Color component marking and convolution-based encoding for polychromatic pattern recognition

A novel method for polychromatic pattern recognition is proposed based on color component marking and convolution-based encoding. Three random phase functions are first chosen as marks to multiply with the three color components of the input images. Then the three marked color components are combined into one image using convolution-based encoding. Finally, the combined images are served as the input images of the JTC to be recognized. The feasibility and effectiveness of the proposed method are demonstrated by numerical results.     

基于模式识别技术的色貌模型评价研究

选用Munsell新标数据集,采用基于模式识别技术的圆度、色相角偏差、明度线性度与空间投影点聚集度四个指标来分别评价目前八个典型色貌模型在彩度均匀性、色相预测准确性、明度均匀性、颜色的空间再现的能力。结果表明,不同的色貌模型在四个方面的颜色再现性上表现出不同的优势。八个色貌模型对彩度预测最好的是RLAB,每个色貌模型对红色和绿色预测比较好,CAM02在色相预测整体上占优势;明度的均匀性预测都比较好,尤其是CIELAB;LLAB的颜色的空间再现能力最好。     

药剂颜色标准的谱识别方法

提出一种药剂颜色标准的谱识别新方法 ,该方法突破了传统的三刺激值测色模式。提出了用光谱特征及非固定的背景模式 ,代替三刺激值及CIE测量模式 ,建立了良好的判别函数 ,适于药剂颜色标准的客观检验与定级 ,结果令人满意。     

Cryptanalysis of a color image encryption algorithm based on chaos

A novel image encryption algorithm based on logistic map is proposed recently. In this paper, a chosen plaintext attack on this algorithm is presented and some other flaws of the algorithm are pointed out. Theoretical analysis and experimental simulation indicate that the plain image can be recovered exactly from the cipher image without secret key. Therefore, this algorithm is not secure enough for practical applications. An improvement is proposed to enhance the security of the original algorithm. Simulation results and theoretical analysis show that the improved scheme has expected cryptographic properties and is more secure than the original algorithm.     

Research on the chaos recognition method based on differential entropy

Phase space reconstruction is the first step to recognizing the chaos from observed time series. On the basis of differential entropy, this paper introduces an efficient method to estimate the embedding dimension and the time delay simultaneously. The differential entropy is used to characterize the disorder degree of the reconstructed attractor. The minimum value of the differential entropy corresponds to the optimum set of the reconstructed parameters. Simulated experiments show that the original phase space can be effectively reconstructed from time series, and the accuracy of the invariants in phase space reconstruction is greatly improved. It provides a new method for the identification of chaotic signals from time series.     

基于双相位编码的单通道彩色图像加密

提出一种基于双相位编码的单通道彩色图像加密方法. 在该方法中,将彩色图像转换到HSI空间,I分量即可作为相位编码的原始待加密图像;而采用双随机相位加密技术对S分量加密后得到的相息图,与H分量一起构成了对I分量加密的双相位. 由于双随机相位加密技术有很高的安全性,在不知密钥的情况下解出S分量几乎是不可能的,由此保证了彩色图像加密的安全性. 模拟实验结果证明了该方法的有效性. 关键词： 彩色图像 单通道 双相位编码     

Wavelet-modified binary phase-only morphological correlation for color pattern recognition

This paper reports a morphological phase-only correlation technique based on bit-map representation for recognition of color as well as grey images in a hybrid digital-optical correlation architecture. The color image is decomposed into its R, G and B components, and each component is further decomposed into eight disjoint elementary images depending upon the bit-map representation of the color value at each pixel. Bit-map representation of the pixel values of an image reduces the required computational time. A set of twenty-four disjoint wavelet-modified binary phase-only filters (WBPOFs) are generated from these bit-map decomposed images. The target image is similarly decomposed into eight disjoint images each of R, G and B and their digital Fourier transforms multiplied with the corresponding WBPOFs. The product functions thus obtained are added up to form a single resultant product function, whose optical Fourier transformation gives the correlation peaks for the presence of R, G and B components in the image. The single product function overcomes the necessity of obtaining the final optical Fourier transformation of the R, G and B components separately. The novelty of this approach lies in the fact that the WBPOFs synthesized by this procedure are thus able to identify both colored as well as gray images and can tolerate salt-and-pepper noise to a considerable extent.     

