Cognitive Neuroscience
A. Bosaghzadeh; M. Shabani; R. Ebrahimpour
Abstract
Background and Objectives: Visual attention is a high order cognitive process of human brain which defines where a human observer attends. Dynamic computational visual attention models are modeled on the behavior of the human brain and can predict what areas a human will pay attention to when viewing ...
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Background and Objectives: Visual attention is a high order cognitive process of human brain which defines where a human observer attends. Dynamic computational visual attention models are modeled on the behavior of the human brain and can predict what areas a human will pay attention to when viewing a scene such as a video. However, several types of computational models have been proposed to provide a better understanding of saliency maps in static and dynamic environments, most of these models are used for specific scenes. In this paper, we propose a model that can generate saliency maps in a variety of dynamic environments with complex scenes.Methods: We used a deep learner as a mediating network to combine basic saliency maps with appropriate weighting. Each of these basic saliency maps covers an important feature of human visual attention, and ultimately the final saliency map is very similar to human visual behavior.Results: The proposed model is run on two datasets and the generated saliency maps are evaluated by different criteria such as ROC, CC, NSS, SIM and KLdiv. The results show that the proposed model has a good performance compared to other similar models.Conclusion: The proposed model consists of three main parts, including basic saliency maps, gating network, and combinator. This model was implemented on the ETMD dataset and the resulting saliency maps (visual attention areas) were compared with some other models in this field by evaluation criteria and their results were evaluated. The results obtained from the proposed model are acceptable and based on the accepted evaluation criteria in this area, it performs better than similar models.
Electronics
J. Khosravi; Mohammad Shams Esfand Abadi; R. Ebrahimpour
Abstract
Background and Objectives: There are numerous applications for image registration (IR). The main purpose of the IR is to find a map between two different situation images. In this way, the main objective is to find this map to reconstruct the target image as optimum as possible. Methods: Needless to ...
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Background and Objectives: There are numerous applications for image registration (IR). The main purpose of the IR is to find a map between two different situation images. In this way, the main objective is to find this map to reconstruct the target image as optimum as possible. Methods: Needless to say, the IR task is an optimization problem. As the optimization method, although the evolutionary ones are sometimes more effective in escaping the local minima, their speed is not emulated the mathematical ones at all. In this paper, we employed a mathematical framework based on the Newton method. This framework is suitable for any efficient cost function. Yet we used the sum of square difference (SSD). We also provided an effective strategy in order to avoid sticking in the local minima. Results: The proposed newton method with SSD as a cost function expresses more decent speed and accuracy in comparison to Gradient descent and genetic algorithms methods based on presented criteria. By considering SSD as the model cost function, the proposed method is able to introduce, respectively, accurate and fast registration method which could be exploited by the relevant applications. Simulation results indicate the effectiveness of the proposed model. Conclusion: The proposed innovative method based on the Newton optimization technique on separate cost functions is able to outperform regular Gradient descent and genetic algorithms. The presented framework is not based on any specific cost function, so any innovative cost functions could be effectively employed by our approach. Whether the objective is to reach accurate or fast results, the proposed method could be investigated accordingly.======================================================================================================Copyrights©2018 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================
