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1.
We propose a novel sequence alignment algorithm for recognizing handwriting gestures by a camera. In the proposed method, an input image sequence is aligned to the reference sequences by phase-synchronization of analytic signals which are transformed from original feature values. A cumulative distance is calculated simultaneously with the alignment process, and then used for the classification. A major benefit of this method is that over-fitting to sequences of incorrect categories is restricted. The proposed method exhibited higher recognition accuracy in handwriting gesture recognition, compared with the conventional dynamic time warping method which explores optimal alignment results for all categories.  相似文献   

2.
模板匹配技术在图像识别中的应用   总被引:2,自引:0,他引:2  
田娟  郑郁正 《传感器与微系统》2008,27(1):112-114,117
在图像目标识别技术的研究应用中,模板匹配技术是其中一个重要的研究方向,它具有算法简单、计算量小以及识别率高的特点。介绍了几种改进的模板匹配技术在图像处理、模式识别等领域的应用,包括有条码识别、生物特征识别技术(人脸识别、指纹识别等)、车牌识别、字符识别、飞机识别等。  相似文献   

3.
We propose a new method for user-independent gesture recognition from time-varying images. The method uses relative-motion extraction and discriminant analysis for providing online learning/recognition abilities. Efficient and robust extraction of motion information is achieved. The method is computationally inexpensive which allows real-time operation on a personal computer. The performance of the proposed method has been tested with several data sets and good generalization abilities have been observed: it is robust to changes in background and illumination conditions, to users’ external appearance and changes in spatial location, and successfully copes with the non-uniformity of the performance speed of the gestures. No manual segmentation of any kind, or use of markers, etc. is necessary. Having the above-mentioned features, the method could be successfully used as a part of more refined human-computer interfaces. Bisser R. Raytchev: He received his BS and MS degrees in electronics from Tokai University, Japan, in 1995 and 1997 respectively. He is currently a doctoral student in electronics and information sciences at Tsukuba University, Japan. His research interests include biological and computer vision, pattern recognition and neural networks. Osamu Hasegawa, Ph.D.: He received the B.E. and M.E. degrees in Mechanical Engineering from the Science University of Tokyo, in 1988, 1990 respectively. He received Ph.D. degree in Electrical Engineering from the University of Tokyo, in 1993. Currently, he is a senior research scientist at the Electrotechnical Laboratory (ETL), Tsukuba, Japan. His research interests include Computer Vision and Multi-modal Human Interface. Dr. Hasegawa is a member of the AAAI, the Institute of Electronics, Information and Communication Engineers, Japan (IEICE), Information Processing Society of Japan and others. Nobuyuki Otsu, Ph.D.: He received B.S., Mr. Eng. and Dr. Eng. in Mathematical Engineering from the University of Tokyo in 1969, 1971, and 1981, respectively. Since he joined ETL in 1971, he has been engaged in theoretical research on pattern recognition, multivariate data analysis, and applications to image recognition in particular. After taking positions of Head of Mathematical Informatics Section (since 1985) and ETL Chief Senior Scientist (since 1990), he is currently Director of Machine Understanding Division since 1991, and concurrently a professor of the post graduate school of Tsukuba University since 1992. He has been involved in the Real World Computing program and directing the R&D of the project as Head of Real World Intelligence Center at ETL. Dr. Otsu is members of Behaviormetric Society and IEICE of Japan, etc.  相似文献   

4.
In this paper, we propose a novel sparse representation based framework for classifying complicated human gestures captured as multi-variate time series (MTS). The novel feature extraction strategy, CovSVDK, can overcome the problem of inconsistent lengths among MTS data and is robust to the large variability within human gestures. Compared with PCA and LDA, the CovSVDK features are more effective in preserving discriminative information and are more efficient to compute over large-scale MTS datasets. In addition, we propose a new approach to kernelize sparse representation. Through kernelization, realized dictionary atoms are more separable for sparse coding algorithms and nonlinear relationships among data are conveniently transformed into linear relationships in the kernel space, which leads to more effective classification. Finally, the superiority of the proposed framework is demonstrated through extensive experiments.  相似文献   

