiency. Our results show that the performance indices in the system were higher. Furthermore, the integration of the cooperation paradigm allows to speed up the segmentation process. Besides, the tests reveal the robustness of our method by proving competitive results. Our proposal achieved an accuracy of 93,51%± 0,8, a sensitivity of 93,53%± 5,08 and a specificity rate of 92,64%± 4,01.
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Baujard, Olivier. "KISS: a multiagent segmentation system." Optical Engineering 32, no. 6 (1993): 1235. http://dx.doi.org/10.1117/12.134190.

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Dissertations / Theses on the topic "SEGMENTATION SYSTEM"

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Jomaa, Diala. "Fingerprint Segmentation." Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4264.

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In this thesis, a new algorithm has been proposed to segment the foreground of the fingerprint from the image under consideration. The algorithm uses three features, mean, variance and coherence. Based on these features, a rule system is built to help the algorithm to efficiently segment the image. In addition, the proposed algorithm combine split and merge with modified Otsu. Both enhancements techniques such as Gaussian filter and histogram equalization are applied to enhance and improve the quality of the image. Finally, a post processing technique is implemented to counter the undesirable effect in the segmented image. Fingerprint recognition system is one of the oldest recognition systems in biometrics techniques. Everyone have a unique and unchangeable fingerprint. Based on this uniqueness and distinctness, fingerprint identification has been used in many applications for a long period. A fingerprint image is a pattern which consists of two regions, foreground and background. The foreground contains all important information needed in the automatic fingerprint recognition systems. However, the background is a noisy region that contributes to the extraction of false minutiae in the system. To avoid the extraction of false minutiae, there are many steps which should be followed such as preprocessing and enhancement. One of these steps is the transformation of the fingerprint image from gray-scale image to black and white image. This transformation is called segmentation or binarization. The aim for fingerprint segmentation is to separate the foreground from the background. Due to the nature of fingerprint image, the segmentation becomes an important and challenging task. The proposed algorithm is applied on FVC2000 database. Manual examinations from human experts show that the proposed algorithm provides an efficient segmentation results. These improved results are demonstrating in diverse experiments.
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Wong, Jennifer L. "A material segmentation and classification system." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85523.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 75).
In this thesis, I developed a material segmentation and classification system that takes in images of an object and identifies the material composition of the object's surface. The 3D surface is first segmented into regions that likely contain the same material, using color as a heuristic measure. The material classification of each region is then based on the cosine lobe model. The cosine lobe model is our adopted reflectance model, which allows for a simple approximation of a material's reflectance properties, which then serves as the material's unique signature.
by Jennifer L. Wong.
M. Eng.
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Östgren, Magnus. "FPGA acceleration of superpixel segmentation." Thesis, Mälardalens högskola, Inbyggda system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48577.

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Superpixel segmentation is a preprocessing step for computer vision applications, where an image is split into segments referred to as superpixels. Then running the main algorithm on these superpixels reduces the number of data points processed in comparison to running the algorithm on pixels directly, while still keeping much of the same information. In this thesis, the possibility to run superpixel segmentation on an FPGA is researched. This has resulted in the development of a modified version of the algorithm SLIC, Simple Linear Iterative Clustering. An FPGA implementation of this algorithm has then been built in VHDL, it is designed as a pipeline, unrolling the iterations of SLIC. The designed algorithm shows a lot of potential and runs on real hardware, but more work is required to make the implementation more robust, and remove some visual artefacts.
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Cho, Jinsoo. "Velocity-based cardiac segmentation and motion-tracking." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04082004-180106/unrestricted/cho%5Fjinsoo%5F200312%5Fphd.pdf.

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King, Stephen. "A machine vision system for texture segmentation." Thesis, Brunel University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310081.

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Kernell, Björn. "Improving Photogrammetry using Semantic Segmentation." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148491.

