Computer vision, or the ability of artificially intelligent systems to "see" like humans, has been a subject of increasing interest and rigorous research now. As a way of emulating the human visual system, the research in the field of computer vision purports to develop machines that can automate tasks that require visual cognition. However, the process of deciphering images, due to the significantly greater amount of multi-dimensional data that needs analysis, is much more complex than understanding other forms of binary information. This makes developing AI systems that can recognize visual data more complicated.
But, the use artificial neural networks is making computer vision more capable of replicating human vision. Computer vision technology of today is powered by deep learning algorithms that use a special kind of neural networks, called convolutional neural network (CNN), to make sense of images. These neural networks are trained using thousands of sample images which helps the algorithm understand and break down everything that's contained in an image. These neural networks scan images pixel by pixel, to identify patterns and "memorize" them. It also memorizes the ideal output that it should provide for each input image (in case of supervised learning) or classifies components of images by scanning characteristics such as contours and colors. This memory is then used by the systems as the reference while scanning more images. And with every iteration, the AI system becomes better at providing the right output.
Web Camera Pro application uses data vision theory to identify objects within video, search through catalogues of images, and extract information out of images. Computer vision provides the functions for recognizing and identifying an image as a specific object.
Object detection deals with detecting instances of semantic objects of a certain class (such as a dog, a human, or a car.) in digital images and videos.
Video tracking is the process of locating a moving object (or multiple objects) over time. Video tracking is an active research topic in the computer vision community and is a prerequisite for many tasks, such as human-computer interaction, security and surveillance, video communication and compression, augmented reality, traffic control, medical imaging, and video editing. The challenge is to track the object irrespective of scale, rotation, perspective projection, occlusions, changes of appearance, and illumination. However, it should be fast enough to maintain transparent interaction with the user.
The digital images taken must be pre-processed to improve their quality before they are analyzed. Using digital filtering, the noise in the image can be removed and the contrast enhanced. Sometimes in this step the color image is converted to a gray-scale image, called the intensity image. The intensity is used to divide the images into disjointed regions with the purpose of separating the region of interest from the background.
Web Camera Pro
- video surveillance with artificial intelligence.