Mohammad Behrouzian Nejad¹, Mohammad Ebrahim Shiri^1,2,*

Computer Systems Science and Engineering, Vol.34, No.2, pp. 91-100, 2019, DOI:10.32604/csse.2019.34.091

Abstract In this paper we propose a new image classification technique. According to this note that most research focuses on extraction of features in the frequency domain, location, and reduction of feature dimensions, in this research we focused on learning step in image classification. The main aim is to use the heuristic methods to increase the function of the estimator of the learning algorithm and continue to achieve the desired state, as well as categorization without user interference and automatically performed by the model produced from the above steps. So, in this paper, a new learning… More >

N. Mohanapriya¹, Dr. B. Kalaavathi²

Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 663-672, 2019, DOI:10.31209/2018.100000041

Abstract Medical images are obtained straight from the medical acquisition devices so that, the image quality becomes poor and may contain noises. Low contrast and poor quality are the major issues in the production of medical images. Medical imaging enhancement technology gives way to solve these issues; it helps the doctors to see the interior portions of the body for early diagnosis, also it improves the features the visual aspects of an image for a right diagnosis. This paper proposes a new blend of Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO) called Hybrid… More >

S. Deivalakshmi^*, P. Palanisamy¹, X. Z. Gao²

Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 459-471, 2019, DOI:10.31209/2018.100000001

Abstract The main aim of this paper is to propose a new technique for enhancing the contrast of dark images using Dynamic Stochastic Resonance (DSR) and Multi Wavelet Transform (MWT), which is computationally more efficient than the conventional methods. In the work, for enhancing the contrast of dark images, the intrinsic noise (darkness) of dark images has been used. The proposed MWT-based DSR scheme (MWT-DSR) can yield better performances in terms of visual information and color preservation than already reported techniques. The desired output response is validated by the Relative Contrast Enhancement Factor (F), Perceptual Quality More >

Fatma Mallouli

Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 243-248, 2019, DOI:10.31209/2019.100000069

Abstract Density estimation via Gaussian mixture modelling has been successfully applied to image segmentation. In this paper, we have learned distributions mixture model to the pixel of an iris image as training data. We introduce the proposed algorithm by adapting the Expectation-Maximization (EM) algorithm. To further improve the accuracy for iris segmentation, we consider the EM algorithm in Markovian and non Markovian cases. Simulated data proves the accuracy of our algorithm. The proposed method is tested on a subset of the CASIA database by Chinese Academy of Sciences Institute of Automation-IrisTwins. The obtained results have shown More >

Lu Wu, Quan Liu, Ping Lou

Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 249-257, 2019, DOI:10.31209/2018.100000010

Abstract The scheme of spatial pyramid matching (SPM) causes feature ambiguity near dividing lines because it divides an image into different scales in a fixed manner. A new method called soft SPM (sSPM) is proposed in this paper to reduce feature ambiguity. First, an auxiliary area rotating around a dividing line in four orientations is used to correlate the feature relativity. Second, sSPM is performed to combine these four orientations to describe the image. Finally, an optimized multiple kernel learning (MKL) algorithm with three basic kernels for the support vector machine is applied. Specifically, for each More >

Mengling Zou¹, Zhengxuan Liu², Xianyi Chen^{3, *}

Journal on Big Data, Vol.1, No.3, pp. 151-158, 2019, DOI:10.32604/jbd.2019.09057

Abstract Image encryption (IE) is a very useful and popular technology to protect the privacy of users. Most algorithms usually encrypt the original image into an image similar to texture or noise, but texture and noise are an obvious visual indication that the image has been encrypted, which is more likely to cause the attacks of enemy. To overcome this shortcoming, many image encryption systems, which convert the original image into a carrier image with visual significance have been proposed. However, the generated cryptographic image still has texture features. In line with the idea of improving More >

Chaowen Lu^1,a, Feifei Lee^1,a,*, Lei Chen¹, Sheng Huang¹, Qiu Chen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.2, pp. 593-608, 2019, DOI:10.32604/cmes.2019.07796

Abstract Image retrieval has become more and more important because of the explosive growth of images on the Internet. Traditional image retrieval methods have limited image retrieval performance due to the poor image expression abhility of visual feature and high dimension of feature. Hashing is a widely-used method for Approximate Nearest Neighbor (ANN) search due to its rapidity and timeliness. Meanwhile, Convolutional Neural Networks (CNNs) have strong discriminative characteristics which are used for image classification. In this paper, we propose a CNN architecture based on improved deep supervised hashing (IDSH) method, by which the binary compact More >

Tianyi Li¹, Xin Gu¹, Qing Zhang^1,*, Dong Lei¹

CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.2, pp. 425-444, 2019, DOI:10.32604/cmes.2019.06700

Abstract Digital image correlation (DIC) measurement technique and peridynamics (PD) method have been applied in specific fields extensively owing to their respective advantages in obtaining full-field deformation and local failure of loaded materials and structures. This study provides a simple way to couple DIC measurements with PD simulations, which can circumvent the difficulties of DIC in dealing with discontinuous deformations. Taking the failure analysis of a compact tension specimen of aluminum alloy and a static three-point bending concrete beam as examples, the DIC experimental system firstly measures the full-field displacements, and then the PD simulation is More >

Yanfen Guo^1,2, Zhe Cui^1,*, Zhipeng Yang³, Xi Wu², Shaahin Madani⁴

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1205-1215, 2019, DOI:10.32604/cmc.2019.06029

Abstract Since the spatial resolution of diffusion weighted magnetic resonance imaging (DWI) is subject to scanning time and other constraints, its spatial resolution is relatively limited. In view of this, a new non-local DWI image super-resolution with joint information method was proposed to improve the spatial resolution. Based on the non-local strategy, we use the joint information of adjacent scan directions to implement a new weighting scheme. The quantitative and qualitative comparison of the datasets of synthesized DWI and real DWI show that this method can significantly improve the resolution of DWI. However, the algorithm ran More >

Hyun Kyu Shin¹, Yong Han Ahn², Sang Hyo Lee³, Ha Young Kim^4,*

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 911-928, 2019, DOI:10.32604/cmc.2019.08269

Abstract Compressive strength of concrete is a significant factor to assess building structure health and safety. Therefore, various methods have been developed to evaluate the compressive strength of concrete structures. However, previous methods have several challenges in costly, time-consuming, and unsafety. To address these drawbacks, this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network (DCNN). The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy. The model was developed with 4,000 digital images and 61,996 images extracted from video More >