Invariant pattern recognition based on centroids

A new method for pattern recognition that is invariant under changes of position, orientation, intensity, and scale is presented. The centroids of objects provide unique points that are related to the energy distribution. For obtaining more such unique points a conformal transform can be used to rearrange the energy distribution of the object. By means of the conformal transform many different centroids can be produced from the same object. A useful pattern-recognition and object-registration method that yields a position-, rotation-, intensity-, and scale-invariant feature vector based on these centroids can be created.     

基于表面评估的工件轮廓识别方法研究

现有加工工件材料和加工方式的多样性使得工件的表面情况多样,导致视觉系统难以准确识别工件轮廓,因此提出了一种适用于不同工件表面的轮廓识别方法。根据纹理区域面积与凸包面积的比值对工件表面进行评估和分类。对于表面质量较好的工件,采用高通线性滤波器对图像进行滤波处理,通过工件表面信息与边缘信息的差异实现工件图像边缘提取;对于表面质量较差的工件,采用一种自适应轮廓提取方法来识别图像边缘。实验表明,与传统的Canny边缘检测算法相比,所提出的方法能够更好地去除噪声干扰,其识别轮廓的精度更高。所提出的轮廓识别方法对不同工件表面有较好的适应性,具有一定的应用价值。     

Invariant pattern recognition by use of wavelength multiplexing

Rotation-invariant pattern recognition can be achieved with circular-harmonic decomposition. A common problem with such a filter is that, because it is only a single term out of the circular decomposition, it does not contain much of the reference object's energy. Thus, the obtained correlation selectivity is low. This problem is solved by use of wavelength multiplexing. First, different harmonic terms are encoded by different wavelengths, and then they all are added incoherently in the output correlation plane. This process leads to rotation-invariant pattern recognition with a higher discrimination ability.     

Invariant pattern recognition by use of a spatial code division multiplexing approach

Invariant pattern recognition can be achieved by use of harmonic decomposition, for example circular harmonics are used for rotation invariant recognition. A common problem with such methods is that often only a single term of the harmonic decomposition is used, and it does not contain a sufficient amount of the reference energy. Thus discrimination capability is limited, especially in the presence of noise or other disturbances. By using several terms of the harmonic decomposition together this problem can be solved; this can be achieved by the use of code division filter multiplexing. Several harmonic terms are encoded onto a single filter, and the signal is simultaneously correlated with all of them, hence producing enhanced discrimination capabilities. Here two methods are suggested for such encoding. The first involves multiplexing the filters in the Fourier plane, while the second involves multiplexing in the image plane.     

Lossless parameterisation of image contour for shape recognition

A parameterised contour that is invariant against affine transformations is a convenient substitution of the object image for shape recognition. Generally, parameterisation needs several representative signals of the image contour to fit the affine transformation model. When the representative signals fail to carry the contour information thoroughly, information loss occurs in the resulting parameterised contour, and so the accuracy of shape recognition may deteriorate. Synthesised feature signals are shown, which represent that an image contour without information loss can be extracted with partial Fourier synthesis or partial cosine synthesis. Lossless parameterisation of the image contour is obtained by substituting the synthesised feature signals into the affine invariant function. Experimental results verify its representative, affine invariance and recognition rate in shape recognition. The results are compared with those by partial wavelet synthesis, which has insignificant information loss.     

Electrical arc contour cutting based on a compound arc breaking mechanism

Electrical arc contour cutting (EACC) is a novel high-efficiency material cutting process that applies arc plasma to perform efficient and economical contour cutting of difficult-to-cut materials. Compared to conventional electrical arc machining (EAM), this process can remove the allowance of open structures and plates in bulk mode, rather than entirely in the form of debris. Compared with existing contour cutting methods, EACC possesses the advantages of high cutting efficiency and a deep cutting depth. Particularly, a compound arc breaking mechanism (CABM), which integrates hydrodynamic force and mechanical motion, has been applied to control the discharge arc column in EACC, while also strengthening the debris expelling effect in the narrow discharge gap. The CABM implementation conditions were studied, based on arc column distortion images captured by a high-speed camera and simulation results of the flow field and debris distribution. A set of machining experiments was designed and conducted to optimize the performance of the proposed process. Finally, a SiC_p/Al metal matrix composite (MMC) space station workpiece was machined to verify the feasibility and efficiency of this process.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00406-0     

