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

A Mechanism Design and Learning Approach for Revenue Maximization on Cloud Dynamic Spot Markets

Modern large-scale computing deployments consist of complex elastic applications running over machine clusters. A current trend adopted by providers is to set unused virtual machines, or else spot instances, in low prices to take advantage of spare capacity. In this paper we present a group of efficient allocation and pricing policies that can be used by vendors for their spot price mechanisms. We model the procedure of acquiring virtual machines as a truthful knapsack auction and we deploy dynamic allocation and pricing rules that achieve near-optimal revenue and social welfare. As the problem is NP-hard our solutions are based on approximate algorithms. First, we propose two solutions that do not use prior knowledge. Then, we enhance them with three learning algorithms. We evaluate them with simulations on the Google Cluster dataset and we benchmark them against the Uniform Price, the Optimal Single Price and the Ex-CORE mechanisms. Our proposed dynamic mechanism is robust, achieves revenue up to 89% of the Optimal Single Price auction, and computes the allocation in polynomial time making our contribution computationally tractable in realtime scenarios. © 2021 IEEE

3T chemical shift‐encoded MRI: Detection of altered proximal femur marrow adipose tissue composition in glucocorticoid users and validation with magnetic resonance spectroscopy

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