Computer Vision Tutorial Last Updated : 06 Aug, 2025 Comments Improve Suggest changes Like Article Like Report Computer Vision (CV) is a branch of Artificial Intelligence (AI) that helps computers to interpret and understand visual information much like humans. This tutorial is designed for both beginners and experienced professionals and covers key concepts such as Image Processing, Feature Extraction, Object Detection, Image Segmentation and other core techniques in CV.Before moving into computer vision, it is recommended to have a foundational understanding of: Machine LearningDeep LearningOpenCVThese areas form the foundation of computer vision which helps us apply techniques and algorithms more effectively If we're unfamiliar with any of these topics, we recommend checking out their respective tutorials to build a solid foundation.Mathematical Prerequisites for Computer VisionBefore moving into Computer Vision, having a foundational understanding of certain mathematical concepts will help us which includes:1. Linear Algebra Linear Algebra VectorsMatrices and TensorsEigenvalues and EigenvectorsSingular Value Decomposition2. Probability and StatisticsProbability and StatisticsProbability Distributions Bayesian Inference and Bayes' TheoremMarkov Chains Kalman Filters 3. Signal Processing Signal Processing Image Filtering and ConvolutionDiscrete Fourier Transform (DFT)Fast Fourier Transform (FFT)Principal Component Analysis (PCA)Key Concepts in Computer Vision1. Image ProcessingIt refers to techniques for manipulating and analyzing digital images. Common image processing tasks include:1. Image Transformation Image Transformation Geometric TransformationsFourier Transform Intensity Transformation 2. Image Enhancement Image Enhancement Histogram Equalization Contrast EnhancementImage SharpeningColor Correction3. Noise Reduction TechniquesNoise Reduction TechniquesMedian Filtering Bilateral Filtering Wavelet Denoising 4. Morphological OperationsMorphological OperationsErosion and Dilation Opening Closing Morphological Gradient2. Feature Extraction It involves identifying distinctive elements within an image for analysis and its techniques include:1. Edge Detection TechniquesComputer Vision AlgorithmsEdge Detection TechniquesCanny Edge DetectorSobel OperatorLaplacian of Gaussian (LoG)2. Corner and Interest Point DetectionHarris Corner Detection3. Feature Descriptors Feature Descriptors SIFT (Scale-Invariant Feature Transform)SURF (Speeded-Up Robust Features)ORB (Oriented FAST and Rotated BRIEF)HOG (Histogram of Oriented Gradients) How Does Computer Vision Work?Computer Vision works much like the human eye and brain. First, our eyes capture the image and send the visual data to our brain. The brain then processes this information and transforms it into a meaningful interpretation, recognizing and categorizing the object based on its properties.In a similar way, Computer Vision uses a camera (acting like the human eye) to capture images. The visual data is then processed by algorithms to recognize and identify the objects based on patterns it has learned. However, before the system can recognize objects in new images, it needs to be trained on a large dataset of labeled images. This training enables the system to identify and associate various patterns with their corresponding labels.For example, imagine providing a computer with thousands of bird song recordings. The system learns by analyzing features like pitch, rhythm and duration. Once trained, it can then recognize whether a new sound resembles a bird song or not.For more details you can refer to: Steps in Computer VisionPopular Libraries for Computer VisionTo implement computer vision tasks effectively, various libraries are used:OpenCV: Mostly used open-source library for computer vision tasks like image processing, video capture and real-time applications.TensorFlow: A popular deep learning framework that includes tools for building and training computer vision models.PyTorch: Another deep learning library that provides great flexibility for computer vision tasks for research and development.scikit-image: A part of the scikit-learn ecosystem, this library provides algorithms for image processing and computer vision.For more details you can refer to: Computer Vision LibrariesDeep Learning for Computer Vision Deep learning has greatly enhanced computer vision by allowing machines to understand and analyze visual data and its key deep learning models include:1. Convolutional Neural Networks (CNNs) Convolutional Neural Networks are designed for learning spatial hierarchies of features from images and its key components include: Deep Learning for Computer Vision Deep learningConvolutional Neural NetworksConvolutional LayersPooling Layers Fully Connected Layers 2. Generative Adversarial Networks (GANs) It consists of two networks (generator and discriminator) that work against each other to create realistic images. There are various types of GANs each designed for specific tasks and improvements: Generative Adversarial Networks (GANs) Deep Convolutional GAN (DCGAN)Conditional GAN (cGAN)Cycle-Consistent GAN (CycleGAN)Super-Resolution GAN (SRGAN)StyleGAN3. Variational Autoencoders (VAEs)They are the probabilistic version of autoencoders which forces the model to learn a distribution over the latent space rather than a fixed point, some other autoencoders used in computer vision are: Autoencoders Variational Autoencoders (VAEs)Denoising Autoencoders (DAE)Convolutional Autoencoder (CAE)4. Vision Transformers (ViT)They are inspired by transformers models to treat images and sequence of patches and process them using self-attention mechanisms, some common vision transformers include: Vision Transformers (ViT) Swin Transformer CvT (Convolutional Vision Transformer)5. Vision Language Models They integrate visual and textual information to perform image processing and natural language understanding. Vision language modelsCLIP (Contrastive Language-Image Pre-training)ALIGN (A Large-scale ImaGe and Noisy-text)BLIP (Bootstrapping Language-Image Pre-training)Computer Vision Tasks 1. Image Classification It involves analyzing an image and assigning it a specific label or category based on its content such as identifying whether an image contains a cat, dog or car.Its techniques are as follows:Computer Vision Tasks Image Classification Image Classification using Support Vector Machine (SVM)Image Classification using RandomForest Image Classification using CNNImage Classification using TensorFlow Image Classification using PyTorch Lightning There are various types for Image Classification which are as follows:Dataset for Image Classification.Multiclass classificationMultilabel classification Zero-shot classification To learn about the datasets for image classification, we can go through the article on Dataset for Image Classification mentioned above.2. Object Detection It involves identifying and locating objects within an image by drawing bounding boxes around them. It includes below following Techniques: Top Computer Vision ModelsObject DetectionYOLO (You Only Look Once)SSD (Single Shot Multibox Detector)Region-Based Convolutional Neural Networks (R-CNNs)Fast R-CNN Faster R-CNN Mask R-CNNObject Detection using TensorFlowObject Detection using PyTorch Type of Object Detection Concepts are as follows: Bounding Box Regression Intersection over Union (IoU)Region Proposal Networks (RPN)Non-Maximum Suppression (NMS)3. Image Segmentation It involves partitioning an image into distinct regions or segments to identify objects or boundaries at a pixel level. Types of image segmentation are: Image SegmentationSemantic Segmentation Instance Segmentation Panoptic SegmentationWe can perform image segmentation using the following methods:Image Segmentation using K Means ClusteringImage Segmentation using UNet Image Segmentation using TensorFlowImage Segmentation with Mask R-CNNNeed for Computer VisionHigh Demand in the Job Market: Critical for careers in AI, machine learning and data science across industries like healthcare, automotive and robotics.Revolutionizing Industries: Powers advancements in self-driving cars, medical diagnostics, agriculture and manufacturing by automating visual tasks.Solving Real-World Problems: Enhances safety, improves medical imaging and optimizes industrial processes.Improving Accessibility: It helps people with disabilities through image recognition and sign language translation.Enhancing Consumer Experiences: It personalizes shopping and improves customer service in retail and entertainment.Applications of Computer VisionHealthcare: Used for disease detection and medical image analysis (X-rays, MRIs).Automotive: Helps self-driving cars to detect objects, lane keeping and traffic sign recognition.Retail: It helps with inventory management, theft prevention and customer behavior analysis.Agriculture: It is used for crop monitoring and disease detection.Security and Surveillance: It recognizes faces and find suspicious activities in security footage.For more details you can refer to: Applications of Computer Vision Comment More infoAdvertise with us K kumar_satyam Follow Improve Article Tags : Computer Vision AI-ML-DS Tutorials Similar Reads Computer Vision Tutorial Computer Vision (CV) is a branch of Artificial Intelligence (AI) that helps computers to interpret and understand visual information much like humans. This tutorial is designed for both beginners and experienced professionals and covers key concepts such as Image Processing, Feature Extraction, Obje 7 min read Introduction to Computer VisionComputer Vision - IntroductionComputer Vision (CV) in artificial intelligence (AI) help machines to interpret and understand visual information similar to how humans use their eyes and brains. It involves teaching computers to analyze and understand images and videos, helping them "see" the world. From identifying objects in ima 4 min read A Quick Overview to Computer VisionComputer vision means the extraction of information from images, text, videos, etc. Sometimes computer vision tries to mimic human vision. 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Using learned features from the initial task, t 8 min read Top 5 PreTrained Models in Natural Language Processing (NLP)Pretrained models are deep learning models that have been trained on huge amounts of data before fine-tuning for a specific task. The pre-trained models have revolutionized the landscape of natural language processing as they allow the developer to transfer the learned knowledge to specific tasks, e 7 min read ML | Introduction to Strided ConvolutionsLet us begin this article with a basic question - "Why padding and strided convolutions are required?" Assume we have an image with dimensions of n x n. If it is convoluted with an f x f filter, then the dimensions of the image obtained are (n-f+1) x (n-f+1). Example: Consider a 6 x 6 image as shown 2 min read Dilated ConvolutionPrerequisite: Convolutional Neural Networks Dilated Convolution: It is a technique that expands the kernel (input) by inserting holes between its consecutive elements. 