Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion

Most of the traditional convolutional neural networks (CNNs) implements bottom-up approach (feed-forward) for image classifications. However, many scientific studies demonstrate that visual perception in primates rely on both bottom-up and top-down connections. Therefore, in this work, we propose a CNN network with feedback structure for Solar power plant detection on middle-resolution satellite images. To express the strength of the top-down connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model used for solar power plant classification on multi-spectral satellite data. Moreover, we introduce a method to improve class activation mapping (CAM) to our FB-Net, which takes advantage of multi-channel pulse coupled neural network (m-PCNN) for weakly-supervised localization of the solar power plants from the features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN, experimental results demonstrated promising results on both solar-power plant image classification and detection task.Comment: 9 pages, 9 figures, 4 table

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

A Novel Method for L Band SAR Image Segmentation Based on Pulse Coupled Neural Network

Pulse Coupled Neural Network (PCNN) is claimed as a third generation neural network. PCNN has wide purpose in image processing  such as segmentation, feature extraction, sharpening etc.  Not like another neural network architecture, PCNN do not need training. The only weaknes point  of PCNN is parameter tune due to  seven parameters in its five equations. In this research we proposed a novel method for segmentation based on modified PCNN.  In order to evaluate the proposed method, we processed L Band Multipolarisation  Synthetic Apperture Radar Image. The Results showed all area extracted both by using PCNN and ICM-PCNN from the SAR image are match to the groundtruth. There fore the proposed method is work properly.Copyright © 2017  International Journal of  Artificial Intelegence Research.All rights reserved

Image Segmentation using Two-Layer Pulse Coupled Neural Network with Inhibitory Linking Field

For over a decade, the Pulse Coupled Neural Network(PCNN) based algorithms have been used for imagesegmentation. Though there are several versions of the PCNNbased image segmentation methods, almost all of them use singlelayerPCNN with excitatory linking inputs. There are fourmajor issues associated with the single-burst PCNN which needattention. Often, the PCNN parameters including the linkingcoefficient are determined by trial and error. The segmentationaccuracy of the single-layer PCNN is highly sensitive to the valueof the linking coefficient. Finally, in the single-burst mode,neurons corresponding to background pixels do not participatein the segmentation process. This paper presents a new 2-layernetwork organization of PCNN in which excitatory andinhibitory linking inputs exist. The value of the linkingcoefficient and the threshold signal at which primary firing ofneurons start are determined directly from the image statistics.Simulation results show that the new PCNN achieves significantimprovement in the segmentation accuracy over the widelyknown Kuntimad’s single burst image segmentation approach.The two-layer PCNN based image segmentation methodovercomes all three drawbacks of the single-layer PCNN