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一种改进的部分Hausdorff距离检测技术和遗传算法在图像匹配识别中的应用

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通信学报 2001年第12期22卷 112-116页

作者：沈庭芝臧铁飞朱少娟方力北京理工大学电子工程系北京100081

本文提出一种改进的部分Hausdorff距离作为模板和图像中物体轮廓相似性的测度,较大地减少计算量。同时把遗传算法引入图像匹配识别,由于GA的高并行性和鲁棒性,可以较快地完成全局搜索,而不会陷入局部最优,因此GA和改进的Hausdorff距离... 详细信息

本文提出一种改进的部分Hausdorff距离作为模板和图像中物体轮廓相似性的测度,较大地减少计算量。同时把遗传算法引入图像匹配识别,由于GA的高并行性和鲁棒性,可以较快地完成全局搜索,而不会陷入局部最优,因此GA和改进的Hausdorff距离相结合能有效地检测出具有平移、旋转和尺度变化的物体,以达到模板和图像间的有效匹配。

关键词： Hausdorff距离图像匹配遗传算法图像识别

A New Adaptive Image Segmentation Method

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Journal of Beijing Institute of Technology 1998年第3期7卷 316-321页

作者：沈庭芝方子文吴玲艳王飞北京理工大学电子工程系北京100081

Aim Researching the optimal thieshold of image segmentation. M^ethods An adaptiveimages segmentation method based on the entropy of histogram of gray-level picture and genetic. algorithm (GA) was presental. Results In our approach, the segmentation problem was formulated as an optimization problem and the fitness of GA which can efficiently search the segmentation parameter space was regarded as the quality criterion. Conclusion The methodcan be adapted for optimal behold segmentation.

关键词： genetic algorithm image segmentation entropy of histogram segmenting threshold

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基于声卡的实时语音质量评价系统

电声技术 2009年第11期33卷 60-63,67页

作者：刘文旭沈庭芝田卉刘朋樟北京理工大学信息与电子学院北京100081

针对目前语音质量评价系统成本高、设计复杂且不能实时评分,提出了基于声卡的实时评价系统。该系统以ITU-TP.862标准中的语音评价算法PESQ为核心,采用声卡作为语音播放和采集装置,利用多线程技术,以廉价的方式实现语音实时评分。并且,... 详细信息

针对目前语音质量评价系统成本高、设计复杂且不能实时评分,提出了基于声卡的实时评价系统。该系统以ITU-TP.862标准中的语音评价算法PESQ为核心,采用声卡作为语音播放和采集装置,利用多线程技术,以廉价的方式实现语音实时评分。并且,以虚拟仪器Labwindows/CVI为平台,方便显示从PESQ算法中提出的语音参数。测试表明,该系统评分准确,可快速判断和分析语音设备的性能。

关键词： PESQ 虚拟仪器声卡实时多线程

Radial-curve-based facial expression recognition

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Journal of Beijing Institute of Technology 2015年第4期24卷 508-512页

作者：岳雷沈庭芝张超赵三元杜部致 School of Information and Electronics Beijing Institute of Technology School of Computer Science and Technology Beijing Institute of Technology

A fully automatic facial-expression recognition （FER） system on 3D expression mesh models was proposed. The system didn＇ t need human interaction from the feature extraction stage till the facial expression classification stage. The features extracted from a 3D expression mesh mod- el were a bunch of radial facial curves to represent the spatial deformation of the geometry features on human face. Each facial curve was a surface line on the 3D face mesh model, begun from the nose tip and ended at the boundary of the previously trimmed 3D face points cloud. Then Euclid dis- tance was employed to calculate the difference between facial curves extracted from the neutral face and each face with different expressions of one person as feature. By employing support vector ma- chine （SVM） as classifier, the experimental results on the well-known 3D-BUFE dataset indicate that the proposed system could better classify the six prototypical facial expressions than state-of-art al- gorithms.

关键词： facial expression radial curve Euclidean distance support vector machine (SVM)

Novel algorithm for pose-invariant face recognition

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Journal of Beijing Institute of Technology 2012年第2期21卷 246-252页

作者：刘朋樟沈庭芝赵三元岳雷闫雪梅 School of Information and Electronics Beijing Institute of Technology

By combining the AdaBoost modular locality preserving projection （AMLPP） algorithm and the locally linear regression （LLR） algorithm, a novel pose-invariant algorithm is proposed to realize high-accuracy face recognition under different poses. In the training stage of this algorithm, the AMLPP is employed to select the crucial frontal blocks and construct effective strong classifier. According to the selected frontal blocks and the corresponding non-frontal blocks, LLR is then applied to learn the linear mappings which will be used to convert the non-frontal blocks to visual frontal blocks. During the testing of the learned linear mappings, when a non-frontal face image is inputted, the non-frontal blocks corresponding to the selected frontal blocks are extracted and converted to the visual frontal blocks. The generated virtual frontal blocks are finally fed into the strong classifier constructed by AMLPP to realize accurate and efficient face recognition. Our algorithm is experimentally compared with other pose-invariant face recognition algorithms based on the Bosphorus database. The results show a significant improvement with our proposed algorithm.

关键词： pose-invariant block-based virtual frontal view locally linear regression (LLR) facerecognition

Feature subset selection method for AdaBoost training

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Journal of Beijing Institute of Technology 2011年第3期20卷 399-402页

作者：赵三元沈庭芝孙晨升刘朋樟岳雷 School of Information and Electronics Beijing Institute of Technology

The feature-selection problem in training AdaBoost classifiers is addressed in this paper. A working feature subset is generated by adopting a novel feature subset selection method based on the partial least square （PLS） regression, and then trained and selected from this feature subset in Boosting. The experiments show that the proposed PLS-based feature-selection method outperforms the current feature ranking method and the random sampling method.

关键词： dimensionality reduction Boosting method feature subset

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一种基于空间自适应的盲图像复原算法

影像技术 2006年第5期18卷 26-28,25页

作者：崔宇沈庭芝朱亚平北京理工大学电子工程系北京100081

针对非负有限支持域约束递归逆滤波(NAS-RIF)图像盲复原算法〔1〕对噪声敏感问题,提出一种基于空间自适应的改进算法。利用最优阈值估计图像支持域,在代价函数中引入空间自适应加权因子和尺度因子,结合正则化技术对图像复原进行局部控制... 详细信息

针对非负有限支持域约束递归逆滤波(NAS-RIF)图像盲复原算法〔1〕对噪声敏感问题,提出一种基于空间自适应的改进算法。利用最优阈值估计图像支持域,在代价函数中引入空间自适应加权因子和尺度因子,结合正则化技术对图像复原进行局部控制,并对不同信噪比和背景的图像进行了实验。结果表明,在较低信噪比情况下,改进的NAS-RIF算法仍具有较好的复原效果,有效地达到了保存图像细节并抑制噪声的目的。

关键词：图像盲复原正则化空间自适应支持域