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5/13/2015CAM Talk G.Kamberova Computer Vision Introduction Gerda Kamberova Department of Computer Science Hofstra University.

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Presentation on theme: "5/13/2015CAM Talk G.Kamberova Computer Vision Introduction Gerda Kamberova Department of Computer Science Hofstra University."— Presentation transcript:

1 5/13/2015CAM Talk G.Kamberova Computer Vision Introduction Gerda Kamberova Department of Computer Science Hofstra University

2 5/13/2015CAM Talk G.Kamberova What is Computer Vision? Trucco and Verri –computing properties of the 3D world from one or more digital images Stockman and Shapiro –To make useful decisions about real physical objects and scenes based on sensed images Ballard and Brown –The construction of explicit, meaningful description of physical objects from images Forsyth and Ponce –...extracting descriptions of the world from pictures or sequences of pictures

3 5/13/2015CAM Talk G.Kamberova From 2D images to 3D model

4 5/13/2015CAM Talk G.Kamberova General Rule If you can’t understand (i.e. model) the forward process, you will have a hard time solving the inverse!

5 5/13/2015CAM Talk G.Kamberova What Information is in Images?

6 5/13/2015CAM Talk G.Kamberova What information is in the image?

7 5/13/2015CAM Talk G.Kamberova Typical Sensor: A CCD Camera Basic process: –photons hit a detector –the detector becomes charged –the charge is read out as brightness –high sensitivity –Noise issues

8 5/13/2015CAM Talk G.Kamberova Pixel Binary 1 bit Grey 1 byte Color 3 bytes Image Representation Each pixel is a measure of the brightness (intensity of light) that falls on an area of an sensor (typically a CCD chip)

9 5/13/2015CAM Talk G.Kamberova Problems of Computer Vision: Radiometry What are the physical and geometric processes that govern (digital) imaging?

10 5/13/2015CAM Talk G.Kamberova Problems of Computer Vision: Imaging Geometry What are the physical and geometric processes that govern (digital) imaging?

11 5/13/2015CAM Talk G.Kamberova Some Related Terms Image Processing: the study of the properties of operators that produce images from other images Machine Vision: a somewhat outdated term which now tends to refer to industrial vision applications where (usually) a single camera is used to solve a structured inspection task Pattern Recognition: typically refers to the recognition of structures in 2D images (usually without reference to any underlying 3D information). Photogrammetry: the science of measurement though non- contact sensing, e.g. terrain maps from satellite images. Usually is more focused on accuracy issues than interpretation.

12 5/13/2015CAM Talk G.Kamberova Computer Vision vs. Graphics Computer Graphics –Produce “plausible” images –You choose the models, conditions, imaging parameters, etc. Computer Vision –Given real images with noise, sampling artifacts … –Estimate physically quantities –Ill-posed ---- what is the minimum world knowledge we need? Is Vision the “Inverse” of Graphics?

13 5/13/2015CAM Talk G.Kamberova From 2D images to 3D model and back

14 5/13/2015CAM Talk G.Kamberova Image Filter Result Problems of Computer Vision: Feature Extraction What are the “informative” areas of an image and how do we detect them?

15 5/13/2015CAM Talk G.Kamberova Filter kernels that are larger see effects at coarser scales Problems of Computer Vision: Feature Extraction

16 5/13/2015CAM Talk G.Kamberova Problems of Computer Vision: Feature Extraction – edge detection

17 5/13/2015CAM Talk G.Kamberova Computer Vision vs. Image Processing Image Processing –Mostly concerned with image-to-image transformations Filtering Enhancement Compression Computer Vision –Concerned with how images reflect the 3D world Filtering for feature extraction Enhancement for recognition/detection Compression that preserves geometric information in images

18 5/13/2015CAM Talk G.Kamberova Problems of Computer Vision: Segmentation and Grouping What portions of an image pertain to one another and to relevant physical phenomena?

