Neural network edge detector

Extracting edges from images is a widely used first step in image processing. A different view on the well known enhancement/thresholding approach for edge detection is presented in this paper. The structure of a two layer feed forward neural network is comparable to the structure of enhancement/thresholding edge detectors. It is possible to calculate an optimal edge detector with a certain predefined network structure and training set, by training the neural network with examples of edge and non-edge patterns. The back propagation learning rule is used for optimization of the network. The choice of the network structure and the training set are very important, because they determine the final behaviour of the network. The paper describes which network structures were selected and how the training sets were generated. Some of the experiments are described, and observations of the convolution kernels for edge enhancement that are formed during training. Finally the results are evaluated and compared with the results of edge detectors based on the Sobel, Marr-Hildreth and Canny edge enhancement algorithms. It appears that the neural network edge detector can be made very robust against noise and blur and in most tests outperforms the others.</p

RAPID BLEEDING REGION DETECTION IN WIRELESS CAPSULE ENDOSCOPY VIDEOS

Wireless Capsule Endoscopy (WCC) is a medical imaging technique used to examine parts of the gastrointestinal tract. Computer aided detection is used to increase the speed of detection, better performance and reduce the time. Before finding the bleeding regions the edge regions are first detected and removed. Both the edge and the bleeding regions will share the same Hue value and the luminance should be same for the bleeding and the non -bleeding regions .We use a canny edge detector operator for detecting the edge regions in L channel. Canny edge detector is used to detect more edge pixels and preserve more bleeding pixels based up on canny edge algorithm. This method in edge removal algorithm includes edge detection, edge dilation and edge masking. After the removal of edges, those regions are made in to segment through super-pixel segmentation and regions are classified using Artificial Neural Network by Radial Bias Function (RBF).Â

Lightweight Real-time Detection of Components via a Micro Aerial Vehicle with Domain Randomization Towards Structural Health Monitoring

Civil structural component detection plays an integral role in Structural Health Monitoring (SHM) pre and post-construction. Challenges including but not limited to labor-intensiveness, cost, and time constraints associated with traditional methods make it a less opti-mal approach in SHM. Despite the success of deep convolutional neural networks in diverse detection problems, the required computational resources are a challenge. This has led to rendering a chunk of resource-constrained edge nodes less applicable with deep convolutional neural networks. In this paper, a computational-efficient deep convolutional neural network is presented based on Gabor filters and a color Canny edge detector. Generic Gabor filters are generated and used as initializers in the computational-efficient deep convolutional neural network presented, afterward trained on building components data. Next, extensive offline and online experimentation with a resource-constrained edge node is conducted and evaluated using diverse metrics. The computational-efficient detection model demonstrates to be effective in detection and via NVIDIA GPU profiler, we observe conservation of around 30% of computational resources during training. The computational-efficient detection model adduces almost a 3% mean average precision higher than two state-of-the-art detectors and records a promising frame processing rate during the online experimentation

Artificial neural network based fast edge detection algorithm for MRI medical images

Currently, magnetic resonance imaging (MRI) has been utilized extensively to obtain high contrast medical image due to its safety which can be applied repetitively. Edges are represented as important contour features in the medical image since they are the boundaries where distinct intensity changes or discontinuities occur. Many traditional algorithms have been proposed to detect the edge, such as Canny, Sobel, Prewitt, Roberts, Zerocross, and Laplacian of Gaussian (LoG). Moreover, many researches have shown the potential of using Artificial Neural Network (ANN) for edge detection. Although many algorithms have been conducted on edge detection for medical images, however higher computational cost and subjective image quality could be further improved. Therefore, the objective of this paper is to develop a fast ANN based edge detection algorithm for MRI medical images. First, we developed features based on horizontal, vertical, and diagonal difference. Then, Canny edge detector will be used as the training output. Finally, optimized parameters will be obtained, including number of hidden layers and output threshold. Results showed that the proposed algorithm provided better image quality while it has faster processing time around three times time compared to other traditional algorithms, such as Sobel and Canny edge detector

Artificial neural network based fast edge detection algorithm for MRI medical images

Currently, magnetic resonance imaging (MRI) has been utilized extensively to obtain high contrast medical image due to its safety which can be applied repetitively. Edges are represented as important contour features in the medical image since they are the boundaries where distinct intensity changes or discontinuities occur. Many traditional algorithms have been proposed to detect the edge, such as Canny, Sobel, Prewitt, Roberts, Zerocross, and Laplacian of Gaussian (LoG). Moreover, many researches have shown the potential of using Artificial Neural Network (ANN) for edge detection. Although many algorithms have been conducted on edge detection for medical images, however higher computational cost and subjective image quality could be further improved. Therefore, the objective of this paper is to develop a fast ANN based edge detection algorithm for MRI medical images. First, we developed features based on horizontal, vertical, and diagonal difference. Then, Canny edge detector will be used as the training output. Finally, optimized parameters will be obtained, including number of hidden layers and output threshold. Results showed that the proposed algorithm provided better image quality while it has faster processing time around three times time compared to other traditional algorithms, such as Sobel and Canny edge detector

A biologically inspired spiking model of visual processing for image feature detection

To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence edge or corner features, commonly called interest points are often used for this purpose. Experimental research has illustrated that biological vision systems use neuronal circuits to extract particular features such as edges or corners from visual scenes. Inspired by this biological behaviour, this paper proposes a biologically inspired spiking neural network for the purpose of image feature extraction. Standard digital images are processed and converted to spikes in a manner similar to the processing that transforms light into spikes in the retina. Using a hierarchical spiking network, various types of biologically inspired receptive fields are used to extract progressively complex image features. The performance of the network is assessed by examining the repeatability of extracted features with visual results presented using both synthetic and real images