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<\/p>\nComputer vision is the field of study which deals with automating tasks that rely on visual inputs. Computer vision aims to give computers the ability to understand images and video as humans do. Today, computer vision is a popular research area that has found applications in various fields such as healthcare, augmented reality, autonomous vehicles, robotics, and more. In this tutorial, we will explore the basics of computer vision.<\/p>\n
Images are often represented as a two-dimensional array of pixel values. Each pixel in the image represents the brightness or color value at that particular location. In grayscale images, each pixel value represents a single brightness value ranging from 0 to 255, where 0 is black and 255 is white. In color images, each pixel is represented as a combination of three color channels: red, green, and blue (RGB). Each color channel also ranges from 0 to 255, with 0 being no contribution and 255 is full contribution of that color to the pixel.<\/p>\n
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In addition to this, images can also be represented in other forms such as histograms, edge maps, and feature vectors. Histograms represent the distribution of pixel values in an image, while edge maps are a representation of the edges or boundaries present in an image. Feature vectors, on the other hand, are a compact representation of an image in terms of features such as color, texture, or shape.<\/p>\n
Image processing involves manipulating images to improve their quality, enhance visual features, or extract information from them. There are various processes involved in image processing such as smoothing, sharpening, edge detection, and thresholding. Let’s take a look at some of these processes in more detail.<\/p>\n
Smoothing, also known as blurring, is a process where the sharp edges and fine details in an image are smoothed out to reduce noise. This is achieved by convolving the image with a kernel or a filter. A kernel is a small matrix of numbers that is used to modify the pixel values in an image. Commonly used filters for smoothing include Gaussian, median, and mean filters.<\/p>\n
![\"smoothing\"](\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2020\/02\/smoothing.png\")
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Sharpening is a process where the edges in an image are enhanced to make them appear more prominent. This can be achieved by subtracting a smoothed image from the original image. The resulting image will have enhanced edges and fine details. A common filter used for sharpening is the Laplacian filter.<\/p>\n
![\"sharpening\"](\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2020\/02\/sharpening.png\")
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Edge detection is a process that identifies the boundaries or edges in an image where there is a significant change in pixel values. Edges are important features in an image and are useful for object recognition and tracking. Commonly used edge detection algorithms include Sobel, Canny, and Prewitt filters.<\/p>\n
![\"edge](\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2020\/02\/edge-detection.png\")
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Thresholding is a process where an image is converted to binary format by comparing each pixel value to a fixed threshold value. If the pixel value is greater than the threshold, it is assigned a value of 1, else it is assigned a value of 0.<\/p>\n
![\"thresholding\"](\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2020\/02\/thresholding.png\")
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Object detection is a process where the position and size of objects in an image are identified and labeled. This is achieved by using a machine learning model trained on a dataset of labeled images. The model learns to recognize the features that are common to the objects in the dataset and uses this knowledge to identify objects in new images.<\/p>\n
There are various object detection models available, including YOLO (You Only Look Once), RCNN (Region-based Convolutional Neural Network), and SSD (Single Shot Detector). These models are typically trained on large datasets such as COCO (Common Objects in Context) and ImageNet, which contain millions of labeled images.<\/p>\n
![\"object](\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2020\/02\/object-detection.png\")
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Computer vision has a wide range of applications, some of which are listed below.<\/p>\n
Computer vision is being used in healthcare for various applications such as disease diagnosis, patient monitoring, and drug discovery. For example, computer vision can be used to analyze medical images such as X-rays, CT scans, and MRI scans to detect abnormalities and diagnose diseases. It can also be used to monitor patients remotely by analyzing video feeds from cameras installed in their homes.<\/p>\n
Augmented reality involves overlaying digital information on the real world. Computer vision is used to track the position and orientation of the device that is displaying the augmented reality content. This allows the digital content to be aligned with the real world, creating a more immersive experience.<\/p>\n
Computer vision is widely used in robotics for tasks such as object recognition, localization, and tracking. Robots can use computer vision to navigate their environment and interact with objects in a human-like manner.<\/p>\n
Autonomous vehicles rely heavily on computer vision for tasks such as lane detection, object detection, and pedestrian detection. Computer vision algorithms are used to process data from cameras and sensors installed on the vehicle to help it navigate the road and avoid obstacles.<\/p>\n
Computer vision is an exciting and rapidly growing field with numerous applications in various industries. In this tutorial, we explored the basics of computer vision, including image representation, image processing, object detection, and some of its many applications. We hope that this tutorial has sparked your interest in computer vision and encouraged you to further explore this fascinating field.<\/p>\n","protected":false},"excerpt":{"rendered":"