19 5/13/2015CAM Talk G.Kamberova Computer Vision vs. Human Vision What is the right segmentation? To us it seems obvious … “common sense”

20 5/13/2015CAM Talk G.Kamberova Grouping

21 5/13/2015CAM Talk G.Kamberova Illusions

22 5/13/2015CAM Talk G.Kamberova Illusions

23 5/13/2015CAM Talk G.Kamberova Stereo(psis) Stereo Recovers information about 3D structure 2 images of the same scene (different viewpoints)

24 5/13/2015CAM Talk G.Kamberova

25 5/13/2015CAM Talk G.Kamberova Autostereogram

26 5/13/2015CAM Talk G.Kamberova Computer Vision Problems: Stereo Correspondence (matching): From two (or more) images, determine the geometry of he scene by matching corresponding areas of the images Reconstruction: from 2D matched elements to 3D pointshe. RIGHT IMAGE PLANE LEFT IMAGE PLANE

27 5/13/2015CAM Talk G.Kamberova THE ORGANIZATION OF AN IMAGE SEQUENCE Frames

28 5/13/2015CAM Talk G.Kamberova THE MOTION FIELD The “instantaneous” velocity of points in an image The focus of expansion 1. Direction of motion 2. Time to collision

29 5/13/2015CAM Talk G.Kamberova MOVING CAMERAS ARE LIKE STEREO: Structure from Motion Locations of points on the object (the “structure”) The change in spatial location between the two cameras (the “motion”)

30 5/13/2015CAM Talk G.Kamberova MOVING CAMERAS ARE LIKE STEREO: Structure from Motion Locations of points on the object (the “structure”) The change in spatial location between the two cameras (the “motion”)

31 5/13/2015CAM Talk G.Kamberova Problems of Computer Vision: Recognition Given a database of objects and an image determine what, if any of the objects are present in the image.

32 5/13/2015CAM Talk G.Kamberova Problems of Computer Vision: Recognition Given a database of objects and an image determine what, if any of the objects are present in the image.

33 5/13/2015CAM Talk G.Kamberova Applications of Computer Vision: Biometrics Face recognition Iris scanning Fingerprint recognition Activity recognition 3D biometric

34 5/13/2015CAM Talk G.Kamberova Applications of Computer Vision: Medical Imaging

35 5/13/2015CAM Talk G.Kamberova Examples: Virtual colonoscopy Patient: data, imaging, rather non-invasive

36 5/13/2015CAM Talk G.Kamberova Applications of Computer Vision: HCI calculator GUI Face tracker Gesture recognition

37 5/13/2015CAM Talk G.Kamberova Applications of Computer Vision: Image Databases (Courtesy D. Forsyth & J. Ponce) From a search for horse pix in 100 horse images and 1086 non-horse images

38 5/13/2015CAM Talk G.Kamberova Applications of Computer Vision: 3D model aquisition (Jitendra Malik, Berkeley)

39 5/13/2015CAM Talk G.Kamberova Applications of Computer Vision: Motion Control

40 5/13/2015CAM Talk G.Kamberova CMU: Vitrualized Reality

41 5/13/2015CAM Talk G.Kamberova Tele-Immersion: UPenn is the technology that enables remotely located users to share the same real or virtual environments in real time.

42 5/13/2015CAM Talk G.Kamberova Teleimmersion sensing of the real world interaction with the real world visualization of real and synthetic data networking Why 3D? Collaborative design Telemedicine Training in 3D Visualization of complex data Entertainment

43 5/13/2015CAM Talk G.Kamberova Tele-immersion for the Common Citizen

44 5/13/2015CAM Talk G.Kamberova Teleimmersion: Collaboration and Working in Virtual 3D Worlds (UPenn, UNC)

45 5/13/2015CAM Talk G.Kamberova Computer Vision Problems: 3D reconstruction A Reconstructed Depth Map

46 5/13/2015CAM Talk G.Kamberova Polynocular Stereo From images to unorganized 3D point cloud

47 5/13/2015CAM Talk G.Kamberova Surface Reconstruction from unorganized 3D point cloud Orient Locally Reconstruction by polyhedral approximation Compute 3D shape ifrom the polyhedral surface: mean and Gauss curvatures

48 5/13/2015CAM Talk G.Kamberova 3D Shape recovery: applications in registration and matching of surfaces/objects G^2 Kamberov: Ongoing program to associate geometric descriptors and invariants directly to an unorganized oriented cloud of points Without polyhedral approximation, or model fitting

49 5/13/2015CAM Talk G.Kamberova Another cloud

50 5/13/2015CAM Talk G.Kamberova Example: Principal directions

51 5/13/2015CAM Talk G.Kamberova Examples: Mean curvature surface

52 5/13/2015CAM Talk G.Kamberova 3D shape: the mean curvature surface

53 5/13/2015CAM Talk G.Kamberova 3D shape from medical data

54 5/13/2015CAM Talk G.Kamberova Medical Data 2

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