Efficient Image Processing Via Compressive Sensing Of Integrate-And-Fire Neuronal Network Dynamics

Integrate-and-fire (I&F) neuronal networks are ubiquitous in diverse image processing applications, including image segmentation and visual perception. While conventional I&F network image processing requires the number of nodes composing the network to be equal to the number of image pixels driving the network, we determine whether I&F dynamics can accurately transmit image information when there are significantly fewer nodes than network input-signal components. Although compressive sensing (CS) theory facilitates the recovery of images using very few samples through linear signal processing, it does not address whether similar signal recovery techniques facilitate reconstructions through measurement of the nonlinear dynamics of an I&F network. In this paper, we present a new framework for recovering sparse inputs of nonlinear neuronal networks via compressive sensing. By recovering both one-dimensional inputs and two-dimensional images, resembling natural stimuli, we demonstrate that input information can be well-preserved through nonlinear I&F network dynamics even when the number of network-output measurements is significantly smaller than the number of input-signal components. This work suggests an important extension of CS theory potentially useful in improving the processing of medical or natural images through I&F network dynamics and understanding the transmission of stimulus information across the visual system

Geometric Morphology of Granular Materials

We present a new method to transform the spectral pixel information of a micrograph into an affine geometric description, which allows us to analyze the morphology of granular materials. We use spectral and pulse-coupled neural network based segmentation techniques to generate blobs, and a newly developed algorithm to extract dilated contours. A constrained Delaunay tesselation of the contour points results in a triangular mesh. This mesh is the basic ingredient of the Chodal Axis Transform, which provides a morphological decomposition of shapes. Such decomposition allows for grain separation and the efficient computation of the statistical features of granular materials.Comment: 6 pages, 9 figures. For more information visit http://www.nis.lanl.gov/~bschlei/labvis/index.htm

Pendeteksian Fitur Wajah Menggunakan Pulse-Coupled Neural Network (PCNN) dan Active Contour

ABSTRAKSI: Computer Vision adalah salah satu bidang penelitian yang sedang berkembang sekarang ini. Inti dari Computer Vision adalah bagaimana sebuah mesin mampu mengenal suatu objek. Salah satu aplikasi praktis yang tengah giat dikembangkan dalam Computer Vision adalah pembangunan sistem pengenalan wajah waktu nyata (Real Time Face Recognition System). Sejauh ini, kendala utama yang dihadapi dalam sistem pengenalan wajah berkisar pada masalah variasi pose, orientasi wajah, variasi pencahayaan dan masalah komputasi ketika prosedur pengenalan dijalankan oleh komputer. Pada kondisi nyata, sistem pengenalan wajah dituntut juga untuk mampu mendeteksi keberadaan wajah dalam citra digital lalu mengekstraknya sebagai citra wajah yang akan dikenali.Pulse-Coupled Neural Network (PCNN) adalah sebuah processing tool yang menjanjikan. Karena Pulse- Coupled Neural Network sangat tergantung pada bentuk gambar, hal ini sesuai untuk Automated Face Segmentation karena gambar muka memiliki bentuk yang serupa. Algoritma ini diimplementasikan untuk pendeteksian otomatis fitur-fitur wajah (mata, hidung dan mulut) pada gambar wajah yang memiliki perbedaan ekspresi berbasis pada model Active Contour (snakes) dengan bantuan PCNN.Pengujian PCNN dan AC dilakukan pada dua kategori citra background dan non background yang masingmasing terdiri dari 30 citra input. Berdasarkan hasil pengujian, algoritma PCNN mampu menghasilkan tingkat akurasi yang baik yaitu berkisar antara 87-98% pada citra grayscale dan rgb. Dan metode Active Contour juga mampu melakukan pendeteksian otomatis fitur yang stabil dan akurat berdasarkan inisialisasi kurva setiap citranya.Kata Kunci : Computer vision, Real Time Face Recognition System, pendeteksian fitur wajah, active contour, Pulse-coupled neural network.ABSTRACT: Computer Vision is a research graetly developed today. The main idea is to solve how a machine be able to recognize an object. The practical aplication of Computer Vission for example is real time face recognition system. So far, there are many kinds of problems faced in face recognition technology, they are pose varians, face orientation, lighting and computational problems. In real world, face recognition system should be able to detect the presence of face and extract it to be recognized.Pulse-Coupled Neural Network (PCNN) is a new promising image processing tool. Since the Pulse-Coupled Neural Network firing scheme depends mainly on the shapes of the image, it is suitable for automated face segmentation (AFC) because face images contains the same shape. In this paper, we present an algorithm for automatic facial features (eye, nose and mouth) detection in face images for different expressions based on PCNN-guided active contour models (snakes).Based on test result, PCNN algorithm is able to produce a good accuracy between 87-98 percent on the grayscale and RGB images. And Active Contour method is also good enough to do segmentation for automatic features detection based on the curve initialization its image.Keyword: Computer vision, Real Time Face Recognition System, facial features detection, active contour, Pulse-coupled neural network

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

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

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

Dual-wavelength thulium fluoride fiber laser based on SMF-TMSIF-SMF interferometer as potential source for microwave generationin 100-GHz region

A dual-wavelength thulium-doped fluoride fiber (TDFF) laser is presented. The generation of the TDFF laser is achieved with the incorporation of a single modemultimode- single mode (SMS) interferometer in the laser cavity. The simple SMS interferometer is fabricated using the combination of two-mode step index fiber and single-mode fiber. With this proposed design, as many as eight stable laser lines are experimentally demonstrated. Moreover, when a tunable bandpass filter is inserted in the laser cavity, a dual-wavelength TDFF laser can be achieved in a 1.5-μm region. By heterodyning the dual-wavelength laser, simulation results suggest that the generated microwave signals can be tuned from 105.678 to 106.524 GHz with a constant step of �0.14 GHz. The presented photonics-based microwave generation method could provide alternative solution for 5G signal sources in 100-GHz region