Image Formation and Preprocessing | Computer vision
Image formation is the process of converting a three-dimensional scene into a two-dimensional image. This is done by a camera, which captures the light reflected from objects in the scene and projects it onto a sensor. The sensor then converts this light into an electrical signal, which is then stored as a digital image.
The process of image formation can be described by the following equation:
- Image is the two-dimensional representation of the scene.
- Scene is the three-dimensional object or environment that is being captured by the camera.
- Camera is the device that is used to capture the image.
- f() is a function that describes the process of image formation.
The function f() depends on a number of factors, including the camera's sensor, lens, and aperture. It also depends on the lighting conditions in the scene.
The image formation process involves several stages:
OpticsThe scene is captured through a lens system that focuses light onto an image sensor. The lens system affects various properties of the image, such as depth of field, perspective, distortion, and focal length.
SensorThe light focused by the lens falls onto an image sensor, such as a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) sensor. The sensor converts the incident light into electrical signals representing the intensity of light at different points on the sensor surface.
SamplingThe continuous image captured by the sensor needs to be sampled to create a discrete representation. The sensor measures the intensity of light at discrete locations called pixels. The spatial resolution of an image is determined by the number of pixels used to sample the scene.
QuantizationThe measured intensities are typically quantized to a finite number of levels to represent the pixel values digitally. For example, in an 8-bit grayscale image, each pixel can take on one of 256 intensity levels ranging from 0 to 255.
Color representationIn addition to grayscale images, color images are commonly used in computer vision. Color sensors capture the intensity of light across different color channels, such as red, green, and blue (RGB). Color information can be represented using different color spaces, such as RGB, CMYK, HSV, or Lab.
Image Preprocessing is an essential step in computer vision that involves various techniques to enhance the quality of images or extract specific features required for subsequent analysis. Some common preprocessing steps include:
Gray-scale conversionConverting color images to grayscale by averaging the intensity values of the color channels. This simplifies processing tasks that do not require color information.
Image resizingAdjusting the image size to a desired resolution. Resizing can be useful for standardizing image dimensions or reducing computational complexity.
Noise removalApplying filters, such as Gaussian filters or median filters, to remove noise from the image. Noise can distort the visual information and affect subsequent analysis.
Contrast enhancementAdjusting the image's contrast to increase the visual distinction between different objects or regions. Techniques like histogram equalization or adaptive contrast stretching can be employed for this purpose.
Image normalizationScaling the pixel values to a standardized range, such as [0, 1] or [-1, 1]. Normalization can help in reducing the influence of varying illumination conditions and improving algorithm performance.
Image croppingSelecting a specific region of interest within the image and discarding the rest. Cropping can be useful for isolating relevant areas or reducing computational load.
Image rotation and transformationApplying geometric transformations like rotation, translation, scaling, or affine transformations to align images, correct perspective distortions, or achieve a desired view.
Here are some of the most common image preprocessing techniques:
- Contrast stretching: This technique increases the contrast between the dark and bright pixels in an image.
- Histogram equalization: This technique equalizes the distribution of pixel intensities in an image.
- Gaussian blurring: This technique blurs an image by averaging the pixel values in a neighborhood.
- Median filtering: This technique removes noise from an image by replacing each pixel with the median of its neighbors.
- Thresholding: This technique converts an image into a binary image by thresholding the pixel values.
These preprocessing techniques are applied to improve the quality and suitability of images for subsequent computer vision tasks, such as object detection, recognition, or segmentation. The choice and order of preprocessing steps depend on the specific requirements of the application and the characteristics of the images being processed.