34 Result(s)
within
Conference Paper
Miaomiao **n
Image Processing and Computer Vision
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Chapter and Conference Paper
Specular Surface Recovery from Reflections of a Planar Pattern Undergoing an Unknown Pure Translation
This paper addresses the problem of specular surface recovery, and proposes a novel solution based on observing the reflections of a translating planar pattern. Previous works have demonstrated that a specular...
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Chapter and Conference Paper
Perspective Two-Frame-Theory for Shape Recovery under Turntable Motion
This paper addresses the problem of shape from shadings under perspective projection and turntable motion.Two-Frame-Theory is a newly proposed method for 3D shape recovery. It estimates shape by solving a first o...
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Chapter and Conference Paper
Bayesian Estimation of Regularization and Atlas Building in Diffeomorphic Image Registration
This paper presents a generative Bayesian model for diffeomorphic image registration and atlas building. We develop an atlas estimation procedure that simultaneously estimates the parameters controlling the sm...
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Chapter and Conference Paper
Bayesian Principal Geodesic Analysis in Diffeomorphic Image Registration
Computing a concise representation of the anatomical variability found in large sets of images is an important first step in many statistical shape analyses. In this paper, we present a generative Bayesian app...
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Chapter and Conference Paper
Finite-Dimensional Lie Algebras for Fast Diffeomorphic Image Registration
This paper presents a fast geodesic shooting algorithm for diffeomorphic image registration. We first introduce a novel finite-dimensional Lie algebra structure on the space of bandlimited velocity fields. We ...
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Chapter and Conference Paper
A Hierarchical Bayesian Model for Multi-Site Diffeomorphic Image Atlases
Image templates, or atlases, play a critical role in imaging studies by providing a common anatomical coordinate system for analysis of shape and function. It is now common to estimate an atlas as a deformable...
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Chapter and Conference Paper
Temporal Registration in In-Utero Volumetric MRI Time Series
We present a robust method to correct for motion and deformations in in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI requires robust alignment across time in the presence of substa...
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Chapter and Conference Paper
Low-Dimensional Statistics of Anatomical Variability via Compact Representation of Image Deformations
Using image-based descriptors to investigate clinical hypotheses and therapeutic implications is challenging due to the notorious “curse of dimensionality” coupled with a small sample size. In this paper, we p...
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Chapter and Conference Paper
SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation
Region-based methods have proven necessary for improving segmentation accuracy of neuronal structures in electron microscopy (EM) images. Most region-based segmentation methods use a scoring function to determ...
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Chapter and Conference Paper
Building Scene Models by Completing and Hallucinating Depth and Semantics
Building 3D scene models has been a longstanding goal of computer vision. The great progress in depth sensors brings us one step closer to achieving this in a single shot. However, depth sensors still produce ...
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Chapter and Conference Paper
Frequency Diffeomorphisms for Efficient Image Registration
This paper presents an efficient algorithm for large deformation diffeomorphic metric map** (LDDMM) with geodesic shooting for image registration. We introduce a novel finite dimensional Fourier representati...
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Chapter and Conference Paper
Fast Geodesic Regression for Population-Based Image Analysis
Geodesic regression on images enables studies of brain development and degeneration, disease progression, and tumor growth. The high-dimensional nature of image data presents significant computational challeng...
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Chapter and Conference Paper
Efficient Laplace Approximation for Bayesian Registration Uncertainty Quantification
This paper presents a novel approach to modeling the posterior distribution in image registration that is computationally efficient for large deformation diffeomorphic metric map** (LDDMM). We develop a Lapl...
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Chapter and Conference Paper
Partial Multi-view Clustering via Auto-Weighting Similarity Completion
With the development of data collection techniques, multi-view clustering (MVC) becomes an emerging research direction to improve the clustering performance. However, most MVC methods assume that the objects a...
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Chapter and Conference Paper
Unsupervised Learning of Endoscopy Video Frames’ Correspondences from Global and Local Transformation
Inferring the correspondences between consecutive video frames with high accuracy is essential for many medical image processing and computer vision tasks (e.g. image mosaicking, 3D scene reconstruction). Imag...
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Chapter and Conference Paper
A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation
A reliable Ultrasound (US)-to-US registration method to compensate for brain shift would substantially improve Image-Guided Neurological Surgery. Develo** such a registration method is very challenging, due ...
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Chapter and Conference Paper
Data-Driven Model Order Reduction for Diffeomorphic Image Registration
This paper presents a data-driven model reduction algorithm to reduce the computational complexity of diffeomorphic image registration in the context of large deformation diffeomorphic metric map** (LDDMM). ...
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Chapter and Conference Paper
Refined Segmentation R-CNN: A Two-Stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants
Accurate segmentation of punctate white matter lesion (PWML) in infantile brains by an automatic algorithm can reduce the potential risk of postnatal development. How to segment PWML effectively has become one...
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Chapter and Conference Paper
Fabric Defect Detection Based on Lightweight Neural Network
Owing to the variety and complexity of defects in the fabric texture image, automatic fabric defect detection is a challenging task in the fields of machine vision. Deep convolutional neural network (CNN) has ...
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Chapter and Conference Paper
On the Applicability of Registration Uncertainty
Estimating the uncertainty in (probabilistic) image registration enables, e.g., surgeons to assess the operative risk based on the trustworthiness of the registered image data. If surgeons receive inaccurately...