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It involves sliding a two-dimensional filter over each channel of a feature map and summarizing the features within the region covered by the fi 5 min read CNN | Introduction to PaddingDuring convolution, the size of the output feature map is determined by the size of the input feature map, the size of the kernel, and the stride. if we simply apply the kernel on the input feature map, then the output feature map will be smaller than the input. This can result in the loss of inform 5 min read What is the difference between 'SAME' and 'VALID' padding in tf.nn.max_pool of tensorflow?Padding is a technique used in convolutional neural networks (CNNs) to preserve the spatial dimensions of the input data and prevent the loss of information at the edges of the image. It involves adding additional rows and columns of pixels around the edges of the input data. There are several diffe 14 min read Convolutional Neural Network (CNN) ArchitecturesConvolutional Neural Network(CNN) is a neural network architecture in Deep Learning, used to recognize the pattern from structured arrays. However, over many years, CNN architectures have evolved. Many variants of the fundamental CNN Architecture This been developed, leading to amazing advances in t 11 min read Deep Transfer Learning - IntroductionDeep transfer learning is a machine learning technique that utilizes the knowledge learned from one task to improve the performance of another related task. This technique is particularly useful when there is a shortage of labeled data for the target task, as it allows the model to leverage the know 8 min read Introduction to Residual NetworksRecent years have seen tremendous progress in the field of Image Processing and Recognition. Deep Neural Networks are becoming deeper and more complex. It has been proved that adding more layers to a Neural Network can make it more robust for image-related tasks. But it can also cause them to lose a 4 min read Residual Networks (ResNet) - Deep LearningAfter the first CNN-based architecture (AlexNet) that win the ImageNet 2012 competition, Every subsequent winning architecture uses more layers in a deep neural network to reduce the error rate. This works for less number of layers, but when we increase the number of layers, there is a common proble 9 min read ML | Inception Network V1Inception net achieved a milestone in CNN classifiers when previous models were just going deeper to improve the performance and accuracy but compromising the computational cost. The Inception network, on the other hand, is heavily engineered. It uses a lot of tricks to push performance, both in ter 4 min read Understanding GoogLeNet Model - CNN ArchitectureGoogLeNet (Inception V1) is a deep convolutional neural network architecture designed for efficient image classification. It introduces the Inception module, which performs multiple convolution operations (1x1, 3x3, 5x5) in parallel, along with max pooling and concatenates their outputs. The archite 3 min read Image Recognition with MobilenetImage Recognition plays an important role in many fields like medical disease analysis and many more. In this article, we will mainly focus on how to Recognize the given image, what is being displayed. What is MobilenetMobilenet is a model which does the same convolution as done by CNN to filter ima 4 min read VGG-16 | CNN modelA Convolutional Neural Network (CNN) architecture is a deep learning model designed for processing structured grid-like data such as images and is used for tasks like image classification, object detection and image segmentation.The VGG-16 model is a convolutional neural network (CNN) architecture t 6 min read Autoencoders in Machine LearningAutoencoders are a special type of neural networks that learn to compress data into a compact form and then reconstruct it to closely match the original input. They consist of an:Encoder that captures important features by reducing dimensionality.Decoder that rebuilds the data from this compressed r 8 min read How Autoencoders works ?Autoencoders is used for tasks like dimensionality reduction, anomaly detection and feature extraction. The goal of an autoencoder is to to compress data into a compact form and then reconstruct it to closely match the original input. The model trains by minimizing reconstruction error using loss fu 6 min read Difference Between Encoder and DecoderCombinational Logic is the concept in which two or more input states define one or more output states. The Encoder and Decoder are combinational logic circuits. In which we implement combinational logic with the help of boolean algebra. To encode something is to convert in piece of information into 9 min read Implementing an Autoencoder in PyTorchAutoencoders are neural networks designed for unsupervised tasks like dimensionality reduction, anomaly detection and feature extraction. They work by compressing data into a smaller form through an encoder and then reconstructing it back using a decoder. The goal is to minimize the difference betwe 4 min read Generative Adversarial Network (GAN)Generative Adversarial Networks (GAN) help machines to create new, realistic data by learning from existing examples. It is introduced by Ian Goodfellow and his team in 2014 and they have transformed how computers generate images, videos, music and more. Unlike traditional models that only recognize 12 min read Deep Convolutional GAN with KerasDeep Convolutional GAN (DCGAN) was proposed by a researcher from MIT and Facebook AI research. It is widely used in many convolution-based generation-based techniques. The focus of this paper was to make training GANs stable. Hence, they proposed some architectural changes in the computer vision pro 9 min read StyleGAN - Style Generative Adversarial NetworksStyleGAN is a generative model that produces highly realistic images by controlling image features at multiple levels from overall structure to fine details like texture and lighting. 