Applications of Image Processing
Visual information is the most important type of information
perceived, processed and
interpreted by the human brain. One third of the cortical area of
the human brain is dedicated
to visual information processing.
Digital image processing, as a computer-based technology, carries
out automatic processing,
manipulation and interpretation of such visual information, and it
plays an increasingly
important role in many aspects of our daily life, as well as in a
wide variety of disciplines and
fields in science and technology, with applications such as
television, photography, robotics,
remote sensing, medical diagnosis and industrial inspection.
• Computerized photography (e.g., Photoshop)
• Space image processing (e.g., Hubble space telescope images,
interplanetary probe
images)
• Medical/Biological image processing (e.g., interpretation of
X-ray images, blood/cellular
microscope images)
• Automatic character recognition (zip code, license plate
recognition)
• Finger print/face/iris recognition
• Remote sensing: aerial and satellite image interpretations
• Reconnaissance
• Industrial applications (e.g., product inspection/sorting)
objectives of image processing
Understand differences between computer vision and image
processing.
Know the basic components of an image processing system.
Understand the basics of the human visual system as they relate to
image processing; including spatial frequency resolution and
brightness adaption.
Understand how images are represented; including optical images,
analog images, and digital images. Understand image types such as
binary images, gray-scale images, color and multi-spectral
images.
Know the key concepts in image file formats.
Understand the model for an image analysis process.
Understand why preprocessing is performed and know about image
geometry, convolution masks, image algebra and basic spatial
filters.
Understand image quantization in both the spatial and brightness
domains.
Understand how discrete transforms work; including concepts of
basis images, orthogonality, orthonormality, separability and
reversibility.
Know about the 2-D Fourier, discrete cosine, Walsh-Hadamard and
wavelet transforms; including implied symmetry, phase, circular
convolution, vector inner and outer products and filtering.
Know why log remapping is necessary for viewing spectral image
data.
Understand lowpass, highpass, bandpass, notch filters; including
ideal and non-ideal filters such as the Butterworth.
Non-Trivial world - Consider Segmentation problem !
Accuracy - You will never get satisfied with the results obtained !
(I have never heard 100% results in Image processing)
Hard Coded / No generic solutions - Consider a filtering problem or
a contrast enhancement problem which needs to be solved generic but
varies according to many factors (source, kernel, methods, etc)
resulting in hard coding.
Diversified - More number of ways and many possible solutions for a
problem. For instance you need boundary of a object -> start
with edge detection (you have around 5 edge detection algorithms)
and end up somewhere near skeletonization or distance
transform.
All-Time Research - Start to work an application -> it is
obvious in IP you are not going to attain 100% accuracy ->
continue to improve the accuracy !
Etc ...Etc ... ( end of thinking capability :-P )
Some of well deserved researches are (personally I have used /
known to me)
1. Viola Jones Face detection ( This undergoes all the above
mentioned issues - But still outperforms other methods
available)
2. HOG -Human / Pedestrian Detection
3. Convolution neural networks - Think Facebook uses this method
for its Face recognition which surpassed human capabilities
4. Part Based Models - Improved method of (2)Hog features for Human
/ Pedestrian detection
Answer the following questions Q1. The image digitisation process consists of, in general, three components to convert...
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