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It will also be of value to researchers of various disciplines from medicine to mathematics with a professional interest in image processing.Ī new technique for color reduction of complex document images is presented in this article. Image Processing: The Fundamentals, Second Edition is an ideal teaching resource for both undergraduate and postgraduate students.
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There is also an accompanying website with slides available for download for instructors as a teaching resource.
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Includes a CD containing the MATLAB® code of the various examples and algorithms presented in the book.
Uses a clear question and answer structure. Illustrates complex algorithms on a step-by-step basis, and lists not only the good practices but also identifies the pitfalls in each case. Focuses on an understanding of how image processing methods work in practice. Contains a large number of fully worked out examples. Key features: Presents material at two levels of difficulty: the main text addresses the fundamental concepts and presents a broad view of image processing, whilst more advanced material is interleaved in boxes throughout the text, providing further reference for those who wish to examine each technique in depth. These updates are combined with coverage of classic topics in image processing, such as orthogonal transforms and image enhancement, making this a truly comprehensive text on the subject.
New material includes image processing and colour, sine and cosine transforms, Independent Component Analysis (ICA), phase congruency and the monogenic signal and several other new topics. This improvement is illustrated in this paper on the binarization task, where the results of four different binarization methods are successfully improved.įollowing the success of the first edition, this thoroughly updated second edition of Image Processing: The Fundamentals will ensure that it remains the ideal text for anyone seeking an introduction to the essential concepts of image processing. We demonstrate the effects of our novel preprocessing technique on a set of challenging historical documents, which we make publicly available for research purpose, and two publicly available datasets. This modification of the usual workflow of historical document analysis eases the binarization task as well as other following tasks like layout analysis, line segmentation, OCR, etc. In fact, by adding a preprocessing step to enhance the input grayscale image, the results on all the following tasks of the analysis chain should be improved. The binarization give then better results on this enhanced grayscale image, and in particular color text is binarized as well as black text. Especially on documents with non-black ink and moreover with diverse colors, e.g., illuminations in historical manuscripts, we expect an increased performance. This new algorithm uses luminance and color information to improve the contrast between the foreground and the background. Before binarization, we propose a grayification step to enhance the input image with the help of a new grayscale conversion algorithm, namely the grayification algorithm. We introduce a novel preprocessing step into the usual document image analysis (DIA) workflow. This paper presents an improvement of handwriting binarization techniques on colored historical documents.