基于差别特征的神经网络人脸识别

根据视觉识别的差别特征分辨特性,该文对自联想神经网络进行了改进,提出了基于差别特征的识别方法。文中采用ＯＲＬ人脸图象库进行的对比识别实验表明,改进后的差别特征神经网络对原人脸图象和加斯噪声的人脸图象,都较自联想神经网络识别高,证实了差别特征的有效性。     

基于模糊联想神经网络分类器的JPEG彩色图象压缩编码

基于模糊联想神经网络分类器的ＪＰＥＧ彩色图象压缩编码尹捷，王煦法（中国科学技术大学电子技术部合肥２３００２６）关键词：模糊，神经网络，ＤＣＴ变换，ＪＰＥＧ图象压缩．工引言ＪＰＥＧ图象压缩中采用了视觉量化技术，即根据人眼视觉特性对不同频率的变换系数进行...     

BP神经网络在空间流体实验数据压缩中的应用

针对利用BP人工神经网络在压缩空间流体实验图象数据是BP网络收敛速度慢和存在局部极小值的问题,给出了一种特殊的处理方法,并将BP神经网络方法与DCT方法的图象压缩效果进行了比较。仿真结果表明,不仅网络训练时间明显减小,而且将BP人工神经网络用于空间流体实验图象压缩中还取得了较高压缩比及较好的重建图象质量,且训练好的网络鲁棒性较高。     

基于多项式近似的图象块分形压缩方法

分形图象压缩方法是一种利用图象自相似特性的新型图象压缩方法。本文介绍一种基于多项式近似的静止图象分形压缩方法，利用它对标准图象“Ｌｅｎａ”进行压缩，得到了令人满意的结果     

基于预测的无损图象压缩技术

预测压缩技术是无损图象压缩的基本技术,分析了基于预测的无损图象压缩方法,综述了预测无损图象压缩技术的研究进展,并对预测压缩算法的设计进行了有益的讨论。     

一种基于分形小波变换的工业电视图象编码方法研究

通过分析工业电视图象的特点,提出了一种基于分形小波变换编码的工业电视图象压缩方法,从而较好地解决了分形编码只能压缩静止图象的不足,该方法不仅适合于煤矿工业电视图象压缩,而且还可用于其它背景区图象相对静止的工业电视图象的压缩编码,实验结果表明,该法可取得较高压缩比和峰值信噪比,因此具有实用价值。     

利用聚类技术改进分形图象压缩方法

分形图象压缩技术是近十年来提出的一种新的图象压缩方法,该方法利用自然界广泛存在的自相似性,以8种简单的仿射变换的组合对图象进行有损压缩,可得到较高的压缩比.但由于分形图象压缩的计算复杂度较高,计算量较大,限制了这一方法在现实中的应用.本文提出一种使用聚类及向量量化技术改进分形图象压缩的方法,降低了分形图象压缩的计算复杂度,提高了压缩效率.本文所述的方法已在c++Builder 6.0集成开发环境下实现了对彩色图象的压缩,并取得较好的实验效果.     

一种基于CMAC的图象恢复算法

由于影响成象和导致图象退化的因素具有模糊性和不确定性,很难准确地建立图象退化过程的数学模型,因而建立退化过程的逆过程图象恢复十分困难,为了解决这一问题,提出了一种基于CMC的图象恢复算法,该方法利用CMAC神经网络的非线性映射和综合能力,通过对影响成象和导致图象退化的过程进行反向学习来恢复图象。仿真结果表明,用CMAC神经网络能很好地恢复出已退化的图象,并且神经网络模型与学习方法十分简单,便于实时图象恢复。     

一种评价图象压缩对量测性能影响的可靠方法

如何评价一种图象压缩方法对图象量测性能的影响，目前还没有切实可行的方法。目前还没有切实可行的方法。本文提出一种评价图象压缩对解压图象量测性能影响的方法，该方法不仅与视觉效果的评价准则相一致，而且能够定量地给出压缩方法对解压图象上象素几何位置的影响程度，因而适用于高精度的数字摄影测量和其它高精度的数字图象处理领域。     

面向图象压缩的图象分类及压缩结果预测

图象数据存在冗余使图象压缩成为可能 ,而不同图象的数据冗余度特别是空间冗余度相差很大 .对被压缩图象的空间冗余度这一图象的本质属性进行研究、减少图象压缩及方法选择时的盲目性是非常必要的 .为此提出了面向图象压缩的图象分类这一新概念以及具体分类算法 .该算法利用图象小波高频系数的分布特点 ,采用图象边缘度作为图象空间冗余度的度量 ,将不同内容的图象按边缘度大小分类 .分类的结果可对不同图象的压缩结果进行预测 .实验结果表明 ,图象分类结果和对压缩结果的预测是有意义的 ,并与人的视觉相吻合 .该分类思想对其他图象处理算法的选择和优化也有参考价值 .     

