Two-dimensional adaptive image stabilisation

Due to the problem that the many digital images can be modified more and more easily, watermarking algorithms are proposed. There are many types of digital watermarking algorithms, such as robust digital watermarking algorithms, watermarking schemes for authentication, and reversible digital watermarking algorithms. However, there are some special images, such as the digital medical images for remote treatment, the military images for tracking object, and the remote sensing images, and they are so sensitive to any modification that we should authenticate the copyright by special technique. And this technique is zero-bit watermarking algorithms. In this paper, about zero-bit watermarking algorithms are surveyed. The basic procedures of these classical algorithms are analyzed, and some suggestion about the zero-bit algorithms is given.

An effective blind digital watermarking algorithm based on neural networks in the wavelet domain is presented. Firstly, the host image is decomposed through wavelet transform. The significant coefficients of wavelet are selected according to the human visual system (HVS) characteristics. Watermark bits are added to them. And then effectively cooperates neural networks to learn the characteristics of the embedded watermark related to them. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results and comparisons with other techniques prove the effectiveness of the new algorithm.

The traveling salesman problem (TSP) is one of the typical \({\cal NP}\)-hard problems. Then, it is inevitable to develop an effective approximate algorithm. We have already proposed an effective algorithm which uses chaotic neurodynamics. The algorithm drives a local search method, such as the 2-opt algorithm and the adaptive k-opt algorithm, to escape from undesirable local minima. In this paper, we propose a new chaotic search method using the Lin-Kernighan algorithm. The Lin-Kernighan algorithm is one of the most effective algorithms for solving TSP. Moreover, to diversify searching states, we introduce the double bridge algorithm. As a result, the proposed method exhibits higher performance than the conventional algorithms. We validate the applicability of the proposed method for very large scale instances, such as 105 order TSPs.KeywordsTravel Salesman ProblemTravel Salesman ProblemCandidate ListLocal Search MethodChaotic Neural NetworkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Photonic crystal fiber as a new type of optical fiber has been extensively applied because of its unique properties. The effective mode area of optical fiber is an important parameter, which has a great influence on the performance of optical fiber. In this study, digital image processing algorithm was used for preprocessing to improve the accuracy of calculation of mode field area. Then the effective mode field area of optical fiber was calculated using Matlab based Gauss fitting method. Take single-mode fiber G.652 as an example, the effective mode field area was calculated using the traditional algorithm and digital image processing algorithm respectively. It was found that the results obtained using digital image processing algorithm were within the allowed error range, suggesting the effectiveness of the algorithm. Then the calculation of the effective mode area of the triangular lattice photonic crystal fiber further verified the reliability of the algorithm.

With the rapid development of digital phones, the digital image forensics system in current times has had a great impact. It will lead to a serious threat for us, and especially the emergence of the recaptured image makes the existing digital image forensics algorithm invalid. So, it needs an effective image detection algorithm for us to identify recaptured images. In this paper, a new detection algorithm of the recaptured image is presented based on gray level co-occurrence matrix by analyzing the differences between the real and recaptured images. In order to analyze the differences, a new image evaluation model was put forward in this paper, which is called image variance ratio. Firstly, the algorithm proposed extracted high-frequency and low-frequency information of images by wavelet transform, based on which we calculated the relative gray level co-occurrence matrices. Secondly, the features of gray level co-occurrence matrix were extracted. At last, the recaptured image was classified by the support vector machine according to the features. The experimental results showed the algorithm proposed can not only effectively identify the recaptured image obtained from different media but also have better identification rate.

A digital image stabilization algorithm based on polar transform and circular block matching is utilized in this paper to stabilize the videos, taken from rolling airborne platform, which have rapid rotation and arbitrary translation changes between adjacent frames. And circular block matching is used to estimate the global motion parameters, the hierarchical search strategy to reduce the amount of computation. The experimental results show that the proposed algorithm can achieve sub-pixel accuracy and the processing speed of 20 frames per second for 360times264 video sequences on a PIV-3.0 GHz CPU.

