Image identification system based on an optical broadcast neural network and a pulse coupled neural network preprocessor stage

We describe the concept of a vision system based on an optoelectronic hardware neural processor. The proposed system is composed of a pulse coupled neural network (PCNN) preprocessor stage that converts an input image into a temporal pulsed pattern. These pulses are inputs to the optical broadcast neural network (OBNN) processor, which classifies the input pattern between a set of reference patterns based on a pattern matching strategy. The PCNN is to provide immunity to the scale, rotation, and translation of objects in the image. The OBNN provides high parallelism and a high speed hardware neural processor.     

一种双目立体视觉系统的校准方法

基于双目立体视觉系统的图像分析以及人工神经网络的三维空间建模算法,设计了一种针对双目立体视觉相机的校准方法,并可应用于运动目标点的轨迹追踪。将均匀分布目标点的校准平面放置在有效视野内的不同位置,通过双目立体视觉系统来捕获处于不同位置的校准平面图像。在图像处理之后,使用校准点中心的二维坐标作为人工神经网络训练的输入样本集,通过建立人工神经网络模型结构,实现目标点二维平面坐标到三维空间坐标的映射关系。采用这种具有通用性的方法,可以有效修正系统中存在的失真因子,获得目标三维位置信息,而无需进行复杂的相机校准操作。实验表明,提出的方案具有良好的可行性和鲁棒性。     

Active contour segmentation by use of a multichannel incoherent optical correlator

We describe an optoelectronic incoherent multichannel processor that is able to segment an object in a real image. The process is based on an active contour algorithm that has been transposed to optical signal processing to accelerate image processing. This implementation requires exact-valued correlations and thus opens attractive perspectives in terms of optical analog computation. Furthermore, this optical multichannel processor setup encourages incoherent processing with high-resolution images.     

Embedding Image Through Generated Intermediate Medium Using Deep Convolutional Generative Adversarial Network

Deep neural network has proven to be very effective in computer vision fields. Deep convolutional network can learn the most suitable features of certain images without specific measure functions and outperform lots of traditional image processing methods. Generative adversarial network (GAN) is becoming one of the highlights among these deep neural networks. GAN is capable of generating realistic images which are imperceptible to the human vision system so that the generated images can be directly used as intermediate medium for many tasks. One promising application of using GAN generated images would be image concealing which requires the embedded image looks like not being tampered to human vision system and also undetectable to most analyzers. Texture synthesizing has drawn lots of attention in computer vision field and is used for image concealing in steganography and watermark. The traditional methods which use synthesized textures for information hiding mainly select features and mathematic functions by human metrics and usually have a low embedding rate. This paper takes advantage of the generative network and proposes an approach for synthesizing complex texture-like image of arbitrary size using a modified deep convolutional generative adversarial network (DCGAN), and then demonstrates the feasibility of embedding another image inside the generated texture while the difference between the two images is nearly invisible to the human eyes.     

On-line fault detection and diagnosis obtained by implementingneural algorithms on a digital signal processor

A measurement instrument for on-line fault detection and diagnosis is proposed. It is based on the implementation of a neural network algorithm on a processor specialized in digital signal processing and provided with suitable data acquisition and generation units. Two specific implementations are detailed. The former uses the neural-network to simulate on-line the correct system behavior, thus allowing the fault detection to be achieved by comparing the neural network output with the measured one. The latter uses the neural network to classify on-line the system as correct or faulty, thus allowing the fault detection and diagnosis to be achieved simultaneously. These two implementations are applied to detect on-line and diagnose faults on a real system in order to point out different fields of application and to highlight the performance of the measurement apparatus     

基于RBF神经网络的结构光三维视觉检测方法研究

研究了基于RBF神经网络的结构光三维视觉检测方法。该方法利用了RBF网络良好的非线性映射能力以及学习、泛化能力，通过采用高精度样本数据训练RBF网络，最终建立起了用于结构光三维视觉检测的RBF网络模型。与常规方法相比，它不需要考虑视觉模型误差、光学调整误差等对视觉检测系统测量精度的影响，因而能够有效地克服常规建模方法的不足，保证了检测系统具有较高的精度。     

基于生理视觉的计算机图象处理系统

报道了一个基于生理视觉原理的计算机图象处理系统，该系统根据对生理视觉过程的分解，采用了一种含有正反馈回路的分级前注意视觉模型，通过模拟视觉通路中信息传递所经的各种神经网络的感受影响应，对输入图象逐级进行处理，实现了对图象的突发组群和合理分割。对有严重阴影的道路图和ＣＴ扫描图进行处理的结果表明，该系统能够在可变的光照条件下提取出不变的物体边界和特征，估于传统的图象处理和机器视觉系统。     

Intelligent detection of microcalcification from digitized mammograms

This paper reports the design and implementation of an intelligent system for detection of microcalcification from digital mammograms. A neuron based thresholding strategy has been developed to reduce the number of candidate pixels. A back propagation neural network (BPNN) classifier has been used to classify the pixels into positive (affected) and normal ones. The false positives generated in the process are eliminated using the connected component analysis and the elongated component removal algorithms in succession. Suspected areas of microcalcification are detected and marked on the mammogram. The system was rigorously tested for the available images and was found to be quite robust, consistent and fast in detection. The output image with prompts generated by the system can form an important input to a radiologist for the final diagnosis.     

