行政院国家科学委员会专题研究计划成果报告

¦æ¬F°|°ê®a¬ì¾Ç©e-û·|±MÃD¬ã¨s-p¹º¦¨ªG³ø§i¤HÅé¤â¶Õ¤§¹B°Ê¤ÀªR»P¿ëÃÑEnergy Spectrum Based HMM for Gesture Recognition -p¹º½s¸¹¡G NSC89-2218-E-011-012°õ¦æ´Á--¡G89¦~8¤ë1¤é¦Ü90¦~7¤ë31¤é¥D«ù¤H¡G³\·s²K±Ð±Â°ê¥ß¥xÆW¬ì§Þ¤j¾Ç¹q¾÷¨t-p¹º°Ñ»P¤H-û¡G·¨¼w¶³°ê¥ß¥xÆW¬ì§Þ¤j¾Ç¹q¾÷¨tAbstractVision based gesture recognition has been a new way of communication between machine and human being. In this paper, we develop an energy spectrum based HMM for both gesture representation and recognition. A symbol sequence is derived from the gesture via vector quantization. The Baum-Welch algorithm is employed to establish the Hidden Markov Model (HMM) during the training phase. The probability of each model producing the symbol sequence associated with the input gesture is computed and the one with maximum probability is recognized.Experiment shows that the recognition rate of the developed energy spectrum based approach outperformed the Fourier descriptor based method as expected.Keywords:gesture recognition, energy spectrum based HMM, vector quantization.I.IntroductionWith the development of the computer technology, it is now possible to take alternative ways to communicate with the computer in addition to the traditional keyboard and mouse. Speech recognition and gesture recognition are the most actively studied approaches [1].Both glove [2, 3] and vision based [4, 5] methods was conducted for gesture recognition. While the former is more precise in the positioning of the 3D information, the latter is more friendly hence is more practical, especially when the machine to be communicated is far from or isolated in an environment.Takahashi and Kishino used data gloves to establish the hand shape code that describe the shape, orientation, and motion of the hand [2]. Cui and Weng combined the action and the shape of hands and extracted the feature vectors for recognition with moderate recognition rate in extremely long time [6]. Liang and Ouhyoung used data gloves to fetch the stretch of the 10 joints of a hand, the orientation of the palm and the motion vector of the hands [3]. Starner and Pentland developed a vision based method with the shape, orientation and trajectory of hands and used a feature vector with only eight elements for HMM [5]. Chen, Huang and Juang applied the hand shape variation and the motion trajectory from 2D images for HMM [4] In this research, the vision based Hidden Markov Model (HMM) will be employed for both hand gesture representation and recognition. Vector quantization will be taken to reduce the size of codebook and the energy spectrum of the symbol sequence will be used as the input for the HMM. In this paper, the features extraction associated with a gesture and the HMM that use energy spectrum as input for training is developed in Section II. Experiment results and discussions are given in Sections III and IV, respectively. II.Energy spectrum based HMMThe selection of features and the application of these feature as input to train the HMM are described.Feature selection is important in both the representation and recognition of gestures. A good type of feature has to satisfy the RST invariance, the ease of computation and high recognition rate. Energy spectrum derived from Fourier descriptor (FD) is such a feature. Instead of using the FDs directly as in [4], we use the energy spectrum to stress the more significant Fourier coefficients to improve the recognition rate. This has been proved to be quite effectivein the experiment: the overall recognition rate has been improved from 94% to 98%.For the recognized ten gestures, twenty training samples used for each gesture and thirty images for each sample, we have a huge size of 6,000 samples. Vector quantization is used to reduce the size and the computation time for recognition. The size of the codebook is selected to be 64 that compromises between the execution time and the average quantization error.HMM is a good vehicle to describe the gesture, in which both state and observation stochastic processes are involved. Ergodic discrete HMM is employed to characterize the evolution of the gesture thoroughly.An HMM λ is usually expressed asλπ=(,,)A B (1)where π is the initial