Ajay.paul@stud.hshl.de am 5. März 2026 um 13:23 Uhr

2026-03-05T13:23:48Z

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	# '''Scale Sensitivity:''' Features like Area and MajorAxisLength depend on the image resolution. If the camera zooms in, the "Area" changes.		# '''Scale Sensitivity:''' Features like Area and MajorAxisLength depend on the image resolution. If the camera zooms in, the "Area" changes.
	#* ''Fix:'' Use scale-invariant dimensionless features like '''Eccentricity''' or '''Circularity''' (<math>4\pi A / P^2</math>) if the camera distance varies.		#* ''Fix:'' Use scale-invariant dimensionless features like '''Eccentricity''' or '''Circularity''' (<math>4\pi A / P^2</math>) if the camera distance varies.

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Ajay.paul@stud.hshl.de: /* Detailed Code Logic */

2026-02-05T08:46:15Z

Detailed Code Logic

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	* '''Coins:''' By inspecting the Area values, we see nickels have an area <math>\approx 2500</math> pixels and dimes have an area <math>\approx 1800</math> pixels. A threshold of '''2000''' cleanly separates the classes.		* '''Coins:''' By inspecting the Area values, we see nickels have an area <math>\approx 2500</math> pixels and dimes have an area <math>\approx 1800</math> pixels. A threshold of '''2000''' cleanly separates the classes.
	* '''Rice:''' Orientation ranges from -90 to 90. The decision boundary is naturally '''45 degrees'''.		* '''Rice:''' Orientation ranges from -90 to 90. The decision boundary is naturally '''45 degrees'''.
	[[~~Datei~~:Classification Size.png\|mini]]
	[[~~Datei~~:Classification Orientation.png\|mini]]		{\| class="wikitable"
			\|-
			\| [[File:Classification Size.png\|mini]]
			\| [[File:Classification Orientation.png\|mini]]
			\|}

	== Common Issues ==		== Common Issues ==

Ajay.paul@stud.hshl.de: Die Seite wurde neu angelegt: „= Classification: Geometric Feature Extraction = '''Image Classification''' in the context of classic image processing involves categorizing objects into classes based on measurable geometric properties (descriptors). Unlike Deep Learning, which learns features automatically, this method relies on '''Feature Engineering'''—mathematically defining properties like Area, Perimeter, or Orientation and using logical thresholds to sort objects. This article…“

2026-02-04T20:26:09Z

Die Seite wurde neu angelegt: „= Classification: Geometric Feature Extraction = '''Image Classification''' in the context of classic image processing involves categorizing objects into classes based on measurable geometric properties (descriptors). Unlike Deep Learning, which learns features automatically, this method relies on '''Feature Engineering'''—mathematically defining properties like Area, Perimeter, or Orientation and using logical thresholds to sort objects. This article…“

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= Classification: Geometric Feature Extraction =

'''Image Classification''' in the context of classic image processing involves categorizing objects into classes based on measurable geometric properties (descriptors). Unlike Deep Learning, which learns features automatically, this method relies on '''Feature Engineering'''—mathematically defining properties like Area, Perimeter, or Orientation and using logical thresholds to sort objects.

This article demonstrates how to classify objects using the MATLAB function <code>regionprops</code>.

__TOC__

== Theoretical Background ==

=== The Workflow ===
# '''Segmentation:''' The image is converted to a binary mask where objects are white (1) and the background is black (0).
# '''Connected Component Analysis (CCA):''' The algorithm scans the binary mask to identify distinct, disconnected "blobs" or objects.
# '''Feature Extraction:''' For every identified object, specific mathematical properties are calculated.
# '''Decision Rule:''' A logical classifier (e.g., an If-Else statement or a Decision Tree) assigns a label to the object based on its feature values.

=== Geometric Descriptors ===
The primary tool for this in MATLAB is <code>regionprops</code>. Common descriptors include:
* '''Area:''' The scalar count of pixels in the region. (Useful for classifying by size).
* '''Orientation:''' The angle (in degrees ranging from -90 to 90) between the x-axis and the major axis of the ellipse that fits the region. (Useful for alignment detection).
* '''Eccentricity:''' A value between 0 and 1 indicating how elongated the shape is (0 is a circle, 1 is a line).
* '''Solidity:''' The proportion of the pixels in the convex hull that are also in the region.

== MATLAB Implementation ==

The following script performs two classification tasks:
# Classifying '''Coins''' by '''Size''' (Large vs. Small).
# Classifying '''Rice Grains''' by '''Orientation''' (Horizontal vs. Vertical).

