Category: Computer Vision
Automatic Number Plate Recognition Project
Naveen
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In recent years, automatic license plate recognition or license plate recognition has become one of the useful approaches for vehicle surveillance. This article presents an automatic license plate recognition project using OpenCV and EasyOCR. Traffic control and vehicle owner identification have become major problems in all countries. It can be difficult to identify the owner of a speeding vehicle that violates the road rules. Therefore, due to the speed of the vehicle, traffic personnel may not be able to obtain the vehicle number of the moving vehicle, so such…
Read MoreCounting Objects in An Image
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Counting objects in an image is the job of computer vision. There are many Python computer vision libraries available for this task. However, this article describes a very simple approach to counting objects in images using Python. How to count objects in an image using Python? Counting objects in an image is a computer vision task. There are many image processing libraries available for this task. B. OpenCV, TensorFlow, PyTorch, Scikit-image, and cvlib. You probably haven’t heard much about Python’s cvlib library. This is a very simple, advanced, and easy-to-use…
Read MoreComputer Vision for Beginners – Part 5
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What is Edge detection? Edge detection is a computer vision technique that uses the edges of an image to find and extract objects. It is widely used in a variety of fields such as photography, video and computer vision. it is used in many ways in computer vision, including image segmentation, which divides an image…
Read MoreComputer Vision for Beginners – Part 4
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In this article, I will be discussing about various algorithms of image feature detection, description using OpenCV. Introduction What do humans typically do when they see the image? He will be able to recognize the faces which are there inside the images. So, in a simple form, computer vision is what allows computers to see…
Read MoreComputer Vision for Beginners – Part 3
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You may be already familiar with the word ‘contour.’ I’ve used this term several times in previous posts. A contour line is a curved line representing values. It is a very simple type of map that outlines the changes in an area, typically separating the different landforms. But then you may ask this. The terms ‘edges’…
Read MoreComputer Vision for Beginners – Part 2
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Today we’re going to talk about how to manipulate images. These are preprocessing steps. When it comes to detecting edges and contours, noise plays a major role in the accuracy of the detection process. -The model needs to focus on the general details of their images in order to produce higher accuracies. -Blurring, thresholding, and…
Read MoreComputer Vision for Beginners – Part 1
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Image Processing is used to perform some operations on images in order to animate them, develop videos, or generate graphics. To get started with data analysis, you can use image preprocessing feature engineering. It’s not difficult to extract useful information from images – this is accomplished via image processing methods. Reduction of noise, adjustment of…
Read MoreTop 40 Data Science Interview Questions and Answers
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1 – What is F1 score? F1 score is a measure of the accuracy of a model. It is defined as the harmonic mean of precision and recall. F1 score is one of the most popular metrics for assessing how well a machine learning algorithm performs on predicting a target variable. F1 score ranges from…
Read MoreWhat is histogram equalizer? How does it work?
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Histogram Equalizer is a computer vision technique that adjusts the luminosity levels of an image to make it more visually appealing. Histogram equalization is a process that lets us take an image and adjust the brightness levels so that each pixel in the image has a similar level of illumination. It does this by mapping…
Read MoreImage processing using Machine Learning
Naveen
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We begin this chapter by examining a number of of the foremost image process algorithmic rule, then march on to machine learning implementation in image processing. The chapter at a look is as follows: Feature Mapping using the SIFT algorithmic rule Suppose we’ve 2 pictures. One image is of a bench in an exceedingly park.…
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