probability vector, A a ij =[] is the state transition matrix and B b ij =[] is the observation matrix, respectively.The energy spectrum obtained from the FDs is employed to train the HMMs to obtain the parameters A and B for each gesture. For the observation sequence of 30 images per gesture and 10 gestures to be tested, the dimensions of A and B are 1010× and 1030×, respectively. Baum-Welch algorithm is employed for training , which is a backward procedure that introduces a backward variable βt i (), two probabilityvariablesγt i () and ξt i j (,), and iteratively update the estimated parameters to achieve the maximum likelihood of P O ()λfor the given observation sequence O whereβλt t t T t i P O O O q i ()(,,,)==++12L , (2)γλt t i P q i O ()(,)== (3)ξλt t t i j P q i q j O (,)(,,)===+1 (4)for the state q and the gesture i, j at time t .Then the parameters areπγi t i =() (5)a i j i ij t t T t t T ==−=−∑∑ξγ(,)()1111 (6)b j j jk t O ktTt t Tt====∑∑γγ()()11(7)Experiments are then conducted based on thisframework.III.Experiment resultsTen gestures, including hand shape change, rotation, and translation are tested. An I-cam USB camera with 160120× at 30 frames per second is employed to fetch the gesture image and a PC Pentium III-500 with 128MB RAM is used to do both training and recognition. The gestures tested are shown in Fig.1 and the total time spent for recognition is 6.228 seconds, including preprocessing time 6.12 seconds. That is, the time used for vector quantization and recognition is only 0.108 second. Some typical recognition results are shown in Table 1 in which the values are calculated using the formula log ()10P O i λ for the given P O i ()λ. Same approach has been taken except that FDs are used directly. The recognition rates for both FDs and the proposed energy spectrum with the 10 gestures are compared as shown in Table 2.IV.Discussion and conclusionAn energy spectrum based HMM for gesture recognition has been developed. Vector quantization was employed to reduce the data size and Baum-Welch algorithm was employed for training. Ten gestures, including hand shape change, rotation, and translation were tested. Experiments showed that the energy spectrum based HMM improved the recognition rate from 94% with FD based to 98%. This is attributed to the stress of the more significant coefficients using the energy spectrum based HMM. The recognition rate of gesture 2 is as low as 93.3%. Comparing gestures 2 and 10, one can find that the beginning two fifth of the two gestures are almost the same. This explains why gesture 2 is taken as gesture 10. However, the situation is not reflective. That is, gesture 10 is not taken as gesture 2. This is because the symbol sequence generated by gesture 2 remains almost unchanged since the hand shape remains unchanged in gesture 2 thus the probability of mistakenly taking the shape varying gesture 10 as gesture 2 is low.Fig.1 Gestures under test. (From top to bottom: gestures 1 to 10)Table 1 Recognition results using energy spectrum based HMMTable 2 Recognition rate using the FD and the energy spectrum approachesReferences[1] Vladimir I. Pavlovic, Rajeev Sharma andThomas S. Huang, “Visual interpretation of hand gesture for human-computer interaction: A review”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.19, no.7, pp.677~pp.694, July, 1997. [2] T.Takahashi and F.Kishino, “A hand gesturerecognition method and its application”, Systems and Computers in Japan, V ol. 23, No.3, pp.38-48, 1992.[3] Liang, R. H. and M, Ouhyoung, “A real-timecontinuous gesture recognition system for sign language”, In Proc. 2 nd Int. Conf. on Automatic Face and Gesture recognition,pp.558-565, 1998.and Gia-Ying Juang, “Gesture recognition using hidden Markov models”, Conference on Computer V ision ,Graphics and Image Processing, pp.477~pp.484, 1999.[5] T. Starner and A. Pentland, “Real-timeAmerican Sign Language recognition form video using hidden Markov models”, Proc.of International Symposium on Computer V ision, pp.265-270, 1995.[6] Cui, Y., J.J. Weng, “Hand sign recognitionfrom intensity image sequences with complex background”, In Proc. IEEE 2nd Int. Conf. on Automatic Face and Gesture Recognition, pp.259 –264, 1996.。

