IEEE Transactions on Pattern Analysis and Machine Intelligence

This revised and expanded new edition of an internationally successful classic presents an accessible introduction to the key methods in digital image processing for both practitioners and teachers. Emphasis is placed on practical application, presenting precise algorithmic descriptions in an unusually high level of detail, while highlighting direct connections between the mathematical foundations and concrete implementation. The text is supported by practical examples and carefully constructed chapter-ending exercises drawn from the authors' years of teaching experience, including easily adaptable Java code and completely worked out examples. Source code, test images and additional instructor materials are also provided at an associated website. Digital Image Processingis the definitive textbook for students, researchers, and professionals in search of critical analysis and modern implementations of the most important algorithms in the field, and is also eminently suitable for self-study.

Digital Image Processing: An Algorithmic Introduction Using Java

Fast connected-component labeling

Today, approximately 7.2 billion people inhabit the Earth and by 2050 this number will have risen to around nine billion, of which about 70% will be living in cities. The population growth and the related global urbanization pose one of the major challenges to a sustainable future. Hence, it is essential to understand drivers, dynamics, and impacts of the human settlements development. A key component in this context is the availability of an up-to-date and spatially consistent map of the location and distribution of human settlements. It is here that the Global Urban Footprint (GUF) raster map can make a valuable contribution. The new global GUF binary settlement mask shows a so far unprecedented spatial resolution of 0.4 ″ ( ∼ 12 m ) that provides – for the first time – a complete picture of the entirety of urban and rural settlements. The GUF has been derived by means of a fully automated processing framework – the Urban Footprint Processor (UFP) – that was used to analyze a global coverage of more than 180,000 TanDEM-X and TerraSAR-X radar images with 3 m ground resolution collected in 2011–2012. The UFP consists of five main technical modules for data management, feature extraction, unsupervised classification, mosaicking and post-editing. Various quality assessment studies to determine the absolute GUF accuracy based on ground truth data on the one hand and the relative accuracies compared to established settlements maps on the other hand, clearly indicate the added value of the new global GUF layer, in particular with respect to the representation of rural settlement patterns. The Kappa coefficient of agreement compared to absolute ground truth data, for instance, shows GUF accuracies which are frequently twice as high as those of established low resolution maps. Generally, the GUF layer achieves an overall absolute accuracy of about 85%, with observed minima around 65% and maxima around 98%. The GUF will be provided open and free for any scientific use in the full resolution and for any non-profit (but also non-scientific) use in a generalized version of 2.8 ″ ( ∼ 84 m ). Therewith, the new GUF layer can be expected to break new ground with respect to the analysis of global urbanization and peri-urbanization patterns, population estimation, vulnerability assessment, or the modeling of diseases and phenomena of global change in general.

Breaking new ground in mapping human settlements from space – The Global Urban Footprint

We present an efficient run-based two-scan algorithm for labeling connected components in a binary image. Unlike conventional label-equivalence-based algorithms, which resolve label equivalences between provisional labels, our algorithm resolves label equivalences between provisional label sets. At any time, all provisional labels that are assigned to a connected component are combined in a set, and the smallest label is used as the representative label. The corresponding relation of a provisional label and its representative label is recorded in a table. Whenever different connected components are found to be connected, all provisional label sets concerned with these connected components are merged together, and the smallest provisional label is taken as the representative label. When the first scan is finished, all provisional labels that were assigned to each connected component in the given image will have a unique representative label. During the second scan, we need only to replace each provisional label by its representative label. Experimental results on various types of images demonstrate that our algorithm outperforms all conventional labeling algorithms.

A Run-Based Two-Scan Labeling Algorithm

A linear-time component-labeling algorithm using contour tracing technique

A new method for finding connected components frombinary images is presented in this article. The main stepof this method is to use a contour tracing technique todetect component contours and also to fill in interior areas.All the component points are traced by this algorithmin a single pass and are assigned either a new label orthe same label as their neighboring pixels. Experimentingon various types of document images (characters, pictures,newspapers, etc.), we find that our method outperformsthe other sequential methods using the equivalencetechnique. Our algorithm, moreover, is a method that notonly labels components but also extracts component contoursat the same time, which proves to be more usefulthan those algorithms that only label components.

A component-labeling algorithm using contour tracing technique

We propose a new prototype classification method that can be combined with support vector machines (SVM) (Cortes, C and Vapnik, V, 1995) for recognizing handwritten numerals and Chinese characters. This method employs a learning process for determining both the number and location of prototypes. The possible techniques used in this process for adjusting the location of prototypes include the K-means (KM) algorithm and the fuzzy c-means (FCM) algorithm (Bezdek, J. C., 1981). When the prototype classification method is applied, the SVM method can be used to process top rank candidates obtained in the prototype learning or matching process. We apply this hybrid method to the recognition of handwritten numerals and Chinese characters. Experiment results show that this hybrid method saves great amount of training and testing time when the number of character types is large, and achieves comparable accuracy rates to those achieved by using SVM solely. Our results also show that the proposed method performs better than the nearest neighbor (NN) classification method. These outcomes suggest that the proposed method can serve as an effective solution for large-scale multiclass classification

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A prototype classification method and its application to handwritten character recognition

We propose a new prototype learning/matching method that can be combined with support vector machines (SVM) in pattern recognition. This hybrid method has the following merits. One, the learning algorithm for constructing prototypes determines both the number and the location of prototypes. This algorithm terminates within a finite number of iterations and assures that each training sample matches in class types with the nearest prototype. Two, SVM can be used to process top-rank candidates obtained by the prototype learning/matching method so as to save time in both training and testing processes. We apply our method to recognizing handwritten numerals and handwritten Chinese/Hiragana characters. Experiment results show that the hybrid method saves great amount of training and testing time in large-scale tasks and achieves comparable accuracy rates to those achieved by using SVM solely. Our results also show that the hybrid method performs better than the nearest neighbour method.

http://www.iis.sinica.edu.tw/papers/fchang/1371-F.pdf

Chun-Jen Chen

Papers

A linear-time component-labeling algorithm using contour tracing technique

A component-labeling algorithm using contour tracing technique

A prototype classification method and its application to handwritten character recognition

Applying a hybrid method to handwritten character recognition