Patch-type Segmentation of Voxel Shapes using Simplified Surface Skeletons

We present a new method for decomposing a 3D voxel shape into disjoint segments using the shape’s simplified surface-skeleton. The surface skeleton of a shape consists of 2D manifolds inside its volume. Each skeleton point has a maximally inscribed ball that touches the boundary in at least two contact points. A key observation is that the boundaries of the simplified fore- and background skeletons map one-to-one to increasingly fuzzy, soft convex, respectively concave, edges of the shape. Using this property, we build a method for segmentation of 3D shapes which has several desirable properties. Our method segments both noisy shapes and shapes with soft edges which vanish over low-curvature regions. Multiscale segmentations can be obtained by varying the simplification level of the skeleton. We present a voxel-based implementation of our approach and illustrate it on several realistic examples.

Image processing for plastic surgery planning

This thesis presents some image processing tools for plastic surgery planning. In particular, it presents a novel method that combines local and global context in a probabilistic relaxation framework to identify cephalometric landmarks used in Maxillofacial plastic surgery. It also uses a method that utilises global and local symmetry to identify abnormalities in CT frontal images of the human body. The proposed methodologies are evaluated with the help of several clinical data supplied by collaborating plastic surgeons

Recognizing the presence of hidden visual markers in digital images

As the promise of Virtual and Augmented Reality (VR and AR) becomes more realistic, an interesting aspect of our enhanced living environment includes the availability — indeed the potential ubiquity — of scannable markers. Such markers could represent an initial step into the AR and VR worlds. In this paper, we address the important question of how to recognise the presence of visual markers in freeform digital photos. We use a particularly challenging marker format that is only minimally constrained in structure, called Artcodes. Artcodes are a type of topological marker system enabling people, by following very simple drawing rules, to design markers that are both aesthetically beautiful and machine readable. Artcodes can be used to decorate the surface of any objects, and yet can also contain a hidden digital meaning. Like some other more commonly used markers (such as Barcodes, QR codes), it is possible to use codes to link physical objects to digital data, augmenting everyday objects. Obviously, in order to trigger the behaviour of scanning and further decoding of such codes, it is first necessary for devices to be aware of the presence of Artcodes in the image. Although considerable literature exists related to the detection of rigidly formatted structures and geometrical feature descriptors such as Harris, SIFT, and SURF, these approaches are not sufficient for describing freeform topological structures, such as Artcode images. In this paper, we propose a new topological feature descriptor that can be used in the detection of freeform topological markers, including Artcodes. This feature descriptor is called a Shape of Orientation Histogram (SOH). We construct this SOH feature vector by quantifying the level of symmetry and smoothness of the orientation histogram, and then use a Random Forest machine learning approach to classify images that contain Artcodes using the new feature vector. This system represents a potential first step for an eventual mobile device application that would detect where in an image such an unconstrained code appears. We also explain how the system handles imbalanced datasets — important for rare, handcrafted codes such as Artcodes — and how it is evaluated. Our experimental evaluation shows good performance of the proposed classification model in the detection of Artcodes: obtaining an overall accuracy of approx. 0.83, F2 measure 0.83, MCC 0.68, AUC-ROC 0.93, and AUC-PR 0.91

Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for eighteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated

Faster and better: a machine learning approach to corner detection

The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is importand because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations [Schmid et al 2000]. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection, and using machine learning we derive a feature detector from this which can fully process live PAL video using less than 5% of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115%, SIFT 195%). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion applied to 3D scenes. We show that despite being principally constructed for speed, on these stringent tests, our heuristic detector significantly outperforms existing feature detectors. Finally, the comparison demonstrates that using machine learning produces significant improvements in repeatability, yielding a detector that is both very fast and very high quality.Comment: 35 pages, 11 figure

COLLECTIVE MOTION AND PHASE DIAGRAM OF SELF-PROPELLED VIBRATED HARD SQUARES

In equilibrium, matter condenses into ordered phases due to the combined effects of inter-particle interactions and entropy. In this dissertation, we explore the self-propulsion of particles as an additional nonequilibrium consideration in the mechanisms for ordering. Our experiments employ square-shaped hard particles; in equilibrium, when particle motions are randomly directed, squares form entropically-stabilized phases in which first their orientations, and then their positions, get locked in relative to each other, depending on the density of coverage. When the square tiles are modified to have small propulsion along some body-fixed axis we find that their tendency to order is profoundly altered. Adding such \u27activity\u27(quantified by the persistence length of motion along the mobility direction) to particles can produce new ‘phases’ and mechanisms for ordering not seen in equilibrium materials. In the first study, we study a system of vibrated self-propelled granular particles with high persistence length on a horizontal plane within a circular boundary. The particles are square and designed to have polar motion along one body diagonal. When they hit the boundary they align along the boundary but also \u27walk\u27 along the boundary. Given a large enough initial density in the plane, particles spontaneously migrate to the boundary, form a ring, and perform a stable 1D rotational gear-like motion with a direction chosen by their net polarization. For a fully polarized single ring, we find that the collective velocity surpasses the free single-particle velocity. This collective velocity increases as the density of particles in the ring increases, which is counterintuitive for a normal traffic problem. The spatial correlations of particle velocity fluctuations decay exponentially with a length scale that increases with density. There is thus increased cooperativity in the system. However, the temporal correlation shows that velocity fluctuations are very short-lived. In a second project, we study the effect of varying the persistence length of individual particle motion in an ensemble of squares held at fixed density. We find that adding activity to the particles qualitatively modifies their phase diagram relative to that of passive squares. At large enough activity (just as in the previous study), particles always migrate to the boundary and form a high-density ordered state. At smaller values of activity, different phases are seen as a function of density. At low density, the particles form an isotropic fluid. As the density increases, particles separate into a high-density ordered region while the remaining particles remain in the fluid state. Above a finite density, the phase coexistence curve terminates and all particles freeze into an ordered state. The start and end density of the coexistence region is found to be a function of activity. %(NM). The coexistence region emerges purely due to the effect of activity in the system. We also discuss dynamics within the dense, ordered state. In the final project in this thesis, we studied by simulation the effect on collective behavior of changing the symmetry of single particle activity. In addition to passive squares (that is, squares with isotropic mobility), we study polar, bipolar, and chiral mobilities. For each of these choices of symmetry we also choose different axes for the activity relative to the particle shape. We thus have six different kinds of particles and compare their corresponding phase behavior. We find that different symmetries of activity have quite different phase states. For a fixed symmetry of activity, changing the direction of symmetry leads to much smaller changes in phase behavior

Improvements on coronal hole detection in SDO/AIA images using supervised classification

We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection techniques (intensity-based thresholding, SPoCA), we prepared data sets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2011 - 2013. By mapping the extracted regions from EUV observations onto HMI line-of-sight magnetograms we also include their magnetic characteristics. We computed shape measures from the segmented binary maps as well as first order and second order texture statistics from the segmented regions in the EUV images and magnetograms. These attributes were used for data mining investigations to identify the most performant rule to differentiate between coronal holes and filament channels. We applied several classifiers, namely Support Vector Machine, Linear Support Vector Machine, Decision Tree, and Random Forest and found that all classification rules achieve good results in general, with linear SVM providing the best performances (with a true skill statistic of ~0.90). Additional information from magnetic field data systematically improves the performance across all four classifiers for the SPoCA detection. Since the calculation is inexpensive in computing time, this approach is well suited for applications on real-time data. This study demonstrates how a machine learning approach may help improve upon an unsupervised feature extraction method.Comment: in press for SWS