多特征模糊推理的虹膜识别算法

为了更加完备地描述虹膜的纹理特征,利用虹膜图像灰度信息提取局部特征点、局部纹理方向和局部纹理的亮暗变化三种特征共同构成纹理的特征空间,克服了之前的多数虹膜识别算法提取单一特征易受干扰影响的局限性.然后,通过设计的模糊推理规则进行模式的分类,这种分段线性分类器的设计提高了算法线性分类的能力.分别在两个图库上进行了实验,识别率分别达到99.41%和99.67%,实验数据表明:结合多种特征能较好的反映虹膜的纹理变化特征,提高了虹膜识别的正确率,使算法具有非常优越的识别性能.     

基于SVM的多生物特征融合识别算法

针对单生物特征识别的局限性,提出融合手背静脉和虹膜两种生物特征实现身份识别.基于尺度不变特征变换(SIFT)提取手背静脉的局部SIFT特征并对特征点进行匹配,利用特征匹配率作为手背静脉图像的相似度测度.通过Haar小波变换实现虹膜特征编码,利用加权汉明距对虹膜进行相似度测试.最后基于支持向量机(SVM)实现两种生物特征在匹配层的融合识别.利用CASIA虹膜数据库和TJU手背静脉数据库对算法性能进行测试,其等错率为0.02%,实验结果表明,该融合算法具有很高的识别性能,为生物特征识别研究提供了新思路.     

用Gabor滤波实现虹膜图像纹理编码

针对虹膜纹理的幅度和相位信息特征,利用二维Gabor滤波器族可以很好地模拟皮层简单细胞的二维感受野轮廓,能够最大限度的提供图像局部方向和频率信息的特点,提出一种基于二维Gabor函数族对虹膜图像进行子块分解滤波的改进编码算法。实验结果表明,该方法能有效的提取虹膜纹理特征,从而达到虹膜识别的目的。     

虹膜纹理的空间频率特性

在虹膜预处理时首先将定位后的虹膜均分8层,然后每层虹膜采用不同的标准分别进行归一化,将虹膜展开为阶梯状,改善了传统归一化造成的图像数据重复或缺漏.利用2DGabor函数的频率选择性,分别在不同的空间频率段上提取虹膜图像特征并进行识别,通过对不同频段上的识别结果进行比较,获得了虹膜纹理的空间频率特性.实验结果表明,在空间频率为0.005～0.04的频段上,能够有效提取可用于识别的虹膜纹理特征,高效率实现虹膜识别;而在低于0.005和高于0.04的频率区间提取的特征主要为图像噪声,无法有效识别虹膜.     

一种基于核主成分分析和组合分类器的虹膜识别方法

提出了一种新的虹膜特征提取与识别方法,该方法利用核主成分分析(KPCA)在高维空间具有较强的特征选择能力来提取虹膜图像的纹理特征。采用了一种距离度量和支持向量机相结合的两级分类方法,前级采用欧式距离来度量图像间的相似性,若符合条件,给出分类结果,否则拒绝,并转入后一级分类器——支持向量机分类,以减少进入支持向量机的样本数目,该组合分类方法充分利用了支持向量机识别率高和距离度量速度快的优点。实验结果表明,该方法提高了虹膜识别率,是一种有效的虹膜识别方法。     

基于振动时频图像全局和局部特征融合的柴油机故障诊断

提出了一种基于振动时频图像全局和局部特征融合的柴油机故障诊断方法。采用平滑伪维格纳分布(SPWVD)方法生成柴油机振动时频图像,分别用核主元分析(KPCA)和局部非负矩阵分解(LNMF)方法提取时频图像的全局和局部特征进行融合,并用独立分量(ICA)分析方法对融合后的特征进行降维,对降维后的融合特征进行分类完成对柴油机的故障诊断。试验结果表明,基于振动时频图像全局和局部特征融合的柴油机故障诊断方法,能够准确诊断柴油机的气门故障。     

