Providing the reader with a comprehensive picture of the overall advances and the current cutting-edge developments in the field of mathematical models for remote sensing image analysis, this book is ideal as both a reference resource and a textbook for graduate and doctoral students as well as for remote sensing scientists and practitioners. Gabriele Moser received the laurea M.
Since , he has been an associate professor of telecommunications at the University of Genoa. In , he spent a period as visiting professor at the National Polytechnic Institute of Toulouse, France. At the University of Genoa, he has been teaching courses on pattern recognition, image processing, remote sensing, and electrical communications in undergraduate, graduate, and doctoral programs.
He is the author of two Italian textbooks for a graduate course on remote sensing and for a doctoral course on multivariate feature space transformations He has been involved in the technical and management activities of several scientific and applicative projects related to the exploitation of remote sensing data and funded by the European Commission, the Italian Space Agency, the Italian Ministry of Education, University, and Research, the Italian Department of Civil Protection, and regional authorities.
From to , she was head of Ayin research group INRIA-SAM dedicated to models of spatio-temporal structure for high-resolution image processing with a focus on remote sensing. She received the M. She is author or co-author of more than scientific publications in international journals, edited books and conference proceedings. Her main research interest is in image processing using probabilistic models.
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The image is segmented into text regions and non-text regions according to their features, respectively. The performance of these methods relies on the text detection algorithm and image complexity. Actually, scene text is usually presented on signboards. Because of the uniform color for the background of signboard, the ideal way for extracting text from scene images is to cut out the signboard regions first, and then detect text from the signboard regions.
Thus, this chapter aims to propose an algorithm for segmenting a natural scene image into homogeneous regions.
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In our method, we first perform the image segmentation in order to detect homogeneous regions. Signboard regions are then detected with a simple criterion in order to remove the noise, such as trees and other non-signboard areas. In the following subsections, the proposed method is described and discussed in detail. A natural scene image, I rgb , is supposed to be a bitmap image based on the RGB red-green-blue color model. The EPSF is applied independently to every pixel using different coefficients as shown in the following convolution mask:.
That is,. The filtering of the image is achieved by applying the convolution mask, Eq. Factor p in Eq. The target pixel of the convolution mask is set to zero to remove impulsive noise. A measure of region homogeneity is variance i. In this section, a mathematical morphological operator, Toggle Mapping TM [ 34 ], is introduced to segment a grayscale image into homogenous regions according to the pixel intensity. This is a simple way to segment a grayscale image into homogeneous regions based on a toggle operator.
Such operator is defined as follows:. In order to meet the needs of the application, Dorini [ 35 ] and Fabrizio [ 36 ] have modified and improved this operator by adding new factors or weight coefficients. In their algorithms, the toggle operator is used one time for segmenting an image, so the values of thresholds and coefficients are fixed in their algorithms. However, for different images, the optimal values for thresholds and coefficients should be different.
In order to overcome over-segmentation or under-segmentation, we propose a new algorithm for grayscale image segmentation. In our method, the toggle operator is applied iteratively on input image, and the value of threshold is changed in each iteration step. This is because, while applying Eq. Figure 1 shows an example for the increment of threshold value.
Based on such feature, we propose an approach that tries to search for homogeneous regions by calculating the standard deviation of intensity for connected components. The detail procedure of our proposed algorithm is described in the following steps. Step 7 : Terminating. Finally, U is a set of homogeneous regions. As shown in Figure 2 , the natural scene image can be segmented into homogeneous regions.
The result showed that our proposed method can work effectively with high accuracy. In our experiment, natural scene images are captured with various signboards, shop names, traffic signs, and more. In order to provide a wide range of real-life scenarios, images are captured with different compact digital cameras at different angles, positions, and under variable lighting and weather conditions. Figure 3 shows some examples used in this experiment.
Table 1 shows the experimental environment. In this subsection, our proposed method is compared to the methods of watershed segmentation using gradient [ 1 ], Canny edge detection [ 18 ], and region growing [ 1 ]. In order to evaluate the accuracy of our proposed method. There are many parameters included in not only our method but also the other three methods. Therefore, images, selected from the total images, are used for training and deciding the value of parameters based on the grid search method.
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The remaining images that differ from the training images are used to do the experiment in order to evaluate the accurancy of segmentation. The purpose of our research is to support visually impaired people to access the scene text. This paper aims to segment the natural scene images into homogenous regions. This is because, after the segmentation, specified criteria can be applied to select the signboard regions and the text can then be extracted from these regions.
Therefore, in the experiment, we only focus on the accuracy of signboard segmentation. The result of segmentation relies on not only segmentation algorithms but also the quality of the images. There are target signboards in experimental images. After the experiment, the accuracy of signboard segmentation and the average image processing time are calculated, respectively.
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The results are shown in Table 2. As shown in Table 2 , the average processing time of our proposed method is not so short.
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This is because our algorithm iteratively applies the toggle operator to segment image and find homogeneous regions. So, it is time-consuming. For the region-growing method, it first searches the seeds in an image and then performs the growing processing. This is also time-consuming. Figures 5 — 8 are the segmentation results of Figure 3 , by applying our menthod, watershed segmentation, Canny edge detection, and region-growing method, respectively.
daurunniustates.tk From the observation, our proposed algorithm can segment an image into homogeneous regions effectively, and some results are better than those applying the Canny edge detector and the region-growing method, because of the Canny edge operator not always detecting the closed boundary of object and the result of region-growing method deeply depending on the initial seeds selection. Each method can achieve a high accuracy value if the quality of images is very good. But if the images include much noise, the accuracy of segmentation is very low any method.
The signboard regions cannot be segmented completely due to the following reasons: 1 the surface of signboard is corroded, for example, Figure 9 a ; 2 shadow exists on the signboard, for example, Figure 9 b ; and 3 reflective effect, for example, Figure 9 c.