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In order to solve the ill-posed problem in super-resolution image reconstruction, this paper proposes an adaptive regularization way use sparse representation. We build a new L1/2 non-convex optimization model and apply reweighted L2 Norm for the adaptive algorithm in this paper. Experimental results show the significant effect in denoising and preserving edge details. It outperforms some traditional methods in the value of peak signal to noise ratio and structural similarity [ABSTRACT FROM AUTHOR]

In order to solve the problem of how to reconstruct the original image under the condition of unknown sparse basis, this paper proposes an image reconstruction method based on blind compressed sensing model. In this model, the image signal is regarded as the product of a sparse coefficient matrix and a dictionary matrix. Based on the existing blind compressed sensing theory, the optimal solution is solved by the alternative minimization method. The proposed method solves the problem that the sparse basis in compressed sensing is difficult to represent, which restrains the noise and improves the quality of reconstructed image. This method ensures that the blind compressed sensing theory has a unique solution and can recover the reconstructed original image signal from a complex environment with a stronger self-adaptability. The experimental results show that the image reconstruction algorithm based on blind compressed sensing proposed in this paper can recover high quality image signals under the condition of under-sampling. [ABSTRACT FROM AUTHOR]

The development of aberrant cell proliferation in the lungs is a problematic condition that has the potential to result in death. On the list of diseases that most frequently result in mortality, lung cancer takes first place. The early stages of lung cancer are notoriously difficult to diagnose due to the fact that cancer cells with dimensions less than very small are notoriously difficult to spot by imaging. If the cell abnormalities are discovered in the early stages, it will be possible to begin therapy sooner, which will result in an improved chance of the patient surviving the illness. Several different image processing strategies can be utilized in the diagnostic phase of patient care to help spot signs of disease. In this paper, classification of Lung Cancer from chest X-ray images has been done using optimized Convolutional Neural Network (OCNN) and Ant Lion Optimization (ALO) algorithm. In pre-processing step, the contrast of all images are enhanced using Histogram Equalization (HE) method and the noises are removed from all images using Median Filtering. After the pre-processing step, feature extraction is performed using Gray Level Spatial Dependence (GLSD) to extract the statistical features. The feature vector is then trained and classified using OCNN-ALO algorithm. The ALO algorithm is used to optimize the hyper parameters of CNN layers. It classifies the lung images into normal and lung tumor affected. Performance results have indicated that OCNN-ALO attains the superior performance with 95.15% accuracy, 85.43% sensitivity, 93.4% specificity and 76.43% F1-score. [ABSTRACT FROM AUTHOR]

A good dictionary has direct impact to the result of super-resolution image reconstruction. For solving the problem that dictionary learning only contains representation ability but no class information using K-SVD algorithm,this paper proposes single image super-resolution algorithm based on LC-KSVD (Label consist K-SVD).The algorithm adds classifier parameter constraints into the process of dictionary learning and classifier parameters in the process, making the dictionary possess good representation and discrimination ability. The experimental results show that the algorithm has high reconstruction results and good robustness. [ABSTRACT FROM AUTHOR]

Xilinx System Generator (XSG) integrates with MATLAB Simulink to build a complex system through Xilinx and Simulink blocks. This paper presents a hardware implementation of various image processing techniques to modify and extract the pixel information from various input images in run time. Complex coding strategies are eradicated with integrated system. The intricacy in structural design is reduced to a huge extent using Xilinx System generator. The reconfigurable attribute of Field Programmable Gate Array (FPGA) bags the massive advantages in image processing like co simulating with selective pixel and verified on FPGA. Image processing algorithms explored in methodical manner by performing diverse operations like edge detection, and color enhancement, complement, contrast stretching etc. The proposed image processing techniques (IPT) intended for various applications. Dissimilar image processing techniques are processed by FPGA and displayed on Video Graphics Array (VGA). Image processing system is designed in XSG, hardware description language (HDL) like VHDL & Verilog code is generated by processing system by system generator token. Design is implemented on Artix-7 and targeted on Basys3 reconfigurable hardware (XC7A35T-1-CPG236C). [ABSTRACT FROM AUTHOR]

