A Multi-Task Semantic Segmentation Network For Threat Detection in X-Ray Security Images
Junhong Liu, Baoping Li
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We develop a novel depth-estimation constrained nonnegative matrix factorization framework for reinforcing neuron extraction, which takes advantage of accurate depth estimation to alleviate the scattering of raw data and compensate for depth loss in the vectorization operation. Established on bright and dark channel priors, a depth-dependent transmission estimation model is effective in estimating the more accurate depth of raw data, where the atmospheric light of each pixel is finely estimated to alleviate the problem of image descattering and overcome the limitation of conventionally constant assumption. Besides, our framework is simply implemented in constrained nonnegative matrix factorization, and can be flexibly accommodated to various neuron extraction approaches. Extensive experiments confirm the superior performance of our framework in terms of reinforcing more accuracy of neuron extraction and demixing better results of spatially overlapping neurons, moreover, the utility of our depth-estimation model is proved for imaging whole brain of zebrafish larvae.