¦æ¬F°|°ê®a¬ì¾Ç©e-û·|±MÃD¬ã¨s-p¹º¦¨ªG³ø§i¥xÆW¨ÅÀù¯f²z¦¨¦]¤§¬ã¨s-§íÀù°ò¦]¦b¨ÅÀù§Î¦¨¤¤¤§-«-n©Ê (²Ä¤T¦~)-p¹º½s¸¹: NSC 88-2314-B-002-365°õ¦æ¦~--: 87¦~08¤ë1¤é¦Ü88¦~7¤ë31¤é¥D«ù¤H : ³\ª÷¥É¥x¤jÂå¾Ç°|¥Í¤Æ©Ò¤¤¤åºK-nÃöÁäµü¡G¨ÅÀù/§íÀù°ò¦]p53,TSG101³\¦hªº¬ã¨s³ø§iÅã¥Ü¦b¨ÅÀù²Ó-M¤¤¦³«Ü°ª¤ñ¨Òªº¬V¦âÅé11p15¤§LOH(Loss of Heterozygosity )Åܲ§¡A Åã¥Ü¦¹°Ï°ì¤§°ò¦]Åܲ§»P¨ÅÀù¤§§Î¦¨¦³Ãö¡F¦ÓTSG101¬O 1997¦~¤~µo ²{¤§§íÀù°ò¦]¡A ¦¹°ò¦]´N ¦ì©ó11p15¡A¦Ó¥B³Ìªñªº³ø¾É«ü¥X ¡A¨ÅÀù²Ó-M¤¤½T¹ê·|§t¦³¦¹°ò¦]¤£¥¿±`ªºmRNA ªí²{¡A ¦]¦¹¦b³o-Ó-pµeùØ¡A §Ú-̥ΨӦۥx¤jÂå°|ªº¤@§å¨ÅÀùÀËÅé°µTSG101ªº¤ÀªR (Table 1)¡A¤S¥Ñ©ó³o§åÀËÅé¡A§Ú-̤w¸g¤ÀªR¹L¨äER¡A erbB2(HER2/neu)¤Îp53ªºªí²{±¡§Î(Table2, 3)¡A±N¨Ó§Ú-Ì¥i¥H¦P®É©Î¤À¶}¨Ó¤ÀªR TSG101¤Î³o¨Ç°ò¦]»PÁ{§É¯fª¬ªº¬ÛÃö©Ê¡A³o±N´£¨Ñ¤@-Ó§ó-ѯS²§©Êªºgenetic markers ¨ÓÀ°§U¤F¸Ñ¦¹Àù¯g¥i¯àªº-P¯f¾÷Âà¡A ¦Ó¥B¥i¥H´£¨Ñ±N¨Ó¬ã¨s T SG101ªº¥Í²z ¥\¯àªº¤è¦V ¡A ¥H¤Î³o ¨Ç°ò¦]¬Û¤¬¶¡ªº§@¥Î¡C -^¤åºK-nKeywords¡G Tumor Suppressor Gene p53 and TSG101/Breast CancerLoss of heterozygosity (LOH) on chromosome 11p15 occurs frequently in breast cancer indicating that this region may have a role in the pathogenesis of breast cancer. TSG101 was identified as a tumor susceptibility gene by homozygous functional inactivation of allelic loci in mouse 3T3fibroblasts.The human homologue of this gene was then isolated and mapped to 11p15.Moreover, abnormalities of TSG101transcripts in human breast cancer and prostate cancer from western country have been reported recently. To determine whether abnormal TSG101 expression has correlation with unique characteristics of breast cancer from Taiwan, TSG101 gene status of breast cancer specimens from National Taiwan University Hospital (NTUH) and several breast cancer cell lines have been studied by reverse transcription-polymerase chain reaction (RT-PCR ) and directly sequencing the cDNA of this gene (Table 1). We haveaccomplished the study about variants ofestrogen receptor (ER), over expression of erbB2(HER2/neu) and the mutation of p53 in same pool of breast cancer specimens mentioned above for the past two years.Those genetic alterations do provide someclues about breast carcinogenesis (Table 2, 3).More genetic analysis will help to developeffective genetic markers for this cancer’sprogression. Therefore, our specific aims ofthis proposed study are the following.Screening a large pool of breast cancer samples with known clinical stages for mutation in the TSG101 gene. The status of ER, erbB2(HER2/neu), p53 and TSG101 in the tumor samples will be correlated with their clinical stages. Moreover, any positivecorrelation between phenotype and mutation would provide scientifically importantdirection to explore inter-relationshipbetween these genes.Background TSG101 was identified as a tumor susceptibility gene by homozygous functional inactivation of allelic loci in mouse 3T3 fibroblasts. The human homologous of this gene was then isolated and mapped to 11P15(6,8). Some previous reports indicate that the major function of TSG101 is the following. TSG101 proteins of putative DNA-binding and transcriptional activation domains, it has suggested that the 43kD TSG101 protein may act to control gene expression and regulate the cell cycle.On the other hand, the coiled-coil domain of TSG101 can interact with stathmin. This finding suggests that this gene may control cell growth and differentiation (4,9,12,14,15,16). Other reports indicate thatTSG101 transcripts are frequently abnormal in human cancer cell, including breast cancer (1,2,3,13,17), prostate cancer (11), and leukemia (5). The major type of abnormality of TSG101 is aberrant splicing but not mutations (7,10). The relaxation of RNA splicing fidelity of TSG101 may be an oncodevelopment marker in cancer. We,therefore, plan to screen the status of TSG101 in a large pool of breast cancer samples that has been analyzed for ER,erbB2(HER2/neu) and p53 alterations in my laboratory. Clinical data including the pathologic stage, histologic type and follow-up will be collected. Then, the statistical analysis will be use to clarity the association between genetic alterations of ER,erbB2(HER2/neu), p53 as well as TSG101and clinical outcome.ResultTo detect the aberrant transcripts of TSG101 gene in breast cancer as well as the normal counterpart.According to the structure of TSG101 gene,two pairs of primer (p1/p2 and p3/p4) have been used to analyze any of the TSG101truncated transcripts by RT-PCR. Further restriction enzyme mapping or sequencing of RT-PCR products have