5.
In this paper, we describe a technique for representing and recognizing human motions using directional motion history images. A motion history image is a single human motion image produced by superposing binarized successive motion image frames so that older frames may have smaller weights. It has, however, difficulty that the latest motion overwrites older motions, resulting in inexact motion representation and therefore incorrect recognition. To overcome this difficulty, we propose directional motion history images which describe a motion with respect to four directions of movement, i.e. up, down, right and left, employing optical flow. The directional motion history images are thus a set of four motion history images defined on four optical flow images. Experimental results show that the proposed technique achieves better performance in the recognition of human motions than the existent motion history images. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008  相似文献   

6.
张鸿宇  刘威  许炜  王辉 《计算机科学》2015,42(9):299-302
在数字化学习场景中,人体姿态的识别有助于分析学习者的学习状态。提出了一种基于深度图像的多学习者姿态识别方法。首先通过Kinect的红外传感器获取包含深度信息的图像,利用深度图像进行人像-背景分离;然后提取人体的轮廓特征Hu矩;最后采用SVM分类器对轮廓特征进行分类和识别。实验结果表明,本方法能有效地识别多个学习者的举手、正坐和低头等姿态。  相似文献   

7.
Automatic fluid intake monitoring can be used to ensure adequate hydration in older people. In this study, a real-time fluid intake monitoring system based on the batteryless Ultra High Frequency Radio Frequency Identification (RFID) technology is proposed. The system is simple, unobtrusive, low cost and maintenance-free. Despite the noisy RFID data stream, we demonstrate the efficacy of using a batteryless RFID enabled fluid container to recognize individual instances of drinking (i.e. drinking episodes), in the presence of non-drinking gestures. We conducted experiments with 10 young and 5 older volunteers and achieved F-scores of 87% and 79% for recognizing drinking episodes, respectively.  相似文献   

8.
A hierarchical scheme for elastic graph matching applied to hand gesture recognition is proposed. The proposed algorithm exploits the relative discriminatory capabilities of visual features scattered on the images, assigning the corresponding weights to each feature. A boosting algorithm is used to determine the structure of the hierarchy of a given graph. The graph is expressed by annotating the nodes of interest over the target object to form a bunch graph. Three annotation techniques, manual, semi-automatic, and automatic annotation are used to determine the position of the nodes. The scheme and the annotation approaches are applied to explore the hand gesture recognition performance. A number of filter banks are applied to hand gestures images to investigate the effect of using different feature representation approaches. Experimental results show that the hierarchical elastic graph matching (HEGM) approach classified the hand posture with a gesture recognition accuracy of 99.85% when visual features were extracted by utilizing the Histogram of Oriented Gradient (HOG) representation. The results also provide the performance measures from the aspect of recognition accuracy to matching benefits, node positions correlation and consistency on three annotation approaches, showing that the semi-automatic annotation method is more efficient and accurate than the other two methods.  相似文献   

9.
10.
两种改进的模板匹配识别算法   总被引:7,自引:1,他引:7  
在开发在线轮胎编码图像自动识别系统时,通过对现有常用的几种识别算法分析与研究,提出了两种改进的标准模板匹配识别算法,分别是基于特征加权的模板匹配算法和基于特征块的模板匹配算法,两种改进的算法都以抽取字符特征为基础,结合模糊原理进行识别,经过理论分析与实际测试,两种改进的识别算法都进一步提高了图像字符的识别率。  相似文献   

11.
A structure-preserved local matching approach for face recognition   总被引:1,自引:0,他引:1  
In this paper, a novel local matching method called structure-preserved projections (SPP) is proposed for face recognition. Unlike most existing local matching methods which neglect the interactions of different sub-pattern sets during feature extraction, i.e., they assume different sub-pattern sets are independent; SPP takes the holistic context of the face into account and can preserve the configural structure of each face image in subspace. Moreover, the intrinsic manifold structure of the sub-pattern sets can also be preserved in our method. With SPP, all sub-patterns partitioned from the original face images are trained to obtain a unified subspace, in which recognition can be performed. The efficiency of the proposed algorithm is demonstrated by extensive experiments on three standard face databases (Yale, Extended YaleB and PIE). Experimental results show that SPP outperforms other holistic and local matching methods.  相似文献   

