Pattern Recognition

1. Topic Title :- Basics of Image Pre-processing
Name :- Sayak Jana
University Roll No :- 13000121008
Registration Number :- 211300100110004 (2021-22)
Department :- Computer Science & Engineering
Subject Name :- Pattern Recognition
Subject Code :- PEC-IT 602D
Exam :- CA1

2. Introduction
Image pre-processing is a critical step in digital image processing. It
involves the application of various techniques to enhance or improve the
quality of an image before it is fed into a machine learning model or used
for analysis.
Image preprocessing is the steps taken to format images before they are
used by model training and inference. This includes, but is not limited to,
resizing, orienting, and color corrections.
Image preprocessing may also decrease model training time and increase
model inference speed. If input images are particularly large, reducing the
size of these images will dramatically improve model training time without
significantly reducing model performance. For example, the standard size
of images on iPhone 11 are 3024×4032. The machine learning model
Apple uses to create masks and apply Portrait Mode performs on images
half this size before its output is rescaled back to full size.

3. Importance of Image Pre-
processing
1 Improves Model Accuracy
By removing noise and standardizing images, pre-processing significantly enhances the
accuracy of models trained on image data.
2 Reduces Computational Load
Pre-processing can optimize images for efficient processing, reducing the
computational load on systems.
3 Enhances Feature Extraction
It aids in highlighting important features and patterns in the images, making them more
identifiable for analysis.

4. Common Techniques Used in Image
Pre-processing
Image Resizing
Resizing images is a
fundamental pre-processing
step used to adjust image
dimensions for specific
applications.
Color Correction
It involves adjusting the colors
to ensure uniformity and
correct any tint or shade
variations in the images.
Histogram
Equalization
It helps in adjusting the
contrast of images, making
them more visually appealing
and analytically useful.

5. Image Resizing and Scaling
1
Improves Display Quality
2X
Reduces Storage Space
4K
Supports High-Resolution
Displays

6. Image Denoising and Filtering
Reduces Noise Enhances Image Clarity
Preserves Edges Improves Feature Detection

7. Image Enhancement and Contrast
Adjustment
Sharpens Details
Enhances edges and fine details within an image for improved visual clarity.
Improves Depth
Enhances contrast to bring out subtle differences and improve visual perception.
Boosts Visual Appeal
Makes images more appealing and engaging for viewers by adjusting brightness and
contrast.

8. Image Normalization and
Standardization
Normalization
Transforms images to a standard scale, ensuring consistency in features
across all images.
Standardization
Standardizes the distribution of pixel values, facilitating effective comparisons
and analysis.

9. Conclusion and Key Takeaways
1 Foundational Role
Image pre-processing forms the foundation for accurate and insightful image analysis
in various fields.
2 Optimization Impacts
Optimizing images significantly impacts the performance and efficiency of machine
learning models and computer vision applications.
3 Continuous Innovation
Advancements in pre-processing techniques continue to drive improvements in
image analysis and its practical applications.

10. References
1. Smith, J. (2020). Image Processing Essentials. New York: Springer.
2. Chen, L. (2019). Advanced Techniques in Image Analysis. San Francisco: O'Reilly.
3. www.analyticsvidya.com
4. www.mygreatlearning.com
5. www.embedded-vision.com
Thank
you!