Computer vision is a field of artificial intelligence that enables computers to derive meaningful information from visual inputs like images and videos. It has various applications including optical character recognition, face detection, pedestrian detection, disease detection, self-driving cars, and medical imaging. Some challenges of computer vision include inadequate hardware, lack of training data, variable lighting conditions, occlusion, scale variability, and contextual understanding.

1. Computer Vision : Applications and challenges

2. 01 02 03 04
MD. Tasadduk
Hossain
ID : 221-15-4830
MD. Raihanul
Islam
ID : 221-15-5150
MD. Moinur
Hasan
ID : 221-15-5692
Rita Faria
Richi
ID : 221-15-5025
Information of Group member

3. What is Computer Vision?
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to
derive meaningful information from digital images, videos and other visual inputs — and take
actions or make recommendations based on that information.
Components of a computer vision
system
Camera
Computer
Scene
Lightin
g

4. Ridiculously brief history of Computer
Vision :
1960’s
Interpretation of
synthetic worlds
1970’s
Progress on interpreting
selected images
ANNs come and go;
shift toward geometry ,
increased mathematical rigor
1980’s
Face recognition;
statistical analysis in vogue
1990’
s
2000’s
Broader recognition;
video processing
starts

5. Image Formation :
Lens forms image on retina,
sensors (rods and cones) respond to light
Lens system forms image,
sensors (CCD, CMOS) respond to light
Computer vision VS Human Vision :
What we see What a computer sees

6. Vision is multidisciplinary :
Compute
r Vision
Control
robotics
Signal
processing
Computer
graphics
Imaging
Artificial
intelligence
Machine
learning
Neurobiology
Mathematics
Robotic vision
Non –linear SP
Multi-variable
SP
Smart camera
Statistics
Geometry
Cognitive vision
Computer
Intelligence
Biological vision

7. Applications of computer vision :
Optical character recognition (OCR) :
Optical Character Recognition
(OCR) is the process that converts
an image of text into a machine-
readable text format.
Digit recognition, AT&T
labs
License plate
readers
Detection :
Face detection :
Many new digital cameras now detect faces
Canon, Sony, Fuji .
Smile detection : Image a camera smart enough to catch
in every smile . In smile Sutter mode, your cyber-shot
camera can automatically trip the Sutter at just the right
instant to catch the perfect expression .

8. Pedestrian detection :
The detection of pedestrians finds applications in fields such as
autonomous driving, traffic management, and transit safety and
efficiency.
Parking occupancy detection :
Computer vision is already widely used for visual parking lot
occupancy detection in Parking Guidance and Information (PGI)
systems.
PPE Detection :
NIOSH reports that over 2,000 work-related injuries occur daily in the
US alone, which could be prevented through the use of PPE
Plant disease detection :
Computer vision is used for the automated detection of plant
diseases, at an early stage of plant growth.
Insect detection :
Camera-based crop monitoring systems can recognize, classify and
count insects threatening the crops.

9. Transportation related issue :
Self-driving or smart or google cars :
Researches working on the ADAS technology combine computer vision
techniques such as pattern recognition, feature extraction, object tracking,
3D vision to develop real-time algorithms that assist driving activity
Traffic flow analysis :
Drone and camera-based traffic flow tracking and estimation have
been possible to the developments in the field of computer
vision.
Road condition monitoring :
Automated Pavement Distress (PD) detection has increasing road
maintenance efficiency and decreasing the safety risk related to accidents.
Agriculture :
Crop and yield monitoring
Automatic weeding
Livestock health monitoring
Aerial survey and imaging

10. Medical imaging :
X-Ray , CT and MRI
analysis
Cancer
detection
Blood loss
measuremen
t
Digital
pathology
Movement
analysis
Games and sports : Vision in space :
Vision systems (JPL) used for several
tasks
* Panorama stitching
* 3D terrain modeling
* Obstacle detection, position tracking
Interactive games
Kinect

11. Manufacturing , Construction and Retail
:
Defect inspection Reading text and barcodes
Predictive maintenance
Product assembly
Automatic replenishment
Self-checkout
Intelligent video
analytics

12. Other sector implementation of computer vision :
Object recognition Shape capture Motion capture Industrial and mobile
robots
Vision as a source of semantic
information :
Object categorization Scene and context categorization Qualitative spatial information

13. Challenges of computer vision :
Challenges
01 Inadequate hardware
02 Lack of train data and data
quality
10 Weak planning for
model development
03 Time shortage ,
04 Need for regular monitoring
05 Lack of experienced
professionals
High costs
07 Variable lighting conditions
06 Occlusio
n
09 Contextual understanding
08 scale variability

14. Challenges of deploying computer
vision :
Viewpoint variation Illumination Scale Deformation
Occlusion Background clutter
Object intra-class
variation
Local ambiguity

15. Thank
you