Interactive Wall (Multi Touch Interactive Surface)

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A graduation project at the Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt. Interactive wall Allows users to interact with the computer using his/her hands gestures, The application uses an optical camera to detect and track the hands using image processing techniques, The desktop is projected on a wall using a projector, which gives the user the free experience of interacting with the computer freely. _________________________________________________________________ Windows Live™: Keep your life in sync. Check it out! http://windowslive.com/explore?ocid=TXT_TAGLM_WL_t1_allup_explore_012009

Multi-touch Interactive Surface Supervisors: Professor Dr. Mohammed Roushdy Dr. Haythem El-Messiry T.A. Ahmad Salah 1

Agenda Introduction Physical Environment and Framework Project Modules and Applications Challenges Conclusion and Future work Tools and References 3

Current Multi-touch devices are:Expensive Heavy Fragile Consume space 4

Problem Definition ,[object Object],5 ,[object Object],[object Object]

Generic Framework for Human Computer Interaction (HCI) using hand gestures.6

Physical Environment Physical environment consists of: A projector. A webcam placed over the projector’s lens capturing the projected surface. 7

Physical Environment Surface 2.15 m Camera Projector 9

Framework 10 Controller Configuration Module Input Module Hand Tracking Hand Segmentation Hand gesture Recognition Interface

Detect Corners Controller Module Color Mapping Search for hand in entry point Segmentation Construct the search window Track the hand Fire Event Gesture Recognition 12

A set of colors are projected and captured for the color calibration process.Desktop Colors Projected Colors 14

[object Object],Geometric Calibration 17 Captured Image Calibrated image

Steps:Initialization Prediction Correction 19

Skin Color Detection ,[object Object],21 Captured image Skin detection applied

Subtraction using Color Calibration ,[object Object]

Convert colors of the desktop image to that of the captured image.22

Color Calibration ,[object Object], each desktop color and its corresponding projected color. ,[object Object]

Calculate the transformation matrix A for each region.

b=A * x ; where b is the calibrated color, A is the transformation matrix, x the desktop color.23

Segmentation Results Largest Blob Extraction Desktop Captured Segmented Global thresholding 24

A bounding box (60 * 60) is constructed around the largest blob.Original Closed Original - Closed

Hand Gesture Recognition 27 Contour Tracing Contour Re-sampling EFD DB EFD Gesture Type

Elliptical Fourier Descriptors ,[object Object],28 harmonically related ellipses. ,[object Object],[object Object]

The interface handles the raised events.

The user can map the gestures to events.32

Applications Puzzle game 34 Painter Image Viewer

Challenges ,[object Object],Multi hands tracking ,[object Object],Similar gestures ,[object Object],Dark and complex backgrounds Arm Extraction 36

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Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.

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Avinash Kumar

Avinash Kumar is a PhD candidate in electrical and computer engineering at the University of Illinois. His research focuses on camera calibration, computational imaging, and computer vision applications to railroad monitoring. He has developed new models and analytical solutions for small field of view camera calibration. His other research includes developing an omnifocus imaging technique, extracting shadows obscured by scattering media, and structure from motion for indoor scenes. He has also worked on a machine vision system to analyze gaps between freight loads on trains.

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IJERA Editor

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Avinash_CV

Avinash Kumar

Avinash Kumar is a PhD candidate in electrical and computer engineering at the University of Illinois. His research focuses on camera calibration, computational imaging, and developing vision systems for analyzing freight trains. He has published papers on non-frontal camera calibration, depth from focus/defocus, and extracting shadow details from transmissive media. Kumar received his dual BTech and MS degrees from the International Institute of Information Technology and is advised by Professor Narendra Ahuja.

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Sybian Technologies Pvt Ltd Final Year Projects & Real Time live Projects JAVA(All Domains) DOTNET(All Domains) ANDROID EMBEDDED VLSI MATLAB Project Support Abstract, Diagrams, Review Details, Relevant Materials, Presentation, Supporting Documents, Software E-Books, Software Development Standards & Procedure E-Book, Theory Classes, Lab Working Programs, Project Design & Implementation 24/7 lab session Final Year Projects For BE,ME,B.Sc,M.Sc,B.Tech,BCA,MCA PROJECT DOMAIN: Cloud Computing Networking Network Security PARALLEL AND DISTRIBUTED SYSTEM Data Mining Mobile Computing Service Computing Software Engineering Image Processing Bio Medical / Medical Imaging Contact Details: Sybian Technologies Pvt Ltd, No,33/10 Meenakshi Sundaram Building, Sivaji Street, (Near T.nagar Bus Terminus) T.Nagar, Chennai-600 017 Ph:044 42070551 Mobile No:9790877889,9003254624,7708845605 Mail Id:sybianprojects@gmail.com,sunbeamvijay@yahoo.com

