In this article, we will be tackling an enjoyable image processing technique called template matching. In particular, template matching can be very useful when used for object detection and recognition. This technique has the same intuition as the ground-truth white balancing technique. It uses the source image to get a reference image; this reference image will be used as a comparison point for other parts of the source image. It would be best to explain this process with an example image of an aircraft.

from import imread, imshow
carrier = imread('aircraft_carrier.jpg')
Aircraft Carrier

For simplicity purposes, we will convert the…

In this article, we will demonstrate several topics in this image processing series. Here, we will be identifying different kind of leaves. Below is a sample of the raw leaf images.

In this 7th article of the image processing series, we will discuss homographgy matrix and texture metrics. We will learn its importance, functionalities, and applications.

In this article, we will be using the scikit-image library to transform our images.

Homography Matrix

The homography matrix is a powerful tool in image processing. It enables one to distort an image with control. A homography matrix is a 3x3 matrix with 8 degrees of freedom.

For example, we need our computer to map out the pieces on this chessboard. Since it is taken from an angle, it will pose a challenge to the computer to…

In this article, we will explore Chromaticity Segmentation.

From part 1 of image segmentation, we observed the threshold and color segmentation have their limitations. Especially with colors that are obscure, such as the fur of the teddy bear.

Let’s explore some image segmentation strategies


In the first part of this article, we will isolate the subject from the background using thresholding. There are two ways to perform thresholding, manually with trial-and-error and the automatic Otsu method. For these examples, we will be using a large teddy bear.

Trial and Error

Trial and error method is straightforward. We manually choose a threshold until we are satisfied with the separation of subject and background. Below, we show an example of a bear in a park with a threshold of 0.6.

from skimage.color import rgb2gray
bear_gray = rgb2gray(bear)
th = 0.6
bear_bw = bear_gray>th

Once upon a time, my brother had to scan one of his favorite baby pictures of me for his school project. After his project was a triumph in school, I immediately requested for it to return safely to the family album. However, he completely forgot until the day I found it sitting on his table, damaged with tea marks. Sadly, my baby photo lost so many details with the damage done. How do I wish I could retract the damage? Fortunately, image processing techniques could do the job.

In this article, we will find out how we could filter, detect…

Blob Detection

Have you ever wondered how do computers detect objects and count? How something so intuitive for us humans is complicated for computers.

Thankfully we have scikit-image feature tools. In this article, we will discuss several methods in detecting blobs. The package has three kinds of blob detection methods:

1. Laplacian of Gaussian (LoG)
2. Difference of Gaussian (DoG)
3. Determinant of Hessian (DoH).

Importing the libraries

from skimage.feature import blob_dog, blob_log, blob_doh
from import imread, imshow
from skimage.color import rgb2gray
from math import sqrt
import matplotlib.pyplot as plt
import numpy as np
from skimage.morphology import erosion, dilation, opening, closing
from skimage.measure import label, regionprops
from skimage.color import…

Have you ever took a photo and realized that the lighting is too dark? Or that the image had too many noises? There was a time when most of my photos had a blue tint because my phone’s UV filter was damaged. It was annoying and awful to see when looking through my gallery. Most of these issues can be solved by image enhancements. We can enhance these with three different techniques.

First, let us tackle the Fourier Transform technique with a sample image of a moon crater. As you can observe, the image has hints of white horizontal lines…

Back when photography and patience mattered to widely respected landscape photographer Ansel Eaton Adams, it was almost impossible to fathom the possibility of digital images. His masterpieces did not require any touchups or alteration. However, it took him decades to perfect his pure photography technique with great technical details.

In this generation of digital images, photoshopping or image processing saves photographers time and effort to perfect the image. We use image processing software such as Adobe Photoshop and GNU Image Manipulation Program to fix imperfections, save damaged photos, create out of this world art, etc. …

Misha Ysabel

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