基于图像模式识别技术的牛顿环检测

利用图像模式识别技术对牛顿环条纹进行自动识别，不仅速度快、精度高，而且克服了通过移测显微镜判读条纹而引起的视差．     

Color component 3D Arnold transform for polychromatic pattern recognition

We propose a novel method for polychromatic pattern recognition based on color component 3D Arnold transform. Three color components (for example RGB) are first transformed into three chaotic images by the use of 3D Arnold transform. And then any one of the chaotic images is chosen as the input image of the JTC to be recognized. As a result, strong color discrimination capability is achieved and common color images as well as some special color images can be recognized, while still preserving compact system and easier analysis of the output. Numerical results demonstrate the feasibility and effectiveness of the proposed method.     

Novel single-channel color image encryption algorithm based on chaos and fractional Fourier transform

A new color image encryption algorithm based on fractional Fourier transform (FrFT) and chaos is proposed. The colors of the original color image are converted to HSI (hue-saturation-intensity), and the S component is transformed by the random-phase encoding based on FrFT to obtain a new random phase. The I component is transformed by double random-phase encoding based on FrFT using the H component and the new random phase as two phase plates. Then chaos scrambling technology is used to encrypt the image, which makes the resulting image nonlinear and disorder both in spatial domain and frequency domain. Additionally, the ciphertext is not a color image but a combination of a gray image and a phase matrix, so the ciphertext has camouflage property to some extent. The results of numerical simulations demonstrate the effectiveness and the security of this algorithm.     

一种基于模式识别的多路盲语音提取方法

盲分离算法能在缺少混合系统参数的条件下仅由观测信号估计初始源,但分离信号存在固有的排列模糊性,这往往导致两次批处理过程中同一信号"对不准",因此很难获得连续的源信号。本文针对盲声源分离中存在的相同问题,根据语音和其他音频信号的特征差异,提出一种修正的自相关函数并以其值作为一个特征基元来表征声音信号的时序相关特性,同时用平均声门波形状参数作为另一个特征基元来表征语音产生的生理效应。以这两个参数作为识别不同音频信号的二维模式特征,采用一种模糊聚类算法提取多路盲分离语音。本方法有效克服了批处理盲声源分离中的信号排列顺序的不确定性,并通过选择合适的阈值提取多路连续语音。仿真给出了5路混合音频信号中盲提取两路连续语音的实验结果。     

Finger crease pattern recognition using Legendre moments and principal component analysis

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper,we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the preprocessing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.     

改进的基于二维主分量分析的掌纹识别

主分量分析(PCA)是一种在众多生物特征识别中获得成功应用的特征提取技术,是一种基于二阶统计的在最小均方误差意义上的最优维数据压缩技术,它所提取的各特征分量之间是互不相关的。传统的PCA变换是对图像向量的分析,但向量维数一般都很高。二维主分量分析方法是最近兴起的针对图像矩阵的主分量分析方法,与一维主分量分析相比能更精确的计算原始数据的协方差矩阵。将其应用于掌纹识别,并在主分量的选取上加以改进,选取了更适合于分类的主分量。实验结果表明,该方法不仅有更高的识别率,而且维数更低。     

Existence problem of optical correlation based pattern recognition

The existence problem of optical correlation based pattern recognition, namely its range of validity and its limitation, is discussed in this paper conjointly with the function approximation theory of neural networks. The conclusion is that only if the sets to be recognized are linearly separable (which is rare) or the subsets, in which a segmental sample of the targets is involved, are linearly separable, can the classical 4f optical correlation system carry out the task of recognition inerrably. The recognition principle of a joint transform correlator is the same as that of a 4f system, and so is its range of validities. Based on the demonstration of the existence problem of optical correlation based pattern recognition an evaluation on some important problems that were studied in this field over the past 40 years is presented explicitly.     

