Multi-Image Semantic Matching by Mining Consistent Features

This work proposes a multi-image matching method to estimate semantic correspondences across multiple images. In contrast to the previous methods that optimize all pairwise correspondences, the proposed method identifies and matches only a sparse set of reliable features in the image collection. In this way, the proposed method is able to prune nonrepeatable features and also highly scalable to handle thousands of images. We additionally propose a low-rank constraint to ensure the geometric consistency of feature correspondences over the whole image collection. Besides the competitive performance on multi-graph matching and semantic flow benchmarks, we also demonstrate the applicability of the proposed method for reconstructing object-class models and discovering object-class landmarks from images without using any annotation.Comment: CVPR 201

Pure Component Adsorption of Methane, Ethylene, Propylene and Carbon Dioxide in Silicalite

Adsorption isotherms are measured for pure methane, ethylene, propylene and carbon dioxide in silicalite. Isotherm data are collected using a volumetric method at three different temperatures of 10C, 35C and 65C at pressure up to 100psi. The Virial equation and Langmuir equation have been applied to represent excess amount adsorbed in each pure component system. The binary adsorption equilibrium for various mixtures are predicted from the pure component adsorption data, using the Ideal Adsorption Solution Theory (IAST