基于轨检车振动问题的钢轨轮廓匹配方法研究

轨检车在进行检测过程中由于轨道不平顺引起的上下振动、左右摆动、倾斜等平面内三自由度随机振动,导致被检测钢轨轮廓图形与标准轮廓图形发生错位,无法进行钢轨轮廓高精度匹配,很难为钢轨磨损修复制定科学合理的策略。利用不共线的三点确定一平面原理,提出一种基于轨检车振动问题的钢轨轮廓匹配方法,对检测钢轨轮廓的位置信息进行先旋转再平移的坐标变换,彻底解决了轨检车在进行检测过程中由于轨道不平顺引起的振动问题,实现了钢轨轮廓的高精度匹配。该方法已经运用在轨道检测小车上,试验证明其匹配精度能达到微米级。     

一种基于边界特征的耳廓提取新方法

在实际的人耳识别系统中,人耳的准确定位是影响识别率的一个重要因素.根据外耳及其所在位置的特征,提出了一种从侧脸图像上准确定位并提取出人耳的新方法.该方法首先对侧脸图像进行阈值化和差分处理,然后将外耳边缘轮廓分为3个区域,利用阈值图像和差分图像以及3个区域的不同走向分别跟踪得到整个外耳轮廓线,并从侧脸图像中提取出外耳图像.该方法运算量小,定位精确,得出的外耳轮廓图像清晰完整无任何干扰,而且定位成功率高.     

人脸识别的一种新的特征提取方法

针对人脸识别中判别特征的提取问题,本文提出了一种新的人脸识别算法—扩展保局投影（ELPP）。普通保局投影（LPP）在构建权图时侧重保持样本的局部结构,属于无监督学习算法。扩展保局投影在保局投影的基础上进行扩展,通过引入可调因子,在保持人脸图像局部流形结构的同时考虑样本的类别信息,从而充分提取样本的判别特征。本文采用最小近邻分类器估算识别率。在Yale人脸库以及AT＆T人脸库的测试结果表明,在姿态、光照、表情、训练样本数目变化的情况下,ELPP都具有较好的识别率。     

Shift- and scale-invariant recognition of contour objects with logarithmic radial harmonic filters

The phase-only logarithmic radial harmonic (LRH) filter has been shown to be suitable for scale-invariant block object recognition. However, an important set of objects is the collection of contour functions that results from a digital edge extraction of the original block objects. These contour functions have a constant width that is independent of the scale of the original object. Therefore, since the energy of the contour objects decreases more slowly with the scale factor than does the energy of the block objects, the phase-only LRH filter has difficulties in the recognition tasks when these contour objects are used. We propose a modified LRH filter that permits the realization of a shift- and scale-invariant optical recognition of contour objects. The modified LRH filter is a complex filter that compensates the energy variation resulting from the scaling of contour objects. Optical results validate the theory and show the utility of the newly proposed method.     

Optimum design of dome contour for filament-wound composite pressure vessels based on a shape factor

Filament-wound composite pressure vessels are an important type of high-pressure container that is widely applied in the commercial and aerospace industries. This study investigates the optimum design of dome contours for filament-wound composite pressure vessels, subjected to geometrical limitations, winding condition, and the Tsai–Wu failure criterion and maximizing shape factor, the feasible direction method being employed. An actual design example, presented by Fukunaga et al. [19], is adopted to study the optimum dome contour using the present method. Results reveal that the dome contours using the present method, Fukunaga et al.’s method and the netting method can be approximated using elliptic curves, and that the depth is the major parameter for optimizing the design of dome contour, and the dome, designed using the present method has stronger structure and greater internal volume than those designed using other approaches. Results reveal that the present method is usable for the optimum design of dome contours for filament-wound composite pressure vessels.     

