Content based image pose manipulation

This thesis proposes the application of space-frequency transformations to the domain of pose estimation in images. This idea is explored using the Wavelet Transform with illustrative applications in pose estimation for face images, and images of planar scenes. The approach is based on examining the spatial frequency components in an image, to allow the inherent scene symmetry balance to be recovered. For face images with restricted pose variation (looking left or right), an algorithm is proposed to maximise this symmetry in order to transform the image into a fronto-parallel pose. This scheme is further employed to identify the optimal frontal facial pose from a video sequence to automate facial capture processes. These features are an important pre-requisite in facial recognition and expression classification systems. The under lying principles of this spatial-frequency approach are examined with respect to images with planar scenes. Using the Continuous Wavelet Transform, full perspective planar transformations are estimated within a featureless framework. Restoring central symmetry to the wavelet transformed images in an iterative optimisation scheme removes this perspective pose. This advances upon existing spatial approaches that require segmentation and feature matching, and frequency only techniques that are limited to affine transformation recovery. To evaluate the proposed techniques, the pose of a database of subjects portraying varying yaw orientations is estimated and the accuracy is measured against the captured ground truth information. Additionally, full perspective homographies for synthesised and imaged textured planes are estimated. Experimental results are presented for both situations that compare favourably with existing techniques in the literature

Revisiting Complex Moments For 2D Shape Representation and Image Normalization

When comparing 2D shapes, a key issue is their normalization. Translation and scale are easily taken care of by removing the mean and normalizing the energy. However, defining and computing the orientation of a 2D shape is not so simple. In fact, although for elongated shapes the principal axis can be used to define one of two possible orientations, there is no such tool for general shapes. As we show in the paper, previous approaches fail to compute the orientation of even noiseless observations of simple shapes. We address this problem. In the paper, we show how to uniquely define the orientation of an arbitrary 2D shape, in terms of what we call its Principal Moments. We show that a small subset of these moments suffice to represent the underlying 2D shape and propose a new method to efficiently compute the shape orientation: Principal Moment Analysis. Finally, we discuss how this method can further be applied to normalize grey-level images. Besides the theoretical proof of correctness, we describe experiments demonstrating robustness to noise and illustrating the method with real images.Comment: 69 pages, 20 figure

Bilateral symmetry of object silhouettes under perspective projection

Symmetry is an important property of objects and is exhibited in different forms e.g., bilateral, rotational, etc. This paper presents an algorithm for computing the bilateral symmetry of silhouettes of shallow objects under perspective distortion, exploiting the invariance of the cross ratio to projective transformations. The basic idea is to use the cross ratio to compute a number of midpoints of cross sections and then fit a straight line through them. The goodness-of-fit determines the likelihood of the line to be the axis of symmetry. We analytically estimate the midpoint’s location as a function of the vanishing point for a given object silhouette. Hence finding the symmetry axis amounts to a 2D search in the space of vanishing points. We present experiments on two datasets as well as internet images of symmetric objects that validate our approach. under perspectivities, we analytically compute a set of midpoints of the object as a function of the vanishing point. Then, we fit a straight line passing through the midpoints. The goodness-of-fit defines the likelihood of this line to be a symmetry axis. Using the proposed method, searching for the symmetry axis is reduced to searching for a vanishing point. Our approach is global in the sense that we consider the whole silhouette of the object rather than small parts of it. The results show that the method presented here is capable of finding axes of symmetry of considerably distorted perspective images. 2 Related Work

PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models

In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching, and completion. Thus it is an important problem to analyze various symmetry forms of 3D shapes. Planar reflective symmetry is the most fundamental one. Traditional methods based on spatial sampling can be time-consuming and may not be able to identify all the symmetry planes. In this paper, we present a novel learning framework to automatically discover global planar reflective symmetry of a 3D shape. Our framework trains an unsupervised 3D convolutional neural network to extract global model features and then outputs possible global symmetry parameters, where input shapes are represented using voxels. We introduce a dedicated symmetry distance loss along with a regularization loss to avoid generating duplicated symmetry planes. Our network can also identify generalized cylinders by predicting their rotation axes. We further provide a method to remove invalid and duplicated planes and axes. We demonstrate that our method is able to produce reliable and accurate results. Our neural network based method is hundreds of times faster than the state-of-the-art methods, which are based on sampling. Our method is also robust even with noisy or incomplete input surfaces.Comment: Corrected typo

