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In this paper, we propose a novel approach to automatically identify plant species using dynamics of plant growth and development or spatiotemporal evolution model (STEM). The online kernel adaptive autoregressive-moving-average (KAARMA) algorithm, a discrete-time dynamical system in the kernel reproducing Hilbert space (RKHS), is used to learn plant-development syntactic patterns from feature-vector...
In this paper, we propose a novel state-based approach to insect flight dynamics modeling and automatic insect recognition (AIR). An emerging technology uses noninvasive laser sensors to capture flight information of flying insects in situ. However current computational entomology methods treat these optical signals as static patterns, rather than time series. We propose to evaluate each insect passage...
This paper presents Data-Driven Tree-structured Bayesian network (DDT), a novel probabilistic graphical model for hierarchical unsupervised image segmentation. The DDT captures long and short-ranged correlations between neighboring regions in each image using a tree-structured prior. Unlike other previous work, DDT first segments an input image into superpixels and learn a tree-structured prior based...
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