been performed to dissect those truncated transcripts as well.The results were listed in table 3.DiscussionIn this preliminary study the abnormal TSG101 transcripts have been detected in tissues of breast cancers but not normal counterparts. These abnormalities of TSG101may play some role in carcinogenesis of breast. Therefore , clinical data including the pathologic stage, histologic type and fellow-up will be collected. Then, the statistical analysis will be used to clearify the associations between genetic alterations of ER, neu, p53 as well as TSG101 and clinical outcome.Table 1:HER-2/neu Row-P=0.018P53+Column Total56.3%43.7%100% HER-2/neuRow -P=0.031PR+Column Total 56.4%43.6%100%p53Row-P=0.004ER+Column Total74.2%25.8%100%Note:HER-2/neu¡G¡Ï¡÷overexpression¡Ð¡÷low level of expression/no expressionp53¡G¡Ï¡÷ point mutation¡Ð¡÷ wild-typeTable 2:p53 Row-P=0.00193Systemic recurrence+Column Total74.1%25.9%100%HER-2/neuRow -P=0.5617Systemic recurrence +Column Total 56.1%43.9%100%Note:HER-2/neu¡G¡Ï¡÷overexpression¡Ð¡÷low level of expression/no expressionp53¡G + ¡÷ point mutatation¡Ð¡÷ wild-typeSystemic recurrence¡G¡Ï¡÷three year’s follow-up¡Ð¡÷no recurrence on follow-up dateREFERENCES1. Benard J. Ahomadegbe JC. TSG101 and breast ca Ali, I.U., Lidereau, R., Theillet, C. & Callahan, R. Reduction to homozygosity of genes on ncer: a correctly named tumor-suppressor gene? Bulletin du Cancer. 84(12):1141-2, 1997Dec.2. Driouch K. Briffod M. Bieche I. Champeme MH. Lidereau R. Location of several putative genes possibly involved in human breast cancer progression. Cancer Research. 58(10):2081-6, 1998 May 15.3. Hofferbert S. Brohm M. Weber BH. Search for TSG101 germ-line mutations in BRCA1/BRCA2-negative breast/ovarian cancer families Cancer Genetics & Cytogenetics. 102(1):86-7, 1998 Apr 1.4. Koonin EV. Abagyan RA. TSG101 may be the prototype of a class of dominant negative ubiquitin regulators Nature Genetics. 16(4):330-1, 1997 Aug.5. Lin PM. Liu TC. Chang JG. Chen TP. Lin SF. Aberrant TSG101 transcripts in acute myeloid leukaemia. British Journal of Haematology. 102(3):753-8, 1998Aug.6. Lee MP. Feinberg AP. Aberrant splicing but not mutations of TSG101 in human breast cancer. Cancer Research. 57(15):3131-4, 1997 Aug 1.7. Li L. Li X. Francke U. Cohen SN. The TSG101 tumor susceptibility gene is located in chromosome 11 band p15 and is mutated in human breast cancer. Cell.88(1):143-54, 1997.8. Li L. Cohen SN. Tsg101: a novel tumor susceptibility gene isolated by controlled homozygous functional knockout of allelic loci in mammalian cells. Cell.85(3):319-29, 1996 May 3.9. Ponting CP. Cai YD. Bork P. The breast cancer gene product TSG101: a regulator of ubiquitination?. Journal of Molecular Medicine. 75(7):467-9, 1997 Jul.10. Steiner P. Barnes DM. Harris WH. Weinberg RA. Absence of rearrangements in the tumour susceptibility gene TSG101 in human breast cancer Nature Genetics. 16(4):332-3, 1997 Aug.11. Sun Z. Pan J. Bubley G. Balk SP.Frequent abnormalities of TSG101 transcripts in human prostate cancer. Oncogene. 15(25):3121-5, 1997 Dec 18.12. Thomson TM. Khalid H. Lozano JJ. Sancho E. Arino J. Role of UEV-1A, a homologue of the tumor suppressor protein TSG101, in protection from DNA damage. FEBS Letters. 423(1):49-52, 1998 Feb 13. 13. Wang Q. Driouch K. Courtois S. Champeme MH. Bieche I. Treilleux I. Briffod M.Rimokh R. Magaud JP. Curmi P. Lidereau R. Puisieux A. Low frequency of TSG101/CC2 gene alterations in invasive human breast cancers. Oncogene. 16(5):677-9, 1998Feb 5.14. Watanabe M. Yanagi Y. Masuhiro Y. Yano T. Yoshikawa H. Yanagisawa J. Kato S. A putative tumor suppressor, TSG101, acts as a transcriptional suppressor through its coiled-coil domain. Biochemical & Biophysical Research Communications.245(3):900-5, 1998 Apr 28.15. Xie W. Li L. Cohen SN. Cell cycle-dependent subcellular localization of the TSG101protein and mitotic and nuclear abnormalities associated with TSG101 deficiency. Proceedings of the National Academy of Sciences of the United States of America.95(4):1595-600, 1998 Feb 17.16. Zhong Q. Chen Y. Jones D. Lee WH. Perturbation of TSG101 protein affects cell cycle progression. [Journal Article] Cancer Research. 58(13):2699-702, 1998 Jul 1.17. Zhong Q. Chen CF. Chen Y. Chen PL. Lee WH. Identification of cellular TSG101 protein in multiple human breast cancer cell lines. Cancer Research.57(19):4225-8, 1997 Oct 1.Table 3 Truncated transcripts of TSG101 in pair specimens4。

