Computer Vision, Lecture
1
http://www.ugrad.cs.jhu.edu/~cs461
| Professor Hager | |
| http://www.cs.jhu.edu/~hager |
| Outline and Organization of the course | |
| What is Computer Vision | |
| Some Applications of Computer Vision | |
| Trucco and Verri | ||
| computing properties of the 3D world from one or more digital image | ||
| 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 | ||
| Image processing: the study of the properties of operators that produce images from other images | ||
| we will touch on image filtering and related operators from image processing | ||
| Machine Vision: a somewhat outdated term which now tends to refer to industrial vision applications where (usually) a single camera is used to solve a structured inspection task | ||
| (demonstration of NI Vision Builder example) | ||
| 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 focussed on accuracy issues. | ||
What Information is in Images?
What Information is in Images?
Problems of Computer Vision: Modeling
Problems of Computer Vision: Modeling
| 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? | ||
Problems of Computer Vision: Feature Extraction
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 | |||
Problems of Computer Vision: Segmentation and Grouping
Computer Vision vs. Human Vision
Illusions: What Do They Tell Us?
Illusions: What Do They Tell Us?
Illusions: What Do They Tell Us?
Illusions: What Do They Tell Us?
Illusions: What Do They Tell Us?
Illusions: What Do They Tell Us?
| The goal of computer vision: to produce a “2 ½ D” sketch | ||
| Shape from stereo | ||
| Shape from shading | ||
| Shape from motion | ||
| . | ||
| . | ||
| . | ||
| . | ||
Problems of Computer Vision: Stereo Vision
THE ORGANIZATION OF AN IMAGE SEQUENCE
MOVING CAMERAS ARE LIKE STEREO
An Example
(Courtesy
Carlo Tomasi)
Problems of Computer Vision: Recognition
Problems of Computer Vision: Recognition
Applications of Computer Vision: Biometrics
| Face recognition | |
| Iris scanning | |
| Fingerprint recognition | |
| Activity recognition |
Applications of Computer Vision: Medical Imaging
Applications of Computer Vision: Medical Imaging
Applications of Computer Vision: HCI
Applications of Computer
Vision: Image Databases
(Courtesy D. Forsyth & J. Ponce)
Applications of Computer
Vision: Data Acquisition
(Jitendra Malik, Berkeley)
Applications of Computer Vision: Motion Control
Applications of Computer
Vision: Motion Control
(CMU)
Applications of Computer Vision: Motion Control
| Use the course WEB site | |||
| http://www.ugrad.cs.jhu.edu/~cs461 | |||
| For information on this course | |||
Computer Vision, Lecture
2
http://www.ugrad.cs.jhu.edu/~cs461
| Professor Hager | |
| http://www.cs.jhu.edu/~hager |
| Basic process: | |||
| photons hit a detector | |||
| the detector becomes charged | |||
| the charge is read out as brightness | |||
| Sensor types: | |||
| CCD (charge-coupled device) | |||
| most common | |||
| high sensitivity | |||
| high power | |||
| cannot be individually addressed | |||
| blooming | |||
| CMOS | |||
| simple to fabricate (cheap) | |||
| lower sensitivity, lower power | |||
| can be individually addressed | |||
THE ORGANIZATION OF A 2D IMAGE
| The “diameter” d of a pixel determines the highest frequency representable in an image | |
| Real scenes may contain higher frequencies resulting in aliasing of the signal. | |
| In practice, this effect is often dominated by other digitization artifacts. | |
| One problem in particular is differing sampling rates between digitizer and camera readout of a row. |
| Non-lossy schemes | |||
| pbm/pgm/ppm/pnm | |||
| code for file type, size, number of bands, and maximum brightness | |||
| tif (lossless and lossy versions) | |||
| bmp | |||
| gif | |||
| Lossy schemes | |||
| jpg | |||
| uses Y Cb Cr color representation; subsamples the color | |||
| Uses DCT on result | |||
| Uses the fact the human system is less sensitive to color than spatial detail | |||
| GIF (Graphics Interchange Format) | ||
| Limited to 8 bits/pixel for both color and gray-scale. | ||
| TIFF (Tagged Image File Format) | |||
| More general than GIF | |||
| Allows 24 bits/pixel | |||
| Supports 5 types of image compression including: | |||
| RLE (Run length encoding) | |||
| LZW (Lempel-Ziv-Welch) | |||
| JPEG (Joint Photographic Experts Group) | |||
A Modern Digital Camera (Firewire)
THE ORGANIZATION OF AN IMAGE SEQUENCE
| 1 CCD cameras | ||
| A Bayer pattern is placed in front of the CCD | ||
| A Demosaicing process reads the pixels in a region and computes color and intensity | ||
| 3 CCD camera use a beam splitter and 3 separate CCDs | ||
| higher color fidelity | ||
| needs lots of light | ||
| requires careful alignment of ccds | ||
| Two different Spectral Energy Distributions with the same RED, GREEN, BLUE response are termed metamers. |
Homogeneous Region: Photometry