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Why is Computer Vision More Than Machine Learning Applied?

Why is Computer Vision More Than Machine Learning Applied

In today’s digital era, computer vision has emerged as a fascinating field with diverse applications. From autonomous vehicles to facial recognition, computer vision is revolutionizing industries across the globe. While “machine learning” is often associated with computer vision, it is vital to understand that it encompasses much more than just machine learning algorithms. In this blog post, we will explore the multifaceted nature of computer vision, its underlying technologies, and why it goes beyond machine learning.

Computer Vision

Computer vision, as the name suggests, is about enabling machines to perceive and understand visual information, mimicking human vision in many respects. It involves techniques and algorithms that allow computers to extract meaningful information from digital images or videos and make decisions based on that information. While machine learning is undoubtedly a crucial component of computer vision, it is essential to grasp the broader picture of this interdisciplinary field.

Computer Vision Techniques

1. Image Processing

Image processing forms the foundation of computer vision. It involves manipulating digital images to enhance quality, extract relevant features, reduce noise, and perform various transformations. Techniques like image filtering, edge detection, image segmentation, and morphological operations are widely used in computer vision applications. While machine learning models can be employed to perform some of these tasks, image processing algorithms provide essential building blocks for computer vision systems.

2. Feature Extraction

Feature extraction involves identifying and extracting relevant information or features from images or video frames. These features act as numerical representations describing specific visual data characteristics, such as edges, corners, textures, or colour patterns. Feature extraction techniques like Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Histogram of Oriented Gradients (HOG) are extensively utilized in computer vision tasks such as object recognition, image classification, and tracking.

3. Object Detection and Recognition

Among the hallmark applications of computer vision, object detection and recognition involve detecting and identifying specific objects or patterns within images or video streams. These tasks enable machines to understand their surroundings and interact with the environment effectively. Various algorithms and techniques, such as Haar cascades, Histogram of Oriented Gradients (HOG), and Convolutional Neural Networks (CNNs), are utilized in object detection and recognition. While CNNs are a form of machine learning, they are not the sole approach to achieving accurate results.

4. Image Classification and Segmentation

Image classification assigns a label or category to an entire image, while image segmentation involves dividing an image into meaningful regions or objects. Both tasks are vital in computer vision applications, including medical image analysis, autonomous vehicles, and scene understanding. Machine learning, particularly deep learning, has revolutionized image classification and segmentation by enabling highly accurate and automated systems. However, it is crucial to recognize that deep learning is only one piece of the puzzle in the broader domain of computer vision.

5. 3D vision

While image and video processing mainly deal with 2D visual data, computer vision also encompasses 3D vision, which involves analyzing and understanding three-dimensional objects or scenes. 3D computer vision plays a vital role in applications like robotics, augmented reality, and virtual reality. Techniques such as structure from motion, 3D reconstruction, and stereo vision extract depth information from images or video streams, enabling machines to perceive and interact with the physical world in three dimensions.

Beyond Machine Learning: The Multidisciplinary Nature of Computer Vision

Computer vision is not limited to machine learning alone. It draws upon various scientific disciplines and technologies to achieve its goals. Let’s explore some of these fields and their relevance to computer vision.

1. Optics

The field of optics plays a fundamental role in capturing visual data. Understanding how light behaves, the properties of lenses, and the principles of imaging systems are crucial in computer vision. Optics helps us design better cameras, improve image quality, and explore innovative techniques such as multi-sensor fusion.

2. Image Sensors

Image sensors like those used in digital cameras and smartphones are at the heart of computer vision systems. These sensors capture raw visual data, converting light into electrical signals that computers can process. Advances in image sensor technology, such as increased pixel resolution and improved low-light sensitivity, contribute to the progress of computer vision applications.

3. Signal Processing

Signal processing techniques are vital in computer vision for denoising, filtering, and compression tasks. Algorithms like the Discrete Fourier Transform (DFT) and wavelet transforms are employed to effectively analyze and manipulate visual data. Signal processing helps improve image quality, extract relevant features, and reduce computational complexity in computer vision algorithms.

