High-level feature recognition using feature relationship graphs

In this paper, we present a method to extract user-specific features from common features. This contrasts with other approaches which work directly off B-rep geometric models. Here, user-specific features are called high-level features which are a set of common features combined in a user-specific manner. A feature relationship graph is used to organize common features in a part and to define high-level feature patterns. The research presented in this paper focuses mainly on feature relationship graph construction and high-level feature recognition using subgraph isomorphic techniques.     

基于特征面的分层特征识别

为降低特征识别的复杂度,提出基于特征实体、特征实面和特征虚面概念的层次性特征分类方法.通过构造2类神经网络输入矩阵,利用神经网络在特征识别中所具有的优势,实现基于特征面的分层特征识别方法.实例表明：该方法在识别去除材料的特征时比较有效,但识别特征的范围受到一定限制.     

Viewer-centered geometric feature recognition

Computer aided design (CAD) and computer aided manufacturing (CAM) systems are now indispensable tools for all stages of product development. The flexibility and ease of use of these systems has dramatically increased productivity and quality of product while reducing lead times. These advances have been largely achieved by automating individual tasks. At present, these islands of automation are poorly linked. One reason for this is that current computer systems are unable to extract geometric and topological information automatically from solid models that is relevant to the down-stream application. In other words, feature information.The objective of the research reported in this paper was to develop a more generic methodology than heretofore, in order to find the generic protrusion and depression features of a CAD model. The approach taken is one relying on a more human type of analysis, one that is viewer-centered as opposed to the object-centered approach of most previous research in this area. The viewer-centered approach to feature recognition described is based on a novel geometric probing or tomographic methodology. A five-step algorithm is described and then applied to a number of components by way of illustration.     

新的基于特征关系表述的步态识别算法

为了快速有效地进行步态识别,利用特征关系非平稳分布的统计特性,提出了一种新的基于特征关系表述的步态识别算法。首先,将剪影轮廓相邻像素点间8邻域相对方向标号作为特征关系属性一,将轮廓边界点与中心点间的距离作为特征关系属性二,经直方图归一化处理,得到两种关系属性的联合概率;其次,结合主成分分析(PCA)降维的方法,提取特征主向量;最后,采用最近邻分类器进行识别分类。实验证明,该算法在CASIA步态数据库上,最高达到了90%以上的识别率,而且与传统的特征关系表述步态识别算法相比,关系属性联合概率矩阵维数由900维下降到240维,大大降低了算法的计算代价。     

Multi-resolution feature fusion for face recognition

For face recognition, image features are first extracted and then matched to those features in a gallery set. The amount of information and the effectiveness of the features used will determine the recognition performance. In this paper, we propose a novel face recognition approach using information about face images at higher and lower resolutions so as to enhance the information content of the features that are extracted and combined at different resolutions. As the features from different resolutions should closely correlate with each other, we employ the cascaded generalized canonical correlation analysis (GCCA) to fuse the information to form a single feature vector for face recognition. To improve the performance and efficiency, we also employ “Gabor-feature hallucination”, which predicts the high-resolution (HR) Gabor features from the Gabor features of a face image directly by local linear regression. We also extend the algorithm to low-resolution (LR) face recognition, in which the medium-resolution (MR) and HR Gabor features of a LR input image are estimated directly. The LR Gabor features and the predicted MR and HR Gabor features are then fused using GCCA for LR face recognition. Our algorithm can avoid having to perform the interpolation/super-resolution of face images and having to extract HR Gabor features. Experimental results show that the proposed methods have a superior recognition rate and are more efficient than traditional methods.     

方向感知的网格模型特征识别

针对网格模型平滑区域提取特征困难,以及现有特征识别方法无法检测仅沿某一特定方向分布的特征点的问题,提出一种方向感知的网格模型特征识别方法。首先,分别从x、y、z三个方向探测网格顶点邻接面法向量沿不同方向变化的情况。设定合适的阈值,只要检测到在任何一个方向上顶点邻接面法向量的变化超过阈值,该顶点即被识别为特征点。然后,针对现有网格模型特征识别算法无法检测三维医学模型普遍存在的一种仅沿z轴方向分布的梯田型结构的问题,单独探测医学模型网格顶点邻接面法向量沿z轴方向变化的情况,将变化超出阈值的顶点识别为梯田型结构顶点,正确地将非正常梯田型结构从人体模型正常结构特征中分离出来。与二面角法的对比实验的结果显示：在相同阈值设置下,所提方法能更好地识别出网格模型特征,解决了二面角法在没有明显折线的平滑区域上无法有效识别特征点的问题;同时,也解决了现有网格模型特征检测算法因不具备方向探测能力而无法将医学模型非正常梯田型结构与正常人体结构区分开来的问题,为医学模型后续数字几何处理工作提供了条件。     

Ontology based automatic feature recognition framework

AFR has long been realized as a key technology for design automation. A significant shortcoming in AFR is that most of them are individual systems that are isolated from each other, due to the absence of a standard feature library or feature modeling techniques. Few studies attempted to overcome this problem by allowing a certain degree of user customization or extension, which are still far from success. In order to address this issue, this paper proposes an ontology-based feature recognition framework. In the framework, features are captured transparently and hierarchically within a formal OWL ontology, and the feature recognition is achieved by applying an efficient backward-chained ontology reasoner to reason through the ontology. The resulting feature recognition system shows a high level of flexibility, maintainability, and explainability, for both representing and recognizing features. The effectiveness of the framework is finally demonstrated with three case studies.     

Newborn footprint recognition using orientation feature

Newborn and infant personal authentication is a critical issue for hospital, birthing centers, and other institutions where multiple births occur, which has not been well studied in the past. In this paper, we propose a novel online newborn personal authentication system for this issue based on footprint recognition. Compared with traditional offline footprinting scheme, the proposed system can capture digital footprint images with high quality. We also develop a preprocessing method for orientation and scale normalization. In this way, a coordinate system is defined to align the images, and a region of interest (ROI) is cropped. In recognition stage, four orientation feature-based approaches, Ordinal Code, BOCV, Competitive Code, and Robust Line Orientation Code, are exploited for recognition. A newborn footprint database is established to examine the performance of the proposed system, and promising experimental results demonstrate the effectiveness of the proposed system.     

SVM-based feature extraction for face recognition

The primary goal of linear discriminant analysis (LDA) in face feature extraction is to find an effective subspace for identity discrimination. The introduction of kernel trick has extended the LDA to nonlinear decision hypersurface. However, there remained inherent limitations for the nonlinear LDA to deal with physical applications under complex environmental factors. These limitations include the use of a common covariance function among each class, and the limited dimensionality inherent to the definition of the between-class scatter. Since these problems are inherently caused by the definition of the Fisher's criterion itself, they may not be solvable under the conventional LDA framework. This paper proposes to adopt a margin-based between-class scatter and a regularization process to resolve the issue. Essentially, we redesign the between-class scatter matrix based on the SVM margins to facilitate an effective and reliable feature extraction. This is followed by a regularization of the within-class scatter matrix. Extensive empirical experiments are performed to compare the proposed method with several other variants of the LDA method using the FERET, AR, and CMU-PIE databases.     

基于分形特征的植物识别

为了获得实用、可靠的植物识别方法,在识别植物识别过程中引入分形方法与分形特征.植物生长的过程在本质上与分形过程一致,都是在原有基础上进行迭代,这种迭代也可以推广到多株植物的情况.因此,分形特征不仅能区分单株植物的类别,也可以识别植物群落.通过多尺度求解图像分形特征,可以进一步检测杂株的分布.实验结果表明,基于分形特征的植物识别方法的识别率可以达到80%以上.     

采用GW-MFCC模型空间参数的语音情感识别

针对单一语音特征对语音情感表达不完整的问题,将具有良好量化和插值特性的LSF参数与体现人耳听觉特性的MFCC参数相融合,提出基于线谱权重的MFCC（WMFCC）新特征。同时,通过高斯混合模型来对该参数建立模型空间,进一步得到GW-MFCC模型空间参数,以获取更高维的细节信息,进一步提高情感识别性能。采用柏林情感语料库进行验证,新参数的识别率比传统的MFCC和LSF分别有5.7%和6.9%的提高。实验结果表明,提出的WMFCC以及GW-MFCC参数可以有效地表现语音情感信息,提高语音情感识别率。     

一种有效的行为识别视频特征

提出了一种行为识别的视频特征。观察人运动的2D视频,不同的运动行为在一定程度上表现为人体内外轮廓不同部位的伸缩变化。以每一帧人运动前景的内、外轮廓凸凹形状来表征当前帧的姿态,以姿态的变化来表征运动。采集姿态变化序列频率与时间平均方差构成的特征向量,利用多种分类方法对采集数据进行交叉检验、特征选择分析和线性判别分析。实验表明特征向量线性可分性好,对人是否背负物品不敏感,包含了恰当的行为区分信息,行为识别精度较高。     

Pattern recognition using feature feedback: Application to face recognition

In this paper, we propose a new pattern recognition method using feature feedback and present its application to face recognition. Conventional pattern recognition methods extract the features employed for classification using PCA, LDA and so on. On the other hand, in the proposed method, the extracted features are analyzed in the original space using feature feedback. Using reverse mapping from the extracted features to the original space, we can identify the important part of the original data that affects the classification. In this way, we can modify the data to obtain a higher classification rate, make it more compact or abbreviate the required sensors. To verify the applicability of the proposed method, we apply it to face recognition using the Yale Face Database. Each face image is divided into two parts, the important part and unimportant part, using feature feedback, and the classification performed using the feature mask obtained from feature feedback. Also, we combine face recognition with image compression. The experimental results show that the proposed method works well.     

