IMAGE ANALYTICS (BCDS061)
B.Tech Semester VI – Exam-Ready Notes
SECTION A
(Attempt all | 2 × 7 = 14 marks)
Write 2–3 crisp lines for each answer.
(a) Characteristics of Histogram in an Image
An image histogram shows the distribution of pixel intensity values.
Key characteristics include contrast, brightness, dynamic range, and peaks representing dominant gray levels.
(b) Different Kinds of Filters
Spatial filters (averaging, median, Gaussian) Frequency domain filters (low-pass, high-pass)
Linear and non-linear filters
(c) Hit-or-Miss Transform
Hit-or-Miss transform is used to detect specific shapes or patterns in binary images by matching a structuring element.
(d) Basic Morphological Algorithms & Applications Erosion – removes noise and small objects
Dilation – fills gaps and connects regions
Applications include shape analysis and object detection.
(e) Pseudo Color Image Processing
Pseudo color processing assigns artificial colors to grayscale images to enhance visual interpretation of features.
(f) Preprocessing Steps for Boundary Detection
Noise removal (smoothing) Image enhancement (contrast improvement)
(g) Key Components of CNN
CNN consists of convolution layers, pooling layers, activation functions, fully connected layers, and output layer.
SECTION B
(Attempt any THREE | 7 × 3 = 21 marks)
(a) Morphological Reconstruction
Morphological reconstruction extracts image components using a marker image and mask image.
Steps: marker selection → iterative dilation → constraint by mask.
Applications: noise removal, object extraction, shape restoration.
(b) Image Smoothing vs Image Sharpening
Smoothing: reduces noise and small variations using filters like averaging or Gaussian.
Sharpening: enhances edges and fine details using high-pass filters.
In color images, smoothing reduces color noise while sharpening highlights boundaries.
(c) Advantages of Deep Learning over Traditional Classification
Automatic feature extraction Higher accuracy for complex images
Robust to noise and variations Better performance on large datasets
(d) Purpose of Feature Extraction
Feature extraction reduces data dimensionality while retaining important information, making classification and recognition faster and more accurate.
(e) Smoothing Spatial Filters
Averaging filter: reduces noise by replacing pixel with neighborhood mean.
Gaussian filter: smooths image while preserving edges better.
Used in preprocessing and noise reduction.
SECTION C
(Attempt any ONE | 7 marks)
(a) Image Segmentation Techniques
Region Growing: groups pixels based on similarity starting from seed points.
Region Splitting & Merging: divides image into regions and merges similar ones.
Comparison: region growing is simpler, splitting-merging is more systematic but computationally expensive.
OR
(b) Scale-Invariant Feature Transform (SIFT)
Steps:
Scale-space extrema detection Keypoint localization
Orientation assignment Keypoint descriptor generation
SIFT is robust to scale, rotation, illumination, and noise.