一种新的甲状腺肿瘤超声图像特征提取算法

本文提出一种新的结合纹理、形状和衰减特征信息的超声图像特征提取算法,并用于甲状腺肿瘤的良恶性鉴别。重点研究并改进了甲状腺肿瘤的纹理特征提取算法,在传统局部二值模式(Local Binary Pattern)算法的基础上,将邻域改成椭圆状,更有利于肿瘤的表示并有效提取了肿瘤的各向异性结构信息;对距离编码采用模糊逻辑建模,克服了超声图像斑点噪声带来的不确定性;此外,提取了肿瘤的圆形度,归一化径向长度的标准差,面积比率、粗糙度指数和衰减系数作为表征甲状腺肿瘤的特征向量;最后,采用支持向量机(Support Vector Machine)对甲状腺结节进行分类识别。与其他特征提取方法相比较,本文提出的特征融合算法描述准确率高,具有较高的分类准确性,通过实验验证了所提方法的合理性和有效性。     

基于手背静脉虹膜和指纹融合身份识别算法

针对单模态生物特征识别的局限性,提出融合手背静脉、虹膜和指纹三种生物特征实现身份识别.首先分别对手背静脉图像、虹膜图像和指纹图像进行独立的图像预处理,特征提取和特征匹配,输出各自的匹配分数.分析匹配分数归一化对识别性能的影响,采用Tarh归一化方法对三种生物特征的匹配分数进行归一化处理,最后利用加权求和法则实现匹配分数的融合,利用最小距离分类器实现身份识别.实验结果表明,融合识别算法的等错率为0.009%,当错误接受率接近0时,对应的错误拒绝率仅为0.2%.     

应用ICA滤波器技术提取图像纹理特征

针对纹理图像分类问题,本文提出了一种应用ICA滤波器技术提取图像纹理特征的方法.该方法首先从训练图像集中随机抽取图像块作为观测信号,应用ICA技术,提取滤波器组.然后根据训练样本图像对滤波器组的响应值来评估和选择滤波器组,达到降维的目的.最后利用滤波器组对测试图像进行滤波,得到该图像的滤波响应结果,从该响应结果中得到最大响应滤波器编号,提取其直方图作为图像的全局特征和局部特征.对Brodatz纹理图像集中108个纹理类别进行了分类实验,结果表明,与MPEG-7纹理描述子相比,该图像特征对纹理图像具有更好的分类效果.     

基于多通道Gabor滤波和特征融合的虹膜识别方法

多通道Gabor滤波器提取的虹膜特征具有冗余信息并存在部分非有效特征,针对此问题提出了改进方法。对同尺度不同方向的Gabor特征,利用幅值信息进行融合,对融合后特征进行相位编码,并运用海明距离匹配。这样,既保证了高识别性能,又将虹膜特征码压缩为传统方法的1／2,可提高匹配速度,并节约存储空间。还提出一种虹膜图像质量评价方法,可有效鉴别不适于识别的低质量虹膜图像。在CASIA和UBIRIS虹膜库的实验结果表明该方法是有效的。     

Research on the Pedestrian Re-Identification Method Based on Local Features and Gait Energy Images

The appearance of pedestrians can vary greatly from image to image, and different pedestrians may look similar in a given image. Such similarities and variabilities in the appearance and clothing of individuals make the task of pedestrian re-identification very challenging. Here, a pedestrian re-identification method based on the fusion of local features and gait energy image (GEI) features is proposed. In this method, the human body is divided into four regions according to joint points. The color and texture of each region of the human body are extracted as local features, and GEI features of the pedestrian gait are also obtained. These features are then fused with the local and GEI features of the person. Independent distance measure learning using the cross-view quadratic discriminant analysis (XQDA) method is used to obtain the similarity of the metric function of the image pairs, and the final similarity is acquired by weight matching. Evaluation of experimental results by cumulative matching characteristic (CMC) curves reveals that, after fusion of local and GEI features, the pedestrian reidentification effect is improved compared with existing methods and is notably better than the recognition rate of pedestrian re-identification with a single feature.     

信息融合的飞机识别方法

以信息融合的理论为基础，利用从可见光图像序列和8-12μm长波红外图像序列中提取的信息对不同种类飞机进行识别。采用矩特征并结合BP神经网络的方法，分别在特征级和决策级两个不同层次上实现了信息融合。实验结果表明，通过信息融合进行飞机识别的准确率可达到90％以上。     

Face recognition using Krawtchouk moment

Feature extraction is one of the important tasks in face recognition. Moments are widely used feature extractor due to their superior discriminatory power and geometrical invariance. Moments generally capture the global features of the image. This paper proposes Krawtchouk moment for feature extraction in face recognition system, which has the ability to extract local features from any region of interest. Krawtchouk moment is used to extract both local features and global features of the face. The extracted features are fused using summed normalized distance strategy. Nearest neighbour classifier is employed to classify the faces. The proposed method is tested using ORL and Yale databases. Experimental results show that the proposed method is able to recognize images correctly, even if the images are corrupted with noise and possess change in facial expression and tilt.     