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Positive Images: These images contain the objects that the classifier is trained to detect.Nega 3 min read R-CNN - Region-Based Convolutional Neural NetworksTraditional Convolutional Neural Networks (CNNs) with fully connected layers often struggle with object detection tasks, especially when dealing with multiple objects of varying sizes and positions within an image. A brute-force method like applying a sliding window across the image to detect object 8 min read YOLO v2 - Object DetectionIn terms of speed, YOLO is one of the best models in object recognition, able to recognize objects and process frames at the rate up to 150 FPS for small networks. However, In terms of accuracy mAP, YOLO was not the state of the art model but has fairly good Mean average Precision (mAP) of 63% when 7 min read Face recognition using Artificial IntelligenceThe current technology amazes people with amazing innovations that not only make life simple but also bearable. Face recognition has over time proven to be the least intrusive and fastest form of biometric verification. The software uses deep learning algorithms to compare a live captured image to t 15+ min read Deep Face RecognitionDeepFace is the facial recognition system used by Facebook for tagging images. It was proposed by researchers at Facebook AI Research (FAIR) at the 2014 IEEE Computer Vision and Pattern Recognition Conference (CVPR). In modern face recognition there are 4 steps: DetectAlignRepresentClassify This app 8 min read ML | Face Recognition Using Eigenfaces (PCA Algorithm)In 1991, Turk and Pentland suggested an approach to face recognition that uses dimensionality reduction and linear algebra concepts to recognize faces. This approach is computationally less expensive and easy to implement and thus used in various applications at that time such as handwritten recogni 4 min read Emojify using Face Recognition with Machine LearningIn this article, we will learn how to implement a modification app that will show an emoji of expression which resembles the expression on your face. This is a fun project based on computer vision in which we use an image classification model in reality to classify different expressions of a person. 7 min read Object Detection with Detection Transformer (DETR) by FacebookFacebook has just released its State of the art object detection Model on 27 May 2020. 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That is, segmentation should stop when the objects or the regions of interest in an application h 7 min read Region and Edge Based SegmentationSegmentationSegmentation is the separation of one or more regions or objects in an image based on a discontinuity or a similarity criterion. A region in an image can be defined by its border (edge) or its interior, and the two representations are equal. There are prominently three methods of perform 4 min read Image Segmentation with Watershed Algorithm - OpenCV PythonImage segmentation is a fundamental computer vision task that involves partitioning an image into meaningful and semantically homogeneous regions. The goal is to simplify the representation of an image or make it more meaningful for further analysis. These segments typically correspond to objects or 9 min read Mask R-CNN | MLThe article provides a comprehensive understanding of the evolution from basic Convolutional Neural Networks (CNN) to the sophisticated Mask R-CNN, exploring the iterative improvements in object detection, instance segmentation, and the challenges and advantages associated with each model. What is R 9 min read 3D ReconstructionPython OpenCV - Depth map from Stereo ImagesOpenCV is the huge open-source library for the computer vision, machine learning, and image processing and now it plays a major role in real-time operation which is very important in today’s systems.Note: For more information, refer to Introduction to OpenCV Depth Map : A depth map is a picture wher 2 min read Top 7 Modern-Day Applications of Augmented Reality (AR)Augmented Reality (or AR), in simpler terms, means intensifying the reality of real-time objects which we see through our eyes or gadgets like smartphones. You may think, How is it trending a lot? The answer is that it can offer an unforgettable experience, either of learning, measuring the three-di 10 min read Virtual Reality, Augmented Reality, and Mixed RealityVirtual Reality (VR): The word 'virtual' means something that is conceptual and does not exist physically and the word 'reality' means the state of being real. So the term 'virtual reality' is itself conflicting. It means something that is almost real. We will probably never be on the top of Mount E 3 min read Camera Calibration with Python - OpenCVPrerequisites: OpenCV A camera is an integral part of several domains like robotics, space exploration, etc camera is playing a major role. It helps to capture each and every moment and helpful for many analyses. In order to use the camera as a visual sensor, we should know the parameters of the cam 4 min read Python OpenCV - Pose EstimationWhat is Pose Estimation? Pose estimation is a computer vision technique that is used to predict the configuration of the body(POSE) from an image. The reason for its importance is the abundance of applications that can benefit from technology. Human pose estimation localizes body key points to accu 7 min read 40+ Top Computer Vision Projects [2025 Updated] Computer Vision is a branch of Artificial Intelligence (AI) that helps computers understand and interpret context of images and videos. It is used in domains like security cameras, photo editing, self-driving cars and robots to recognize objects and navigate real world using machine learning.This ar 4 min read Like