Mach-Zehnder joint transform correlator with multi-channel quantized reference functions for color pattern recognition

Based on the Mach-Zehnder interferometer, we introduce a novel construction of the optical correlator for color pattern recognition. This system could remove the zero order term, which decreases the discrimination ability of the system, in only one step. Since each color can be separated into 3 channels, we propose the multi-level quantized reference functions (MQRF) for each color channel, and utilize them in spatial domain. Consequently, a total of 3 MQRFs and 3 color channels of the test image could be implemented at the input plane of liquid crystal spatial light modulator (LCSLM) of Mach-Zehnder joint transform correlator. Each LCSLM is modulated along the real-valued axis.     

Color pattern recognition based on the joint fractional Fourier transform correlator

A new system of multi-channel single-output joint fractional Fourier transform correlator (JFRTC) for color pattern recognition is proposed based on the conventional system of multi-channel single-output joint transform correlator (JTC). The theoretical analysis and optical experiments are performed. With this method, one can obtain three correlation peaks at the output plane which show a pair of desired cross-correlation peaks and one auto-correlation peak. In comparison, the conventional system leads to more correlation peaks playing a noise role in color pattern recognition.     

Data processing in BOTDR distributed strain measurement based on pattern recognition

As a distributed fiber optic sensor able to monitor strain distribution within dozens of miles, BOTDR is widely applied in structural health monitoring (SHM). However, the difficulty of data processing has been in the way of health monitoring of civil structures and hence an obstacle against BOTDR’s development. In order to realize the rapid processing of distributed strain data, the strain measurement line was divided into a series of waveform units to confirm whether waveform units are normal or not by pattern recognition while taking both mean and standard deviation of characteristic vector into consideration, as well as combine neighboring abnormal units as abnormal area and give evaluation and warning to this area by the correspondence between waveforms and fiber deformation. The application of data processing in distributed monitoring of a certain tunnel showed that the abnormal area can be located fast and accurately and the deformation of optical fiber and structure can be evaluated with this technology.     

Reconfigurable optical differential phase-shift-keying pattern recognition based on incoherent photonic processing

We propose and experimentally demonstrate asynchronous optical differential phase-shift-keying (DPSK) pattern recognition using a fully reconfigurable technique. The proposed method uses optical phase-to-bipolar intensity conversion through all-optical differentiation in conjunction with an incoherent time-spectrum convolution system where the pattern to be recognized is implemented directly in the spectral domain through optical amplitude-only linear filtering. Full reconfigurability in terms of bit rate, pattern sequence, and pattern length is achieved using electronically programmable optical filters. We demonstrate dynamically switching recognition of different 64?bit patterns in a continuous 12?Gb/s DPSK pseudorandom optical bit stream with contrast ratio up to 3.8?dB.     

A blind color image watermarking based on DC component in the spatial domain

Color image watermarking has become essential and important copyright protection or authentication scheme. It is noted that most of the existing color image watermarking algorithms are performed only in the single domain (spatial domain or frequency domain), and not to integrate these merits of the different domains. By utilizing the generating principle and distribution feature of the direct current (DC) coefficient, a novel blind watermarking algorithm is proposed for color host images in this paper. Firstly, the Y luminance of host image is divided into 8 × 8 sub-blocks and the DC coefficients of each block are directly calculated in the spatial domain without DCT transform. Secondly, according to the watermark information and the quantization step, the DC coefficients are calculated and their increments are further utilized to modify directly the values of all pixels in the spatial domain instead of the DCT domain to embed watermark. When watermark extraction, only the watermarked image and the quantization step are needed in the spatial domain. Experimental results show that the proposed method not only can resist both traditional signal processing attacks and geometric attacks, but also has more efficient in computational complexity. Comparisons also demonstrate the advantages of the method.     

Composite wavelet based morphological correlation for continuous-scale-invariant pattern recognition

A composite nonlinear correlation is proposed to perform invariant recognition for the input image with continuous-scale distortions. The proposed correlation can be considered as a summation of many composite wavelet matched filters. Every matched filter consists of a linear combination of binary slices that are generated from threshold decomposition of the training images, and an adaptive wavelet filter. The adaptive wavelet is optimized to produce the sparse image features. Computer simulations are carried out to prove the scale-invariance and noise robustness of the new scheme.