=== Code Example ===

<syntaxhighlight lang="matlab">
%% Task 6: Geometric Classification
clc; clear; close all;

%% --- CASE 1: Classification by Size (Area) ---

% 1. Load and Preprocess
I_coins = imread('coins.png');
% Otsu's thresholding for binarization
level = graythresh(I_coins);
BW_coins = imbinarize(I_coins, level);
% Fill holes so the Area calculation represents the solid coin
BW_coins = imfill(BW_coins, 'holes');

% 2. Feature Extraction
% Measure Area, Centroid (for labeling), and BoundingBox (for drawing)
stats_coins = regionprops(BW_coins, 'Area', 'Centroid', 'BoundingBox');

% 3. Classification Logic and Visualization
figure('Name', 'Task 6: Classification by Size', ...
'NumberTitle', 'off', 'Position', [100, 500, 600, 500]);
imshow(I_coins);
title('Classifying Coins: Red=Large, Green=Small');
hold on;

% Threshold determined by observing the data
area_threshold = 2000;

for k = 1:length(stats_coins)
% Get features for the k-th object
current_area = stats_coins(k).Area;
bbox = stats_coins(k).BoundingBox;
centroid = stats_coins(k).Centroid;

% --- THE CLASSIFIER ---
if current_area > area_threshold
% Class 1: Large
color = 'r';
label = 'L';
else
% Class 2: Small
color = 'g';
label = 'S';
end

% Draw bounding box
rectangle('Position', bbox, 'EdgeColor', color, 'LineWidth', 2);
% Place label
text(centroid(1), centroid(2), label, 'Color', 'k', ...
'FontSize', 12, 'FontWeight', 'bold', 'BackgroundColor', 'w');
end

%% --- CASE 2: Classification by Orientation ---

% 1. Load and Preprocess (Rice requires background subtraction)
I_rice = imread('rice.png');
background = imopen(I_rice, strel('disk', 15));
I_rice_adj = I_rice - background;
BW_rice = imbinarize(I_rice_adj);

% 2. Feature Extraction
% Measure Orientation and MajorAxisLength (to draw lines)
stats_rice = regionprops(BW_rice, 'Orientation', 'Centroid', 'MajorAxisLength');

% 3. Visualization
figure('Name', 'Task 6: Classification by Orientation', ...
'NumberTitle', 'off', 'Position', [750, 500, 600, 500]);
imshow(I_rice);
title('Rice Orientation: Cyan=Horizontal, Magenta=Vertical');
hold on;

for k = 1:length(stats_rice)
angle = stats_rice(k).Orientation;
centroid = stats_rice(k).Centroid;

% --- THE CLASSIFIER ---
% MATLAB orientation is between -90 and 90.
% < 45 degrees is horizontal-ish. > 45 degrees is vertical-ish.
if abs(angle) < 45
color = 'c'; % Cyan for Horizontal
else
color = 'm'; % Magenta for Vertical
end

% Draw orientation lines (Geometric calculation)
len = stats_rice(k).MajorAxisLength;
theta = deg2rad(-angle);
x1 = centroid(1) - (len/2) * cos(theta);
y1 = centroid(2) - (len/2) * sin(theta);
x2 = centroid(1) + (len/2) * cos(theta);
y2 = centroid(2) + (len/2) * sin(theta);

plot([x1 x2], [y1 y2], 'Color', color, 'LineWidth', 2);
end
hold off;
</syntaxhighlight>

== Detailed Code Logic ==

=== 1. Preprocessing ===
Before features can be extracted, the image must be binary.
* For the coins, simple '''Otsu thresholding''' (`graythresh`) works well because the background is uniform.
* For the rice, '''Top-hat filtering''' (Background subtraction using `imopen`) is used first because the lighting is uneven.

=== 2. regionprops ===
This is the workhorse function.
<syntaxhighlight lang="matlab">
stats = regionprops(BW, 'Property1', 'Property2', ...);
</syntaxhighlight>
It returns a structure array where `stats(1)` refers to the first object found, `stats(2)` the second, and so on.

=== 3. The Decision Boundary ===
In classical classification, the engineer must manually determine the cutoff point.
* '''Coins:''' By inspecting the Area values, we see nickels have an area <math>\approx 2500</math> pixels and dimes have an area <math>\approx 1800</math> pixels. A threshold of '''2000''' cleanly separates the classes.
* '''Rice:''' Orientation ranges from -90 to 90. The decision boundary is naturally '''45 degrees'''.
[[Datei:Classification Size.png|mini]]
[[Datei:Classification Orientation.png|mini]]
== Common Issues ==

# '''Under-Segmentation:''' If two objects touch, `regionprops` sees them as one single object.
#* ''Impact:'' The Area will be double the expected size, causing a misclassification (e.g., two small coins classified as one large coin).
#* ''Fix:'' Use `imopen` with a disk structuring element or Watershed segmentation to break connections before feature extraction.
# '''Scale Sensitivity:''' Features like Area and MajorAxisLength depend on the image resolution. If the camera zooms in, the "Area" changes.
#* ''Fix:'' Use scale-invariant dimensionless features like '''Eccentricity''' or '''Circularity''' (<math>4\pi A / P^2</math>) if the camera distance varies.

Image Processing: Classification - Versionsgeschichte

Ajay.paul@stud.hshl.de am 5. März 2026 um 13:23 Uhr

Ajay.paul@stud.hshl.de: /* Detailed Code Logic */