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Topic: Categories of Digital Imaging

Requirements:

Title Page and Reference Page

Minimum 5 pages

APA 7th ed formatting

Minimum 4-5 references

Digital Image Process

By: Temeika Williams

Agenda

Introduction

Categories of Digital Image Process

Descriptions

Regulatory Issues

Companies

Summary

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Introduction

Digital processing is almost used in all image interpretation and analysis in the modern world, where most remote sensing data are stored digitally (Ekstrom, 2012). Digital imaging and its processes are used in a wide range of fields; such as, space, industrial automation, satellites, and video surveillance are the major fields. The data must be available in digital format and recorded for it to be suitable for storage on a computer disk for remote sensing imagery to be processed digitally.

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Categories of Digital Image Process

1.Image Enhancement

2. Image Restoration

3. Image Segmentation

4. Image Recognition

5. Image Compression

6. Image Generation

7. Image Transformation

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Category 1: Image Enhancement

This involves adjusting the visual quality of an image, such as adjusting brightness, contrast, sharpness, and smoothing.

Category 2: Image Restoration

This involves removing artifacts or noise from an image, such as blurring, speckle, and dust and scratches

Category 3: Image Segmentation

This involves dividing an image into multiple segments or regions, each of which corresponds to a different object or part of the image

Category 4: Image Recognition

This involves identifying objects, people, and patterns in an image, and classifying them into different categories

Category 5: Image Compression

This involves reducing the size of an image while preserving its visual quality as much as possible

Category 6: Image Generation

This involves creating new images based on existing images or synthesizing new images from scratch using algorithms

Category 7: Image Transformation

This involves changing the appearance or shape of an image, such as rotating, resizing, or warping an image

Regulatory Issues

Privacy: Digital images often contain personal information, and there are concerns about how the information is collected, stored, and used.

Intellectual Property: Digital images can be protected by copyright and trademark laws

Accuracy: Digital Images can be manipulated or altered, and that raises red flags in the accuracy when it comes to scientific research, journalism, and other applications

Security: Digital Images can be vulnerable to hacking, theft, and other cybercrimes and then concerns arise about the security of sensitive images, such as military imagery or medical images

Bias and Discrimination: Digital Images can be used to train machine learning algorithms, which arises concerns in the training data and the resulting models.

Companies

Many companies are involved in digital image processing, ranging from small start ups to large multinational corporations.

Google: technology company that specializes in search, advertising, and artificial intelligence. Several digital image processing products for example google photos

Adobe: multinational company that specializes in multimedia and creativity software including photo editing and graphic design software such as Photoshop

NVIDA: technology company that specializes in high performance graphics processing units (GPU) and computer vision althorithms. It is used in machine learning,and computer vision

Summary

It is the digital manipulation of images by considering of the many different factors. Pixels and image elements are a couple of names for these components. The dots and squares that make up the pixel would be the sample unit of the image. Each pixel is represented by the RGB parameters.

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References

Ekstrom, M. P. (2012). Digital image processing techniques. Academic Press.

Furht, B., Akar, E., & Andrews, W. A. (2018). Digital image processing: Practical approach. Springer.

Gonzalez, R. C., & Woods, R. E. (2018). Digital image processing.

Umbaugh, S. E. (2022). Digital image processing and visual perception. Digital Image Processing and Analysis, 87-125. 
https://doi.org/10.1201/9781003221142-3

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Thank you

Temeika Williams

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