融合分数维特征的水岸彩色图像水岸界线识别

针对复杂的水岸景物彩色图像水岸界线识别,提出了融合多特征的分数维方法。该方法用提取边界来降低维数;再将横向细长窗口内的 Lebesgue 测度(μmin =260)和水岸界线形状位置等特征作为限定条件,只对符合条件点应用基于 ε-blanket 方法的覆盖技术进行迭代计算,找到分数维数最接近 1 的窗口;用经过部分改造的最小二乘法拟合窗口内符合条件点即为水岸界线。这样可以大幅度减少计算量,需迭代计算像素点数量仅为原算法的 0.88%。对多种自然条件下水岸图像 1026 幅进行识别,完全识别率达 85.3%,反应出该方法的鲁棒性与实用性。     

基于超像素的联合能量主动轮廓CT图像分割方法

为解决医学CT图像主动轮廓分割方法中对初始轮廓敏感的问题,提出一种基于超像素和卷积神经网络的人体器官CT图像联合能量函数主动轮廓分割方法。该方法首先基于超像素分割对CT图像进行超像素网格化,并通过卷积神经网络进行超像素分类确定边缘超像素;然后提取边缘超像素的种子点组成初始轮廓;最后在提取的初始轮廓基础上,通过求解本文提出的综合能量函数最小值实现人体器官分割。实验结果表明,本文方法与先进的U-Net方法相比平均Dice系数提高5%,为临床CT图像病变诊断提供理论基础和新的解决方案。     

Emotion recognition based on facial components

Machine analysis of facial emotion recognition is a challenging and an innovative research topic in human–computer interaction. Though a face displays different facial expressions, which can be immediately recognized by human eyes, it is very hard for a computer to extract and use the information content from these expressions. This paper proposes an approach for emotion recognition based on facial components. The local features are extracted in each frame using Gabor wavelets with selected scales and orientations. These features are passed on to an ensemble classifier for detecting the location of face region. From the signature of each pixel on the face, the eye and the mouth regions are detected using the ensemble classifier. The eye and the mouth features are extracted using normalized semi-local binary patterns. The multiclass Adaboost algorithm is used to select and classify these discriminative features for recognizing the emotion of the face. The developed methods are deployed on the RML, CK and CMU-MIT databases, and they exhibit significant performance improvement owing to their novel features when compared with the existing techniques.     

隐马尔可夫模型在舰船噪声目标识别中的应用

将Mel频率倒谱作为特征参数,实现了基于连续型隐马尔可夫模型的舰船辐射噪声目标识别。对五艘不同类别舰船的辐射噪声建立模型,并进行了识别实验。模型的状态数和高斯混合数分别确定为1和3,五艘舰船的平均识别率达到91．6％。利用两艘不同类型舰船的噪声分析了舰船的工况对识别结果的影响。     

改进测地线活动轮廓模型的肝脏超声图像分割

针对超声医学图像的特点,系统地发展和应用测地线活动轮廓模型技术进行肝脏超声图像的边缘提取,提出一种改进的测地线活动轮廓模型.首先把总变分和各向异性扩散方程的有机结合构造出新的边缘停止函数;其次,提出三种边缘停止函数,通过实验比较得出最佳模型.新模型能有效地保留目标信息,消除噪音,增大图像梯度值,具有更强的收敛效果.实验表明,该方法能更加准确地提取弱边缘,达到更理想的分割效果.     

An efficient gait recognition based on a selective neural network ensemble

The neural network ensemble is a learning paradigm where a collection of neural networks is trained for the same task. Generally, the ensemble shows better generalization performance than a single neural network. In this article, a selective neural network ensemble is applied to gait recognition. The proposed method selects some neural network based on the minimization of generalization error. Since the selection rule is directly incorporated into the cost function, we can obtain adequate component networks to constitute an ensemble. Experiments are performed with the NLPR database to show the performance of the proposed algorithm. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 237–241, 2008; Published online in Wiley InterScience (www.interscience.wiley.com).     

Modernization of a conceptual model of a data bank for physicotechnical effects based on contemporary information technology

Requirements for a conceptual model of a data bank for physicomechanical effects and methods for achieving them are studied. On the basis of presenting converters as an information system, a method for modelling them is suggested based on the theory of an energy-information model, parametric structural schemes, SADT-technology, and an objectively orientated paradigm. Advantages of the conceptual model developed are discussed. __________ Translated from Izmeritel’naya Tekhnika, No. 1, pp. 7–9, January, 2007.