Computer vision, a pivotal subfield of artificial intelligence (AI), has revolutionized the way machines interpret and interact with the visual world. This presentation delves into the foundational concepts, applications, and transformative potential of computer vision, offering a comprehensive overview of its current state and future directions. By bridging the gap between human visual perception and machine understanding, computer vision enables systems to analyze images and videos, extract meaningful information, and perform tasks ranging from object recognition to complex scene interpretation. The presentation underscores the interdisciplinary nature of computer vision, highlighting its reliance on machine learning, deep learning, and vast datasets to achieve human-like accuracy and beyond. The introduction sets the stage by defining computer vision as the technology that empowers machines to "see" and interpret visual data, akin to human cognition. It emphasizes the synergy between computer vision and AI, particularly through machine learning algorithms that train systems to recognize patterns, edges, shapes, and colors in visual inputs. Examples such as facial recognition in smartphones, self-driving cars, and medical image analysis illustrate the pervasive impact of computer vision in everyday life. The title, "Understanding Computer Vision: Giving Eyes to Machines," encapsulates the essence of the field, portraying it as a transformative force that equips machines with the ability to perceive and understand their surroundings. A significant portion of the presentation is dedicated to exploring the diverse applications of computer vision across industries. In healthcare, computer vision plays a critical role in diagnosing diseases through advanced imaging techniques like X-rays and MRI scans, enabling early detection of conditions such as tumors. The retail sector leverages computer vision for cashier-free checkout systems and in-store analytics, tracking shopper behavior to optimize business operations. Surveillance systems utilize real-time video analysis to enhance security by identifying suspicious activities, while space missions employ computer vision to process satellite imagery and explore extraterrestrial terrain. Agriculture benefits from precision farming techniques, where computer vision monitors crop health and guides automated harvesting robots. Autonomous vehicles, perhaps one of the most prominent applications, rely on computer vision to navigate safely by detecting and interpreting road conditions, traffic signals, and obstacles. These examples collectively demonstrate the versatility and far-reaching implications of computer vision in solving real-world problems. The presentation also addresses the research background and objectives, shedding light on the evolution of computer vision from simple image processing to sophisticated tasks like facial recognition and scene understanding.

1. NAME: ALI CHUDARY AKBAR
STUDENT ID: 2415066005
Presentation on the Topic of Computer Vision

4. Introduction
◦ As its core, computer vision is the ability of computers to understand and analyse visual
content in the same way humans do. This includes tasks such as recognizing objects
and faces, reading text and understanding the context of an image or video.
◦ Computer vision is closely related to artificial intelligence (AI) and often uses AI
techniques such as machine learning to analyse and understand visual data. Machine
learning algorithms are used to “train” a computer to recognize patterns and features
in visual data, such as edges, shapes and colours.

5. TITLE: Understanding computer
vision: Giving eyes to Machine
◦ What is computer vision?
Computer Vision is a field of Artificial Intelligence that enables machines to "see" and
understand images or videos just like do humans .
Example:
 Face recognition in phones
 Self-driving cars
 Medical image analysis

7. Application of CV:

8. Computer vision in Facial
Recognition:

9. Application of CV:

10. CV in augmented reality and
Mixed Reality:

11. CV in augmented reality and
Mixed Reality:
One week I go Hubei Normal university
Where I experience Live Demo like
Augmented reality.
They gave us AR glasses or headsets and
Where I experience digital information
onto the real world.

12. Computer vision in Healthcare

13. Application of CV:

14. Importance of Computer Vision:
•It helps computers interact with the world visually.
• It's used in real-life systems like traffic monitoring, autonomous driving, and
security.
• In healthcare, it helps detect diseases through X-rays and MRI scans.
• In apps, it powers features like Snapchat filters and Google Lens.
Computer vision is crucial because it enables machines to "see" and understand
the visual world, mimicking human vision and cognitive abilities. This allows for
automation, improved decision-making, and enhanced efficiency across various
industries by identifying, analysing, and extracting insights from images and videos.
It's also important for tasks like object recognition, scene understanding, and even
surpassing human capabilities in some areas.

15. Research Background and Objectives
◦ Research Background:
Computer Vision, a vital subfield of Artificial Intelligence, empowers machines to
interpret and understand the visual world. With the rapid advancement of deep
learning techniques and the availability of large datasets, computer vision has
transitioned from simple image processing to complex tasks like facial recognition,
object detection, and scene understanding. Today, CV is applied in healthcare
for medical imaging, in automotive systems for autonomous driving, and in
agriculture for precision farming. Despite significant progress, challenges remain in
achieving human-level understanding of dynamic scenes, occluded objects, and
cross-environment adaptability. This research aims to explore how to improve CV
systems by leveraging deep learning and real-time processing methods.

16. Research Objectives:
◦ To develop systems capable of detecting and recognizing objects in static
and dynamic visual data
◦ To enhance understanding of scenes and contextual relationships within
images
◦ To track motion and actions in video sequences
◦ To improve image quality through advanced enhancement and demonising
techniques
◦ To contribute to real-world applications such as smart surveillance, healthcare
diagnostics, and autonomous navigation

17. Current state of research
◦ Computer Vision has grown a lot due to deep learning and big data.
◦ Modern Techniques:
◦ Convolutional Neural Networks (CNNs) for image classification
◦ YOLO & Faster R-CNN for object detection
◦ GANs (Generative Adversarial Networks) for image generation
◦ Transformers for image segmentation and understanding

18. Coors real world Application:
◦ Medical Diagnosis:
◦ Retail– Smart:
◦ Surveillance:
◦ Space Exploration:

19. Computer Vision: Transforming Industries and Exploration
◦ Computer vision (CV) is an AI-based technology that enables systems to analyse visual
data (images or videos) and extract useful information smmirror.com.
◦ In healthcare, CV aids in medical imaging by detecting and classifying tumours in MRI
scans for early diagnosis smmirror.com.
◦ In retail, CV powers cashier-free checkout (tracking items for automatic billing)
ultralytics.com.
◦ And monitors in-store camera feeds to track shopper traffic, queue lengths, and other
behaviours for business insights aws.amazon.com

20. Computer Vision: Transforming Industries and Exploration
◦ Surveillance systems use CV to analyse live video and flag unusual or suspicious
activities (e.g. unattended luggage, loitering) in real time for improved security
tracevision.com.
◦ Space missions leverage CV to process satellite and rover images, mapping alien
terrain and detecting objects like asteroids or exoplanets for scientific research
ultralytics.com.
◦ In agriculture, CV enables precision farming: it monitors crop and fruit growth, detects
pests or weeds, and can even guide robots in automated harvesting ultralytics.com.
◦ Autonomous vehicles rely on CV to “see” the road by detecting and tracking other
cars, pedestrians, traffic signals, and obstacles in real time, enabling safe navigation
opencv.ai.

21. My expected Contribution
◦ As a student passionate about Computer Vision, I plan to:
◦ Learn and apply deep learning tools like CNNs and Open CV.
◦ Work on a small project like face mask detection or number plate recognition.
◦ I want Join the research group for better understanding, where how to solve real-
world problems.
◦ Improve my knowledge to eventually publish research in this field.

22. Companies working in the field of
computer vision:

23. Opportunity for CV JOBS:
◦ There are several type of computer being jobs in the field including engineer
researcher, developer, programmer, and scientist opportunities are panful and the
compensation for these roles in high competitive on glassdoor.com.
◦ There are listing for over $68000 computer van jobs in the United States alone.
◦ The salary rate for computer being jobs around from $86000-136000 with a medium
salary just shy of 1,00000 per year.