Fast interactive 2D and 3D segmentation tools.

by Kevin Chun-Ho Wong.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 74-79).Abstract also in Chinese.Chinese Abstract --- p.vAbstract --- p.viAcknowledgements --- p.viiChapter 1 --- Introduction --- p.1Chapter 2 --- Prior Work : Image Segmentation Techniques --- p.3Chapter 2.1 --- Introduction to Image Segmentation --- p.4Chapter 2.2 --- Region Based Segmentation --- p.5Chapter 2.2.1 --- Boundary Based vs Region Based --- p.5Chapter 2.2.2 --- Region growing --- p.5Chapter 2.2.3 --- Integrating Region Based and Edge Detection --- p.6Chapter 2.2.4 --- Watershed Based Methods --- p.8Chapter 2.3 --- Fuzzy Set Theory in Segmentation --- p.8Chapter 2.3.1 --- Fuzzy Geometry Concept --- p.8Chapter 2.3.2 --- Fuzzy C-Means (FCM) Clustering --- p.9Chapter 2.4 --- Canny edge filter with contour following --- p.11Chapter 2.5 --- Pyramid based Fast Curve Extraction --- p.12Chapter 2.6 --- Curve Extraction with Multi-Resolution Fourier transformation --- p.13Chapter 2.7 --- User interfaces for Image Segmentation --- p.13Chapter 2.7.1 --- Intelligent Scissors --- p.14Chapter 2.7.2 --- Magic Wands --- p.16Chapter 3 --- Prior Work : Active Contours Model (Snakes) --- p.17Chapter 3.1 --- Introduction to Active Contour Model --- p.18Chapter 3.2 --- Variants and Extensions of Snakes --- p.19Chapter 3.2.1 --- Balloons --- p.20Chapter 3.2.2 --- Robust Dual Active Contour --- p.21Chapter 3.2.3 --- Gradient Vector Flow Snakes --- p.22Chapter 3.2.4 --- Energy Minimization using Dynamic Programming with pres- ence of hard constraints --- p.23Chapter 3.3 --- Conclusions --- p.25Chapter 4 --- Slimmed Graph --- p.26Chapter 4.1 --- BSP-based image analysis --- p.27Chapter 4.2 --- Split Line Selection --- p.29Chapter 4.3 --- Split Line Selection with Summed Area Table --- p.29Chapter 4.4 --- Neighbor blocks --- p.31Chapter 4.5 --- Slimmed Graph Generation --- p.32Chapter 4.6 --- Time Complexity --- p.35Chapter 4.7 --- Results and Conclusions --- p.36Chapter 5 --- Fast Intelligent Scissor --- p.38Chapter 5.1 --- Background --- p.39Chapter 5.2 --- Motivation of Fast Intelligent Scissors --- p.39Chapter 5.3 --- Main idea of Fast Intelligent Scissors --- p.40Chapter 5.3.1 --- Node position and Cost function --- p.41Chapter 5.4 --- Implementation and Results --- p.42Chapter 5.5 --- Conclusions --- p.43Chapter 6 --- 3D Contour Detection: Volume Cutting --- p.50Chapter 6.1 --- Interactive Volume Cutting with the intelligent scissors --- p.51Chapter 6.2 --- Contour Selection --- p.52Chapter 6.2.1 --- 3D Intelligent Scissors --- p.53Chapter 6.2.2 --- Dijkstra's algorithm --- p.54Chapter 6.3 --- 3D Volume Cutting --- p.54Chapter 6.3.1 --- Cost function for the cutting surface --- p.55Chapter 6.3.2 --- "Continuity function (x,y, z) " --- p.59Chapter 6.3.3 --- Finding the cutting surface --- p.61Chapter 6.3.4 --- Topological problems for the volume cutting --- p.61Chapter 6.3.5 --- Assumptions for the well-conditional contour used in our algo- rithm --- p.62Chapter 6.4 --- Implementation and Results --- p.64Chapter 6.5 --- Conclusions --- p.64Chapter 7 --- Conclusions --- p.71Chapter 7.1 --- Contributions --- p.71Chapter 7.2 --- Future Work --- p.72Chapter 7.2.1 --- Real-time interactive tools with Slimmed Graph --- p.72Chapter 7.2.2 --- 3D slimmed graph --- p.72Chapter 7.2.3 --- Cartoon Film Generation System --- p.7

Optimization-based interactive segmentation interface for multiregion problems.

