Direct Estimation of Appearance Models for Segmentation

Abstract

Image segmentation algorithms often depend on appearance models that characterize the distribution of pixel values in different image regions. We describe a novel approach for estimating appearance models directly from an image, without explicit consideration of the pixels that make up each region. Our approach is based on algebraic expressions that relate local image statistics to the appearance models of spatially coherent regions. We describe two algorithms that can use the aforementioned algebraic expressions for estimating appearance models. The first algorithm is based on solving a system of linear and quadratic equations. The second algorithm is a spectral method based on an eigenvector computation. We present experimental results that demonstrate the proposed methods work well in practice and lead to effective image segmentation algorithms.

Plant Disease Diagnosis in the Visible Spectrum

“…The literature of the models that incorporate texture features is very vast, and a thorough summary is available in [18]. There are some methodologies for image segmentation that impose some type of spatial regularization, for example, the integration of local characteristics; methods based on graphs reach that objective by formulating the segmentation as the partition of a graph [19][20][21]. In addition, vegetation indices have been used for image segmentation problems in agricultural settings [22].…”