Image Formation and Preprocessing | Computer vision

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:

1. Optics

   The 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.

2. Sensor

   The 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.

3. Sampling

   The 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.

4. Quantization

   The 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.

5. Color representation

   In 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:

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:

1. Gray-scale conversion

   Converting color images to grayscale by averaging the intensity values of the color channels. This simplifies processing tasks that do not require color information.

2. Image resizing

   Adjusting the image size to a desired resolution. Resizing can be useful for standardizing image dimensions or reducing computational complexity.

3. Noise removal

   Applying filters, such as Gaussian filters or median filters, to remove noise from the image. Noise can distort the visual information and affect subsequent analysis.

4. Contrast enhancement

   Adjusting 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.

5. Image normalization

   Scaling 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.

6. Image cropping

   Selecting a specific region of interest within the image and discarding the rest. Cropping can be useful for isolating relevant areas or reducing computational load.

7. Image rotation and transformation

   Applying 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:

1. Contrast stretching: This technique increases the contrast between the dark and bright pixels in an image.
2. Histogram equalization: This technique equalizes the distribution of pixel intensities in an image.
3. Gaussian blurring: This technique blurs an image by averaging the pixel values in a neighborhood.
4. Median filtering: This technique removes noise from an image by replacing each pixel with the median of its neighbors.
5. Thresholding: This technique converts an image into a binary image by thresholding the pixel values.

Conclusion

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.