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3D reconstruction is the process of constructing a three-dimensional model from images. It contains multiple steps where each step can induce errors. When doing 3D reconstruction of outdoor scenes, there are some types of scene content that regularly cause problems and affect the resulting 3D model. Two of these are water, due to its fluctuating nature, and sky because of it containing no useful (3D) data. These areas cause different problems throughout the process and do generally not benefit it in any way. Therefore, masking them early in the reconstruction chain could be a useful step in an outdoor scene reconstruction pipeline. Manual masking of images is a time-consuming and boring task and it gets very tedious for big data sets which are often used in large scale 3D reconstructions. This master thesis explores if this can be done automatically using Convolutional Neural Networks for semantic segmentation, and to what degree the masking would benefit a 3D reconstruction pipeline.
3D-rekonstruktion är teknologin bakom att skapa 3D-modeller utifrån bilder. Det är en process med många steg där varje steg kan medföra fel. Vid 3D-rekonstruktion av stora utomhusmiljöer finns det vissa typer av bildinnehåll som ofta ställer till problem. Två av dessa är vatten och himmel. Vatten är problematiskt då det kan fluktuera mycket från bild till bild samt att det kan innehålla reflektioner som ger olika utseenden från olika vinklar. Himmel å andra sidan ska aldrig ge upphov till 3D-information varför den lika gärna kan maskas bort. Manuell maskning av bilder är väldigt tidskrävande och dyrt. Detta examensarbete undersöker huruvida denna maskning kan göras automatiskt med Faltningsnät för Semantisk Segmentering och hur detta skulle kunna förbättra en 3D-rekonstruktionsprocess.
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Fergusson, Robert Johnstone. "Human visual system based object extraction for video coding." Thesis, University of Strathclyde, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366673.

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Chen, Cheng. "A General System for Supervised Biomedical Image Segmentation." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/214.

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Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before used in a different application. We describe a system that, with few modifications, can be used in a variety of image segmentation problems. The system is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. In summary, we have several innovations: (1) A general framework for such a system is proposed, where rotations and variations of intensity neighborhoods in scales are modeled, and a multi-scale classification framework is utilized to segment unknown images; (2) A fast algorithm for training data selection and pixel classification is presented, where a majority voting based criterion is proposed for selecting a small subset from raw training set. When combined with 1-nearest neighbor (1-NN) classifier, such an algorithm is able to provide descent classification accuracy within reasonable computational complexity. (3) A general deformable model for optimization of segmented regions is proposed, which takes the decision values from previous pixel classification process as input, and optimize the segmented regions in a partial differential equation (PDE) framework. We show that the performance of this system in several different biomedical applications, such as tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar or better than several algorithms specifically designed for each of these applications. In addition, we describe another general segmentation system for biomedical applications where a strong prior on shape is available (e.g. cells, nuclei). The idea is based on template matching and supervised learning, and we show the examples of segmenting cells and nuclei from microscopy images. The method uses examples selected by a user for building a statistical model which captures the texture and shape variations of the nuclear structures from a given data set to be segmented. Segmentation of subsequent, unlabeled, images is then performed by finding the model instance that best matches (in the normalized cross correlation sense) local neighborhood in the input image. We demonstrate the application of our method to segmenting cells and nuclei from a variety of imaging modalities, and quantitatively compare our results to several other methods. Quantitative results using both simulated and real image data show that, while certain methods may work well for certain imaging modalities, our software is able to obtain high accuracy across several imaging modalities studied. Results also demonstrate that, relative to several existing methods, the template based method we propose presents increased robustness in the sense of better handling variations in illumination, variations in texture from different imaging modalities, providing more smooth and accurate segmentation borders, as well as handling better cluttered cells and nuclei.
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Fatemi-Ghomi, Navid. "Performance measures for wavelet-based segmentation algorithms." Thesis, University of Surrey, 1997. http://epubs.surrey.ac.uk/794/.

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Brodie, James Cameron. "Investigation of ephrin regulation during hindbrain segmentation." Thesis, University College London (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249431.

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Books on the topic "SEGMENTATION SYSTEM"

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Storm, Philipp. Alternative Freiverkehrssegmente im Kapitalmarktrecht: Zugleich ein Beitrag zur rechtsökonomischen Analyse emittentenbezogener Regulierung durch einen Marktveranstalter und zum System der Segmentierung. Frankfurt am Main: Lang, 2010.

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Elbehri, Aziz. The Changing face of the U.S. grain system: Differentiation and identity preservation trends. Washington, D.C: U.S.Dept. of Agriculture, Economic Research Service, 2007.

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F, Nielsen Poul M., Miller Karol, and SpringerLink (Online service), eds. Computational Biomechanics for Medicine: Soft Tissues and the Musculoskeletal System. New York, NY: Springer Science+Business Media, LLC, 2011.

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Liew, Alan Wee-Chung. Visual speech recognition: Lip segmentation and mapping. Edited by Wang Shilin. Hershey PA: Medical Information Science Reference, 2009.

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Jaklič, Aleš. Segmentation and Recovery of Superquadrics. Dordrecht: Springer Netherlands, 2000.

