Author: Naveen
Naveen PandeyWhat are the top important topic of computer vision?
Computer vision is the science of understanding the world by interpreting images. It is a branch of both computer science and electrical engineering, and can be divided into image processing, pattern recognition, machine learning, and robotics. Computer vision is a difficult field to study due to its complexity. It is concerned with the understanding and…
Read MoreImage Processing using PIL (Python Imaging Library)
Python Image Library is used for manipulating images and working with the different formats of images. 1. Reading We can read the image using open function of image module of Python Image Library. This function will take path of your image as parameter. 2. Display properties of the image we can see properties of the…
Read MoreWhat are the major algorithms in Computer Vision?
The major algorithm in Computer Vision is the Viola-Jones object detection framework. The framework is based on two key aspects: Another important algorithm in Computer Vision is the Deep Learning Network (DLN). DLN can be used to classify deep neural networks and produce better information about the images they process. Deep Learning networks consist of…
Read MoreReading an image using multiple Libraries?
PIL – Python Imaging Library Pillow is an image processing and manipulation library To install pillow – pip install pillow To import the package you need to import PIL Matplotlib Matplotlib is a plotting library for the Python programming language To install Matplotlib – pip install matplotlib To import the package you need to use…
Read MoreWhen is Template Matching used in real life?
Template matching is used in real-time video surveillance to retrieve templates. This section will discuss the use cases of template matching in computer vision. Template matching is used to compare the details between two images and retrieve templates. These templates are then matched to identify structures within an image, such as facial features or license…
Read MoreWhen are histograms used in real life?
Histograms are used in computer vision mainly to filter or detect edges. They are also used for histogram matching, which is based on the idea that two images will have similar histograms if they represent the same object. They can be used in different applications such as medical, astronomy, and computer vision. They are often…
Read MoreHow to Solve Underfitting in Machine Learning Models
Underfitting is a common problem in machine learning models. This happens when the model is too simple to capture the complexity of the real data, resulting in poor performance on the training and testing datasets. In this article, we will explore what underfitting is and how to solve it using different techniques. What is Underfitting?…
Read MoreOverfitting in Machine Learning: What it is and When it Occurs
In machine learning, overfitting refers to the phenomenon where a model performs well with training data, but does not generalize well to new, unseen data. Overfitting occurs when the model is too complex for the amount of training data. To understand overfitting, let’s look at an analogy. Imagine you are in a foreign country and…
Read MoreUnderstanding Different Types of Machine Learning: Batch, Online, Instance-Based, and Model-Based Learning
Machine learning is an integral part of artificial intelligence (AI). It allows computer systems to learn from data and improve their performance. There are different types of machine learning, such as batch learning, online learning, example-based learning, and model-based learning. In this article, we will explore each of these types in detail and understand their…
Read MoreImportant Supervised and Unsupervised Algorithms for Machine Learning
Machine learning is a branch of computer science and artificial intelligence that allows machines to learn automatically without special programming. It involves using algorithms and statistical models to analyze and interpret data and make predictions based on that analysis. Machine learning can be broadly divided into two types of algorithms: supervised and unsupervised. In this…
Read MoreWhat are the Challenges of Natural Language Processing
Natural Language Processing is that the field of design methods and algorithms that takes as input or produce as output unstructured. Human language is highly ambiguous (consider the sentence I ate pizza with friends, and compare it to I ate pizza with olives), and also highly variable (the core message of I ate pizza with…
Read MoreStemming vs Lemmatization Difference: Explained in Detail
Introduction When dealing with large amount of text data, it becomes essential to preprocess and analyze the text effectively. Stemming and lemmatization are text processing techniques that help reduce words to their base forms, helps you in better analysis and understanding. Stemming Stemming is a technique that aims to reduce words to their root form,…
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