An efficient nonlinear approach for removing fixed-value impulse noise from grayscale images

The latest-generation earth observation instruments on airborne and satellite platforms are currently producing an almost continuous high-dimensional data stream. This exponentially growing data poses a new challenge for real-time image processing and recognition. Making full and effective use of the spectral information and spatial structure information of high-resolution remote sensing image is the key to the processing and recognition of high-resolution remote sensing data. In this paper, the adaptive multipoint moment estimation (AMME) stochastic optimization algorithm is proposed for the first time by using the finite lower-order moments and adding the estimating points. This algorithm not only reduces the probability of local optimum in the learning process, but also improves the convergence rate of the convolutional neural network (Lee Cun et al. in Advances in neural information processing systems, 1990). Second, according to the remote sensing image with characteristics of complex background and small sensitive targets, and by automatic discovery, locating small targets, and giving high weights, we proposed a feature extraction method named weighted pooling to further improve the performance of real-time image recognition. We combine the AMME and weighted pooling with the spatial pyramid representation (Harada et al. in Comput Vis Pattern Recognit 1617–1624, 2011) algorithm to form a new, multiscale, and multilevel real-time image recognition model and name it weighted spatial pyramid networks (WspNet). At the end, we use the MNIST, ImageNet, and natural disasters under remote sensing data sets to test WspNet. Compared with other real-time image recognition models, WspNet achieve a new state of the art in terms of convergence rate and image feature extraction compared with conventional stochastic gradient descent method [like AdaGrad, AdaDelta and Adam (Zeiler in Comput Sci, 2012; Kingma and Ba in Comput Sci, 2014; Duchi et al. in J Mach Learn Res 12(7):2121–2159, 2011] and pooling method [like max-pooling, avg-pooling and stochastic-pooling (Zeiler and Fergus in stochastic-pooling for regularization of deep convolutional neural networks, 2013)].     

Binary object extraction using bi-directional self-organizing neural network (BDSONN) architecture with fuzzy context sensitive thresholding

A novel neural network architecture suitable for image processing applications and comprising three interconnected fuzzy layers of neurons and devoid of any back-propagation algorithm for weight adjustment is proposed in this article. The fuzzy layers of neurons represent the fuzzy membership information of the image scene to be processed. One of the fuzzy layers of neurons acts as an input layer of the network. The two remaining layers viz. the intermediate layer and the output layer are counter-propagating fuzzy layers of neurons. These layers are meant for processing the input image information available from the input layer. The constituent neurons within each layer of the network architecture are fully connected to each other. The intermediate layer neurons are also connected to the corresponding neurons and to a set of neighbors in the input layer. The neurons at the intermediate layer and the output layer are also connected to each other and to the respective neighbors of the corresponding other layer following a neighborhood based connectivity. The proposed architecture uses fuzzy membership based weight assignment and subsequent updating procedure. Some fuzzy cardinality based image context sensitive information are used for deciding the thresholding capabilities of the network. The network self organizes the input image information by counter-propagation of the fuzzy network states between the intermediate and the output layers of the network. The attainment of stability of the fuzzy neighborhood hostility measures at the output layer of the network or the corresponding fuzzy entropy measures determine the convergence of the network operation. An application of the proposed architecture for the extraction of binary objects from various degrees of noisy backgrounds is demonstrated using a synthetic and a real life image.

基于多特征加权融合的静态手势识别

针对人工提取特征的单一性及卷积神经网络提取特征的遗漏性问题,提出了一种基于多特征加权融合的静态手势识别方法.首先,提取分割后的手势图像的傅里叶和Hu矩等形状特征,将两者融合作为手势图像的局部特征;设计双通道卷积神经网络提取手势图像的深层次特征,采用主成分分析方法对提取的特征进行降维;然后,将提取的局部特征和深层次特征进行加权融合作为手势识别的有效特征描述;最后,使用Softmax分类器进行手势图像分类.实验结果验证了提出方法的有效性,在手势图像数据集上的识别准确率达到了99%以上.     

