Automatic Image Segmentation by Dynamic Region Merging

This paper addresses the automatic image segmentation problem in a region merging style. With an initially over-segmented image, in which the many regions (or super-pixels) with homogeneous color are detected, image segmentation is performed by iteratively merging the regions according to a statistical test. There are two essential issues in a region merging algorithm: order of merging and the stopping criterion. In the proposed algorithm, these two issues are solved by a novel predicate, which is defined by the sequential probability ratio test (SPRT) and the maximum likelihood criterion. Starting from an over-segmented image, neighboring regions are progressively merged if there is an evidence for merging according to this predicate. We show that the merging order follows the principle of dynamic programming. This formulates image segmentation as an inference problem, where the final segmentation is established based on the observed image. We also prove that the produced segmentation satisfies certain global properties. In addition, a faster algorithm is developed to accelerate the region merging process, which maintains a nearest neighbor graph in each iteration. Experiments on real natural images are conducted to demonstrate the performance of the proposed dynamic region merging algorithm.Comment: 28 pages. This paper is under review in IEEE TI

A random forest system combination approach for error detection in digital dictionaries

When digitizing a print bilingual dictionary, whether via optical character recognition or manual entry, it is inevitable that errors are introduced into the electronic version that is created. We investigate automating the process of detecting errors in an XML representation of a digitized print dictionary using a hybrid approach that combines rule-based, feature-based, and language model-based methods. We investigate combining methods and show that using random forests is a promising approach. We find that in isolation, unsupervised methods rival the performance of supervised methods. Random forests typically require training data so we investigate how we can apply random forests to combine individual base methods that are themselves unsupervised without requiring large amounts of training data. Experiments reveal empirically that a relatively small amount of data is sufficient and can potentially be further reduced through specific selection criteria.Comment: 9 pages, 7 figures, 10 tables; appeared in Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data, April 201

Dancing with the Dragon Heads: Enforcement, Innovations and Efficiency of Contracts between Agricultural Processors and Farmers in China

Contractual breaches are very common in developing countries such as China. In order to prevent breaches of contract, the contractual designs between farmers and agricultural processors (Dragon Head Firms) in China are innovating in two ways: organizational innovations and contractual innovations. Due to contractual innovations, simple price-quantity contracts are evolving into complex cooperation contracts. Using data for over 500 state key processors in 2003 from the Chinese Ministry of Agriculture, we construct econometric models to study contractual choices, contract intensity, and their impacts on sales and profits for agricultural processors in China. The results indicate that capital and the number of contracted farmers are endogenous to contract choices. Processors are more likely to use cooperation contracts compared with price-quantity contracts as the number of contracted farmers increases, because the costs of coordinating, monitoring and enforcing price-quantity contracts may increase dramatically under these circumstances . On the other hand, contract types are not important for the number of contracted farmers, the intensity of contracts, or sales and profits for processors. The results also indicate that the elasticity of profits with respect to capital is 0.52, which implies that the returns to investing in the food processing industry are relatively high in China.Agribusiness,

Geometry of unsteady fluid transport during fluid–structure interactions

We describe the application of tools from dynamical systems to define and quantify the unsteady fluid transport that occurs during fluid–structure interactions and in unsteady recirculating flows. The properties of Lagrangian coherent structures (LCS) are used to enable analysis of flows with arbitrary time-dependence, thereby extending previous analytical results for steady and time-periodic flows. The LCS kinematics are used to formulate a unique, physically motivated definition for fluid exchange surfaces and transport lobes in the flow. The methods are applied to numerical simulations of two-dimensional flow past a circular cylinder at a Reynolds number of 200; and to measurements of a freely swimming organism, the Aurelia aurita jellyfish. The former flow provides a canonical system in which to compare the present geometrical analysis with classical, Eulerian (e.g. vortex shedding) perspectives of fluid–structure interactions. The latter flow is used to deduce the physical coupling that exists between mass and momentum transport during self-propulsion. In both cases, the present methods reveal a well-defined, unsteady recirculation zone that is not apparent in the corresponding velocity or vorticity fields. Transport rates between the ambient flow and the recirculation zone are computed for both flows. Comparison of fluid transport geometry for the cylinder crossflow and the self-propelled swimmer within the context of existing theory for two-dimensional lobe dynamics enables qualitative localization of flow three-dimensionality based on the planar measurements. Benefits and limitations of the implemented methods are discussed, and some potential applications for flow control, unsteady propulsion, and biological fluid dynamics are proposed