Application of image segmentation and adaptive interpolation techniques to 3D reconstruction of the human temporal bones


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Author Zhenfeng Zhao, Saint Mary's University (Halifax, N.S.). Department of Mathematics and Computing Science.
Language English
ISBN 9780494829035 0494829036
Genre Academic theses
Available formats PDF, fb2, ePub, mobi, RTF, Doc, txt, LRF, AZW, DJVU

PyImageSearch - Be awesome at OpenCV, …

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08.06.2017 · In this tutorial you’ll discover the difference between Keras and tf.keras , including what’s new in TensorFlow 2.0. Today’s tutorial is inspired from an email I received last Tuesday from PyImageSearch reader, Jeremiah. Jeremiah asks: Hi Adrian, I …

(PDF) Liver Segmentation Using Sparse 3D Prior …

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Liver Segmentation Using Sparse 3D Prior Models with Optimal Data Support. ... application in segmentation. ... 2D to 3D interpolation methods based on shape models to reconstruct the whole 3D ...

Statistical Image Processing and

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A great many textbooks have been written on image processing. However this book does not so much focus on images, per se, but rather on spatial data sets, with one or more measurements taken over a two or higher dimensional space, and to which standard image-processing algorithms may not apply.

Development of automated 3D knee bone

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Canny Edge Detector — OpenCV 2.4.13.7 …

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Asks the user to enter a numerical value to set the lower threshold for our Canny Edge Detector (by means of a Trackbar) Applies the Canny Detector and generates a mask (bright lines representing the edges on a black background). Applies the mask obtained on the original image and display it in a window. The tutorial code’s is shown lines below.

Category:Image processing - Wikipedia

en.wikipedia.org/wiki/Category:Image_processing

Digital image processing is the application of signal processing techniques to the domain of images — two-dimensional signals such as photographs or video. Image processing does typically involve filtering or enhancing an image using various types of functions in addition to other techniques to extract information from the images.

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Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to form pictures of the anatomy and the physiological processes of the body. MRI scanners use strong magnetic fields, magnetic field gradients, and radio waves to generate images of the organs in the body.

Interpolation-Dependent Image Downsampling | …

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Recently, Y. Zhang et at. generated these ideas and proposed an interpolation-dependent image downsampling algorithm, where the downsampled image is …

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Publications | Vision and Image Processing Lab ...

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3D Reconstruction of Underwater Scenes; ... Vision and Image Processing Lab ... adaptive_color_image_segmentation_using_markov_random_fields.pdf (293.05 KB) Wesolkowski, S., and P. Fieguth, "Gibbs random field based vector quantization", NATO ASI Learning Theory and Practice Workshop, Belgium, 2002.

Journals | IDEAgroup

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“MRF regularizaton models for adaptive segmentation of non-uniform images“, IEE Proceedings on Vision, Image and Signal Processing, Vol. 145, Issue 5, pp.322-332, October 1998. [Dinggang Shen and Horace H S Ip] “An affine-invariant active contour model (AI-snake) for model-based segmentation“, Image and Vision Computing 16

Medical Imaging 2001: Image Processing | …

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Cone-beam image reconstruction for detectors with nonsquare detector elements ... Automatic generation of object shape models and their application to tomographic image segmentation Author(s): ... Automating measurement of subtle changes in articular cartilage from MRI of the knee by combining 3D image registration and segmentation Author(s ...

Image Segmentation - MATLAB & Simulink

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Using a Variety of Image Segmentation Techniques. With functions in MATLAB and Image Processing Toolbox™, you can experiment and build expertise on the different image segmentation techniques, including thresholding, clustering, graph-based segmentation, and region growing.. Thresholding. Using Otsu’s method, imbinarize performs thresholding on a 2D or 3D grayscale image to create a binary ...

A vector quantization approach for image

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Image Segmentation for Fruit Detection and

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Image segmentation is the task of transforming individual pixels in an image into class labels. In this paper we present multiple image segmentation architectures for the binary classification of orchard image data into fruit/non-fruit classes. These include a multi-scale MLP and

Medical Image Segmentation: A Brief Survey | …

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Medical image segmentation Survey Segmentation features Appearance Shape Edges Segmentation techniques Statistical models Atlas Deformable models This …

Application of image segmentation and

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Get this from a library! Application of image segmentation and adaptive interpolation techniques to 3D reconstruction of the human temporal bones. [Zhenfeng Zhao; Saint Mary's University (Halifax, N.S.). Department of Mathematics and Computing Science.]

Microsoft Research – Emerging Technology, …

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10.09.2018 · Explore research at Microsoft, a site featuring the impact of research along with publications, products, downloads, and research careers.

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Adaptive object recognition with image feature interpolation

International Journal of Geographical …

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Computer vision - Wikipedia

en.wikipedia.org/wiki/Computer_Vision

Computer graphics produces image data from 3D models, computer vision often produces 3D models from image data. There is also a trend towards a combination of the two disciplines, e.g., as explored in augmented reality. The following characterizations appear …

Edge Detection - MATLAB & Simulink

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Edge detection is an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision.. Common edge detection algorithms include Sobel, Canny, Prewitt, Roberts, and fuzzy logic methods.