采用小波与神经网络的高效图像数据压缩方案

通过构造特别的映射、整函数和BP神经网络,获得一套基于神经网络的无损数据压缩方案。由于该方案能压缩已被小波编码压缩过的数据,因此将其嵌套入一好的小波编码系统就可以获得一种基于小波与神经网络的高效图像数据压缩方案。实验证明,该高效方案对于Lenna图像的压缩比为43∶1, 并且恢复的图像有较好的视觉效果。     

Super‐resolution of the undersampled and subpixel shifted image sequence by a neural network

Numerous approaches to super‐resolution (SR) of sequentially observed images (image sequence) of low resolution (LR) have been presented in the past two decades. However, neural network methods are almost ignored for solving SR problems. This is because the SR problem traditionally has been regarded as the optimization of an ill‐posed large set of linear equations. A designed neural network based on this has a large number of neurons, thereby requiring a long learning time. Also, the deduced cost function is overly complex. These defects limit applications of a neural network to an SR problem. We think that the underlying meaning of the SR problem should refer to super‐resolving an imaging system by image sequence observation, instead of merely improving the image sequence itself. SR can be regarded as a pattern mapping from LR to SR images. The parameters of the pattern mapping can be learned from the imaging process of the image sequence. This article presents a neural network for SR based on learning from the imaging process of the image sequence. In order to speed up the convergence, we employ vector mapping to train the neural network. A mapping vector is composed of some neighbor subpixels. Such a well‐trained neural network has powerful generalization ability so that it can be used directly to estimate the SR image of the other image sequences without learning again. Our simulations show the effectiveness of the proposed neural network. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 8–15, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20001     

多方法融合技术在多目标位置检测中的应用

提出了将多种技术融合一体定位多目标的方法。首先根据计算机视觉原理，采用正交光轴的双摄像机配置检测系统；然后建立去除复杂实际背景和自适应二值化的动态图象预处理模型，并根据矩法搜索和求取目标质心；再利用神经网络模拟人眼感知事物的功能，确立图象点与空间点的映射关系，并将目标质心象点坐标作为输入节点，采用两个BP网并行处理左右两幅图象，快速求得目标空间位置。实践表明，此方法具有一定的实用性，并且在实际应用中取得了满意的效果。     

热轧重轨表面缺陷在线检测识别的关键技术研究

目前热轧重轨表面缺陷检测速度慢、精度低。为此,提出了一种基于机器视觉的热轧重轨表面缺陷在线检测系统。分析了过暗过曝区域交叠融合法与图像像素线互相关校验法两种方法提取特征缺陷等关键技术,并对模糊脉冲神经网络的表面缺陷分类效果进行了研究。实际应用证明,采用上述机器视觉的检测关键技术对热轧重轨表面进行缺陷检测识别,较大提高了检测速度和精度,且检测正确率在90%以上。     

气辅注塑成型注气压力的模糊神经网络控制研究

针对气辅注塑成形的注气压力精确控制要求,设计了具有５层结构的模糊神经网络控制器和控制算 法,利用神经网络的学习能力实现对模糊逻辑规则的优化,改善了系统的适应性。对系统３段压力控制的仿真 分析,验证了模糊神经网络控制模型的可行性,控制效果良好。     

Optical implementation of neural networks for face recognition by the use of nonlinear joint transform correlators

We describe a nonlinear joint transform correlator-based two-layer neural network that uses a supervised learning algorithm for real-time face recognition. The system is trained with a sequence of facial images and is able to classify an input face image in real time. Computer simulations and optical experimental results are presented. The processor can be manufactured into a compact low-cost optoelectronic system. The use of the nonlinear joint transform correlator provides good noise robustness and good image discrimination.     