12.
Existing gesture segmentations use the backward spotting scheme that first detects the end point, then traces back to the start point and sends the extracted gesture segment to the hidden Markov model (HMM) for gesture recognition. This makes an inevitable time delay between the gesture segmentation and recognition and is not appropriate for continuous gesture recognition. To solve this problem, we propose a forward spotting scheme that executes gesture segmentation and recognition simultaneously. The start and end points of gestures are determined by zero crossing from negative to positive (or from positive to negative) of a competitive differential observation probability that is defined by the difference of observation probability between the maximal gesture and the non-gesture. We also propose the sliding window and accumulative HMMs. The former is used to alleviate the effect of incomplete feature extraction on the observation probability and the latter improves the gesture recognition rate greatly by accepting all accumulated gesture segments between the start and end points and deciding the gesture type by a majority vote of all intermediate recognition results. We use the predetermined association mapping to determine the 3D articulation data, which reduces the feature extraction time greatly. We apply the proposed simultaneous gesture segmentation and recognition method to recognize the upper-body gestures for controlling the curtains and lights in a smart home environment. Experimental results show that the proposed method has a good recognition rate of 95.42% for continuously changing gestures.  相似文献   

13.
傅里叶变换的多视角步态识别   总被引:1,自引:0,他引:1  
步态识别作为一种全新的生物特征识别技术,通过人走路的姿势实现对个人身份的识别和认证。步态能量图将一个周期的步态组合在一起,增强了各帧的相关性,减少了噪声的干扰。对步态能量图进行傅里叶变换,利用傅里叶变换的低频分量对多个视角的步态进行识别。在CASIA数据库中进行实验,结果表明算法简单快速,取得了较好的识别效果。  相似文献   

14.
15.
Segmentation and recognition of continuous gestures are challenging due to spatio-temporal variations and endpoint localization issues. A novel multi-scale Gesture Model is presented here as a set of 3D spatio-temporal surfaces of a time-varying contour. Three approaches, which differ mainly in endpoint localization, are proposed: the first uses a motion detection strategy and multi-scale search to find the endpoints; the second uses Dynamic Time Warping to roughly locate the endpoints before a fine search is carried out; the last approach is based on Dynamic Programming. Experimental results on two arm and single hand gestures show that all three methods achieve high recognition rates, ranging from 88% to 96% for the two arm test, with the last method performing best.  相似文献   

16.
In this study a new approach is presented for the recognition of human actions of everyday life with a fixed camera. The originality of the presented method consists in characterizing sequences by a temporal succession of semi-global features, which are extracted from “space-time micro-volumes”. The advantage of this approach lies in the use of robust features (estimated on several frames) associated with the ability to manage actions with variable durations and easily segment the sequences with algorithms that are specific to time-varying data. Each action is actually characterized by a temporal sequence that constitutes the input of a Hidden Markov Model system for the recognition. Results presented of 1,614 sequences performed by several persons validate the proposed approach.  相似文献   

17.
Gesture recognition error rates and the qualitative nature of the errors made are heavily influenced by the choice of visual representation. A direct empirical comparison of two contrasting approaches, namely trajectory- and history-based representation, is presented. Skin colour is used as a common visual cue and recognition is based on hidden Markov models, moment features and normalised template matching. Two novel representation schemes are proposed and evaluated: (i) skin history images and (ii) composite history images which represent occluded motion. Results are reported for an application in which able-bodied and disabled subjects specify their own gesture vocabularies.  相似文献   

18.
静态手势识别是以手势驱动的人机交互系统的核心技术。针对静态手势识别问题,提出了一种基于深度图像进行静态手势识别的方法。为了消除静态手势识别过程中的平移、旋转和缩放不变性,提取手势轮廓的Hu不变矩,并以Hu不变矩作为特征构建静态手势深度感知神经网络模型,以此实现对静态手势进行分类识别。在VisualStudio的开发环境下实现了对该方法的验证,取得了良好的效果,并与传统的模板匹配法与基于卷积神经网络的深度学习方法作比较,静态手势识别准确率总体可达95%,识别效率高,能满足实时性要求。  相似文献   

19.
Applications of approximate string matching to 2D shape recognition   总被引:7,自引:0,他引:7  
H Bunke  U Bü  hler 《Pattern recognition》1993,26(12):1797-1812
A new method for the recognition of arbitrary two-dimensional (2D) shapes is described. It is based on string edit distance computation. The recognition method is invariant under translation, rotation, scaling and partial occlusion. A set of experiments are described demonstrating the robustness and reliability of the proposed approach.  相似文献   

20.
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