The mosaic image is an essential process for merging several images, especially for very high-resolution satellite images, as its width swath is small. The main issue of mosaic images is the geometric accuracy between the images. This paper proposed a sub-pixel geometric evaluation to assess the geometric shift between the images. It is an essential step for image registration. Image registration is a process to determine a geometric shift involving two or more images with the same object but different acquisition dates, viewpoints, and sensors. The basic idea of image registration in the frequency domain is estimating the shift between the test image and the reference image. In this study, we used the phase cross-correlation function to determine the geometric shift between two different acquisition Pleiades images from each polygon. Phase correlation is based on the translation property of the Fourier Transform. It transforms the displacement of two correlated images in the spatial domain into a phase difference in the frequency domain. The phase correlation algorithm calculates a phase difference map containing a single peak. The peak location is proportional to the value of the shift between the two images. Several Pleiades images were used in the experiment. The image processing level is an ortho-image, which is already pan-sharped. As a result, the highest geometric shift is on Polygon 2 based on the R-value, and the lowest error value is in polygon 1. The experiments also showed that the most significant error value is in the blue band, and the lowest is in the red band. The average geometric shift between the two Pleiades data is 1.187 pixels, with the most shift located in the blue band with a 1.282 pixels shift, and the Root Mean Square Error (RMSE) is 1.8. [ABSTRACT FROM AUTHOR]

Mobile cloud technology is currently playing a vital role in the field of mobile communication. Frequently new updates are coming in the field of image processing, online games, Natural language processing and other application using mobile and cloud. On mobile phones, the execution of sophisticated software and Apps might result in poor performance in terms of battery consumption and response time. Therefore a new method is required to utilize the benefits of mobile cloud by using computation offloading technique. In this paper we proposed EDOMAC method for execution of Picasa and Mathdroid application using cloudlet environment. We have computed response time and energy consumption for above mentioned applications based on mobile and cloud. At the end compared results with POMAC and M-POMAC. [ABSTRACT FROM AUTHOR]

One of the research emergence as per studied problem on the image processing based computer vision is that object detection in a image with bounding boxes. This complicated processing has to be done with help of machine leaning based algorithms only. In the recent years research has done with machine leaning algorithms like CNN,RCNN, Fast RCNN,FasterRCNN,Yolo algorithm and etc.These algorithms have achieved the proposed concept in different levels. In this paper we presented the comparative study of each algorithm and provided efficiency and weakness contexts. [ABSTRACT FROM AUTHOR]

In order to optimize the recognition accuracy of maize kernels breakage detection and improve the detection efficiency of maize kernels breakage, this paper using computer vision technology and detecting of the maize kernels breakage based on K-means clustering algorithm. First, the collected RGB images are converted into Lab images, then the original images clarity evaluation are evaluated by the energy function of Sobel 8 gradient. Finally, the detection of maize kernels breakage using different pixel acquisition equipments and different shooting angles. In this paper, the broken maize kernels are identified by the color difference between integrity kernels and broken kernels. The original images clarity evaluation and different shooting angles are taken to verify that the clarity and shooting angles of the images have a direct influence on the feature extraction. The results show that K-means clustering algorithm can distinguish the broken maize kernels effectively. [ABSTRACT FROM AUTHOR]

Self Organization Map (SOM) is an automatic tool in data analysis in data mining,it is used to explore the multi-dimentional data which simplifies complexity and produce meaningful relation with each other or high dimentional into low dimentional .The powerful method of SOM i.e learning method results excellent performance .the SOM algorithum have various steps from starting stage to the final neuron and their weight updation and modification ,these procedure resultant a lot of complexity according to the parameters on the basis of experiments, this paper will compare and discuss various parameters and their result or factors that can improve and refine the image through various process of SOM. [ABSTRACT FROM AUTHOR]

Maintaining the integrity of pipelines over long distances is an ongoing challenge for oil and gas companies. Among many factors, corrosion is one of the major causes of pipeline failure. Therefore, timely and accurate detection of corrosion is crucial. Along with the growing use of In-Pipe Inspection Robot (IPIR) technologies, a camera-based visual inspection has become a reliable technique for pipe defects detection. However, the conventional manual surveying process by the operators shows a lack of efficiency in this task. This paper studies the application of YOLO, an image-processing algorithm based on Convolutional Neural Network (CNN), for automating corrosion inspection. The results demonstrate that the proposed method is capable of performing detection with an accuracy rate of 64% under AP75 threshold. The system developed can be a promising tool in providing real-time autonomous defect detection to enhance IPIR devices. [ABSTRACT FROM AUTHOR]

This paper presents a tool that would calculate traffic density through vehicle detection using image processing. Haar-Cascading Algorithm was used in vehicle detection. The developed tool used video recordings from identified Cross and T-type road junctions in the City of Mandaue. Traffic video recording were provided by the Traffic Enforcement Agency of Mandaue (TEAM). The system will classify traffic status as low, moderate or heavy based on the calculated traffic density. It is also capable of simulating traffic light control by displaying a Go signal in one part of the road in a particular road junction at a time. The duration of this Go signal will depend on the traffic status determined by the system. Results showed that the system has an average accuracy rate of 80% in vehicle detection and can correctly classify traffic status from the calculated traffic density. [ABSTRACT FROM AUTHOR]

In this paper, first anew modified classical conjugate gradient CG method is proposed by deriving a new conjugancy coefficient based on three terms direction, the sufficient descent and the global conjugancy properties are proved. Second: One of the flock algorithms was hybridized which is the particle flock PSO algorithm with a modified conjugate gradient method. The modified PSO Algorithm is normalize the initialization the best by modified CG Algorithm. The numerical results on test functions showed the efficiency of the hybrid algorithm on each of PSO and CG original algorithms. Third: the hybrid algorithm was also used in image processing, by which it was able to estimate image parameter distribution represented by Gibbs random distribution. The estimated parameters was also used in the hybrid algorithm in restoring images and the results were very encouraging. [ABSTRACT FROM AUTHOR]

Indirect Immunofluorescence (IIF) is a good standard used for antinuclear autoantibody (ANA) test using Hep- 2 cells to determine specific diseases. Different classifier algorithm methods have been proposed in previous works however, there still no valid set as a standard to classify the fluorescence intensity. This paper presents the use of fuzzy logic to classify the fluorescence intensity and to determine the positivity of the Hep-2 cell serum samples. The fuzzy algorithm involves the image pre-processing by filtering the noises and smoothen the image, converting the red, green and blue (RGB) color space of images to luminosity layer, chromaticity layer 'a' and 'b' (LAB) color space where the mean value of the lightness and chromaticity layer 'a' was extracted and classified by using fuzzy logic algorithm based on the standard score ranges of antinuclear autoantibody (ANA) fluorescence intensity. Using 100 data sets of positive and intermediate fluorescence intensity for testing the performance measurements, the fuzzy logic obtained an accuracy of intermediate and positive class as 85% and 87% respectively. [ABSTRACT FROM AUTHOR]

In this paper, a new detection algorithm based on Convolutional Neural Network is presented in order to realize the fast and convenient ID information extraction in multiple scenarios. The algorithm uses the mobile device equipped with Android operating system to locate and extract the ID number; Use the special color distribution of the ID card, select the appropriate channel component; Use the image threshold segmentation, noise processing and morphological processing to take the binary processing for image; At the same time, the image rotation and projection method are used for horizontal correction when image was tilting; Finally, the single character is extracted by the projection method, and recognized by using Convolutional Neural Network. Through test shows that, A single ID number image from the extraction to the identification time is about 80ms, the accuracy rate is about 99%, It can be applied to the actual production and living environment. [ABSTRACT FROM AUTHOR]

The problem of the automatic image calibration is considered in this paper. The most challenging task of the automatic calibration is a proper detection of the calibration object. The solving of this problem required the appliance of the methods and algorithms of the digital image processing, such as morphology, filtering, edge detection, shape approximation. The step-by-step process of the development of the algorithm and its adopting to the specific conditions of the log cuts in the image's background is presented. Testing of the automatic calibration module was carrying out under the conditions of the production process of the logging enterprise. Through the tests the average possibility of the automatic isolating of the calibration object is 86.1% in the absence of the type 1 errors. The algorithm was implemented in the automatic calibration module within the mobile software for the log deck volume measurement. [ABSTRACT FROM AUTHOR]

Due to the importance of the two-phase flow researches for the industrial safety analysis, many researchers developed various methods and techniques to study the two-phase flow phenomena on the industrial cases, such as in the chemical, petroleum and nuclear industries cases. One of the developing methods and techniques is image processing technique. This technique is widely used in the two-phase flow researches due to the non-intrusive capability to process a lot of visualization data which are contain many complexities. Moreover, this technique allows to capture direct-visual information data of the flow which are difficult to be captured by other methods and techniques. The main objective of this paper is to present an improved algorithm of image processing technique from the preceding algorithm for the stratified flow cases. The present algorithm can measure the film thickness (hL) of stratified flow as well as the geometrical properties of the interfacial waves with lower processing time and random-access memory (RAM) usage than the preceding algorithm. Also, the measurement results are aimed to develop a high quality database of stratified flow which is scanty. In the present work, the measurement results had a satisfactory agreement with the previous works. [ABSTRACT FROM AUTHOR]

Eddy current probes (EC) with Giant Magnetoresistive (GMR) sensors have recently emerged as a promising tool for rapid scanning of multilayer aircraft panels that helps detect cracks under fastener heads. However, analysis of GMR data is challenging due to the complexity of sensed magnetic fields. Further, probes that induce unidirectional currents are insensitive to cracks parallel to the current flow. In this paper, signal processing algorithms are developed for mixing data from two orthogonal EC-GMR scans in order to generate pseudo-rotating electromagnetic field images of fasteners with bottom layer cracks. Finite element simulations demonstrate that the normal component of numerically computed rotating field has uniform sensitivity to cracks emanating in all radial directions. The concept of pseudo-rotating field imaging is experimentally validated with the help of MAUS bilateral GMR array (Big-MR) designed by Boeing. [ABSTRACT FROM AUTHOR]

We discuss a range of Bayesian modelling approaches for spatial data and investigate some of the associated computational challenges. This paper commences with a brief review of Bayesian mixture models and Markov random fields, with enabling computational algorithms including Markov chain Monte Carlo (MCMC) and integrated nested Laplace approximation (INLA). Following this, we focus on the Potts model as a canonical approach, and discuss the challenge of estimating the inverse temperature parameter that controls the degree of spatial smoothing. We compare three approaches to addressing the doubly intractable nature of the likelihood, namely pseudo-likelihood, path sampling and the exchange algorithm. These techniques are applied to satellite data used to analyse water quality in the Great Barrier Reef. [ABSTRACT FROM AUTHOR]

The paper is devoted to solving the problem of excluding areas of irregularities in material surface images. To solve this problem, we propose an algorithm for automatic mask construction with identifying the areas with material, areas without material, and crack areas; this combines image processing methods (pattern search, threshold flood filling, boundary search) with crack detection algorithms. It is demonstrated that this algorithm is applicable to experimental images with a large number of frames in a series. [ABSTRACT FROM AUTHOR]

In the modern world, there are numerous models for classifying and identifying paddy rice on the market today as it is more crucial to use technologies like image processing since they have made it feasible to serve high-quality food to more people. When algorithms are run in Indian farmer sheds using this model-based application, higher profits are made with less investments. The major issue with the current image processing algorithm system are the data gathered were from specific ethic groups. The canny methods on machine learning algorithms works in a few data sample categories. However, in the proposed work, various large category datasets that were gathered from Kaggle were used to analyze the performance of the canny algorithm. The primary goal of the research was to increase the accuracy of paddy rice adulteration prediction utilizing novel Canny algorithms and comparison with image processing techniques. [ABSTRACT FROM AUTHOR]

With the media industry in its full swing, the need for video editing and censoring has increased in drastic proportions. But as every sector has its own advancements, there are also certain areas where it lags. With more number of media items pouring in, the need to segregate the categories has also increased. Presently, this censoring is done manually by viewer's who view the content. Though there are strict rules as to which videos should fall under which category, discrepancies occur due to difference in perspectives. Moreover, a content running for 3 min would be judged by an algorithm at faster rates than what is being done manually. Therefore, there is a need for a platform where end users need only upload the content while the sorting would be done automatically. The data sets available from ImageNet API would make it easier for training the model for convolutional neural networks. [ABSTRACT FROM AUTHOR]