This paper investigated the problem of the watermark algorithm for digital color image. An digital image watermark embedding and blind extraction algorithm is proposed based on edge detection and Compressive Sensing (CS) theory. Firstly, the carrier image is divided into non-overlapping image blocks. Then, the target image blocks suitable for concealing the watermark information are chosen by combining the edge detection algorithm. Finally, watermarks are embedded into target image blocks by utilizing the Singular Value Decomposition (SVD) and CS theory. The experimental result shows that the proposed digital images watermark algorithm has excellent invisibility and robustness performance in some scenarios.

Real-time image processing is one of the fundamental elements in achieving IR 4.0. The rapid development of digital image processing techniques has enabled various applications in fields such as healthcare, transportation, and manufacturing. People are seeking higher-performance image processing as traditional image processing is no longer fulfilling the demands. FPGA-based digital image processing has become one of the choices for the public due to its parallel pipelining, which enables shorter processing time and better performance. Several digital image processing algorithms have been developed in this project, which are gray level transformation, brightness manipulation, contrast adjustment, thresholding, and inversion. They are the most popular algorithms used in digital image processing. Microsoft Paint is used to convert the format of the color input image to bitmap format, followed by MATLAB to convert it into a hexadecimal file to be read and written in FPGA. Platforms such as ModelSim Altera and Intel Quartus II are used to write Verilog HDL for digital image processing algorithms. As a result, five hexadecimal files are obtained from the simulation. The output hexadecimal files are further processed in MATLAB to generate respective images.

This paper proposes a simple and effective digital feedforward algorithm to improve the power quality of a boundary-conduction-mode (BCM) boost converter for power-factor-correction (PFC) applications. BCM boost converters suffer from a zero-crossing distortion of the line current caused by the valley switching of the converter. The feedforward algorithm works by increasing the switch on-time around the zero crossing of the line voltage, which in turn increases the line current at this point. The feedforward algorithm has a very simple design procedure and implementation requiring only a single look-up table (LUT) in microcontroller software. The algorithm is implemented on a 1 kW interleaved 3-channel BCM boost converter which operates with an input rms line voltage of 85 V to 265 V, and an output voltage of 400 V. Experimental results are given for the prototype converter demonstrating a significantly improved power quality, when the feedforward algorithm is used.

Quantitation of thin-layer chromatography (TLC) using image analysis is attractive for its low cost and convenience. The image analysis is investigated by designing a digital imaging system with simple equipment, developing an image analysis software based on our algorithm, and validated the system in the TLC quantitative assay of cichoric acid present in Echinacea purpurea (L.) Moench. TLC used a polyamide thin-layer plate with chloroform-methanol formic acid-water (3:6:1:1) as the mobile phase and 3% (m/v) aqueous aluminum chloride solution as the visualization reagent. Images are acquired with a standard digital camera under a UV viewing lamp (365 nm) in a dark room. The three-dimensional gray scale digital image dataset (x, y, gray) is reduced to two-dimensional dataset (distance, accumulative gray) and then plotted as a curve. The area under the peak corresponding to the cichoric acid spot is integrated and used for quantitation. The whole method was validated by the assay tests of detection limit, calibration curve, repeatability, reproducibility, and recovery. The results showed that our digital imaging method and image analysis algorithm were applicable for the quantification of TLC. The whole method is convenient, efficient, and moderately accurate for the quantitative assay of cichoric acid present in Echinacea purpurea (L.) Moench.