PRS-Net: planar reflective symmetry detection net for 3D models

In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching, and completion. Thus it is an important problem to analyze various symmetry forms of 3D shapes. Planar reflective symmetry is the most fundamental one. Traditional methods based on spatial sampling can be time-consuming and may not be able to identify all the symmetry planes. In this paper, we present a novel learning framework to automatically discover global planar reflective symmetry of a 3D shape. Our framework trains an unsupervised 3D convolutional neural network to extract global model features and then outputs possible global symmetry parameters, where input shapes are represented using voxels. We introduce a dedicated symmetry distance loss along with a regularization loss to avoid generating duplicated symmetry planes. Our network can also identify generalized cylinders by predicting their rotation axes. We further provide a method to remove invalid and duplicated planes and axes. We demonstrate that our method is able to produce reliable and accurate results. Our neural network based method is hundreds of times faster than the state-of-the-art methods, which are based on sampling. Our method is also robust even with noisy or incomplete input surfaces

Symmetry breaking and strong coupling in planar optical metamaterials

We demonstrate narrow transmission resonances at near-infrared wavelengths utilizing coupled asymmetric split-ring resonators (SRRs). By breaking the symmetry of the coupled SRR system, one can excite dark (subradiant) resonant modes that are not readily accessible to symmetric SRR structures. We also show that the quality factor of metamaterial resonant elements can be controlled by tailoring the degree of asymmetry. Changing the distance between asymmetric resonators changes the coupling strength and results in resonant frequency tuning due to resonance hybridization

Observation of Solitonic Vortices in Bose-Einstein Condensates

We observe solitonic vortices in an atomic Bose-Einstein condensate after free expansion. Clear signatures of the nature of such defects are the twisted planar density depletion around the vortex line, observed in absorption images, and the double dislocation in the interference pattern obtained through homodyne techniques. Both methods allow us to determine the sign of the quantized circulation. Experimental observations agree with numerical simulations. These solitonic vortices are the decay product of phase defects of the BEC order parameter spontaneously created after a rapid quench across the BEC transition in a cigar-shaped harmonic trap and are shown to have a very long lifetime.Comment: 7 pages, 7 figure

Structure, dynamics and bifurcations of discrete solitons in trapped ion crystals

We study discrete solitons (kinks) accessible in state-of-the-art trapped ion experiments, considering zigzag crystals and quasi-3D configurations, both theoretically and experimentally. We first extend the theoretical understanding of different phenomena predicted and recently experimentally observed in the structure and dynamics of these topological excitations. Employing tools from topological degree theory, we analyze bifurcations of crystal configurations in dependence on the trapping parameters, and investigate the formation of kink configurations and the transformations of kinks between different structures. This allows us to accurately define and calculate the effective potential experienced by solitons within the Wigner crystal, and study how this (so-called Peierls-Nabarro) potential gets modified to a nonperiodic globally trapping potential in certain parameter regimes. The kinks' rest mass (energy) and spectrum of modes are computed and the dynamics of linear and nonlinear kink oscillations are analyzed. We also present novel, experimentally observed, configurations of kinks incorporating a large-mass defect realized by an embedded molecular ion, and of pairs of interacting kinks stable for long times, offering the perspective for exploring and exploiting complex collective nonlinear excitations, controllable on the quantum level.Comment: 25 pages, 10 figures, v2 corrects Fig. 2 and adds some text and reference

The Three-Dimensional Circumstellar Environment of SN 1987A

We present the detailed construction and analysis of the most complete map to date of the circumstellar environment around SN 1987A, using ground and space-based imaging from the past 16 years. PSF-matched difference-imaging analyses of data from 1988 through 1997 reveal material between 1 and 28 ly from the SN. Careful analyses allows the reconstruction of the probable circumstellar environment, revealing a richly-structured bipolar nebula. An outer, double-lobed ``Peanut,'' which is believed to be the contact discontinuity between red supergiant and main sequence winds, is a prolate shell extending 28 ly along the poles and 11 ly near the equator. Napoleon's Hat, previously believed to be an independent structure, is the waist of this Peanut, which is pinched to a radius of 6 ly. Interior to this is a cylindrical hourglass, 1 ly in radius and 4 ly long, which connects to the Peanut by a thick equatorial disk. The nebulae are inclined 41\degr south and 8\degr east of the line of sight, slightly elliptical in cross section, and marginally offset west of the SN. From the hourglass to the large, bipolar lobes, echo fluxes suggest that the gas density drops from 1--3 cm^{-3} to >0.03 cm^{-3}, while the maximum dust-grain size increases from ~0.2 micron to 2 micron, and the Si:C dust ratio decreases. The nebulae have a total mass of ~1.7 Msun. The geometry of the three rings is studied, suggesting the northern and southern rings are located 1.3 and 1.0 ly from the SN, while the equatorial ring is elliptical (b/a < 0.98), and spatially offset in the same direction as the hourglass.Comment: Accepted for publication in the ApJ Supplements. 38 pages in apjemulate format, with 52 figure