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Jaklič, Aleš. Segmentation and recovery of superquadrics. Dordrecht: Kluwer Academic Publishers, 2000.

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Guo, Ju. Semantic Video Object Segmentation for Content-Based Multimedia Applications. Boston, MA: Springer US, 2002.

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Li, Jia. Image Segmentation and Compression Using Hidden Markov Models. Boston, MA: Springer US, 2000.

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Rathgeb, Christian. Iris Biometrics: From Segmentation to Template Security. New York, NY: Springer New York, 2013.

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Guo, Ju. Semantic video object segmentation for content-based multimedia applications. Boston: Kluwer Academic Publishers, 2001.

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Book chapters on the topic "SEGMENTATION SYSTEM"

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Huttunen, O., O. Lipponen, T. Vehkomäki, X. Yu, J. Ylä-Jääski, and T. Katila. "Segmentation System for Medical Images." In Computer Assisted Radiology / Computergestützte Radiologie, 612–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-00807-2_98.

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Markic, Brano, and Drazena Tomic. "Marketing Intelligent System for Customer Segmentation." In Marketing Intelligent Systems Using Soft Computing, 79–111. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15606-9_10.

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Eckhorn, Reinhard. "Segmentation Coding by the Visual System." In Neural Networks: Artificial Intelligence and Industrial Applications, 5–12. London: Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3087-1_1.

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Nguyen, Khai, Bo Peng, Tianrui Li, and Qin Chen. "Online Evaluation System of Image Segmentation." In Advances in Intelligent Systems and Computing, 527–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54927-4_50.

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Woo, Wing Hon, and Kin Sam Yen. "Moiré Fringe Segmentation Using Fuzzy Inference System." In 9th International Conference on Robotic, Vision, Signal Processing and Power Applications, 247–55. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1721-6_27.

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Irwin, P. D. S., and A. J. Wilkinson. "An integrated image segmentation/image analysis system." In Lecture Notes in Computer Science, 26–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/3-540-19036-8_2.

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Cordelia, L. P., M. De Santo, G. Percannella, C. Sansone, and M. Vento. "A Multi-expert System for Movie Segmentation." In Multiple Classifier Systems, 304–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45428-4_30.

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Mota, S., E. Ros, J. Díaz, and F. de Toro. "General Purpose Real-Time Image Segmentation System." In Reconfigurable Computing: Architectures and Applications, 164–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11802839_23.

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Amaral, Rui, and Isabel Trancoso. "Topic Segmentation in a Media Watch System." In Lecture Notes in Computer Science, 272–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85980-2_37.

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Vargas, Jheyson, Jairo Andres Velasco, Gloria Ines Alvarez, Diego Luis Linares, and Enrique Bravo. "False Positive Reduction in Automatic Segmentation System." In Advances in Intelligent Systems and Computing, 103–8. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01568-2_15.

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Conference papers on the topic "SEGMENTATION SYSTEM"

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Les, T., T. Markiewicz, M. Dziekiewicz, and M. Lorent. "Automatic system for the renal and cancer segmentation in CT images." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.092.

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Chen, Bi-ke, Chen Gong, and Jian Yang. "Importance-Aware Semantic Segmentation for Autonomous Driving System." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/208.

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Semantic Segmentation (SS) partitions an image into several coherent semantically meaningful parts, and classifies each part into one of the pre-determined classes. In this paper, we argue that existing SS methods cannot be reliably applied to autonomous driving system as they ignore the different importance levels of distinct classes for safe-driving. For example, pedestrians in the scene are much more important than sky when driving a car, so their segmentations should be as accurate as possible. To incorporate the importance information possessed by various object classes, this paper designs an "Importance-Aware Loss" (IAL) that specifically emphasizes the critical objects for autonomous driving. IAL operates under a hierarchical structure, and the classes with different importance are located in different levels so that they are assigned distinct weights. Furthermore, we derive the forward and backward propagation rules for IAL and apply them to deep neural networks for realizing SS in intelligent driving system. The experiments on CamVid and Cityscapes datasets reveal that by employing the proposed loss function, the existing deep learning models including FCN, SegNet and ENet are able to consistently obtain the improved segmentation results on the pre-defined important classes for safe-driving.
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Nickisch, Hannes, Carsten Rother, Pushmeet Kohli, and Christoph Rhemann. "Learning an interactive segmentation system." In the Seventh Indian Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1924559.1924596.

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Paplanus, S., A. Graham, D. Thompson, and P. H. Bartels. "Expert system-guided scene segmentation." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1988. http://dx.doi.org/10.1109/iembs.1988.95180.