瓶頸。 （二） 國內對於工業製品的需求必然有限。解決國內市場不足的方法可能只有組 成多國或區域性貿易協會，有如拉丁美洲自由貿易協會、中美洲共同市場。拉丁 美洲國家曾在這方面有所努力，但仍未能解決問題。 （三）拉丁美洲工業運用高度的科技，因此只創造了有限的就業機會。這導致了 兩個無法避免的不利結果：使國內消費品市場的大小受到限制；未能改善日益嚴 重的失業問題。 在這樣的情況下，拉丁美洲國家進入第二階段的進口替代工業化的第一個 時期，將由本地製造商生產原先仰賴進口的各種資本及技術密集的製造品包括： 耐久性消費財（如，汽車） 、中間性財貨（如，石化及鋼鐵） 、資本財（如，重機 械） 。這意味建立更技術性勞力的、資本的、技術密集的工業，能夠生產資本財 和耐久性消費財，並能加工處理原料；這也意味持續對內導向的發展，包括維持 前一階段保護性、控制取向的政策結構。此外，拉丁美洲繼續出口相對豐富的天 然資源。6基於以上的特徵，我們又稱之為垂直性的進口替代工業化。 拉丁美洲在一九六○年代末期都面臨赤字與通貨膨脹的問題，垂直性的進口 替代工業化並不能解決與外在世界之經濟關係處於不平衡的狀況。7因之，拉丁 美洲國家修正了第二階段進口替代的策略，包括了促進出口。換言之，以行政部 門的措施，選擇性鼓勵特定工業或廠商，推動出口，但並未改變保護性的結構， 也沒有走向市場自由化。促進出口需要給予補貼，這項補貼有來自公共部門，也 有來自民間部門。這是在既有進口替代工業化的大架構外，給予額外的管制與獎 勵。8 而出口促進策略使該地區主要國家工業製品出口都增加了一倍以上。伴隨 此政策而來的是主要拉美國家與全球跨國企業策略的進一步聯繫，於此同時，垂 直性進口替代仍繼續在資本財部分努力。巴西在此階段中由於先前創造了良好的 投資環境而具有優勢，而墨國則繼續其「墨西哥化」政策，吸引外資的結果當然 也有差異。9 在這個階段，經濟整合運動的發展，和擴大市場有密切的關係。從一九五○ 年代開始，拉丁美洲區域整合的運動即已出現，但遲至一九六○年代才有正式的 組織。最早兩個主要組織分別是中美洲共同市場（ Central American Common