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Ara V. Nefian is seeking a challenging research position involving computer vision, machine learning, robotics, and multimedia processing. She has a PhD in electrical engineering from Georgia Tech and over 10 years of research experience. Her skills include computer vision, Bayesian networks, image and video processing. She has published 40 papers and holds 10 patents related to these fields. Her most recent role is as a Senior System Scientist at Carnegie Mellon University where she leads projects in 3D terrain reconstruction from planetary images and autonomous robotics.

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butest

Ara V. Nefian is seeking a challenging research position involving computer vision, machine learning, robotics, and multimedia processing. She has over 10 years of research experience and 40 publications. Her background includes a PhD in electrical engineering focused on face recognition using HMMs. She has led numerous projects at companies like CMU, Intel, and Nokia involving 3D reconstruction, computer vision, Bayesian networks, and multimedia processing. She has also filed 20 patents related to these areas.

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Top Cited Article in Informatics Engineering Research: October 2020

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The document presents a project report on machine learning. It discusses several projects completed including implementing neural networks to compute averages, extracting histogram of joints features, and developing a gesture recognition system using Hidden Markov Models. The gesture recognition system uses a Kinect sensor to capture skeleton data, extracts features, builds a codebook using clustering, trains HMM models for each gesture, and achieves over 85% accuracy on a dataset of 15 gestures. Future work to improve the system is also outlined.

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Day by day lots of efforts are been taken towards developing an intelligent and natural interface between computer system and users. And looking at the technologies now a day’s it has become possible by means of variety of media information like visualization, audio, paint etc. Gesture has become important part of human communication to convey the information. Thus In this paper we proposed a method for HAND GESTURE RECOGNIZATION which includes Hand Segmentation, Hand Tracking and Edge Traversal Algorithm. We have designed a system which is limited to the hardware parts such as computer and webcam. The system consists of four modules: Hand Tracking and Segmentation, Feature Extraction, Neural Training, and Testing. The objective of this system to explore the utility of a neural network-based approach to the recognition of the hand gestures that create a system that will easily identify the gesture and use them for device control and convey information instead of normal inputs devices such as mouse and keyboard.

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永久可查学历认证【微信：A575476】【(QMUE毕业证书)英国爱丁堡玛格丽特女王大学毕业证成绩单Offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：A575476】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：A575476】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才 → 【关于价格问题（保证一手价格）我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！

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一比一原版新西兰奥塔哥大学毕业证（otago毕业证）如何办理

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一模一样【微信：A575476】【新西兰奥塔哥大学毕业证（otago毕业证）成绩单Offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：A575476】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：A575476】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才 → 【关于价格问题（保证一手价格）我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！

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Interactive Wall (Multi Touch Interactive Surface)

1. Multi-touch Interactive Surface Supervisors: Professor Dr. Mohammed Roushdy Dr. Haythem El-Messiry T.A. Ahmad Salah 1

2. Team Members 2

3. Agenda Introduction Physical Environment and Framework Project Modules and Applications Challenges Conclusion and Future work Tools and References 3

5. Current Multi-touch devices are:Expensive Heavy Fragile Consume space 4

7. Generic Framework for Human Computer Interaction (HCI) using hand gestures.6

8. Physical Environment Physical environment consists of: A projector. A webcam placed over the projector’s lens capturing the projected surface. 7

9. 1.25 m 0.8 m 8

10. Physical Environment Surface 2.15 m Camera Projector 9

11. Framework 10 Controller Configuration Module Input Module Hand Tracking Hand Segmentation Hand gesture Recognition Interface

12. Controller Module 11

13. Detect Corners Controller Module Color Mapping Search for hand in entry point Segmentation Construct the search window Track the hand Fire Event Gesture Recognition 12

14. Configuration Module 13

15.

16. A set of colors are projected and captured for the color calibration process.Desktop Colors Projected Colors 14

17.

18. Input Module 16

19.

20. Hand Tracking Module 18

21.

22. Steps:Initialization Prediction Correction 19

23. Hand Segmentation Module 20

24.

25.

26. Convert colors of the desktop image to that of the captured image.22

27.