Interactive segmentation is becoming of increasing interest to the medical imaging community in that it combines the positive aspects of both manual and automated segmentation. However, general-purpose tools have been lacking in terms of segmenting multiple regions simultaneously with a high degree of coupling between groups of labels. Hierarchical max-flow segmentation has taken advantage of this coupling for individual applications, but until recently, these algorithms were constrained to a particular hierarchy and could not be considered general-purpose. In a generalized form, the hierarchy for any given segmentation problem is specified in run-time, allowing different hierarchies to be quickly explored. We present an interactive segmentation interface, which uses generalized hierarchical max-flow for optimization-based multiregion segmentation guided by user-defined seeds. Applications in cardiac and neonatal brain segmentation are given as example applications of its generality

Interactive Perception Based on Gaussian Process Classification for House-Hold Objects Recognition and Sorting

We present an interactive perception model for object sorting based on Gaussian Process (GP) classification that is capable of recognizing objects categories from point cloud data. In our approach, FPFH features are extracted from point clouds to describe the local 3D shape of objects and a Bag-of-Words coding method is used to obtain an object-level vocabulary representation. Multi-class Gaussian Process classification is employed to provide and probable estimation of the identity of the object and serves a key role in the interactive perception cycle – modelling perception confidence. We show results from simulated input data on both SVM and GP based multi-class classifiers to validate the recognition accuracy of our proposed perception model. Our results demonstrate that by using a GP-based classifier, we obtain true positive classification rates of up to 80%. Our semi-autonomous object sorting experiments show that the proposed GP based interactive sorting approach outperforms random sorting by up to 30% when applied to scenes comprising configurations of household objects

Elastic map: interactive image segmentation using a few seed-points

Thesis supervisor: Dr. K. Palaniappan.Includes vita.Over the past two decades interactive methods for clinical and biomedical image segmentation have been investigated since the pioneering work of Live-Wire, Live-Lane [17] and Intelligent Scissors [1]. Fully automatic image segmentation is essential for quantitative analysis but remains an unsolved problem, so user driven interactive methods continue to be a powerful alternative when extremely precise segmentation is required. However, manual methods although routinely used are tedious, time-consuming, expensive, inconsistent between experts and error prone. In semi-supervised interactive segmentation the goal is for the user to provide a small amount of partial information or hints for an automatic algorithm to use in order to produce accurate boundaries suitable for the user. The coupled interaction between the user provided input and the semi-supervised segmentation algorithm should be minimal and robust. Commonly used drawing tools for interactive segmentation interfaces include active contour or boundary drawing, scribbles to identify foreground and background regions, and rectangles to outline the object of interest. But interactive segmentation using a sparse set of seed-points has not been widely investigated. In this work we investigate the use of sparse seed point-based for interactive image segmentation task. We have also proposed a new regression based framework, making use of Elastic Body Splines (EBS) to perform interactive image segmentation. Elastic Body Splines belonging to the family of 3D splines were recently introduced to capture tissue deformations within a physical model-based approach for non-rigid biomedical image registration [18]. ElasticMap model the displacement of points in a 3D homogeneous isotropic elastic body subject to forces. We propose a novel extension of using elastic body splines for interactive learning-based figure-ground segmentation. The task of interactive image segmentation, with user provided foreground-background labeled seeds or samples, is formulated as learning a spatially dependent interpolating pixel classification function that is then used to assign labels for all unlabeled pixels in the image. The spline function we chose to model the semisupervised pixel classifier is the ElasticMap which can use sparse point-scribble input from the user and has a closed form solution. Experimental results demonstrate the applicability of the EBS approach for image segmentation. The ElasticMap method for interactive foreground segmentation uses on an average just four to six labeled pixels as input from the user. Using such sparsely labeled information the proposed EBS method produces very accurate results with an average accuracy consistently exceeding 95 percent on three different benchmark datasets and outperforms eleven other popular interactive image segmentation methods.Includes bibliographical references (pages 110-120)