The document discusses image preprocessing techniques used to prepare raw image data for analysis. It describes challenges in processing images including variability in image sizes and formats, noise and artifacts, and irrelevant information. Common preprocessing techniques are outlined that help address these challenges, such as resizing images, removing noise, and extracting meaningful features through techniques like segmentation. The goal of preprocessing is to enhance image quality and extract useful information to improve performance of computer vision algorithms.

1. Image Preprocessing Techniques in Unstructured Data
Presented By
Chethan M C

2. INTRODUCTION TO
IMAGE PREPROCESSING

3. Image
Preprocessing
Image preprocessing refers to a set of
techniques used to enhance, clean, and prepare
images for analysis or further processing.
These techniques are applied to raw image data
to address various challenges and improve the
quality of the data before it is fed into
algorithms for tasks such as object detection,
image recognition, and medical imaging.

4. Images come in various sizes, resolutions,
and formats, making it challenging to process
them uniformly. Preprocessing techniques
such as resizing and scaling help standardize
image dimensions for analysis.
01
Variability in Image
Sizes, Resolutions, and
Formats
Images come in various sizes, resolutions,
and formats, making it challenging to process
them uniformly. Preprocessing techniques
such as resizing and scaling help standardize
image dimensions for analysis.
02
Noise and Artifacts in
Images
Images come in various sizes, resolutions,
and formats, making it challenging to process
them uniformly. Preprocessing techniques
such as resizing and scaling help standardize
image dimensions for analysis.
03
Irrelevant Information
Images come in various sizes, resolutions,
and formats, making it challenging to process
them uniformly. Preprocessing techniques
such as resizing and scaling help standardize
image dimensions for analysis.
04
Complexity of
Extracting Meaningful
Features
Challenges

5. Images come in various sizes, resolutions,
and formats, making it challenging to process
them uniformly. Preprocessing techniques
such as resizing and scaling help standardize
image dimensions for analysis.
01
Variability in Image
Sizes, Resolutions, and
Formats
Images come in various sizes, resolutions,
and formats, making it challenging to process
them uniformly. Preprocessing techniques
such as resizing and scaling help standardize
image dimensions for analysis.
02
Noise and Artifacts in
Images
Images come in various sizes, resolutions,
and formats, making it challenging to process
them uniformly. Preprocessing techniques
such as resizing and scaling help standardize
image dimensions for analysis.
03
Irrelevant Information
Images come in various sizes, resolutions,
and formats, making it challenging to process
them uniformly. Preprocessing techniques
such as resizing and scaling help standardize
image dimensions for analysis.
04
Complexity of
Extracting Meaningful
Features
Challenges

6. Images come in various sizes, resolutions,
and formats, making it challenging to process
them uniformly. Preprocessing techniques
such as resizing and scaling help standardize
image dimensions for analysis.
01
Variability in Image
Sizes, Resolutions, and
Formats
Images often contain noise, artifacts, or
distortions that can degrade their quality and
affect the accuracy of algorithms. Noise
reduction techniques, such as applying filters,
help improve image quality by removing
unwanted elements
02
Noise and Artifacts in
Images
Images come in various sizes, resolutions,
and formats, making it challenging to process
them uniformly. Preprocessing techniques
such as resizing and scaling help standardize
image dimensions for analysis.
03
Irrelevant Information
Images come in various sizes, resolutions,
and formats, making it challenging to process
them uniformly. Preprocessing techniques
such as resizing and scaling help standardize
image dimensions for analysis.
04
Complexity of
Extracting Meaningful
Features
Challenges

7. Images come in various sizes, resolutions,
and formats, making it challenging to process
them uniformly. Preprocessing techniques
such as resizing and scaling help standardize
image dimensions for analysis.
01
Variability in Image
Sizes, Resolutions, and
Formats
Images often contain noise, artifacts, or
distortions that can degrade their quality and
affect the accuracy of algorithms. Noise
reduction techniques, such as applying filters,
help improve image quality by removing
unwanted elements
02
Noise and Artifacts in
Images
Irrelevant information present in images, such
as text overlays or watermarks, can interfere
with analysis tasks. Data cleaning techniques
are employed to remove such artifacts and
focus on relevant image features. Eg Text
Overlays
03
Irrelevant Information
Images come in various sizes, resolutions,
and formats, making it challenging to process
them uniformly. Preprocessing techniques
such as resizing and scaling help standardize
image dimensions for analysis.
04
Complexity of
Extracting Meaningful
Features
Challenges

8. Images come in various sizes, resolutions,
and formats, making it challenging to process
them uniformly. Preprocessing techniques
such as resizing and scaling help standardize
image dimensions for analysis.
01
Variability in Image
Sizes, Resolutions, and
Formats
Images often contain noise, artifacts, or
distortions that can degrade their quality and
affect the accuracy of algorithms. Noise
reduction techniques, such as applying filters,
help improve image quality by removing
unwanted elements
02
Noise and Artifacts in
Images
Irrelevant information present in images, such
as text overlays or watermarks, can interfere
with analysis tasks. Data cleaning techniques
are employed to remove such artifacts and
focus on relevant image features. Eg Text
Overlays
03
Irrelevant Information
Extracting meaningful features from images is
challenging due to the complexity and high
dimensionality of image data. Feature extraction
techniques, such as edge detection and
segmentation, help identify important patterns and
structures in images
04
Complexity of
Extracting Meaningful
Features
Challenges

9. 01
Resizing and Scaling:
Standardizing image dimensions for
analysis and reducing computational
complexity.
02
Normalization:
Scaling pixel values to a standard range
for consistency in data representation.
03
Grayscale Conversion:
Simplifying processing and preserving
essential information for certain tasks.
04
Noise Reduction:
Applying filters to remove noise and
improve image quality.
05
Dimensionality Reduction:
Reducing dimensionality while preserving
information for computational efficiency.
06
Feature Extraction:
Extracting meaningful features using
techniques such as SIFT, SURF, and
CNNs.
07
Segmentation:
Dividing images into meaningful regions
or segments for object detection and
medical imaging.
08
Data Cleaning:
Removing artifacts and irrelevant
information to enhance data quality.
Common Image Preprocessing Techniques