The document provides an overview of computer vision, describing its capabilities in interpreting images and its applications across various industries. It covers key concepts such as digital images, pixel manipulation, image transformations, and methods for object detection and classification. It highlights techniques like convolution, histogram equalization, and classifiers used in processing and analyzing images.

1. COMPUTER
VISION

2. WEEK 1

3. WHAT IS COMPUTER VISION?
• Provides computers with the ability to see and understand images.
• Has an anonymous impact on industries like automotive, manufacturing, etc.
• Revolutionizing the hiring process.

4. APPLICATIONS
• Sifting videos
• Detection of metal and other structures in electric towers(divide into the
grid)
• Rust detection
• Rusted areas of the bridge
• Damage detection

5. RESEARCH IN COMPUTER VISION
• Detection of objects
• Detecting objects in self-driving cars
• Image-to-image translation(winter to summer)/(zebra to horse)
• Dance pose detection

6. WEEK 2

7. What is a digital image?
• A digital image can be interpreted as a rectangular array of a number
• The image is comprised of rectangular blocks called pixels
• Pixels can be represented as numbers called intensity values
• PIL import image module
• Show-plot image or matplotlib.imshow
• Attribute size is the no of pixels that make height and width
• Attribute mode color space

8. Manipulating Images
• Id function is used to find the object memory address
• Flipping images changes the images orientation
• Changing the index value of the image  flip the image
• PIL  images are flipped by using (flip, mirror, and transpose)
• Openvc  images are flipped by using (flip , rotate)
• Cropping is cutting out the part of image and throwing out the rest
• PIL  Imagedraw function to draw PIL image object
• ImageFont module for fonts

9. Pixels Transformations
• A histogram counts the no of occurrences of a pixel
• It understands and manipulates images
• Intensity transformation changes a image one pixel at a time
• S=2r+1
• Alpha-contrast control
• Beta-brightness control
• convertscaleAbs is used instead of array operations after applying transformations
• Histogram equalization—an algorithm that uses image histogram to adjust contrast

10. Pixels Transformations
• Func (equalizeHist)---improves contrast

11. Spatial operation in image processing
• Convolution or linear filtering is a standard way to Filter an image the filter is
called the kernel
• Z=wx(x is input image,w = kernel filter)

12. IMAGE CLASSIFICATION
• Image classification is the process of taking an image or picture and getting a
computer to automatically classify it, or try to provide the probability of the class
of the image.
• A large portion of data is used for training and a smaller for testing
• K nearest neighbour (knn) is slow.
• The use of all the samples in the dataset is called an epoch
• When we use all samples it is called batch gradient descent where one iteration
equals one epoch

13. Methods of object detection
• Sliding windows detection-----we start from one region and classify that
sub-image---we then shift the window and classify the next sub-image—
repeat
• Bounding boxes----- rectangular box
• An adaboost classifier is used to reduce the no. of features
• Strong classifier is the combination of weak classifiers