Phase field models and higher-order active contours

The representation and modelling of regions is an important topic in computer vision. In this paper, we represent a region via a level set of a 'phase field' function. The function is not constrained, e.g. to be a distance function; nevertheless, phase field energies equivalent to classical active contour energies can be defined. They represent an advantageous alternative to other methods: a linear representation space; ease of implementation (a PDE with no reinitialization); neutral initialization; greater topological freedom. We extend the basic phase field model with terms that reproduce 'higher-order active contour' energies, a powerful way of including prior geometric knowledge in the active contour framework via nonlocal interactions between contour points, in addition to the above advantages, the phase field greatly simplifies the analysis and implementation of the higher-order terms. We define a phase field model that favours regions composed of thin arms meeting at junctions, combine this with image terms, and apply the model to the extraction of line networks from remote sensing images

Gap Closure in (Road) Networks Using Higher-Order Active Contours

We present a new model for the extraction of networks from images in the presence of occlusions. Such occlusions cause gaps in the extracted network that need to he closed. Using higher-order active contours, which allow the incorporation of sophisticated geometric information, we introduce a new, non-local, 'gap closure' force that causes pairs of network extremities that are close together to extend towards one another and join, thus closing the gap between them. We demonstrate the benefits of the model using the problem of road network extraction, presenting results on aerial images

Inflection point model under phase field higher-order active contours for network extraction from VHR satellite images

The segmentation of networks is important in several imaging domains, and models incorporating prior shape knowledge are often essential for the automatic performance of this task. We incorporate such knowledge via phase fields and higher-order active contours (HOACs). In this paper: we introduce an improved prior model, the phase field HOAC `inflection point' model of a network; we present an improved data term for the segmentation of road networks; we confirm the robustness of the resulting model to choice of gradient descent initialization; and we illustrate these points via road network extraction results on VHR satellite images

Active contours with weighted external forces for medical image segmentation

Parametric active contours have been widely used for image segmentation. However, high noise levels and weak edges are the most acute issues that hinder their performance, particularly in medical images. In order to overcome these issues, we propose an external force that weights the gradient vector flow (GVF) field and balloon forces according to local image features. We also propose a mechanism to automatically terminate the contour's deformation. % process. %Our approach improves performance over noisy images and weak edges and allows snake's initialization using a limited number of manually selected points. Evaluation results on real MRI and CT slices show that the proposed approach attains higher segmentation accuracy than snakes using traditional external forces, while allowing initialization using a limited number of selected points

Real-time visual tracking using image processing and filtering methods

The main goal of this thesis is to develop real-time computer vision algorithms in order to detect and to track targets in uncertain complex environments purely based on a visual sensor. Two major subjects addressed by this work are: 1. The development of fast and robust image segmentation algorithms that are able to search and automatically detect targets in a given image. 2. The development of sound filtering algorithms to reduce the effects of noise in signals from the image processing. The main constraint of this research is that the algorithms should work in real-time with limited computing power on an onboard computer in an aircraft. In particular, we focus on contour tracking which tracks the outline of the target represented by contours in the image plane. This thesis is concerned with three specific categories, namely image segmentation, shape modeling, and signal filtering. We have designed image segmentation algorithms based on geometric active contours implemented via level set methods. Geometric active contours are deformable contours that automatically track the outlines of objects in images. In this approach, the contour in the image plane is represented as the zero-level set of a higher dimensional function. (One example of the higher dimensional function is a three-dimensional surface for a two-dimensional contour.) This approach handles the topological changes (e.g., merging, splitting) of the contour naturally. Although geometric active contours prevail in many fields of computer vision, they suffer from the high computational costs associated with level set methods. Therefore, simplified versions of level set methods such as fast marching methods are often used in problems of real-time visual tracking. This thesis presents the development of a fast and robust segmentation algorithm based on up-to-date extensions of level set methods and geometric active contours, namely a fast implementation of Chan-Vese's (active contour) model (FICVM). The shape prior is a useful cue in the recognition of the true target. For the contour tracker, the outline of the target can be easily disrupted by noise. In geometric active contours, to cope with deviations from the true outline of the target, a higher dimensional function is constructed based on the shape prior, and the contour tracks the outline of an object by considering the difference between the higher dimensional functions obtained from the shape prior and from a measurement in a given image. The higher dimensional function is often a distance map which requires high computational costs for construction. This thesis focuses on the extraction of shape information from only the zero-level set of the higher dimensional function. This strategy compensates for inaccuracies in the calculation of the shape difference that occur when a simplified higher dimensional function is used. This is named as contour-based shape modeling. Filtering is an essential element in tracking problems because of the presence of noise in system models and measurements. The well-known Kalman filter provides an exact solution only for problems which have linear models and Gaussian distributions (linear/Gaussian problems). For nonlinear/non-Gaussian problems, particle filters have received much attention in recent years. Particle filtering is useful in the approximation of complicated posterior probability distribution functions. However, the computational burden of particle filtering prevents it from performing at full capacity in real-time applications. This thesis concentrates on improving the processing time of particle filtering for real-time applications. In principle, we follow the particle filter in the geometric active contour framework. This thesis proposes an advanced blob tracking scheme in which a blob contains shape prior information of the target. This scheme simplifies the sampling process and quickly suggests the samples which have a high probability of being the target. Only for these samples is the contour tracking algorithm applied to obtain a more detailed state estimate. Curve evolution in the contour tracking is realized by the FICVM. The dissimilarity measure is calculated by the contour based shape modeling method and the shape prior is updated when it satisfies certain conditions. The new particle filter is applied to the problems of low contrast and severe daylight conditions, to cluttered environments, and to the appearing/disappearing target tracking. We have also demonstrated the utility of the filtering algorithm for multiple target tracking in the presence of occlusions. This thesis presents several test results from simulations and flight tests. In these tests, the proposed algorithms demonstrated promising results in varied situations of tracking.Ph.D.Committee Chair: Eric N. Johnson; Committee Co-Chair: Allen R. Tannenbaum; Committee Member: Anthony J. Calise; Committee Member: Eric Feron; Committee Member: Patricio A. Vel

