Computer Vision techniques: 3 examples you need to know
What is Artificial Intelligence?
It is a field that combines computer science and robust data sets to solve problems.
Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind. It can iteratively improve upon the information it gathers.
AI manifests itself in a variety of ways. In this note we will show a variety of techniques developed within the field of computer vision, which focuses on making machines capable of extracting useful information from images.
Instagram filters (and other applications) are excellent examples of applications utilizing techniques developed in computer vision.
For machines to recognize what needs to be detected, they must be trained with thousands of images to generate a model.
In this case, the operation of instagram filters (and other applications) usually relies on four essential facial recognition tasks: face detection, detection of eyes, nose, and/or mouth (through Landmarks Detection) and depth estimation.
Image analysis using Deep Learning (a sub-area within Machine Learning and Artificial Intelligence) typically involves passing the images through a network of layers of neurons. Each layer performs a series of operations before returning the expected result. After passing through multiple layers (which gives the name to “Deep Learning”), the AI (or predictive model) is able to extract a prediction about different characteristics of the object in the image. These layers of neurons, which form a convolutional neural network, can identify patterns in the image useful for predicting or estimating the detection target. Depending on the images and what is expected to be identified in them, the predictive model is able to learn and adjust the parameters to provide the most accurate results.
In this case, the images are analyzed to determine or classify the type of garment shown. It is also possible to use techniques to extract color information from the pixels that make up the garment. With this information, a search by garment type and color is performed to provide suggestions to the user.
Applications in agriculture (seedling counting)
Precision agriculture involves using available technology to optimize crops. As a result, human capabilities are enhanced and assistance is provided in solving problems. In addition, it allows farmers to invest safely in their crops’ profitability. The use of sensors, flaggers, drones, robots and other technological advances, combined with developments in artificial intelligence, are currently being used successfully by farmers.
In this case we can see an example that uses traditional image processing techniques and an object detection model to identify seedlings in the image. As a result of the detections, the number of seedlings present in a crop area can be determined.