Acquiring Image Datasets
To initiate the process, we analyze the business goals and create a database of images extracted from multiple sources. Structured, relevant, and quality data is prepared to serve as a guideline for future comparison.
In image processing, labeling helps to make the database more search-friendly. Filtering similar patterns and making object comparisons become more efficient with this method. Variables like color, contour, intensity, and size are used to create labels and organize the data.
Processing the Data
The labeled dataset undergoes a meticulous quality check by being tested against training data. We run a series of automated processes to enhance the images like adding or removing pixels, removing noise, sorting misclassified data, and so on.
To improve the training data, the images are modified with a variety of techniques like flipping (horizontally or vertically), cropping, blurring, zooming, and compression to train the model for more accurate image recognition results.
Understanding the Image
In the final stage, the model is able to correctly interpret and categorize the object identified. The software is now adequately trained to recognize images from new input sources. This iterative process ensures that the model continues to enhance its capabilities over time.