It is important to analyze and evaluate images you use for research , study, and presentations. Images should be analyzed and evaluated like any other source, such as journal articles or books, to determine their quality, reliability, and appropriateness.
Images should be analyzed evaluated on several levels. Visual analysis is an important step in evaluating an image and understanding its meaning. It is also important to consider textual information provided with the image, the image source and original context of the image, and the technical quality of the image. The following questions can help guide your analysis and evaluation.
Image analysis is a broad term that may be defined quite differently by those working in diverse fields. Originally, image analysis was used to describe the extraction of numerical information from pictures. Since the process of placing pictures into a form that could be analyzed digitally was cumbersome, and the computers used to analyze such pictures were slow, image analysis was performed ‘off line’ and quite often at sites far removed from where the image was originally recorded. As software techniques for image analysis improved, and computers became faster and more affordable, image analysis became more widely used, in many fields and disciplines. Image analysis encompasses many areas: machine vision, graphic arts, pattern matching, photometry,
Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions.
scikit-image is a collection of algorithms for image processing. It is available free of charge and free of restriction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers.