Picture autonomous vehicles zooming about. They drive cars using computer vision! Pretty cool, right? This tech is also implemented in medical tools. It is able to diagnose diseases with remarkable precision. Even stores utilize it to monitor how shoppers behave. This all goes to show how transformative computer vision is.
Computer vision is what is known when machines “see” and understand images. It is getting really crucial everywhere. Let’s explore the basics!
What is Computer Vision?
With computer vision, just like us, computers can learn from photographs. It’s not just about seeing. It’s about understanding what we see. This discipline combines AI, machine learning, and image processing. AI helps computers think. But one approach they can take is machine learning, which enables them to learn. Compressed images are cleaned up with image processing. Combined, they allow machines to “see.”
Core Concepts Explained
So, Let’s decompose key ideas. In Image formation: How light becomes images Pixels are the little dots that compose a picture. E.g. Color spaces (RGB) define colors. Red, green, and blue are used by RGB. Grayscale utilizes hues of gray. HSV stands for hue, saturation, and value or brightness. Image manipulations (operations) transform images. It’s the same as adjusting brightness and contrast.
Ever think about how computers see a cat? Each pixel has a color value. An RGB value for a pixel might be (255, 0, 0) for red. As far as the computer is concerned, this combination of numbers is the image.
Steps to action: Span an image with the code Then get the RGB values for a certain pixel. This will get you an idea of how images are represented.
Understand the differences between computer vision and image processing
They are both associated with computer vision and image processing. But they’re not the same thing. Image processing alters the images. Consider brightening an image. Computer vision decides what’s in the photo. One is about manipulation; the other is about comprehension.
Actionable tip: A great place to start is using image processing to sharpen a blurry photo. Then, apply computer vision to the cleaned-up image to identify objects within it.
Key Tasks in Computer Vision
In general, there are different duties performed by Computer vision systems. Other types of tasks can be done separately or together. Let’s explore some key ones.
Image Classification
Image classification assigns a category to an entire image. Is it a cat? A dog? An apple? It classifies what it sees. This is done using a CNN (Convolutional Neural Networks). They’re great at detecting patterns.
Imagine using ImageNet. It has thousands of different categories. Give it a picture of a sunflower. In response, the system might say, “Sunflower: 95%, Daisy: 3%, Rose: 1%.”
Actionable tip: Building open source image classification model based on pre-trained neural networks to identify images of animals. There are several online tools that can assist you.
Object Detection
Object detection is the process of finding objects in an image and locating them. It puts boxes around whatever it finds. There are many detection algorithms like YOLO, SSD, R-CNN, etc.
Using OpenCV, you can easily detect faces in images or videos. It draws boxes around every face it detects.
Actionable tip: Leverage your webcam. Carry out object detection on a live video feed. Pre-trained models simplify this a lot.
Image Segmentation
Image segmentation divides an image into its segments. Each segment is a region or object. Two examples of this type are semantic segmentation and instance segmentation.
Think about medical imaging. Segmentation might separate healthy tissue from unhealthy tissue. That helps doctors make diagnoses.
Procedural Essentials: Building Blocks
The working of computer vision is achieved through a few key techniques.
Feature Extraction
Feature extraction extracts useful information from images. All features, including edges, corners, and textures. So SIFT, SURF, HOG are the feature descriptors.
Edge detection detects edges or boundaries in an image. Similar to how you would do feature extraction. This allows computers to “see” the shape of various objects.
Actionable tip: Experiment HOG features on images of people. Then, you can classify various poses based on these features.
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks are the foundation of computer vision. They function by recognizing patterns with images. CNNs are well suited for image classification and such tasks.
For example, consider the task of hand-written digit classification using MNIST. The CNN learns what pixel patterns correspond to a “1,” a “2,” etc.
Computer Vision Dataset and Tools
In order to build computer vision systems, you have the data and the tools.
Popular Datasets
Datasets usually provide images for training. There are 14 million+ images in 20,000 categories available in ImageNet. COCO dataset here contains more than 330K images including 1.5 million object instances. This dataset is MNIST which contains 70,000 gray scale images of handwritten digits. Pascal VOC gives standardized image dataset to perform object detection.
Software Libraries & Frameworks
An OpenCV is one of the most widely used libraries for realtime computer vision. Machine learning framework to TensorFlow. PyTorch is another flexible ML framework. Keras is a front-end engineering API for neural networks.
Actionable advice: Create a desktop computer vision emulator. Choose a framework and install all necessary libraries.
The Future of Computer Vision
Advancements and Applications
“They are starting to be significant in vision. Few-shot learning allows the system to learn from very few examples. Computer vision explains image reasoning by utilizing explainable AI. Computer vision will be applied increasingly in areas like augmented reality and robotics.
Ethical Considerations
Ethical issues of computer vision Algorithms can be biased. This can raise concerns about privacy, however.
Says AI ethics expert, Dr. Maya Green: “AI ethics needs careful attention paid to both fairness and transparency if we want to ensure the technology benefits everyone.”
Conclusion
Computer vision enables machines to “see” and understand images. These are the three major tasks categorized under this domain – image classification, object detection, and image segmentation. Key methods are feature extraction and CNNs. This is often done on popular datasets like ImageNet. Computer vision applications are made possible by frameworks such as OpenCV, TensorFlow, and PyTorch. We worked hard every single week over a very short period to train this neural network, and add more modules of learning over the whole of General.ai to enable the neural network to learn that data and be tuned into all the updates it has undergone till now.
Our future will be impacted greatly by computer vision. Habituate it and learn from it.
