Introduction
Computer Vision is a field in Science and Technology that is enabling machines or computers to understand, analyze and understand the visual information in the same way as humans do. Today it has become a subfield of Artificial Intelligence.
Similar to humans using their eyes to interpret the surrounding and make decisions, Computer Vision trains machines to perform the same actions, through programs and algorithms developed digitally. For example, someone wants to identify objects around them using machine, Computer Vision is the technology being used. You show an image to Computer Vision enabled machine and tells you what type of objects are there with certain level of confidence.
Today Computer Vision is being widely used in all types of industries: Manufacturing to identify good items and defective items during the process, In Automotive for ADAS adoption, In Medical industry to aid in disease detection, diagnosis. In Retail industry to enable personalized shopping.
Working of Computer Vision
Computer Vision follows certain set of steps to enable machines to understand and interpret visual data. This involves a complex process of analyzing images and videos to extract meaningful information. The process of computer vision typically involves the following steps:
1.Acquiring the images
The first step in computer vision is acquiring the visual data. It can be in the form of images or videos. This data can come from various sources, such as cameras, sensors, or pre-existing image databases. Images can be acquired in real time as well for analysis.
2. Preprocessing
The next step involves cleaning data, removing noise, correcting for distortions, and adjusting brightness and contrast to enhance the quality of images. This step is important as here we ensure that the resulting algorithms receive accurate and reliable visual input.
3. Feature Extraction
The process involving identification and capture of distinctive patterns or features within the Image is Feature Extraction. Features may be edges, corners, textures, shapes or color distributions. This helps in simplifying the data and extract relevant information that would be later used for classification, detection or analysis
4. Object Classification, Detection, Tracking
With trained deep learning model, we are ready to identify and classify objects by comparing extracted features with patterns, it learned during training. This process enables computers to distinguish between different classes of objects eg: whether an image contains a cat or a bird. Once classified, we aim to identify specific instances of objects within the image. Further to this, when we tend to follow the movement of object in the scene over time, it is identified as tracking
By combining the power of deep learning, machine learning, pattern recognition, and image processing, computer vision systems can perform an array of tasks ranging from basic image understanding to complex visual analysis.
Uses of Computer Vision
Medical Industry
Computer Vision along with AI models is being widely used for Medical Imaging X-rays, CT scans, MRI images, Ultrasound). It has also enabled the development of surgical robots with enhanced precision, improving surgical outcomes.
Financial Industry
Computer Vision is helping Financial institutions via automating document processing tasks, such as invoice processing, document verification, and data extraction. Another way the financial sector uses computer vision is for security and compliance purposes. Facial recognition algorithms can be employed for identity verification in mobile banking apps or at ATMs, enhancing security and reducing fraud.
Government
Government agencies are using Computer Vision technology to improve operational efficiency, enhance security measures, and support decision-making processes.
Retail and E-Commerce
The retail industry has been revolutionized using Computer Vision technology, enhancing customer experiences and optimizing operational efficiency. It has enabled personalized shopping recommendations based on image analysis of customer preferences.
Automotive
Modern vehicles use a suite of cameras and sensors to perceive their surroundings in real-time. Basis these analysis, safety mechanism are applied to avoid collision, aligning with speed limits, and adjust vehicle behavior.
Manufacturing and Quality Control
Cameras on assembly lines scan products for microscopic cracks, misalignments, or surface flaws that a human might miss.
Future of Computer Vision
Computer Vision is evolving everyday from simple pattern recognition to awareness intelligence. Improvements in processing performance and algorithms will further enhance the capabilities of computer vision systems. This translates to more accurate and efficient processing of visual data. The future of computer vision holds the potential to reshape the way we interact with the world and the way businesses operate.