Pattern fusion in feature recognition neural networks forhandwritten character recognition

B. Hussain and M.R. Kabuka (1994) proposed a feature recognition neural network to reduce the network size of neocognitron. However, a distinct subnet is created for every training pattern. Therefore, a big network is obtained when the number of training patterns is large. Furthermore, recognition rate can be hurt due to the failure of combining features from similar training patterns. We propose an improvement by incorporating the idea of fuzzy ARTMAP in the feature recognition neural network. Training patterns are allowed to be merged, based on the measure of similarity among features, resulting in a subnet being shared by similar patterns. Because of the fusion of training patterns, network size is reduced and recognition rate is increased.     

Emotional speech feature normalization and recognition based on speaker-sensitive feature clustering

In this paper we propose a feature normalization method for speaker-independent speech emotion recognition. The performance of a speech emotion classifier largely depends on the training data, and a large number of unknown speakers may cause a great challenge. To address this problem, first, we extract and analyse 481 basic acoustic features. Second, we use principal component analysis and linear discriminant analysis jointly to construct the speaker-sensitive feature space. Third, we classify the emotional utterances into pseudo-speaker groups in the speaker-sensitive feature space by using fuzzy k-means clustering. Finally, we normalize the original basic acoustic features of each utterance based on its group information. To verify our normalization algorithm, we adopt a Gaussian mixture model based classifier for recognition test. The experimental results show that our normalization algorithm is effective on our locally collected database, as well as on the eNTERFACE’05 Audio-Visual Emotion Database. The emotional features achieved using our method are robust to the speaker change, and an improved recognition rate is observed.     

Face recognition using transform domain feature extraction and PSO-based feature selection

This paper presents two new techniques, viz., DWT Dual-subband Frequency-domain Feature Extraction (DDFFE) and Threshold-Based Binary Particle Swarm Optimization (ThBPSO) feature selection, to improve the performance of a face recognition system. DDFFE uses a unique combination of DWT, DFT, and DCT, and is used for efficient extraction of pose, translation and illumination invariant features. The DWT stage selectively utilizes the approximation coefficients along with the horizontal detail coefficients of the 2-dimensional DWT of a face image, whilst retaining the spatial correlation of pixels. The translation variance problem of the DWT is compensated in the following DFT stage, which also exploits the frequency characteristics of the image. Then, all the low frequency components present at the center of the DFT spectrum are extracted by drawing a quadruple ellipse mask around the spectrum center. Finally, DCT is used to lay the ground for BPSO based feature selection. The second proposed technique, ThBPSO, is a novel feature selection algorithm, based on the recurrence of selected features, and is used to search the feature space to obtain a feature subset for recognition. Experimental results obtained by applying the proposed algorithm on seven benchmark databases, namely, Cambridge ORL, UMIST, Extended Yale B, CMUPIE, Color FERET, FEI, and HP, show that the proposed system outperforms other FR systems. A significant increase in the recognition rate and a substantial reduction in the number of features required for recognition are observed. The experimental results indicate that the minimum feature reduction obtained is 98.2% for all seven databases.     

A novel feature recognition neural network and its application tocharacter recognition

Presents a feature recognition network for pattern recognition that learns the patterns by remembering their different segments. The base algorithm for this network is a Boolean net algorithm that the authors developed during past research. Simulation results show that the network can recognize patterns after significant noise, deformation, translation and even scaling. The network is compared to existing popular networks used for the same purpose, especially the Neocognitron. The network is also analyzed as regards to interconnection complexity and information storage/retrieval     

Multi-scale gist feature manifold for building recognition

Multi-scale gist (MS-gist) feature manifold for building recognition is presented in the paper. It is described as a two-stage model. In the first stage, we extract the multi-scale gist features that represent the structural information of the building images. Since the MS-gist features are extrinsically high dimensional and intrinsically low dimensional, in the second stage, an enhanced fuzzy local maximal marginal embedding (EFLMME) algorithm is proposed to project MS-gist feature manifold to low-dimensional subspace. EFLMME aims to preserve local intra-class geometry and maximize local interclass margin separability of MS-gist feature manifold of different classes at the same time. To evaluate the performance of our proposed model, experiments were carried out on the Sheffield buildings database, compared with the existing works: (a) the visual gist based building recognition model (VGBR) and (b) the hierarchical building recognition model (HBR). Moreover, EFLMME is evaluated on Sheffield buildings database compared with some linear dimensionality reduction methods. The results show that the proposed model is superior to other models in practice of building recognition and can handle the building recognition problem caused by rotations, variant lighting conditions and occlusions very well.     

基于游程特征的文本图像识别方法

提出了一种基于游程特征的中英文本图像识别方法,用游程统计特征提供的图像信息作为图像模式识别的描述特征,在此基础上利用神经网络来训练分类器。实验结果表明,该方法的识别精度较高,具有一定的容错能力。     

Human interaction recognition using spatial-temporal salient feature

Multimedia Tools and Applications - Depth sensor is widely used today and has great impact in object pose estimation, camera tracking, human actions, and scene reconstruction. This paper presents a...