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I would like to know about some preprocessing practices recommended for applying to images before their features are computed, in order to increase the efficiency of the process as much as possible. (e.g. Croping, changing format, etc...)
Thanks in advance :)
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OpenCV has a bunch of pre-trained classifiers that can be used to identify objects such as trees, number plates, faces, eyes, etc. We can use any of these classifiers to detect the object as per our need.
OpenCV is a pre-built, open-source CPU-only library (package) that is widely used for computer vision, machine learning, and image processing applications. It supports a good variety of programming languages including Python.
OpenCV – Open Source Computer Vision. It is one of the most widely used tools for computer vision and image processing tasks.
Haar cascade: Basically, the Haar cascade technique is an approach based on machine learning where we use a lot of positive and negative images to train the classifier to classify between the images. Haar cascade classifiers are considered as the effective way to do object detection with the OpenCV library.
Preprocessing is heavily dependent on feature extraction method and input image type. Some common methods are:
Denoising: applying a Gaussian or simple box filter for denoising.
Contrast Enhancement: If gray level image is too dark or too bright, this may be applied.
Downsampling to increase speed.
Morphological operations for binary images.
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