Quaternion Fourier Descriptors for the Preprocessing and Recognition of Spoken Words Using Images of Spatiotemporal Representations

This paper presents an application of the quaternion Fourier transform for the preprocessing for neural-computing. In a new way the 1D acoustic signals of French spoken words are represented as 2D signals in the frequency and time domain. These kind of images are then convolved in the quaternion Fourier domain with a quaternion Gabor filter for the extraction of features. This approach allows to greatly reduce the dimension of the feature vector. Two methods of feature extraction are tested. The features vectors were used for the training of a simple MLP, a TDNN and a system of neural experts. The improvement in the classification rate of the neural network classifiers are very encouraging which amply justify the preprocessing in the quaternion frequency domain. This work also suggests the application of the quaternion Fourier transform for other image processing tasks.

Virtual reality of recognition technologies of the improved contour coding image based on level set and neural network models

Image contour-based feature extraction method has been applied to some fields of image recognition and virtual reality. However, image contour features are easily susceptible to factors like noise, rotation and thresholds during extraction and processing. To solve the above problem, this paper proposes a contour coding image recognition algorithm based on level set and BP neural network models. Firstly, level set model is employed to extract the contours of images. Secondly, image coding method proposed herein is used to code images horizontally, vertically and obliquely. At last, BP neural network model is trained to recognize the image codes. Validity of the proposed algorithm is verified by using a set of actual engineering part images as well as MPEG and PLANE databases. The results show that the proposed method achieves high recognition rate and requires small samples, which also exhibits good robustness to external disturbances such as noise and image scaling and rotation.

     

Fuzzy auto-associative neural networks for principal component extraction of noisy data.

In this paper, we propose a fuzzy auto-associative neural network for principal component extraction. The objective function is based on reconstructing the inputs from the corresponding outputs of the auto-associative neural network. Unlike the traditional approaches, the proposed criterion is a fuzzy mean squared error. We prove that the proposed objective function is an appropriate fuzzy formulation of auto-associative neural network for principal component extraction. Simulations are given to show the performances of the proposed neural networks in comparison with the existing method.     

Image change detection from difference image through deterministic simulated annealing

This paper proposes an automatic method based on the deterministic simulated annealing (DSA) approach for solving the image change detection problem between two images where one of them is the reference image. Each pixel in the reference image is considered as a node with a state value in a network of nodes. This state determines the magnitude of the change. The DSA optimization approach tries to achieve the most network stable configuration based on the minimization of an energy function. The DSA scheme allows the mapping of interpixel contextual dependencies which has been used favorably in some existing image change detection strategies. The main contribution of the DSA is exactly its ability for avoiding local minima during the optimization process thanks to the annealing scheme. Local minima have been detected when using some optimization strategies, such as Hopfield neural networks, in images with large amount of changes, greater than the 20%. The DSA performs better than other optimization strategies for images with a large amount of changes and obtain similar results for images where the changes are small. Hence, the DSA approach appears to be a general method for image change detection independently of the amount of changes. Its performance is compared against some recent image change detection methods.

高光谱图像小样本分类的卷积神经网络方法

目的 与传统分类方法相比,基于深度学习的高光谱图像分类方法能够提取出高光谱图像更深层次的特征。针对现有深度学习的分类方法网络结构简单、特征提取不够充分的问题,提出一种堆叠像元空间变换信息的数据扩充方法,用于解决训练样本不足的问题,并提出一种基于不同尺度的双通道3维卷积神经网络的高光谱图像分类模型,来提取高光谱图像的本质空谱特征。方法 通过对高光谱图像的每一像元及其邻域像元进行旋转、行列变换等操作,丰富中心像元的潜在空间信息,达到数据集扩充的作用。将扩充之后的像素块输入到不同尺度的双通道3维卷积神经网络学习训练集的深层特征,实现更高精度的分类。结果 5次重复实验后取平均的结果表明,在随机选取了10%训练样本并通过8倍数据扩充的情况下,Indian Pines数据集实现了98.34%的总体分类精度,Pavia University数据集总体分类精度达到99.63%,同时对比了不同算法的运行时间,在保证分类精度的前提下,本文算法的运行时间短于对比算法,保证了分类模型的稳定性、高效性。结论 本文提出的基于双通道卷积神经网络的高光谱图像分类模型,既解决了训练样本不足的问题,又综合了高光谱图像的光谱特征和空间特征,提高了高光谱图像的分类精度。