Wenting Qiao^1,2, Xiaoguang Wu^1,*, Wen Sun³, Qiande Wu^4,*

Structural Durability & Health Monitoring, Vol.14, No.4, pp. 355-374, 2020, DOI:10.32604/sdhm.2020.011479

Abstract To solve the problem that the digital image recognition accuracy of concrete structure cracks is not high under the condition of uneven illumination and complex surface color of concrete structure, this paper has proposed a block segmentation method of maximum entropy threshold based on the digital image data obtained by the ACTIS automatic detection system. The steps in this research are as follows: 1. The crack digital images of concrete specimens with typical features were collected by using the Actis system of KURABO Co., Ltd., of Japan in the concrete beam bending test. 2. The images are segmented into blocks… More >

H. M. Rehan Afzal^1,2,*, Suhuai Luo¹, Saadallah Ramadan^1,2, Jeannette Lechner-Scott^1,2,3, Mohammad Ruhul Amin³, Jiaming Li⁴, M. Kamran Afzal⁵

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 977-991, 2021, DOI:10.32604/cmc.2020.012448

Abstract The diagnosis of multiple sclerosis (MS) is based on accurate detection of lesions on magnetic resonance imaging (MRI) which also provides ongoing essential information about the progression and status of the disease. Manual detection of lesions is very time consuming and lacks accuracy. Most of the lesions are difficult to detect manually, especially within the grey matter. This paper proposes a novel and fully automated convolution neural network (CNN) approach to segment lesions. The proposed system consists of two 2D patchwise CNNs which can segment lesions more accurately and robustly. The first CNN network is implemented to segment lesions accurately,… More >

Rizwan Ali Naqvi¹, Dildar Hussain², Woong-Kee Loh^3,*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 715-732, 2021, DOI:10.32604/cmc.2020.013249

Abstract Multiple ocular region segmentation plays an important role in different applications such as biometrics, liveness detection, healthcare, and gaze estimation. Typically, segmentation techniques focus on a single region of the eye at a time. Despite the number of obvious advantages, very limited research has focused on multiple regions of the eye. Similarly, accurate segmentation of multiple eye regions is necessary in challenging scenarios involving blur, ghost effects low resolution, off-angles, and unusual glints. Currently, the available segmentation methods cannot address these constraints. In this paper, to address the accurate segmentation of multiple eye regions in unconstrainted scenarios, a lightweight outer… More >

Yuanyuan Liu^1,2, Shuo Zhang¹, Haiye Yu³, Yueyong Wang^4,*, Yuehan Feng¹, Jiahui Sun¹, Xiaokang Zhou¹

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 247-262, 2021, DOI:10.32604/cmc.2020.012328

Abstract Intelligent straw coverage detection plays an important role in agricultural production and the ecological environment. Traditional pattern recognition has some problems, such as low precision and a long processing time, when segmenting complex farmland, which cannot meet the conditions of embedded equipment deployment. Based on these problems, we proposed a novel deep learning model with high accuracy, small model size and fast running speed named Residual Unet with Attention mechanism using depthwise convolution (RADw–UNet). This algorithm is based on the UNet symmetric codec model. All the feature extraction modules of the network adopt the residual structure, and the whole network… More >

Jiajie Mai¹, Xuemiao Xu^2,*, Guorong Xiao³, Zijun Deng², Jiaxing Chen²

Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 847-855, 2020, DOI:10.32604/iasc.2020.010119

Abstract The Salient object detection aims to segment out the most visually distinctive objects in an image, which is a challenging task in computer vision. In this paper, we present the PGCA-Net equipped with the pyramid guided channel attention fusion block (PGCAFB) for the saliency detection task. Given an input image, the hierarchical features are extracted using a deep convolutional neural network (DCNN), then starting from the highest-level semantic features, we stage-by-stage restore the spatial saliency details by aggregating the lowerlevel detailed features. Since for the weak discriminative ability of the shallow detailed features, directly introducing them to the semantic features… More >

Eslam Mohammed Abdelkader^1,2,*, Osama Moselhi³, Mohamed Marzouk⁴, Tarek Zayed⁵

Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 643-661, 2020, DOI:10.32604/iasc.2020.010100

Abstract Image segmentation is one of the fundamental stages in computer vision applications. Several meta-heuristics have been applied to solve the segmentation problems by extending the Otsu and entropy functions. However, no single-objective function can optimally handle the diversity of information in images besides the multimodality issues of gray-level images. This paper presents a self-adaptive multi-objective optimization-based method for the detection of crack images in reinforced concrete bridges. The proposed method combines the flexibility of information theory functions in addition to the invasive weed optimization algorithm for bi-level thresholding. The capabilities of the proposed method are demonstrated through comparisons with singleobjective… More >

Guangli Zhu, Wenting Liu, Shunxiang Zhang^*, Xiang Chen , Chang Yin

Computer Systems Science and Engineering, Vol.35, No.3, pp. 223-232, 2020, DOI:10.32604/csse.2020.35.223

Abstract The current method of extracting new login sentiment words not only ignores the diversity of patterns constituted by new multi-character words (the number of words is greater than two), but also disregards the influence of other new words co-occurring with a new word connoting sentiment. To solve this problem, this paper proposes a method for extracting new login sentiment words from Chinese micro-blog based on improved mutual information. First, micro-blog data are preprocessed, taking into consideration some nonsense signals such as web links and punctuation. Based on preprocessed data, the candidate strings are obtained by applying the N-gram segmentation method.… More >

Thamotharan B^1,*, Venkatraman B², Chandrasekaran S³

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 335-340, 2020, DOI:10.31209/2019.100000156

Abstract Accumulation of impurities within cold trap device results in degradation of efficient performance in a nuclear reactor systems. The impurities have to be identified and the device has to be replaced periodically based on the accumulation level. Though there are a few techniques available to identify these impurities from the cold trap device, there are certain limitations in these techniques. In order to overcome these constraints, a new harmless and easy approach for identifying and separating the impurities using the radiographic images of cold traps is proposed in this paper. It includes a new segmentation algorithm to segregate the deposited… More >

R.V.V. Krishna^1,*, S. Srinivas Kumar²

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 281-290, 2020, DOI:10.31209/2019.100000121

Abstract In this paper, a color image segmentation algorithm is proposed by extracting both texture and color features and applying them to the one -against-all multi class support vector machine (MSVM) classifier for segmentation. Local Binary Pattern is used for extracting the textural features and L*a*b color model is used for obtaining the color features. The MSVM is trained using the samples obtained from a novel soft rough fuzzy c-means (SRFCM) clustering. The fuzzy set based membership functions capably handle the problem of overlapping clusters. The lower and upper approximation concepts of rough sets deal well with uncertainty, vagueness, and incompleteness… More >

Yong Luo¹, Xiaojie Li², Chao Luo², Feng Wang¹, Xi Wu², Imran Mumtaz³, Cheng Yi^{1, *}

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1771-1780, 2020, DOI:10.32604/cmc.2020.010069

Abstract Tissue segmentation is a fundamental and important task in nasopharyngeal images analysis. However, it is a challenging task to accurately and quickly segment various tissues in the nasopharynx region due to the small difference in gray value between tissues in the nasopharyngeal image and the complexity of the tissue structure. In this paper, we propose a novel tissue segmentation approach based on a two-stage learning framework and U-Net. In the proposed methodology, the network consists of two segmentation modules. The first module performs rough segmentation and the second module performs accurate segmentation. Considering the training time and the limitation of… More >