Adaptive Ranking Based Constraint Handling for Explicitly Constrained Black-Box Optimization

A novel explicit constraint handling technique for the covariance matrix adaptation evolution strategy (CMA-ES) is proposed. The proposed constraint handling exhibits two invariance properties. One is the invariance to arbitrary element-wise increasing transformation of the objective and constraint functions. The other is the invariance to arbitrary affine transformation of the search space. The proposed technique virtually transforms a constrained optimization problem into an unconstrained optimization problem by considering an adaptive weighted sum of the ranking of the objective function values and the ranking of the constraint violations that are measured by the Mahalanobis distance between each candidate solution to its projection onto the boundary of the constraints. Simulation results are presented and show that the CMA-ES with the proposed constraint handling exhibits the affine invariance and performs similarly to the CMA-ES on unconstrained counterparts.Comment: 9 page

ROBOMIRROR: A SIMULATED MIRROR DISPLAY WITH A ROBOTIC CAMERA

Simulated mirror displays have a promising prospect in applications, due to its capability for virtual visualization. In most existing mirror displays, cameras are placed on top of the displays and unable to capture the person in front of the display at the highest possible resolution. The lack of a direct frontal capture of the subject\u27s face and the geometric error introduced by image warping techniques make realistic mirror image rendering a challenging problem. The objective of this thesis is to explore the use of a robotic camera in tracking the face of the subject in front of the display to obtain a high-quality image capture. Our system uses a Bislide system to control a camera for face capture, while using a separate color-depth camera for accurate face tracking. We construct an optical device in which a one-way mirror is used so that the robotic camera behind can capture the subject while the rendered images can be displayed by reflecting off the mirror from an overhead projector. A key challenge of the proposed system is the reduction of light due to the one-way mirror. The optimal 2D Wiener filter is selected to enhance the low contrast images captured by the camera

Nonrigid reconstruction of 3D breast surfaces with a low-cost RGBD camera for surgical planning and aesthetic evaluation

Accounting for 26% of all new cancer cases worldwide, breast cancer remains the most common form of cancer in women. Although early breast cancer has a favourable long-term prognosis, roughly a third of patients suffer from a suboptimal aesthetic outcome despite breast conserving cancer treatment. Clinical-quality 3D modelling of the breast surface therefore assumes an increasingly important role in advancing treatment planning, prediction and evaluation of breast cosmesis. Yet, existing 3D torso scanners are expensive and either infrastructure-heavy or subject to motion artefacts. In this paper we employ a single consumer-grade RGBD camera with an ICP-based registration approach to jointly align all points from a sequence of depth images non-rigidly. Subtle body deformation due to postural sway and respiration is successfully mitigated leading to a higher geometric accuracy through regularised locally affine transformations. We present results from 6 clinical cases where our method compares well with the gold standard and outperforms a previous approach. We show that our method produces better reconstructions qualitatively by visual assessment and quantitatively by consistently obtaining lower landmark error scores and yielding more accurate breast volume estimates