浅谈《历体略》[摘要]《历体略》是明末天文学西学东渐的产物,作者在书中试图对西方天文学和中国传统天文学进行融会。

透过此书可以对明末知识分子对待新知识和旧有传统的态度以及此书产生的社会环境做一些分析。

[关键词]王英明《历体略》天文学明末一、《历体略》简介《历体略》是明人王英明撰,成书于1612年或略早。

全书共分三卷:上卷六篇介绍了一些中国传统天文学知识;中卷三篇,介绍步天歌,也主要是关于传统天学;下卷七篇侧重介绍欧洲天文学知识。

此书可能是中国士人最早的一部试图融会中西历体知识的作品,也很可能是最早的一部通俗天文学著作。

作者王英明(?—1614),字子晦,自号太常吉星,开州澶渊人(今河南濮阳)。

万历三十四年(1606年)举人。

重刻《历体略》序言中提到他重“实学”,“才识渊博,于历律、兵屯、河防、水衡之事无弗沉究”,著述颇丰。

王英明是明末先进的知识分子的代表,此书在研究明清之际思想观念方面有重要价值。

二、著书背景1.实学思想。

序言和作者自序中提到这部书的编写,是王氏其重视“实学”的一贯态度使然,这就引出了我们对“实学”的思考。

自南宋末期起,封建社会母体中就开始酝酿一种自我否定因素,经济上表现为资本主义萌芽,意识形态领域则是出现了一股批判思潮,这种思潮就是实学思想。

明末实学思想正是这种思潮的延续,这也是西学东渐的一个思想基础。

2.政府控制减弱。

古代,天象观测和星占术称“天文”,历朝都禁止民间私习。

属于历法的内容称为“推步”,在明以前并不禁止私习,入明之后开始确立了全面禁令,在至少100余年的时间里这一禁令都被严格地执行着。

直到明代中后期,随着国家机器的腐朽,这一禁令才逐渐在事实上被废止,这是《历体略》成书的重要社会背景。

没有这一背景,西方的天文学知识不可能得到传布,《历体略》这样的私人天文学著作也不可能产生。

三、本书所体现的作者观念1.作者对待天文学的态度。

作者对待天文学的态度可以从自序中看出来,他提到“历者,帝王经世之洪范。