A Semi-automated Test and Evaluation System for Multi-Person Pose Estimation


A Semi-automated Test and Evaluation System for Multi-Person Pose Estimation – Person re-identification is an important problem in many areas including robotics and artificial intelligence. In this paper, we investigate the challenge in Re-ID for the purpose of re-identification of the human-body connection from images. Following the previous work on this problem, we propose a novel two-phase re-identification algorithm based on the idea of re-scented image classification and localization. Under this framework, image re-ID is used to classify the human-body connection between the images. This paper considers re-ID as a supervised model which can easily be designed to re-identify the person and the person re-ID. The proposed re-ID algorithm is implemented using ImageNet, which handles image classification and localization for a semi-automated test and evaluation system. Furthermore, it is implemented using a machine learning framework which handles the classification and localization for an automatic re-ID system.

The recently developed deep learning (DL) network has been shown to be effective for image denoising. However, there is no formal definition of these methods. Deep learning is one method which aims at learning the parameters to map images to the correct ones. In this paper, we extend DL network by learning deep descriptors to recognize denoising images. To do this, we first define the denoising parameters. Then, we learn an efficient DL network from deep descriptors. Experiments show that the network learns discriminative model over discriminative labels given image. Our DL network can automatically recognize the denoising parameters without any costly training process.

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A Semi-automated Test and Evaluation System for Multi-Person Pose Estimation

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    Fast Color Image Filtering Using a Generative Adversarial NetworkThe recently developed deep learning (DL) network has been shown to be effective for image denoising. However, there is no formal definition of these methods. Deep learning is one method which aims at learning the parameters to map images to the correct ones. In this paper, we extend DL network by learning deep descriptors to recognize denoising images. To do this, we first define the denoising parameters. Then, we learn an efficient DL network from deep descriptors. Experiments show that the network learns discriminative model over discriminative labels given image. Our DL network can automatically recognize the denoising parameters without any costly training process.


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