GitHub - …

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What Is Object Detection? - MATLAB & Simulink

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Object detection is a computer vision technique for locating instances of objects in images or videos. Object detection algorithms typically leverage machine learning or deep learning to produce meaningful results. When humans look at images or video, we can recognize and locate objects of interest within a matter of moments.

Noise Removal - MATLAB & Simulink

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This approach often produces better results than linear filtering. The adaptive filter is more selective than a comparable linear filter, preserving edges and other high-frequency parts of an image. In addition, there are no design tasks; the wiener2 function handles all preliminary computations and implements the filter for an input image.

Application of AI Techniques in Medical Image

researchgate.net/publication/50864267_Application_ ... ovel_Categorization_of_Available_Methods_and_Tools

A number of medical image segmentation techniques have been proposed, but there is no standard automatic algorithm that can generally be used to segment a real 3D image obtained in daily routine ...

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Volume Table of Contents

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Image Reconstruction and Synthesis. ... Two-level training of a 3D U-Net for accurate segmentation of the intra-cochlear anatomy in head CTs with limited ground truth training data. ... Localization and labeling of cervical vertebral bones in the micro-CT images of rabbit fetuses using a 3D deep convolutional neural network.

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LoAd: A locally adaptive cortical segmentation

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Atlas-based segmentation can be further improved by incorporating intensity information from the unseen image through a Gaussian mixture model (GMM) (Van Leemput et al., 1999; Fischl et al., 2002).

Morphological operations on binary images - …

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bwmorph supports the generation of C code (requires MATLAB ® Coder™).Note that if you choose the generic MATLAB Host Computer target platform, bwmorph generates code that uses a precompiled, platform-specific shared library. Use of a shared library preserves performance optimizations but limits the target platforms for which code can be generated.

Image processing - PDF Free Download

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Image processing Image processing In electrical engineering and computer science, image processing is any form of signal processing for which the input is an image, such as photographs or frames of video; the output of image processing can be either an image or a set of characteristics or parameters related to the image.

US8170350B2 - Foreground/background …

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An analysis and classification tool compares at least a portion of a captured image and a reference image of nominally the same scene. One of the captured and reference images is taken with flash and the other is taken without flash. The tool provides a measure of the difference in illumination between the captured image and the reference image.

Volume rendering - Wikipedia

en.wikipedia.org/wiki/Volume_rendering

Volume rendering is distinguished from thin slice tomography presentations, and is also generally distinguished from projections of 3D models, including maximum intensity projection. Still, technically, all volume renderings become projections when viewed on a 2-dimensional display, making the distinction between projections and volume renderings a bit vague.

Medical Imaging 2006: Image Processing | …

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Improved 3D live-wire method with application to 3D CT chest image analysis ... Large-scale validation of non-rigid registration algorithms for atlas-based brain image segmentation Author(s): ... Optical flow based interpolation of temporal image sequences Author(s): ...

A review of medical image segmentation: …

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Automatic medical image segmentation is an unsolved problem that has captured the attention of many researchers. The purpose of this survey is to identify a representative set of methods that have ...

Medical Imaging 2017: Image Processing | …

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Image segmentation - Wikipedia

en.wikipedia.org/wiki/Image_segmentation

Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

US20100260396A1 - integrated segmentation

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The first part provides an exposure to different kinds of visual data (e.g. 2D images, videos and 3D geometry) and the core mathematical techniques that are required for their processing (e.g. interpolation and linear regression.) The second part of the book on Image Based Visual Computing deals with several fundamental techniques to process 2D ...

Data reconstruction using directional …

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09.11.2010 · The 3D data comprises 2D images. In some embodiments, the 2D images are directionally interpolated to generate directionally-int Data reconstruction using directional interpolation techniques - Georgia Tech Research Corporation

dl.acm.org

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Digital image segmentation is the process of assigning distinct labels to different objects in a digital image, and the fuzzy segmentation algorithm has been used successfully in

A segmentation and reconstruction technique

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In the context of stroke therapy simulation, a method for the segmentation and reconstruction of human vasculature is presented and evaluated. Based on CTA scans, semi-automatic tools have been developed to reduce dataset noise, to segment using active ...

GitHub - kjw0612/awesome-deep-vision: A …

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13.03.2017 · Awesome Deep Vision . A curated list of deep learning resources for computer vision, inspired by awesome-php and awesome-computer-vision.. Maintainers - Jiwon Kim, Heesoo Myeong, Myungsub Choi, Jung Kwon Lee, Taeksoo Kim We are looking for a maintainer! Let me know ([email protected]) if interested.Contributing

Digital Image Processing - Tutorialspoint

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19.04.2018 · Digital image processing deals with manipulation of digital images through a digital computer. It is a subfield of signals and systems but focus particularly on images. DIP focuses on developing a computer system that is able to perform processing on an image. The input of …

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