基于卷积神经网络和Transformer网络的鸟声识别

针对传统鸟声识别算法中特征提取方式单一、分类识别准确率低等问题,提出一种结合卷积神经网络和Transformer网络的鸟声识别方法。该方法综合考虑网络局部特征学习和全局上下文依赖性构造,从原始鸟声音频信号中提取短时傅里叶变换(Short Time Fourier Transform,STFT)语谱图特征,将其输入到卷积神经网络(ConvolutionalNeural Network,CNN)中提取局部频谱特征信息,同时提取鸟声信号的对数梅尔特征及一阶差分、二阶差分特征用于合成梅尔频率倒谱系数(Mel Frequency Cepstrum Coefficient,MFCC)混合特征向量,将其输入到Transformer网络中获取全局序列特征信息,最后融合所提取的特征可得到更丰富的鸟声特征参数,通过Softmax分类器得到鸟声识别结果。在Birdsdata和xeno-canto鸟声数据集上进行实验,平均识别准确率分别达到了97.81%和89.47%。实验结果表明该方法相较于其他现有的鸟声识别模型具有更高的识别准确率。     

Weighted residual fusion with multi-modality features for high-resolution aerial scene classification

In scene-level classification of remote sensing, fusion of multi-feature can significantly boost the performance. However, most methods directly fuse the features of different modalities without considering the importance of each feature modality. Based on the above considerations, in this work, multi-modality features weighted residual fusion method is proposed. First, the extracted high-level and low-level features of the scene image are encoded into a unified feature representation. Then the reconstruction residuals of each modality of each scene class are calculated based on two representation-based classification, i.e. sparse representation (SR) and collaborative representation (CR). After fusing the weighted reconstruction residuals of these two modalities with SR and CR, the class label is assigned to the category with the smallest residual. We make extensive evaluations on two challenging remote sensing data sets. The comparison with the state-of-the-art methods demonstrates the effectiveness of our proposed method.     

Statistical feature extraction based iris recognition system

Iris recognition systems have been proposed by numerous researchers using different feature extraction techniques for accurate and reliable biometric authentication. In this paper, a statistical feature extraction technique based on correlation between adjacent pixels has been proposed and implemented. Hamming distance based metric has been used for matching. Performance of the proposed iris recognition system (IRS) has been measured by recording false acceptance rate (FAR) and false rejection rate (FRR) at different thresholds in the distance metric. System performance has been evaluated by computing statistical features along two directions, namely, radial direction of circular iris region and angular direction extending from pupil to sclera. Experiments have also been conducted to study the effect of number of statistical parameters on FAR and FRR. Results obtained from the experiments based on different set of statistical features of iris images show that there is a significant improvement in equal error rate (EER) when number of statistical parameters for feature extraction is increased from three to six. Further, it has also been found that increasing radial/angular resolution, with normalization in place, improves EER for proposed iris recognition system.     

基于非对称增强注意力与特征交叉融合的行人重识别方法

针对现有的行人重识别方法提取到的特征信息充分性与辨识性不足导致检索精度低的问题,提出一种基于非对称增强注意力与特征交叉融合的行人重识别方法。首先,构建非对称增强注意力模块,通过多重池化聚合的跨邻域通道交互注意力增强显著特征表示,使网络聚焦于图像中的行人区域;其次,考虑到网络各层特征间的差异性与关联性,构建特征交叉融合模块,利用交叉融合方式实现同层不同级特征的跨层级融合,进而实现多尺度融合;最后,水平切分输出特征以获取局部特征,从而实现在特定区域上描述行人。在Market1501、DukeMTMC-reID与CUHK03这3个公开数据集上对提出的方法进行了验证,首位命中率(Rank-1)分别达到了93.5%、85.1%和64.3%,证明了该方法在提升行人重识别性能上具有优越性。