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A Novel Distant Target Region Detection Method Using Hybrid Saliency-Based Attention Model Under Complex Textures
Computer Science and Information Systems, Tome 18 (2021) no. 2.

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In this paper, a hybrid visual attention model to effectively detect a distant target is proposed. The model employs the human visual attention mechanism and consists of two models, the training model, and the detection model. In the training model, some of the features are selected to train in the process of extracting and combining the early visual features from the training image of the target by bottom-up manner, and these features are trained and accumulated as trained data. When the image containing the target is input into the detection model, a task of selectively promoting only features of the target using pre-trained data is performed. As a result, the desired target is detected through the saliency map created as a result of the feature combination. The model has been tested on various images, and the experimental results demonstrate that the proposed model detected the target more accurately and faster than other previous models.
Keywords: target, hybrid, saliency, attention 
@article{CSIS_2021_18_2_a1,
     author = {JaepilKo and Kyung Joo Cheoi},
     title = {A {Novel} {Distant} {Target} {Region} {Detection} {Method} {Using} {Hybrid} {Saliency-Based} {Attention} {Model} {Under} {Complex} {Textures}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {18},
     number = {2},
     year = {2021},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2021_18_2_a1/}
}
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JaepilKo; Kyung Joo Cheoi. A Novel Distant Target Region Detection Method Using Hybrid Saliency-Based Attention Model Under Complex Textures. Computer Science and Information Systems, Tome 18 (2021) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2021_18_2_a1/