4. Robotics

Computer vision and robotics are closely intertwined, as computer vision allows robots to perceive and understand their environment. Robotic vision systems employ various computer vision techniques to navigate, interact with objects, and perform tasks autonomously. Computer vision in robotics enables applications ranging from industrial automation to unmanned aerial vehicles (UAVs) and self-driving cars.

5. Human-Computer Interaction

Computer vision plays a vital role in human-computer interaction (HCI). Computer vision enables intuitive and natural interfaces by allowing machines to recognize human gestures, facial expressions, and eye movements. Applications like gesture-based control systems, facial recognition for user authentication, and eye tracking for human behaviour analysis rely on computer vision techniques.

6. Augmented Reality and Virtual Reality

Augmented reality (AR) and virtual reality (VR) heavily rely on computer vision for creating immersive experiences. Computer vision algorithms enable virtual objects to interact with the real world in AR, enhance human perception in VR, and track the position and movements of users. Computer vision expands the possibilities of AR and VR applications, making them more engaging and interactive.

Further Steps and Resources

Computer vision is a vast and rapidly evolving field. If you’re interested in delving deeper into the subject, here are some practical steps you can take:

  1. Enroll in online courses or university programs: Many reputable institutions offer courses and degrees in computer vision and related fields. Online platforms such as Coursera, Udacity, and edX provide accessible learning options from top experts.
  2. Explore open-source libraries and frameworks: OpenCV, TensorFlow, PyTorch, and Caffe are popular libraries and frameworks used in computer vision. These resources offer tutorials, documentation, and examples to help you get started with practical implementations.
  3. Read research papers and publications: Keeping up with the latest research advancements in computer vision can deepen your understanding of the field. Platforms like arXiv and IEEE Xplore provide access to a vast collection of papers.
  4. Join computer vision communities: Engage with like-minded individuals by joining online forums, discussion groups, or conferences and meetups. Connecting with experts and peers can provide valuable insights, collaborations, and career opportunities.

Remember, while machine learning is an integral part of computer vision, embracing the field’s multidisciplinary nature is essential. By combining diverse techniques and technologies, computer vision continues to push the boundaries of what machines can see and understand. So, whether you’re a newcomer or already familiar with the topic, exploring the various dimensions of computer vision will undoubtedly expand your horizons in this exciting domain.


Computer vision is more than just applying machine learning algorithms. It is a multidisciplinary field that combines image processing, feature extraction, object detection and recognition, image classification and segmentation, 3D vision, and various other techniques. By leveraging optics, image sensors, signal processing, robotics, human-computer interaction, and augmented/virtual reality, computer vision enables machines to perceive and understand visual information. 

Embracing the broader perspective of computer vision allows us to unlock its full potential and create intelligent systems that interact with the visual world around us. So, let’s dive into this captivating field, explore its many facets, and witness the ever-evolving wonders of computer vision.


Q: What is the primary difference between computer vision and machine learning?

 A: While machine learning is a subset of artificial intelligence that enables systems to learn from data and make predictions, computer vision encompasses a broader range of techniques that involve perceiving and understanding visual information.

Q: How does computer vision go beyond machine learning in terms of application? 

A: Computer vision extends beyond machine learning by incorporating multiple sensory inputs, enabling scene understanding, perceiving three-dimensional information, and bridging the gap between visual perception and language understanding.

Q: Can computer vision be utilized in sectors other than healthcare and transportation? 

A: Absolutely! Computer vision finds applications in various sectors, including agriculture, manufacturing, retail, security, and entertainment, just to name a few.

Q: What challenges does computer vision face in terms of data availability? 

A: One of the significant challenges in computer vision is the availability of large, diverse datasets for training robust models. Collecting, annotating, and curating such datasets require substantial effort and resources.

Q: What are the key advancements leading the future of computer vision?

A: Recent advancements in computer vision include technologies like augmented reality, which merges the virtual and real worlds, and computational photography, which redefines visual capture by leveraging computer vision techniques. These advancements hold tremendous potential for the future of computer vision.

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