Robust Adaptive Median Binary Pattern for noisy texture classification and retrieval

Texture is an important cue for different computer vision tasks and applications. Local Binary Pattern (LBP) is considered one of the best yet efficient texture descriptors. However, LBP has some notable limitations, mostly the sensitivity to noise. In this paper, we address these criteria by introducing a novel texture descriptor, Robust Adaptive Median Binary Pattern (RAMBP). RAMBP based on classification process of noisy pixels, adaptive analysis window, scale analysis and image regions median comparison. The proposed method handles images with high noisy textures, and increases the discriminative properties by capturing microstructure and macrostructure texture information. The proposed method has been evaluated on popular texture datasets for classification and retrieval tasks, and under different high noise conditions. Without any train or prior knowledge of noise type, RAMBP achieved the best classification compared to state-of-the-art techniques. It scored more than $90\%$ under $50\%$ impulse noise densities, more than $95\%$ under Gaussian noised textures with standard deviation $\sigma = 5$, and more than $99\%$ under Gaussian blurred textures with standard deviation $\sigma = 1.25$. The proposed method yielded competitive results and high performance as one of the best descriptors in noise-free texture classification. Furthermore, RAMBP showed also high performance for the problem of noisy texture retrieval providing high scores of recall and precision measures for textures with high levels of noise

An alternative inference tool to total probability formula and its applications

Total probability and Bayes formula are two basic tools for using prior information in the Bayesian statistics. In this paper we introduce an alternative tool for using prior information. This new toold enables us to improve some traditional results in statistical inference. However, as far as the authors know, there is no work on this subject, except [1]. The results of this paper can be extended to other branches of probability and statistics. In Section 2 total probability formula based on median is defined and its basic properties are proved. A few applications of this new tool are given in Section 3.Comment: Presented at the 23th Int. worskhop on Bayesian and Maximum Entropy methods (MaxEnt23), Aug. 3-7, 2003, Jackson Hole, US

Bayesian segmentation of hyperspectral images

In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.Comment: 8 pages, 2 figures, presented at MaxEnt 2004, Inst. Max Planck, Garching, German

Steganography-based secret and reliable communications: Improving steganographic capacity and imperceptibility

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Unlike encryption, steganography hides the very existence of secret information rather than hiding its meaning only. Image based steganography is the most common system used since digital images are widely used over the Internet and Web. However, the capacity is mostly limited and restricted by the size of cover images. In addition, there is a tradeoff between both steganographic capacity and stego image quality. Therefore, increasing steganographic capacity and enhancing stego image quality are still challenges, and this is exactly our research main aim. Related to this, we also investigate hiding secret information in communication protocols, namely Simple Object Access Protocol (SOAP) message, rather than in conventional digital files. To get a high steganographic capacity, two novel steganography methods were proposed. The first method was based on using 16x16 non-overlapping blocks and quantisation table for Joint Photographic Experts Group (JPEG) compression instead of 8x8. Then, the quality of JPEG stego images was enhanced by using optimised quantisation tables instead of the default tables. The second method, the hybrid method, was based on using optimised quantisation tables and two hiding techniques: JSteg along with our first proposed method. To increase the steganographic capacity, the impact of hiding data within image chrominance was investigated and explained. Since peak signal-to-noise ratio (PSNR) is extensively used as a quality measure of stego images, the reliability of PSNR for stego images was also evaluated in the work described in this thesis. Finally, to eliminate any detectable traces that traditional steganography may leave in stego files, a novel and undetectable steganography method based on SOAP messages was proposed. All methods proposed have been empirically validated as to indicate their utility and value. The results revealed that our methods and suggestions improved the main aspects of image steganography. Nevertheless, PSNR was found not to be a reliable quality evaluation measure to be used with stego image. On the other hand, information hiding in SOAP messages represented a distinctive way for undetectable and secret communication.The Ministry of Higher Education in Syria and the University of Alepp

Object-Based Coastal Morphological Change Analysis Based on LiDAR and Hurricane Events

