Enhancing the Way We See the World
Vision, being among the most crucial senses, heavily relies on the brain. Approximately 50% of our brain areas engage in processing the abundance of visual information from our eyes. The intricate process includes representing color, shape, movement, detail, and beauty, making it a complex task of information processing.
Computer vision, in turn, delves into the extraction of diverse aspects from images. This study also investigates the nature of internal representations guiding decisions in our thoughts and actions. Understanding these mechanisms sheds light on how we perceive the world around us and make sense of its complexities through our eyes.
What is Computer Vision?
In today's world, images and videos are ubiquitous. Social media platforms and video-sharing sites host billions of them. Almost every mobile phone and computer comes with built-in cameras, resulting in people accumulating gigabytes of photos and videos on their devices. The field of computer vision is concerned with programming computers and designing algorithms to understand the content of these photos and videos.
Computer vision involves the automated extraction of information from images. This information encompasses various aspects, such as 3D models, camera positions, object recognition, content clustering, and search. It also includes tasks like image deformation, noise reduction, and augmented reality. Often, computer vision aims to mimic human vision, but sometimes geometry or a more statistical approach becomes essential to solve specific problems. It is an interdisciplinary field combining programming, modeling, and mathematics, making it an extremely attractive area of study.
Applications of Computer Vision
Computer vision has been an area of research for over 40 years, resulting in the development of numerous applications, including:
- Optical Character Recognition (OCR): Automatic identification of symbols or characters from an image to store them as data.
- Robotic Inspection: Rapid inspection of parts to ensure manufacturing component quality using stereo vision with specialized lighting.
- Retail: Classic barcode readers in supermarkets for product price recognition at checkout counters.
- 3D Model Construction: Automated construction of 3D models from photographs.
- Medical Imaging: Technology used for taking X-rays and techniques to detect malignant tumors and anomalies.
- Automotive Safety: Assisting in obstacle detection through assisted driving systems using various cameras.
- Motion Capture: Utilizing retro-reflective markers viewed from multiple cameras for capturing actors' movements in computer animation.
- Surveillance: Intruder monitoring, traffic analysis, and pool monitoring for drowning victims.
- Fingerprint and Biometrics Recognition: Automatic access identification and forensic applications.
- Face Detection: Improving camera focus and enhancing relevant people search in images.
These applications represent only a few of the vast possibilities offered by computer vision techniques.
Convolutional Neural Networks
The human brain's visual processing area is extensively studied and well-understood, revealing a complex hierarchical arrangement of neurons. For example, visual information enters the cortex through the primary visual area, V1, which deals with low-level visual features like small contour segments, small-scale motion components, binocular disparity, and basic contrast and color information. Subsequently, V1 feeds information to other areas such as V2, V4, and V5, each handling more specific or abstract aspects of the information. For instance, V4 neurons process moderately complex objects like star shapes in different colors. The animal visual cortex serves as a powerful inspiration for creating artificial neural networks for image identification, leading to the development of Convolutional Neural Networks (CNNs).
CNNs consist of neurons with learnable weights and biases. Each neuron receives inputs, performs a dot product, and then applies an activation function. CNNs are explicitly designed to process image inputs, which allows certain properties to be encoded in the architecture, enhancing efficiency and reducing the number of network parameters. They work by progressively modeling small pieces of information and combining them in deeper layers of the network. For example, the first layer detects edges and establishes edge detection patterns, while subsequent layers combine them into simpler shapes and patterns of different object positions, lighting, scales, etc. The final layers match an input image with all these patterns, arriving at a final prediction as a weighted sum of them, enabling CNNs to model complex variations and achieve accurate predictions.
In summary, the visual processing principles of the human brain have significantly influenced the design and success of Convolutional Neural Networks in image-related tasks.
Structure of Convolutional Neural Networks
In general, CNNs are constructed with a structure containing three distinct types of layers:
- The Convolutional Layer distinguishes CNNs from other neural networks by utilizing the convolution operation. This replaces typical matrix multiplication. The input image undergoes convolution with a filter or kernel, generating a map of the original features. This reduces parameters significantly. Convolution leverages three essential ideas to enhance any Machine Learning system: Sparse interactions with smaller filter sizes, shared parameters among different filters, and equivariant representations, where inputs and outputs change similarly.
- Pooling Layer: Usually placed after the convolutional layer, its main purpose is to reduce the spatial dimensions (width x height) of the input volume for the next convolutional layer. It does not affect the depth dimension of the volume. The operation performed by this layer is often called down-sampling, as the size reduction also leads to information loss. However, such a loss can be beneficial for the network for two reasons: reduced size means less computational overhead for the next network layers, and it also helps in reducing overfitting.
- Fully Connected Classifier Layer: After applying the convolutional and pooling layers, CNNs usually incorporate fully connected layers, treating each pixel as an individual neuron, similar to regular neural networks. The final classifier layer contains neurons equal to the number of classes to be predicted.
Deep learning models based on CNNs have achieved impressive results in various computer vision problems, such as image classification and object detection.
OpenCV: Empowering Computer Vision
OpenCV is an Open Source computer vision library created in 1999 by Gary Bradski, who worked at Intel. Its goal was to accelerate computer vision and AI development, providing a robust infrastructure for the field. The library is written in C and C++ and supports Linux, Windows, and macOS. Moreover, OpenCV offers interfaces for Python, Java, MATLAB, Android, and iOS.
OpenCV encompasses over 500 functions, serving diverse computer vision areas such as factory product inspection, medical imaging, security, user interface, camera calibration, stereo vision, and robotics. It also offers a machine learning library for statistical pattern recognition and clustering, making it a go-to choice for computer vision enthusiasts.
In conclusion, Computer vision has revolutionized the way we perceive and interact with the world around us. With the power of convolutional neural networks and tools like OpenCV, computers are now capable of understanding, analyzing, and recognizing visual information with incredible accuracy and speed. The applications of computer vision span various domains, from healthcare and manufacturing to retail and entertainment. As technology continues to advance, we can expect computer vision to play an increasingly critical role in shaping our future, unlocking new possibilities, and enhancing our daily lives.
