Computer Vision
Segmentation, Classification and Detection
The primary objective of computer vision is to understand the content of videos and still images. With this understanding, it is possible to form useful information from these images and solve an ever-increasing number of problems. Computer vision combines cameras, edge computing, cloud-based computing, software, and artificial intelligence (AI) to help systems identify objects. Machines powered by artificial intelligence (AI) are increasingly able to identify objects with the aid of convolutional neural networks (CNN).
Computer vision applications based on CNNs are able to detect and recognize objects with high accuracy.
AI-driven computer vision systems are becoming increasingly popular in a variety of industries, from healthcare to automotive, agriculture, security and beyond.
At InnoWave, we build and train models using supervised (classification or regression-based), unsupervised (clustering or association based) and reinforcement (reaction to an environment based) learning techniques.
We optimize the model for maximum accuracy, and ensure it meets the customer requirements.
Contact us for detailed information.
Computer Vision Use Cases
Automotive
Use of cameras and sensors to detect and identify objects in the environment. It can be used to detect obstacles such as other vehicles, pedestrians, and other objects, and can also be used to detect and respond to traffic signals as well as provide real-time data to traffic control systems, allowing for better traffic flow.
Computer Vision for Smart Cities
Computer Vision for Smart Cities is a technology that is being used to improve the efficiency of public services and infrastructure. It can provide real-time monitoring of public spaces, detect criminal activity, and alert authorities when issues arise. This technology can also be used to detect traffic congestion and recommend alternative routes.
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Object Detection and Recognition
Object detection and recognition is a field of computer vision that deals with detecting and recognizing objects from images. It involves methods such as convolution neural networks, which are trained to detect and recognize objects from images
Medical Imaging
Medical imaging is a field of research that uses computer vision algorithms to process medical images such as X-rays, CT scans, MRIs, etc. These algorithms are designed to automate the process of diagnosing medical conditions and detecting abnormalities in medical images.