28. Calculate the transformation matrix A for each region.

29. b=A * x ; where b is the calibrated color, A is the transformation matrix, x the desktop color.23

30. Segmentation Results Largest Blob Extraction Desktop Captured Segmented Global thresholding 24

31.

32. A bounding box (60 * 60) is constructed around the largest blob.Original Closed Original - Closed

33. Hand Gesture Recognition Module 26

34. Hand Gesture Recognition 27 Contour Tracing Contour Re-sampling EFD DB EFD Gesture Type

35.

36. Testing Results 30

37. Interface Module 31

38.

39. The interface handles the raised events.

40. The user can map the gestures to events.32

41. Main Gestures 33

42. Applications Puzzle game 34 Painter Image Viewer

43. Demo 35

44.

45.

46. Image processing can be very powerful if used in the appropriate environment.37

47.

48. Multi hands’ and multi users’ interaction.

49. Interactive Wall can be used with another surface other than the projector, for example a large screen can be used.

50. Recognize dynamic gestures.38

51.

52. MatLab

53.

54. Projector39

55.

56. AsanterabiMalima, ErolÖzgür, and MüjdatÇetin, A Fast algorithm for vision-based hand gesture recognition robot control, Faculty of Engineering and Natural Science, Sabancı University, Tuzla, İstanbul, Turkey, 2006.40

57.

58.

59.

60.

61. NgonT.Truong, Jae-GyunGwag, Yong-Jin Park, and Suk-Ha Lee, Genetic Diversity of Soybean Pod Shape Based on Elliptical Fourier Descriptors, Dep. of Plant Science, Seoul National University, Seoul 151-742, Korea Dep. Of Crop Sciences, Can Tho University, Can Tho, Viet Nam Genetic Resources Div., National Institute of Agricultural Biotechnology, Suwon 441-707, Korea, 2005.

62.

63.

64. Stephen Wolf,Color Correction Matrix for Digital Still and Video Imaging Systems, U.S. DEPARTMENT OF COMMERCE, December 2003.

65. Qing Chen, Evaluation of OCR Algorithms for Images with Different Spatial Resolutions and Noises, School of Information Technology and Engineering Faculty of Engineering University of Ottawa ©, Ottawa, Canada, 2003.

66.

67. Dengsheng Zhang and Guojun Lu,A Comparative Study on Shape Retrieval Using Fourier Descriptors with Different Shape Signatures, Gippsland School of Computing and Information Technology Monash University Churchill, Victoria 3842, Australia, 2001.43

68.

69. Kenny Teng, Jeremy Ng, Shirlene Lim, Computer Vision Based Sign Language Recognition for Numbers.

70.

71. A. M. Hamad, Fawziashaaban, Mona Gabr, NohaSayed, RababHussien, Robot Vision, Faculty of computer and Information Sciences Ain Shams University, Cairo, Egypt, 2008.44

72. Thank You ! 45

Editor's Notes

Our goal is the development of a more natural interface; a camera-projector system using hand gesture analysis.Optical cameraProjector Framework Hand gesture , ApplicationsA development framework that gives any application using it the ability to allow the user to interact with this application using “Hand Gestures”.A stream of video is captured using low cost webcam then processed in the framework to extract the hand position, recognize the gesture and finally the application handles the fired events.
Our goal is the development of a more natural interface; a camera-projector system using hand gesture analysis.Optical cameraProjector Framework Hand gesture , ApplicationsA development framework that gives any application using it the ability to allow the user to interact with this application using “Hand Gestures”.A stream of video is captured using low cost webcam then processed in the framework to extract the hand position, recognize the gesture and finally the application handles the fired events.
1. Initialization (k=0). In this step it is looked for the object in the whole image due we do not know previously the object position. We obtain this way. Also we can considerer initially a big error tolerance.2. Prediction (k>0). In this stage using the Kalman filter we predict the relative position of the object, such position is considered as search center to find the object.3. Correction (k>0). In this part we locate the object (which is in the neighborhood point predicted in the previous stage) and we use its realposition (measurement) to carry out the state correction using the Kalman filter finding this way .The steps 2 and 3 are carried out while the object tracking runs.the equations for the Kalman filter fall into two groups: Time update equations and measurement update equations. The time update equations are responsible for projecting forward (in time) the current state and error covarianceestimates to obtain the a priori estimates for the next time step. The measurement update equations are responsible for the feedback—i.e. for incorporating a new measurement intothe a priori estimate to obtain an improved a posteriori estimate.
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