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The processing of the images simultaneously degraded by blur and affine transformation has become a key task in many applications and many novel methods are designed specifically for it in which the moment-based methods play an important role. However, the existing moment-based methods all resort to non-orthogonal moments invariants which have problem of(More)
In this paper, we proposed a set of translation and rotation invariants extracted from Tchebichef moments. A set of Tchebichef moment invariants is derived from the relationship between Tchebichef moments of the original image and those of the transformed image. These invariants are then used for symmetric image recognition. Contrarily to the methods based(More)
Iterative image reconstruction algorithms have been widely used in the field of positron emission tomography (PET). However, such algorithms are sensitive to noise artifacts so that the reconstruction begins to degrade when the number of iterations is high. In this paper, we propose a new algorithm to reconstruct an image from the PET emission projection(More)
The linear-chain CRFs is one of the most popular discriminative models for human action recognition, as it can achieve good prediction performance in temporal sequential labeling by capturing the one-or few-timestep interactions of the target states. However, existing CRFs formulations have limited capabilities to capture deeper intermediate representations(More)
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