Computer Vision

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					Artificial Intelligence (CS607)




1 Computer vision
It is a subfield of Artificial Intelligence. The purpose of computer vision is to study
algorithms, techniques and applications that help us make machines that can "understand"
images and videos. In other words, it deals with procedures that extract useful information
from static pictures and sequence of images. Enabling a machine to see, percieve and
understand exactly as humans see, percieve and understand is the aim of Computer Vision.

Computer vision finds its applications in medicine, military, security and surveillance, quality
inspection, robotics, automotive industry and many other areas. Few areas of vision in
which research is benig actively conducted thoughout the world are as follows:

        The detection, segmentation, localisation, and recognition of certain objects in
        images (e.g., human faces)

        Tracking an object through an image sequence

        Object Extraction from a video sequence

        Automated Navigation of a robot or a vehicle

        Estimation of the three-dimensional pose of humans and their limbs

        Medical Imaging, automated analysis of different body scans (CT Scan, Bone Scan,
        X-Rays)

        Searching for digital images by their content (content-based image retrieval)

        Registration of different views of the same scene or object


Computer vision encompases topics from pattern recognition, machine learning, geometry,
image processing, artificial intelligence, linear algebra and other subjects.

Apart from its applications, computer vision is itself interesting to study. Many detailed
turorials regarding the field are freely avalible on the internet. Readers of this text are
encouraged to read though these tutorials get indepth knowledge about the limits and
contents of the field.



1.1 Exercise Question

Search though the internet and read about intersting happeneing and reseach going on
around the globe in the are of Computer Vision.


                                  © Copyright Virtual University of Pakistan
Artificial Intelligence (CS607)




http://www.cs.ucf.edu/~vision/

The above link might be useful to explore knowledge about computer vision.


2 Robotics
Robotics is the highly advanced and totally hyped field of today. Literally speaking, robotics
is the study of robots. Robots are nothing but a complex combination of hardware and
intelligence, or mechanics and brains. Thus robotics is truly a multi-disciplinary area, having
active contributions from, physics, mechanics, biology, mathematics, computer science,
statistics, control thory, philosophy, etc.

The features that constitute a robot are:

    •   Mobility
    •   Perception
    •   Planning
    •   Searching
    •   Reasoning
    •   Dealing with uncertainty
    •   Vision
    •   Learning
    •   Autonomy
    •   Physical Intelligence

What we can see from the list is that robotics is the most profound manifestation of AI in
practice. The most crucial or defining ones from the list above are mobility, autonomy and
dealing with uncertainety

The area of robotics have been followed with enthusiasm by masses from fiction, science
and industry. Now robots have entered the common household, as robot pets (Sony Aibo
entertainment robot), oldage assistant and people carriers (Segway human transporter).

2.1 Exercise Question

Search though the internet and read about intersting happeneing and reseach going on
around the globe in the are of robotics.

http://www.cs.dartmouth.edu/~brd/Teaching/AI/Lectures/Summaries/robotics.html

The above link might be useful to explore knowledge about robotics.




                                   © Copyright Virtual University of Pakistan
Artificial Intelligence (CS607)




3 Softcomputing
Softcomputing is a relatively new term coined to encapsulate the emergence of new hybrid
area of work in AI. Different technologies including fuzzy systems, genetic algorithms, neural
networks and a few statistical methods have been combined together in different
orientations to successfully solve today’s complex real-world problems.

The most common combinations are of the pairs
   • genetic algorithms – fuzzy systems (genetic fuzzy)
   • Neural Networks – fuzzy systems (neuro-fuzzy systems)
   • Genetic algorithms – Neural Networks (neuro-genetic systems)

Softcomputing is naturally applied in machine learning applications. For example one usage
of genetic-fuzzy system is of ‘searching’ for an acceptable fuzzy system that conforms to the
training data. In which, fuzzy sets and rules combined, are encoded as individuals, and GA
iterations refine the individuals i.e. fuzzy system, on the basis of their fitness evaluations.
The fitness function is usually MSE of the invidual fuzzy system on the training data. Very
similar applications have been developed in the other popular neuro-fuzzy systems, in
which neural networks are used to find the best fuzzy system for the given data through
means of classical ANN learning algorithms.


Genetic algorithms have been employed in finding the optimal initial weights of neural
networks.



3.1 Exercise Question

Search though the internet and read about intersting happeneing and reseach going on
around the globe in the are of softcomputing.

http://www.soft-computing.de/

The above link might be useful to explore knowledge about softcomputing.




                                  © Copyright Virtual University of Pakistan
Artificial Intelligence (CS607)




4 Clustering
Clustering is a form of unsupervised learning, in which the training data is available but
without the classification information or class labels. The task of clustering is to identify and
group similar individual data elements based on some measure of similarity. So basically
using clustering algorithms, classification information can be ‘produced’ from a training data
which has no classification data at the first place. Naturally, there is no supervision of
classification in clustering algorithms for their learning/clustering, and hence they fall under
the category of unsupervised learning.

The famous clustering algorithms are Self-organizing maps (SOM), k-means, linear vector
quantization, Density based data analysis, etc.



4.1 Exercise Question

Search though the internet and read about intersting happeneing and reseach going on
around the globe in the are of clustering.

http://www.elet.polimi.it/upload/matteucc/Clustering/tutorial_html/

The above link might be useful to explore knowledge about clustering.




                                  © Copyright Virtual University of Pakistan

				
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posted:5/26/2010
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