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In this paper we propose a Hidden Markov Model for modeling and extracting vine structure from images. We built up from previous research to infer connectivity of cane segments extracted from binary images. We use skeletonisation and polylines to model cane segments and we use simulated annealing to optimize an energy function defined in terms of attributes observed for each connection. We formulate...
This paper proposes the sequential context inference (SCI) algorithm for Markov random field (MRF) image analysis. This algorithm is designed primarily for fast inference on an MRF model, but its application requires also a specific modeling architecture. The architecture is composed of a sequence of stages, each modeling the conditional probability of the labels, conditioned on a neighborhood of...
The notion of using context information for solving high-level vision and medical image segmentation problems has been increasingly realized in the field. However, how to learn an effective and efficient context model, together with an image appearance model, remains mostly unknown. The current literature using Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) often involves specific...
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