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FET Blogs
04 May 2026
The CV full form stands for Computer Vision. Computer vision focuses on imitating human vision to derive meaningful information from visual data.
Computer vision and artificial intelligence (AI) are related concepts within the field of technology, but they have different applications and focus points.
Computer vision is a subfield of AI that helps machines process, analyze, and interpret visual inputs such as images and videos. It uses machine learning to help computers and other systems make decisions based on visual data.
Besides machine learning, computer vision also uses deep learning. In this approach, neural networks are trained with large volumes of data to identify patterns and features in a manner that resembles the functions of a human brain.
The computer vision system consists of several complex components. On a broader scale, it is divided into two distinct parts; a sensory device, which is a camera, and, on the other hand, a processing unit, which is a computer.
The visual information is captured by a sensory device that gathers information about the environment. Conversely, an interpreting system processes this data to derive meaningful information.
Computer vision tasks can be solved using different models and techniques, but a typical workflow generally follows these steps:
| Step | Overview |
| Data Gathering | Relevant visual data (images or videos) is collected and labeled so the model can learn patterns (e.g., identifying pneumonia in X-rays). |
| Preprocessing | The data is cleaned and improved by resizing, enhancing image quality, and applying techniques like data augmentation to increase diversity. |
| Model Selection | An appropriate model (such as CNNs for images, RNNs for videos, or Vision Transformers) is chosen based on the task and required performance. |
| Model Training | The model learns from the data by extracting features, making predictions, calculating errors, and adjusting its parameters using techniques like backpropagation. |
| Model Evaluation | The trained model is tested on new, unseen data to measure its performance using metrics like accuracy, precision, recall, and F1-score, ensuring it can generalize well to real-world scenarios. |
The different types of Computer Vision are discussed below:
| Task | Description | Use Cases |
| Image Classification | Assigns a single label to an entire image based on its content. |
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| Semantic Segmentation | Labels every pixel in an image, including the background, based on category. |
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| Instance Segmentation | Detects object boundaries and labels each object instance separately. |
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| Edge Detection | Detects object boundaries by identifying changes in brightness or intensity. |
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| Feature Matching | Matches features across images despite variations in angle, lighting, or size. |
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There are many advancements made in the areas of computer vision and artificial intelligence, which in turn have led to diverse use cases. Some of the real-life applications that use computer vision and artificial intelligence are as follows:
Cameras, sensors, and lidar help robots to perceive the environment. Computer vision helps them to carry out activities like supporting surgeries, warehouse navigation, running assembly lines, and even picking ripe crops accurately.
In the agricultural sector, cameras, drones, and satellites take high-resolution pictures of crops and farmlands. These images are analyzed by computer vision systems to determine plant health, pests, and weeds. It also helps in the precise application of herbicides to increase the crop yield.
Self-driving cars in the automotive industry create a 3D map of the surrounding environment with cameras, lidar, radar, and sensors. Object detection, image segmentation, and scene understanding are computer vision methods that help these vehicles navigate safely by detecting road lanes, obstacles, traffic lights, and signs.
Computer vision is crucial in medical imaging because it interprets scans, such as X-rays, CT scans, MRIs, and ultrasounds. It assists in identifying signs of the disease, and properly describes the organ system, tissues, and tumors to enhance the diagnosis process and make more informed treatment choices.
Computer vision systems are used in retail to provide automated checkout systems, which can track and identify items chosen by customers and may require some level of user interaction or verification. In e-commerce, it is used to facilitate virtual try-on services with augmented reality. This feature enables customers to see items such as clothes, eyewear, or make-up before ordering them.
Computer vision solutions are based on deep learning, neural networks, and generative AI to process the information present in pictures, videos, and sensors. These systems are capable of recognizing objects and patterns, and help produce meaningful insights to make decisions.
They contribute to increased efficiency, safety, sustainability, and overall performance by providing near real-time analysis.
For example:
Computer Vision is quickly changing the way machines perceive the world. Its application is growing in industries such as agriculture, healthcare, robotics, retail, and others. This expansion contributes to CV being one of the most in-demand technologies in the present day.
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A1: Examples include facial recognition, self-driving cars, medical image analysis, object detection in photos, and augmented reality applications.
A2: Yes. Computer vision is a subfield of artificial intelligence that enables machines to interpret and understand visual data.
A3: The 3 R’s are Recognition (identifying objects), Reconstruction (understanding structure), and Reorganization (interpreting scenes meaningfully).
A4: Computer vision does not have a single inventor. The field gained momentum from earlier research in the 1940s and 50s, such as Warren McCulloch and Walter Pitts' work on neural modelling, and early image digitization by Russell Kirsch in 1959.
Later, pioneers like Larry Roberts helped lay its foundation in the 1960s and David Marr established a computational framework for vision in the 1970s and early 1980s.
A5: The four primary tasks of computer vision are labeling an image, locating objects, semantic segmentation, and instance segmentation (identifying individual object boundaries).
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