Abstract:The just noticeable distortion (JND) model plays an important role in measuring the visual visibility for spread transform dither modulation (STDM) watermarking. However, the existing JND model characterizes the suprathreshold distortions with an equal saliency level. Visual saliency (VS) has been widely studied by psychologists and computer scientists during the last decade, where the distortions are more likely to be noticeable to any viewer. With this consideration, we proposed a novel STDM watermarking method for a monochrome image by exploiting a visual saliency-based JND model. In our proposed JND model, a simple VS model is employed as a feature to reflect the importance of a local region and compute the final JND map. Extensive experiments performed on the classic image databases demonstrate that the proposed watermarking scheme works better in terms of the robustness than other related methods.

Abstract:It has been known that human visual systems (HVSs) can be applied to describe the underlying masking properties for the image processing. In general, HVS can only perceive small changes in a scene when they are greater than the just noticeable distortion (JND) threshold. Recently, the cognitive resources of huma visual attention mechanisms are limited, which can not concentrate on all stimuli. To be specific, only more important stimuli will react from the mechanisms. When it comes to visual attention mechanisms, we need to introduce the visual saliency to model the human perception more accurately. In this paper, we presents a new wavelet-based JND estimation method that takes into account the interrelationship between visual saliency and JND threshold. In the experimental part, we verify it from both subjective and objective aspects. In addition, the experimental results show that extracting the saliency map of the image in the discrete wavelet transform (DWT) domain and then modulating its JND threshold is better than the non-modulated JND effect.

DNA chips are becoming increasingly popular as a convenient way to perform vast amounts of experiments related to genes on a single chip. And the importance of analyzing the data that is provided by such DNA chips is becoming significant. A very important analysis on DNA chip data would be clustering genes to identify gene groups which have similar properties such as cancer. Clustering data for DNA chips usually deal with a large search space and has a very fuzzy characteristic. The Particle Swarm Optimization algorithm which was recently proposed is a very good candidate to solve such problems. In this paper, we propose a clustering mechanism that is based on the Particle Swarm Optimization algorithm. Our experiments show that the PSO-based clustering algorithm developed is efficient in terms of execution time for clustering DNA chip data, and thus be used to extract valuable information such as cancer related genes from DNA chip data with high cluster accuracy and in a timely manner. Availability: The codes for the developed algorithm may be obtained from the author under consent to intellectual property agreements. The code is a demo version and thus some configuration methods are done through editing configuration files. The results are shown in a text window which will be improved in the future.

DNA chips are used for experiments on genes and provide useful information that could be further analyzed. Using the data extracted from the DNA chips to find useful patterns or information has become a very important issue. In this paper, we explain the application developed for classifying DNA chip data using a classification method based on the Particle Swarm Optimization (PSO) algorithm. Considering that DNA chip data is extremely large and has a fuzzy characteristic, an algorithm that imitates the ecosystem such as the PSO algorithm is suitable to be used for analyzing such data. The application enables researchers to customize the PSO algorithm parameters and see detail results of the classification rules. Availability: The codes for the developed algorithm may be obtained from the author under consent to intellectual property agreements. The code is a demo version and thus some configuration methods are done through editing configuration files. The results are shown in a text window which will be improved in the future.