In this paper, a new method for the segmentation of color images is presented. This method searches for an acceptable segmentation of 1D-histograms, according to a “monotone” hypothesis. The algorithm uses recurrence to localize all the modes in the histogram. The algorithm is applied on the hue, saturation and intensity histograms of the image. As a result, an optimal and accurately segmented ...

Even for a well-trained statistician the construction of a histogram for a given real-valued data set is a difficult problem. It is even more difficult to construct a fully automatic procedure which specifies the number and widths of the bins in a satisfactory manner for a wide range of data sets. In this paper we compare several histogram construction methods by means of a simulation study. Th...

Despite a huge literature on this topic, image segmentation remains a difficult problem in the sense there does not exist a general method which works in all cases. One reason is in that the expected segmentation generally depends on the final application goal. Generally researchers focus on the development of specific algorithms according to the type of images they are processing and their fin...

Histograms are a widely used tool for data analysis. In this paper, we propose a theory to segment a 1D-histogram without a priori assumptions about the undelying density function. Our approach considers a rigorous definition of an admissible segmentation, which provides a criterion allowing to avoid over and undersegmentation problems. A simple, parameter-free and fast algorithm leading to suc...

One of the most utilised criteria for segmenting an image is the gray level values of the pixels in it. The information for identifying similar gray values is usually extracted from the image histogram. We have analysed the problems that may arise when the histogram is automatically characterised in terms of multiple Gaussian distributions and solutions have been proposed for special situations...

ID: 18746 Title: FDG-PET Histogram Distances: Novel Predictors of Tumor Response to Chemoradiotherapy Purpose: To examine the accuracy of FGD-PET histogram distances as predictors of pathologic tumor response to chemo-radiotherapy (CRT) in esophageal cancer. Methods: Twenty patients were included. A rigid registration was used to align the post-CRT PET/CT with the pre-CRT PET/CT images. The pri...

Data hiding is an important way of realising copyright protection for multimedia. In this study, a new predictive method is proposed to enhance the histogram-based reversible data hiding approach on grey images. In those developed histogram-based reversible data hiding approaches, their drawbacks are the number of predictive values less to the number of pixels in an image. In these interleaving...

In this paper, a reversible data hiding scheme based on modification of prediction errors (MPE) is proposed. For the existing histogram-shifting based reversible data hiding techniques, though the distortion caused by embedding is low, the embedding capacity is limited by the frequency of the most frequent pixel. To remedy this problem, the proposed method modifies the histogram of prediction e...

Data sets across many disciplines are becoming consistently large and they bring with them the need of new methods for processing information. We introduce the analysis of interval-valued and histogram-valued data sets as an alternative to classic single-valued data sets and we show the promise of this approach on dealing with economic and financial data. Being our current focus the prediction ...

For reversible data hiding, the histogram-based difference expansion (DE) is one family of the generic methods where secret bits are embedded into an image by DE on prediction error. However, the ratio of the count of bit 0 to that of bit 1, embedded into the pixels with the same prediction error, will vary with the image and the secret bits, which leads to the embedding unbalance and image dis...