Computer vision is a fascinating field that enables machines to observe and interpret their environment. Computer vision applications are becoming more widespread in our daily lives, ranging from self-driving automobiles to facial recognition systems. This blog will take you on a journey through the fundamentals of computer vision to more advanced concepts, serving as a thorough introduction for novices.
What is Computer Vision?
Computer Vision is a subfield of AI and ML that focuses on teaching computers to behave more like a human, and have interpreting skills, like we can make judgments, so can they, based on visual data. To extract relevant information from photos and videos, it is necessary to observe and analyze them. The ultimate goal is to emulate human eyesight skills in machines.
How Does Computer Vision Work?
Computer vision works through a series of steps that transform pixels into perception:
Image Acquisition: Capturing an image using cameras or sensors, which informs us about its pixels, specific color and intensity value.
Image Preprocessing: Enhancing the image quality by applying techniques like noise reduction, contrast adjustment, and resizing. We work majorly on this part for better results.
Feature Extraction: Its important to identify what's important and extracting relevant features from the image, such as edges, textures, and shapes.
Segmentation: Simply dividing the image into meaningful segments for easy work analysis.
Object Detection and Recognition: Identifying and classifying objects or shapes within the image.
Post-processing: Not necessarily a part mentioned anywhere, but refining the results and performing further analysis makes the results better.
Tools and Libraries
Most famous tools and libraries in this field, that keep us going:
OpenCV: An open-source computer vision library that provides numerous tools and functions for image processing, object detection, face recognition, text analysis and so much more. It's available in multiple languages, including Python and C++.
TensorFlow and Keras: Deep learning libraries that offer powerful tools for building and training neural networks, including those used for computer vision tasks.
PyTorch: Another deep learning library that's popular for its flexibility and ease of use, especially in research settings.
Applications of Computer Vision
Autonomous Vehicles: Self-driving cars are a hype now-a-days, and rightly so, they use computer vision to detect and interpret road signs, obstacles, and lane markings.
Facial Recognition: Systems that identify and verify individuals based on facial features. All of you must be aware of your face lock features on your smartphones.
Medical Imaging: Analyzing medical images (e.g., X-rays, MRIs) to assist in diagnosis and treatment. It gives real insights.
Surveillance: Monitoring and analyzing video feeds for security purposes.
Augmented Reality (AR): Overlaying digital information onto the real world through devices like smartphones and AR glasses.
These are just out in the air, but knowing the true use of computer vision, you would know it is much more powerful tool than your imagination.
Mind blowing concepts in Computer Vision
Pixels and Images: Are we all aware of an image being a grid of pixels, where each pixel represents a color value. Images can be grayscale or colored (usually RGB). The changing of the range of pixels and colors would be a start to explore this field.
Image Processing: Have you ever manipulated images, or trying editing it like:
Filtering: Everyone has used one on Instagram or Snapchat, but what they really do is remove noise or enhance features in an image. Here we have Gaussian, Median filters.
Edge Detection: Identifying edges, in an image using algorithms like Canny or Sobel. It likes an artist starting with outline of his/her drawing.
Morphological Operations: Techniques like dilation and erosion to modify the structure of objects in an image. It is more fun to try.
Image Segmentation: Working with the small regions of an image. Techniques include:
Thresholding: Separating objects based on pixel intensity.
Clustering: Grouping pixels with similar characteristics.
Deep Learning-based Segmentation: Using models like U-Net for more accurate segmentation.
Object Detection: What our eyes see, and try to identify the objects around, in similar way, it works for identifying and locating objects within an image. Popular methods include:
HaarCascades: Have several files for human detection, like every part of it, eyes, face, body, limbs etc.
Histogram of Oriented Gradients: Used for detecting pedestrians.
Deep Learning Models: Using Convolutional Neural Networks (CNNs) for more complex object detection tasks. They are complex yet useful models.
Feature Detection and Extraction: Identifying key points and features within an image, such as corners, blobs, or edges. The three basic methods include:
SIFT (Scale-Invariant Feature Transform)
SURF (Speeded Up Robust Features)
ORB (Oriented FAST and Rotated BRIEF)
Conclusion
Computer vision is, not to forget I am mentioning again, a fascinating and rapidly evolving field with immense potential. By understanding the basics and experimenting with tools like OpenCV, you can unlock a world of possibilities and an interesting career path. Whether you're a hobbyist or a budding AI specialist, there's always something new to learn and explore in the realm of computer vision, because world never stops to impress, and computer vision has eyes on everything.