The aim of this retrospective pilot study was to evaluate the Aperio nuclear V9 algorithm as an image analysis tool to observe the histopathological changes of ocular surface squamous neoplasia (OSSN). A histopathological assessment, including the Ki-67 proliferative index (PI) of immunohistochemically-stained tumor conjunctiva (TC) and healthy conjunctiva (HC) tissues, was performed in six cases of OSSN. The Aperio V9 algorithm was applied to digital images of the tissue specimens to count the Ki-67 PI and to measure the nuclear area indices. This digital algorithm was validated using stereological and visual analysis methods. The visual scoring of Ki-67 PI ranged from 22 to 60% (mean, 38.5%), and from 5 to 20% (mean 9.5%) in TC and HC tissue, respectively. The computer-aided analysis, using the Aperio nuclear V9 algorithm, revealed that the Ki-67 PI ranged from 21.5 to 43.5% (mean, 33.6%), and from 1.9 to 21.0% (mean, 11.8%) in the TC and HC tissue, respectively. The stereological method demonstrated that the Ki-67 PI ranged from 30.1 to 51.5% (mean, 41.0%), and from 3.2 to 30.1% (mean, 15.1%) in the TC and HC tissues, respectively. The strongest association in the collinearity of regression analysis was observed between the Aperio nuclear V9 algorithm/stereological models in the TC tissue (r2=0.7; P=0.04) and the HC tissue (r2=0.7; P=0.03), and the visual/stereological models in the TC tissue (r2=0.7; P=0.04) and the visual/Aperio nuclear V9 algorithm models in the HC tissue (r2=0.7; P=0.04). A weak and statistically insignificant association was identified between the visual/Aperio nuclear V9 algorithm analysis in the TC tissue (r2=0.4; P=0.2) and the visual/stereological models in the HC tissue (r2=0.5; P=0.13). No significant difference was observed between the nuclear area of the TC (mean, 36.5 μm2) and HC (mean, 35.7 μm2; P=0.88) tissues. It was concluded that the Aperio nuclear V9 algorithm is a useful tool for the reliable analysis of histopathological changes of OSSN. The results of this computer-aided algorithm correlate strongly with the stereological method when assessing the Ki-67 PI.

Index of a point of 3-D digital binary image and algorithm for computing its Euler characteristic

An alternative approach for soil characterization is proposed, using digital images and machine learning. The present work investigated the Red, Green, and Blue (RGB) pattern with Artificial Neural Network (ANN) and Random Forest (RF) in the classification of Oxisol and Inceptisol collected in South Brazil. Furthermore, we also built ANN, RF, Partial Least Squares (PLS), and Support Vector Machine (SVM) models from digital images to quantify Total Carbon (TC) content in soil samples. Preliminary evaluations considered the hypothesis that alternative methods of classification and quantification of soil properties might provide results comparable to traditional methods. The TC content of 91 soil samples, 40 from Oxisol and 51 from Inceptisol were determined by dry combustion. ANN and RF as machine learning algorithms were applied to classify the two kinds of soils. The classification quality was assessed by the percentage of correctly classified instances. The results suggest using digital images and machine learning algorithms as a new methodology to soil classification. The best machine learning method was RF which can be used to classify soil types by the RGB channel colors of the soil images. The use of digital images and RF is a clean (eco-friendly), rapid, and affordable strategy analysis for TC quantification in soils. Therefore, the proposed methods represent an advancement in soil classification, such as the determination of the TC content of soils.

Hill Cipher is one of the methods used in Cryptography. In the Hill Cipher algorithm, the key square matrix must have an inverse modulo. One special matrix that definitely has an inverse is the unimodular matrix. The unimodular matrix can be used as a key in an encryption process. The purpose of this research is to show an alternative in securing digital image data. The type of cryptography used is symmetric cryptography. The algorithm presented by generating a unimodular matrix using the Logistic Map. First, to get a unimodular matrix, we use an identity matrix. The sequence of real number in (0,1) of Logistic Map then converted into integer number from 0 to 255. That number then occupies upper triangular entries of the unimodular matrix. We use the Elementary Row Operation to obtain the complete matrix as a key. Then the multiplication of matrix modulo is used as an encryption process. The unimodular matrix generated by the proposed algorithm guarantees a key in the Hill Cipher for all matrix sizes (n > 4) and only need two parameters (password 1 and password 2). We also tested the algorithm in python language on two digital images (grayscale and color) with different sizes. The results show that the encrypted images are very difficult to read by third parties. The time needed is based on password 1, that is the size of the key matrix. So, the unimodular matrix and logistic map work very well with Hill Cipher to encrypt a digital image.

Most of the subway stations are below the groundwater level, so it is particularly important to do a good job in waterproofing. For the leakage disease of subway station structure, detection is the method and identification is the purpose. There are many types of urban subway station structural leakage diseases, and it is not easy to identify the diseases in all directions. Based on the summary and analysis of the common identification methods of seepage water diseases in subway stations, the crack digital images obtained in a non-contact way are taken as the research objects. Starting from the crack characteristics and the principle of digital image processing algorithm, the traditional algorithm is improved and optimized to obtain a detection algorithm more suitable for the digital image of concrete structural cracks, which is applied to the identification of structural cracks in subway stations. Compared with other manual methods, this method is more accurate and can save a lot of time and cost.