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Chan, Kelby K., Alek S. Hayrapetian, Christina C. Lau, and Robert B. Lufkin. "Neural network based segmentation system." In Medical Imaging 1993, edited by Murray H. Loew. SPIE, 1993. http://dx.doi.org/10.1117/12.154548.

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Ottesen, Georg E. "An automatic diphone segmentation system." In 2nd European Conference on Speech Communication and Technology (Eurospeech 1991). ISCA: ISCA, 1991. http://dx.doi.org/10.21437/eurospeech.1991-188.

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Xiaoyu Wu, Yangsheng Wang, and Xiaolong Zheng. "Monocular video foreground segmentation system." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761326.

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Aboul-Yazeed, Rasha S., Abdalla S. A. Mohamed, and Ahmed El-Bialy. "Edge-based IVD segmentation system." In 2014 Middle East Conference on Biomedical Engineering (MECBME). IEEE, 2014. http://dx.doi.org/10.1109/mecbme.2014.6783213.

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Elmoataz, Abderrahim, Christine Porquet, and Marinette Revenu. "General-purpose segmentation system using knowledge on images and segmentation operators." In Robotics - DL tentative, edited by David P. Casasent. SPIE, 1992. http://dx.doi.org/10.1117/12.57075.

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Vinh, Truong Quang, Bui Minh Thanh, and Nguyen Ngoc Tai. "Dental intraoral system supporting tooth segmentation." In 2013 International Conference on Computing, Management and Telecommunications (ComManTel). IEEE, 2013. http://dx.doi.org/10.1109/commantel.2013.6482414.

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Reports on the topic "SEGMENTATION SYSTEM"

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Palmer, David D. SATZ - An Adaptive Sentence Segmentation System. Fort Belvoir, VA: Defense Technical Information Center, December 1994. http://dx.doi.org/10.21236/ada632260.

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Seybold, Patricia. Designing a Customer Flight Deck(SM) System - Customer Segmentation. Boston, MA: Patricia Seybold Group, January 2002. http://dx.doi.org/10.1571/fw1-31-02cc.

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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform lighting conditions. In the second part of the framework, the context-based segmentation is developed to eliminate regions from imagery that are not considered to be a threat to the pipeline. Context based segmentation makes use of a cascade of pre-trained classifiers to search for regions that are not threats. The context based segmentation algorithm accelerates threat identification and improves object detection rates. The last phase of the framework is an efficient object detection model. Efficient object detection �follows a three-stage approach which includes extraction of the local phase in the image and the use of local phase characteristics to locate machinery threats. The local phase is an image feature extraction technique which partially removes the lighting variance and preserves the edge information of the object. Multiple orientations of the same object are matched and the correct orientation is selected using feature matching by histogram of local phase in a multi-scale framework. The classifier outputs locations of threats to pipeline.�The advanced automatic image analysis system is intended to be capable of detecting construction equipment along the ROW of pipelines with a very high degree of accuracy in comparison with manual threat identification by a human analyst. �
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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.
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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.
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Fisher, Andmorgan, Timothy Middleton, Jonathan Cotugno, Elena Sava, Laura Clemente-Harding, Joseph Berger, Allistar Smith, and Teresa Li. Use of convolutional neural networks for semantic image segmentation across different computing systems. Engineer Research and Development Center (U.S.), March 2020. http://dx.doi.org/10.21079/11681/35881.

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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Profile of clients of different providers of family planning services in Egypt. Social Planning, Analysis & Administration Consultants (SPAAC), 1994. http://dx.doi.org/10.31899/rh1994.1006.

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The Government of Egypt launched its national family planning (FP) program in 1966. The early phase was mainly supply oriented and aimed at ensuring wide distribution of contraceptives primarily through pharmacies to meet rising demand. Between 1988 and 1992 a number of additional sources of FP services appeared, with an increase in clinic-based services through both the private and public sectors. In 1992 about one-fourth of users relied on pharmacies to get their FP supplies compared to about one-half in 1988. This change evolved through the influence of two forces: the initiation of a number of FP projects, and expanded and improved FP services in Ministry of Health facilities. Because of these changes in the service delivery systems, senior program managers required information on current market segmentation to identify any overlap among activities of various service delivery systems. As noted in this report, this study assessed the complementary/competitive roles of these systems. It probes into factors that influence clients movements from one type of service provision to another, and their experience with services received. Six governorates were selected and sampled.
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