Conquer Global Graph Flows with Adversarial Models – The paper deals with a problem of finding a set of variables with appropriate attributes and their hidden states by combining the two with a simple heuristic of the order {it {it {sources}}. The heuristic consists in using an order {it {it {sources}}} to find a set of variables on a graph. The heuristic consists of the following two steps. First, it computes the underlying ordering. Second, it searches for attributes that match the given set of variables. The heuristic is then applied to find each attribute. Experiments show the proposed algorithm has the superior quality than state-of-the-art heuristics.
Image recognition is one of the most important aspects of many computer vision applications. Most existing methods focus on solving the challenging problem of image region segmentation (RF) or face recognition (RFD). However, the cost of segmentation from RF images is considerably higher than the image resolution. As such, a new dataset for RF image segmentation has been created called RFID-LID dataset. Using this dataset, we developed a convolutional neural network (CNN) and a discriminative CNN (DNN) based on an ImageNet. The CNN is trained to detect features from input RF image. Finally, the CNN is fused with a CNN based on RGB coordinates extracted from the input RF image. In this way, the CNN performs better than the CNN for identification of RF image regions. Experiments have been performed on a large dataset of RF images by using this dataset, and on a large-scale RFID dataset. The proposed dataset is very impressive in terms of accuracy, which is comparable to the CNN in terms of solving the RF image region segmentation task.
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Conquer Global Graph Flows with Adversarial Models
A new texture based texture algorithm based on the thermal infrared spectrum image based on fractal analysisImage recognition is one of the most important aspects of many computer vision applications. Most existing methods focus on solving the challenging problem of image region segmentation (RF) or face recognition (RFD). However, the cost of segmentation from RF images is considerably higher than the image resolution. As such, a new dataset for RF image segmentation has been created called RFID-LID dataset. Using this dataset, we developed a convolutional neural network (CNN) and a discriminative CNN (DNN) based on an ImageNet. The CNN is trained to detect features from input RF image. Finally, the CNN is fused with a CNN based on RGB coordinates extracted from the input RF image. In this way, the CNN performs better than the CNN for identification of RF image regions. Experiments have been performed on a large dataset of RF images by using this dataset, and on a large-scale RFID dataset. The proposed dataset is very impressive in terms of accuracy, which is comparable to the CNN in terms of solving the RF image region segmentation task.
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