In this article, an algorithm for palm-dorsa vein image enhancement is proposed. Instead of using expensive high-quality vein image capture device, a low-cost webcam was used to capture the vein image. The webcam has been modified to make it possible to capture the vein image of palm-dorsa. In this research, the algorithm to enhance the low-quality vein image, remove the noise and finally detect the palm-dorsa vein pattern has been developed. First, the vein image is applied with image normalization technique to obtain standard image. Then, the Gaussian filter is used to remove the noise in the vein image. Next, Difference of Gaussian and threshold techniques are used to segment the vein image. Median filter is used to remove and smooth the noise introduced from the previous image segmentation process. Finally, thinning technique is used to get single line palm-dorsa vein pattern. Even though the vein images from webcam are noisy and very low contrast, results show that the proposed algorithm still can get good vein image enhancement. [ABSTRACT FROM AUTHOR]

We propose a novel method for template matching named THTM – a template matching algorithm based on HOG (histogram of gradient) and two-stage matching. We rely on the fast construction of HOG and the two-stage matching that jointly lead to a high accuracy approach for matching. TMTM give enough attention on HOG and creatively propose a twice-stage matching while traditional method only matches once. Our contribution is to apply HOG to template matching successfully and present two-stage matching, which is prominent to improve the matching accuracy based on HOG descriptor. We analyze key features of THTM and perform compared to other commonly used alternatives on a challenging real-world datasets. Experiments show that our method outperforms the comparison method. [ABSTRACT FROM AUTHOR]

In order to objectively evaluate the fusion effect of infrared and visible image, a fusion evaluation method for infrared and visible images based on energy-weighted average structure similarity and edge information retention value is proposed for drawbacks of existing evaluation methods. The evaluation index of this method is given, and the infrared and visible image fusion results under different algorithms and environments are made evaluation experiments on the basis of this index. The experimental results show that the objective evaluation index is consistent with the subjective evaluation results obtained from this method, which shows that the method is a practical and effective fusion image quality evaluation method. [ABSTRACT FROM AUTHOR]

Turbidity is the degree to which the optical clarity of water is reduced by impurities in the water. High turbidity values in rivers and lakes promote the growth of pathogen, reduce dissolved oxygen levels and reduce light penetration. The conventional ways of on-site turbidity measurements involve the use of optical sensors similar to those used in commercial turbidimeters. However, these instruments require frequent maintenance due to biological fouling on the sensors. Thus, image processing was proposed as an alternative technique for continuous turbidity measurement to reduce frequency of maintenance. The camera was kept out of water to avoid biofouling while other parts of the system submerged in water can be coated with anti-fouling surface. The setup developed consisting of a webcam, a light source, a microprocessor and a motor used to control the depth of a reference object. The image processing algorithm quantifies the relationship between the number of circles detected on the reference object and the depth of the reference object. By relating the quantified data to turbidity, the setup was able to detect turbidity levels from 20 NTU to 380 NTU with measurement error of 15.7 percent. The repeatability and sensitivity of the turbidity measurement was found to be satisfactory. [ABSTRACT FROM AUTHOR]

This study aims to retrieve face images from a database according to a target face image. For this purpose, Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR) is taken into consideration. TMEMPR is a recursive algorithm based on Enhanced Multivariance Products Representation (EMPR). TMEMPR decomposes a matrix into three components which are a matrix of left support terms, a tridiagonal matrix of weight parameters for each recursion, and a matrix of right support terms, respectively. In this sense, there is an analogy between Singular Value Decomposition (SVD) and TMEMPR. However TMEMPR is a more flexible algorithm since its initial support terms (or vectors) can be chosen as desired. Low computational complexity is another advantage of TMEMPR because the algorithm has been constructed with recursions of certain arithmetic operations without requiring any iteration. The algorithm has been trained and tested with ORL face image database with 400 different grayscale images of 40 different people. TMEMPR's performance has been compared with SVD's performance as a result. [ABSTRACT FROM AUTHOR]