Real-time three-dimensional object recognition with multiple perspectives imaging

A novel system for recognizing three-dimensional (3D) objects by use of multiple perspectives imaging is proposed. A 3D object under incoherent illumination is projected into an array of two-dimensional (2D) elemental images by use of a microlens array. Each elemental 2D image corresponds to a different perspective of the 3D object. Multiple perspectives imaging based on integral photography has been used for 3D display. In this way, the whole set of 2D elemental images records 3D information about the input object. After an optical incoherent-to-coherent conversion, an optical processor is employed to perform the correlation between the input and the reference 3D objects. Use of micro-optics allows us to process the 3D information in real time and with a compact optical system. To the best of our knowledge this 3D processor is the first to apply the principle of integral photography to 3D image recognition. We present experimental results obtained with both a digital and an optical implementation of the system. We also show that the system can recognize a slightly out-of-plane rotated 3D object.     

Real-time implementation of a measurement-based adaptive wide-area control system considering communication delays

A wide-area control system (WACS) uses wide-area measurement signals to provide auxiliary stabilising controls to power system devices. An adaptive WACS has been designed to provide damping control signals to the excitations of generators. The delays in signal transmission and the reliability of the communication network is a major concern with wide-area measurement- based control. The adaptive WACS is designed to compensate for a wide range of communication delays and provide robust damping to mitigate system oscillations. A single simultaneous recurrent neural network is used in the realisation of the adaptive WACS for both identification and control of the power system. The WACS has been implemented on a digital signal processor and its performance is evaluated on a power system implemented on a real-time platform - the real-time digital simulator. The additional damping provided by the WACS is enumerated using Prony analysis.     

Acoustic Emission Recognition Based on a Two-Streams Convolutional Neural Network

The Convolutional Neural Network (CNN) is a widely used deep neural network. Compared with the shallow neural network, the CNN network has better performance and faster computing in some image recognition tasks. It can effectively avoid the problem that network training falls into local extremes. At present, CNN has been applied in many different fields, including fault diagnosis, and it has improved the level and efficiency of fault diagnosis. In this paper, a two-streams convolutional neural network (TCNN) model is proposed. Based on the short-time Fourier transform (STFT) spectral and Mel Frequency Cepstrum Coefficient (MFCC) input characteristics of two-streams acoustic emission (AE) signals, an AE signal processing and classification system is constructed and compared with the traditional recognition methods of AE signals and traditional CNN networks. The experimental results illustrate the effectiveness of the proposed model. Compared with single-stream convolutional neural network and a simple Long Short-Term Memory (LSTM) network, the performance of TCNN which combines spatial and temporal features is greatly improved, and the accuracy rate can reach 100% on the current database, which is 12% higher than that of single-stream neural network.     

基于神经网络的印刷墨量在线检测研究

提出了使用机器视觉和神经网络等技术构建印刷墨量在线检测系统,介绍了由机器视觉采集印刷图像,以及使用神经网络计算印张上各墨区CMYK四色油墨墨层厚度的方法。最后通过实验验证了神经网络的可行性,并编程实现了这一系列过程。     

An Atomically Thin Optoelectronic Machine Vision Processor

2D semiconductors, especially transition metal dichalcogenide (TMD) monolayers, are extensively studied for electronic and optoelectronic applications. Beyond intensive studies on single transistors and photodetectors, the recent advent of large-area synthesis of these atomically thin layers has paved the way for 2D integrated circuits, such as digital logic circuits and image sensors, achieving an integration level of ≈100 devices thus far. Here, a decisive advance in 2D integrated circuits is reported, where the device integration scale is increased by tenfold and the functional complexity of 2D electronics is propelled to an unprecedented level. Concretely, an analog optoelectronic processor inspired by biological vision is developed, where 32 × 32 = 1024 MoS₂ photosensitive field-effect transistors manifesting persistent photoconductivity (PPC) effects are arranged in a crossbar array. This optoelectronic processor with PPC memory mimics two core functions of human vision: it captures and stores an optical image into electrical data, like the eye and optic nerve chain, and then recognizes this electrical form of the captured image, like the brain, by executing analog in-memory neural net computing. In the highlight demonstration, the MoS₂ FET crossbar array optically images 1000 handwritten digits and electrically recognizes these imaged data with 94% accuracy.     

基于机器视觉的玻璃瓶口缺陷检测方法

目的为提高玻璃瓶口缺陷检测精度,确保生产线包装效率。方法基于机器视觉设计一种瓶口缺陷检测方法,并简要介绍检测系统的整体框架。分别论述基于最大熵值法的图像分割方法、瓶口定位方法以及图像特征提取方法,其中图像特征主要包括周长、圆形度、相对圆心距离。利用BP神经网络实现瓶口缺陷的准确识别,将瓶口破损程度转换为具体数值,最后进行实验验证。结果文中检测方法对破损瓶口的检测成功率为99%,对于不同的破损类型均有较高的检测准确度。结论基于机器视觉的玻璃瓶口缺陷检测方法能够满足生产线对准确性和实时性的要求。