Antonio Scuri The IM library provides a toolkit for digital image processing. It supports image acquisition from a variety of devices, provides numerous image transformations and can save images in a number of popular formats. The article presents a concise overview of the library’s structure and underlying mechanisms, demonstrating its use in several practical examples.

IM is a toolkit for Digital Imaging. It provides support for image capture, several image file formats and many image processing operations. Image representation includes scientific data types and attributes. Animation, video and volumes are supported as image sequences. The main goal of the library is to provide a simple API and abstraction of images for scientific applications. It has a very flexible license and can be used for public and commercial applications. The library and its API are implemented in C, but it has a binding to the Lua language.

This article will present a small overview of the library concepts and show a few examples.

Getting Started

IM is based on four concepts: Image Representation, Storage, Processing and Capture. Image Visualization is a task that it is left for a graphics library, like OpenGL or the Canvas Draw toolkit. Figure 1. Illustrates the relationship of these concepts.

Image Representation describes the image model and its details: which color systems are going to be used, which data types, how the data is organized in memory and how other image characteristics are managed.

Image Storage describes the file format model and how images are loaded and saved. Image Capture describes how to obtain an image from a capture device. And Image Processing describes the image processing operations.

There are multiple ways to implement these concepts, but there is no common definition in the literature. Although there is a standard called Programmer's Imaging Kernel System (PIKS), it is a very complete and also very complex standard, so it is difficult to implement and not very popular.

The idea behind IM was to create a toolkit that was not complex, neither big or widespread, but one that can be used as a solid base to the development of thesis and dissertations, as for commercial applications. As this environment is very heterogeneous, the IM project choose some directives:

• Portability (Windows and UNIX)

• C API

• Totally Free and Open Source

• Focus in Scientific Applications

• Easy to Learn

• Easy to Reuse

Considering these directives there are only a few similar toolkits, but none can be considered equivalent. The most popular like VTK or OpenCV are too complex, too big, or to hard to reuse code.

Figure 1. IM Toolkit Concepts

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