Storms are considered one of the rapid climatic events that have a dramatic impact on coastal morphology, hence they require further investigation and quantifying of coastal changes and responses. Light detection and ranging (LiDAR) is the most advanced technology to be widely used by researchers for coastal geomorphological studies. The purpose of this study is to apply an object-based approach using repeated LiDAR surveys to understand the short-term morphological changes that occurred on Santa Rosa Island, Florida after category 3 hurricanes Ivan (2004) and Dennis (2005), making it the first study to apply this method, as opposed to previous studies’ commonly used field-based approaches. The first analysis was conducted using a coastal morphology analysis (CMA) tool. In the second analysis, the extracted mean elevation change values were linked to three factors—mean vegetation, mean slope, and mean elevation—to demonstrate their contribution to the change using ordinary least square (OLS) analysis. The third analysis was carried out using the classification and regression tree (CART) analysis. Of the study area, 18.64% encountered erosional processes and 11.35% with depositional processes during Hurricane Ivan, whereas during Hurricane Dennis, 5.91% faced erosional processes and 8.18% was affected by depositional processes. Both hurricanes resulted in a net sediment loss; 283,167 m3 during Hurricane Ivan and 52,440 m3 during Hurricane Dennis. Generally, objects tended to be irregular, asymmetrical, and shaped with smooth boundaries. Along the coast, most objects tended to have an elongated shape, but inland the shapes were more irregular. The overall OLS model during Hurricane Ivan yielded statistically significant results for the three factors, with a confidence level of 0.00 and an adjusted r-square of 0.40; and during Hurricane Dennis, the mean vegetation and mean elevation results yielded significant statistical results (p-value 0.00), while slope did not show significance and had an adjusted r-square of 0.47. CART analysis of both hurricanes ranked the mean elevation as the most important factor in predicting the mean elevation change, followed by the mean slope and finally the mean vegetation variable

MobileDenseNet: A new approach to object detection on mobile devices

Object detection problem solving has developed greatly within the past few years. There is a need for lighter models in instances where hardware limitations exist, as well as a demand for models to be tailored to mobile devices. In this article, we will assess the methods used when creating algorithms that address these issues. The main goal of this article is to increase accuracy in state-of-the-art algorithms while maintaining speed and real-time efficiency. The most significant issues in one-stage object detection pertains to small objects and inaccurate localization. As a solution, we created a new network by the name of MobileDenseNet suitable for embedded systems. We also developed a light neck FCPNLite for mobile devices that will aid with the detection of small objects. Our research revealed that very few papers cited necks in embedded systems. What differentiates our network from others is our use of concatenation features. A small yet significant change to the head of the network amplified accuracy without increasing speed or limiting parameters. In short, our focus on the challenging CoCo and Pascal VOC datasets were 24.8 and 76.8 in percentage terms respectively - a rate higher than that recorded by other state-of-the-art systems thus far. Our network is able to increase accuracy while maintaining real-time efficiency on mobile devices. We calculated operational speed on Pixel 3 (Snapdragon 845) to 22.8 fps. The source code of this research is available on https://github.com/hajizadeh/MobileDenseNet

Advancing Water Resources Systems Modeling Cyberinfrastructure to Enable Systematic Data Analysis, Modeling, and Comparisons

Water resources systems models aid in managing water resources holistically considering water, economic, energy, and environmental needs, among others. Developing such models require data that represent a water system’s physical and operational characteristics such as inflows, demands, reservoir storage, and release rules. However, such data is stored and described in different formats, metadata, and terminology. Therefore, Existing tools to store, query, and visualize modeling data are model, location, and dataset-specific, and developing such tools is time-consuming and requires programming experience. This dissertation presents an architecture and three software tools to enable researchers to more readily and consistently prepare and reuse data to develop, compare, and synthesize results from multiple models in a study area: (1) a generalized database design for consistent organization and storage of water resources datasets independent of study area or model, (2) software to extract data out of and populate data for any study area into the Water Evaluation and Planning system, and (3) software tools to visualize online, compare, and publish water management networks and their data for many models and study areas. The software tools are demonstrated using dozens of example and diverse local, regional, and national datasets from three watersheds for four models; the Bear and Weber Rivers in the USA and the Monterrey River in Mexico