A Multispectral Data Model for Higher-Order Active Contours and its Application to Tree Crown Extraction

Forestry management makes great use of statistics concerning the individual trees making up a forest, but the acquisition of this information is expensive. Image processing can potentially both reduce this cost and improve the statistics. The key problem is the delineation of tree crowns in aerial images. The automatic solution of this problem requires considerable prior information to be built into the image and region models. Our previous work has focused on including shape information in the region model; in this paper we examine the image model. The aerial images involved have three bands. We study the statistics of these bands, and construct both multispectral and single band image models. We combine these with a higher-order active contour model of a `gas of circles' in order to include prior shape information about the region occupied by the tree crowns in the image domain. We compare the results produced by these models on real aerial images and conclude that multiple bands improves the quality of the segmentation. The model has many other potential applications, e.g. to nano-technology, microbiology, physics, and medical imaging

A higher-order active contour model of a `gas of circles' and its application to tree crown extraction

Many image processing problems involve identifying the region in the image domain occupied by a given entity in the scene. Automatic solution of these problems requires models that incorporate significant prior knowledge about the shape of the region. Many methods for including such knowledge run into difficulties when the topology of the region is unknown a priori, for example when the entity is composed of an unknown number of similar objects. Higher-order active contours (HOACs) represent one method for the modelling of non-trivial prior knowledge about shape without necessarily constraining region topology, via the inclusion of non-local interactions between region boundary points in the energy defining the model. The case of an unknown number of circular objects arises in a number of domains, e.g. medical, biological, nanotechnological, and remote sensing imagery. Regions composed of an a priori unknown number of circles may be referred to as a `gas of circles'. In this report, we present a HOAC model of a `gas of circles'. In order to guarantee stable circles, we conduct a stability analysis via a functional Taylor expansion of the HOAC energy around a circular shape. This analysis fixes one of the model parameters in terms of the others and constrains the rest. In conjunction with a suitable likelihood energy, we apply the model to the extraction of tree crowns from aerial imagery, and show that the new model outperforms other techniques

Phase diagram of a long bar under a higher-order active contour energy: application to hydrographic network extraction from VHR satellite images

The segmentation of networks is important in several imaging domains, and models incorporating prior shape knowledge are often essential for the automatic performance of this task. Higher-order active contours provide a way to include such knowledge, but their behaviour can vary significantly with parameter values: e.g. the same energy can model networks or a dasiagas of circlespsila. In this paper, we present a stability analysis of a HOAC energy leading to the phase diagram of a long bar. The results, which are confirmed by numerical experiments, enable the selection of parameter values for the modelling of network shapes using the energy. We apply the resulting model to the problem of hydrographic network extraction from VHR satellite images

An Infrared through Radio Study of the Properties and Evolution of IRDC Clumps

We examine the physical properties and evolutionary stages of a sample of 17 clumps within 8 Infrared Dark Clouds (IRDCs) by combining existing infrared, millimeter, and radio data with new Bolocam Galactic Plane Survey (BGPS) 1.1 mm data, VLA radio continuum data, and HHT dense gas (HCO+ and N2H+) spectroscopic data. We combine literature studies of star formation tracers and dust temperatures within IRDCs with our search for ultra-compact (UC) HII regions to discuss a possible evolutionary sequence for IRDC clumps. In addition, we perform an analysis of mass tracers in IRDCs and find that 8 micron extinction masses and 1.1 mm Bolocam Galactic Plane Survey (BGPS) masses are complementary mass tracers in IRDCs except for the most active clumps (notably those containing UCHII regions), for which both mass tracers suffer biases. We find that the measured virial masses in IRDC clumps are uniformly higher than the measured dust continuum masses on the scale of ~1 pc. We use 13CO, HCO+, and N2H+ to study the molecular gas properties of IRDCs and do not see any evidence of chemical differentiation between hot and cold clumps on the scale of ~1 pc. However, both HCO+ and N2H+ are brighter in active clumps, due to an increase in temperature and/or density. We report the identification of four UCHII regions embedded within IRDC clumps and find that UCHII regions are associated with bright (>1 Jy) 24 micron point sources, and that the brightest UCHII regions are associated with "diffuse red clumps" (an extended enhancement at 8 micron). The broad stages of the discussed evolutionary sequence (from a quiescent clump to an embedded HII region) are supported by literature dust temperature estimates; however, no sequential nature can be inferred between the individual star formation tracers.Comment: 33 pages, 26 figures, 6 tables, accepted for publication in ApJ. Full resolution version available here: http://casa.colorado.edu/~battersb/Publications.htm