Zhe Liu¹, Charlie Maere^1,*, Yuqing Song¹

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 669-686, 2019, DOI:10.32604/cmc.2019.04590

Abstract Accurately finding the region of interest is a very vital step for segmenting organs in medical image processing. We propose a novel approach of automatically identifying region of interest in Computed Tomography Image (CT) images based on temporal and spatial data . Our method is a 3 stages approach, 1) We extract organ features from the CT images by adopting the Hounsfield filter. 2)We use these filtered features and introduce our novel approach of selecting observable feature candidates by calculating contours’ area and automatically detect a seed point. 3) We use a novel approach to track the growing region changes… More >

Qun Mo¹, Heng Yao¹, Fang Cao², Zheng Chang³, Chuan Qin^1,*

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 119-133, 2019, DOI:10.32604/cmc.2019.05770

Abstract Recently, reversible data hiding in encrypted image (RDHEI) has attracted extensive attention, which can be used in secure cloud computing and privacy protection effectively. In this paper, a novel RDHEI scheme based on block classification and permutation is proposed. Content owner first divides original image into non-overlapping blocks and then set a threshold to classify these blocks into smooth and non-smooth blocks respectively. After block classification, content owner utilizes a specific encryption method, including stream cipher encryption and block permutation to protect image content securely. For the encrypted image, data hider embeds additional secret information in the most significant bits… More >

Yuhan Kang¹, Fenlin Liu¹, Chunfang Yang^1,*, Xiangyang Luo¹, Tingting Zhang²

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 315-329, 2019, DOI:10.32604/cmc.2019.05242

Abstract This study proposes a color image steganalysis algorithm that extracts high-dimensional rich model features from the residuals of channel differences. First, the advantages of features extracted from channel differences are analyzed, and it shown that features extracted in this manner should be able to detect color stego images more effectively. A steganalysis feature extraction method based on channel differences is then proposed, and used to improve two types of typical color image steganalysis features. The improved features are combined with existing color image steganalysis features, and the ensemble classifiers are trained to detect color stego images. The experimental results indicate… More >

Lili Pan¹, Jiaohua Qin^1,*, Hao Chen², Xuyu Xiang¹, Cong Li¹, Ran Chen¹

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 297-313, 2019, DOI:10.32604/cmc.2019.04097

Abstract Image retrieval for food ingredients is important work, tremendously tiring, uninteresting, and expensive. Computer vision systems have extraordinary advancements in image retrieval with CNNs skills. But it is not feasible for small-size food datasets using convolutional neural networks directly. In this study, a novel image retrieval approach is presented for small and medium-scale food datasets, which both augments images utilizing image transformation techniques to enlarge the size of datasets, and promotes the average accuracy of food recognition with state-of-the-art deep learning technologies. First, typical image transformation techniques are used to augment food images. Then transfer learning technology based on deep… More >

Xianyi Chen^1,2,*, Haidong Zhong^1,2, Zhifeng Bao³

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 239-255, 2019, DOI:10.32604/cmc.2019.03572

Abstract Recently, a reversible image transformation (RIT) technology that transforms a secret image to a freely-selected target image is proposed. It not only can generate a stego-image that looks similar to the target image, but also can recover the secret image without any loss. It also has been proved to be very useful in image content protection and reversible data hiding in encrypted images. However, the standard deviation (SD) is selected as the only feature during the matching of the secret and target image blocks in RIT methods, the matching result is not so good and needs to be further improved… More >

Xiaoliang Wang^{1, *}, Wenhua Song¹, Bowei Zhang¹, Brandon Mausler², Frank Jiang^{1, 3}

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 135-147, 2019, DOI:10.32604/cmc.2019.05687

Abstract Nowadays, the number of vehicles in China has increased significantly. The increase of the number of vehicles has also led to the increasingly complex traffic situation and the urgent safety measures in need. However, the existing early warning devices such as geomagnetic, ultrasonic and infrared detection have some shortcomings like difficult installation and maintenance. In addition, geomagnetic detection will damage the road surface, while ultrasonic and infrared detection will be greatly affected by the environment. Considering the shortcomings of the existing solutions, this paper puts forward a solution of early warning for vehicle turning meeting based on image acquisition and… More >

M. Morii¹, N. Hu^1,2, H. Fukunaga³, J.H. Li¹, Y.L. Liu¹, S. Atobe³, Alamusi³, J.H. Qiu⁴

CMC-Computers, Materials & Continua, Vol.26, No.1, pp. 37-66, 2011, DOI:10.3970/cmc.2011.026.037

Abstract Lamb wave tomography (LWT) is a potential and efficient technique for non-destructive tomographic reconstruction of damage images in structural components or materials. A new two-stage inverse algorithm with a small amount of scanning data for quickly reconstructing damage images in aluminum and CFRP laminated plates was proposed in this paper. Due to its high sensitivity to damages, the amplitude decrease of transmitted Lamb waves after travelling through the inspected region was employed as a key signal parameter related to the attenuation of Lamb waves in propagation routes. A through-thickness circular hole and a through-thickness elliptical hole in two aluminum plates,… More >

Ligang Zheng^1,*, Chao Song²

CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 529-539, 2018, DOI: 10.3970/cmc.2018.03780

Abstract There is a steep increase in data encoded as symmetric positive definite (SPD) matrix in the past decade. The set of SPD matrices forms a Riemannian manifold that constitutes a half convex cone in the vector space of matrices, which we sometimes call SPD manifold. One of the fundamental problems in the application of SPD manifold is to find the nearest neighbor of a queried SPD matrix. Hashing is a popular method that can be used for the nearest neighbor search. However, hashing cannot be directly applied to SPD manifold due to its non-Euclidean intrinsic geometry. Inspired by the idea… More >

Manyu Jin¹, Tao Wang¹, Zexuan Ji^1,*, Xiaobo Shen²

CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 501-515, 2018, DOI: 10.3970/cmc.2018.02371

Abstract Perceptual image quality assessment (IQA) is one of the most indispensable yet challenging problems in image processing and computer vision. It is quite necessary to develop automatic and efficient approaches that can accurately predict perceptual image quality consistently with human subjective evaluation. To further improve the prediction accuracy for the distortion of color images, in this paper, we propose a novel effective and efficient IQA model, called perceptual gradient similarity deviation (PGSD). Based on the gradient magnitude similarity, we proposed a gradient direction selection method to automatically determine the pixel-wise perceptual gradient. The luminance and chrominance channels are both took… More >

Qidi Wu¹, Yibing Li¹, Yun Lin^1,*, Ruolin Zhou²

CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 91-105, 2018, DOI: 10.3970/cmc.2018.02771

Abstract The conventional sparse representation-based image classification usually codes the samples independently, which will ignore the correlation information existed in the data. Hence, if we can explore the correlation information hidden in the data, the classification result will be improved significantly. To this end, in this paper, a novel weighted supervised spare coding method is proposed to address the image classification problem. The proposed method firstly explores the structural information sufficiently hidden in the data based on the low rank representation. And then, it introduced the extracted structural information to a novel weighted sparse representation model to code the samples in… More >