Thresholding image segmentation opencv python Binarization separate objects from the background

Binarization or thresholding is one problem that must be solved in pattern recognition and it has a very important influence on the sequent steps in imaging applications. Thresholding is used to separate objects from the background, and diminish the amount of data alter the computational speed. Recently, interest in multilevel thresholding has been altered. However, when the levels are altered, the computation time alters so single threshold methods are accelerated than multilevel methods. Moreover, for every new application, new methods are is acquired.
Thresholding
Computer Vision: OpenCV
Deep Learning: TensorFlow, PyTorch, Caffe, Torch
Optimization: OpenVINO, OpenCL, CUDA
Language: C++, Python
Hardware: Raspberry pi, Google Coral, Intel® Neural Compute Stick
Cloud: AWS, Docker, Seldon Core, MLflow, Kubeflow
What is Thresholding in image processing?
Binarization or thresholding is one problem that have to solve in pattern recognition methods and applications. Moreover, it has a very important influence on the sequent steps in computer vision applications such as, Optical Character Recognition (OCR), image segmentation, and tracking objects.
Binarization or thresholding is one problem that must be solved in pattern recognition and it has a very important influence on the sequent steps in imaging applications. Thresholding is used to separate objects from the background, and diminish the amount of data alter the computational speed. Recently, interest in multilevel thresholding has been altered. However, when the levels are altered, the computation time alters so single threshold methods are accelerated than multilevel methods. Moreover, for every new application, new methods are is acquired.
Thresholding is one of the critical steps in pattern recognition and has a significant effect on the upcoming steps of image application, the important objectives of thresholding are as follows, separating objects from background, decreasing the capacity of data consequently increases speed. Handwritten recognition is one of the important issues, which have various applications in mobile devices.
OpenCV library in Python
Install using pip
pip install opencv-python
pip install opencv-contrib-python
How to perform Threshold by using OpenCV library in Python

for i in range(rows):
for j in range(cols):
else:
cv2.THRESH_BINARY
cv2.THRESH_BINARY_INV
cv2.THRESH_TRUNC
cv2.THRESH_TOZERO
cv2.THRESH_TOZERO_INV

Example of different thresholding methods in OpenCV

PSNR
Thresholding

• Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
• GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
• Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
• Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
• Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
• License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
• Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)

[malekbenabdallah]

hi sir
I watch your video and he is helpful, but when I repeated with my image he didn't work with me .
actually I am working with landsat 8 and landsat tm to extract only the built up area using Optimal thresholding Otsu’s algorithm for separating built-up from non-built-up regions but the didn't work also.
did you help me sir