Noreen Mushtaq¹, Arfat Ahmad Khan², Faizan Ahmed Khan³, Muhammad Junaid Ali⁴, Malik Muhammad Ali Shahid⁵, Chitapong Wechtaisong^2,*, Peerapong Uthansakul²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5793-5806, 2022, DOI:10.32604/cmc.2022.024316

Abstract Astrocytoma IV or glioblastoma is one of the fatal and dangerous types of brain tumors. Early detection of brain tumor increases the survival rate and helps in reducing the fatality rate. Various imaging modalities have been used for diagnosing by expert radiologists, and Medical Resonance Image (MRI) is considered a better option for detecting brain tumors as MRI is a non-invasive technique and provides better visualization of the brain region. One of the challenging issues is to identify the tumorous region from the MRI scans correctly. Manual segmentation is performed by medical experts, which is a time-consuming task and got… More >

Jung-Hwa Kim¹, Jin-Woo Jeong^2,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5107-5132, 2022, DOI:10.32604/cmc.2022.021107

Abstract Gaze estimation is one of the most promising technologies for supporting indoor monitoring and interaction systems. However, previous gaze estimation techniques generally work only in a controlled laboratory environment because they require a number of high-resolution eye images. This makes them unsuitable for welfare and healthcare facilities with the following challenging characteristics: 1) users’ continuous movements, 2) various lighting conditions, and 3) a limited amount of available data. To address these issues, we introduce a multi-view multi-modal head-gaze estimation system that translates the user’s head orientation into the gaze direction. The proposed system captures the user using multiple cameras with… More >

Yunping Zhao¹, Weixuan Liang¹, Jianzhuang Lu^1,*, Xiaowen Chen¹, Nijiwa Kong²

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2709-2722, 2021, DOI:10.32604/cmc.2021.013389

Abstract Late fusion multi-view clustering (LFMVC) algorithms aim to integrate the base partition of each single view into a consensus partition. Base partitions can be obtained by performing kernel k-means clustering on all views. This type of method is not only computationally efficient, but also more accurate than multiple kernel k-means, and is thus widely used in the multi-view clustering context. LFMVC improves computational efficiency to the extent that the computational complexity of each iteration is reduced from O(n³) to O(n) (where n is the number of samples). However, LFMVC also limits the search space of the optimal solution, meaning that… More >

Jiabin Wang^*, Kai Peng

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 345-363, 2020, DOI:10.32604/cmes.2020.011046

Abstract In many existing multi-view gait recognition methods based on images or video sequences, gait sequences are usually used to superimpose and synthesize images and construct energy-like template. However, information may be lost during the process of compositing image and capture EMG signals. Errors and the recognition accuracy may be introduced and affected respectively by some factors such as period detection. To better solve the problems, a multi-view gait recognition method using deep convolutional neural network and channel attention mechanism is proposed. Firstly, the sliding time window method is used to capture EMG signals. Then, the back-propagation learning algorithm is used… More >

Minjae Seo¹, Jong-Ho Paik^{2, *}

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2571-2589, 2020, DOI:10.32604/cmc.2020.011347

Abstract The trend in video viewing has been evolving beyond simply providing a multiview option. Recently, a function that allows selection and viewing of a clip from a multiview service that captures a specific range or object has been added. In particular, the freeview service is an extended concept of multi-view and provides a freer viewpoint. However, since numerous videos and additional data are required for its construction, all of the clips constituting the content cannot be simultaneously provided. Only certain clips are selected and provided to the user. If the video is not the preferred video, change request is made,… More >

Rong Duan¹, Junshan Tan^{1, *}, Jiaohua Qin¹, Xuyu Xiang¹, Yun Tan¹, Neal N. Xiong²

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2335-2350, 2020, DOI:10.32604/cmc.2020.012161

Abstract In recent years, with the massive growth of image data, how to match the image required by users quickly and efficiently becomes a challenge. Compared with single-view feature, multi-view feature is more accurate to describe image information. The advantages of hash method in reducing data storage and improving efficiency also make us study how to effectively apply to large-scale image retrieval. In this paper, a hash algorithm of multi-index image retrieval based on multi-view feature coding is proposed. By learning the data correlation between different views, this algorithm uses multi-view data with deeper level image semantics to achieve better retrieval… More >

Junshan Tan¹, Rong Duan¹, Jiaohua Qin^{1, *}, Xuyu Xiang¹, Yun Tan¹

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 675-689, 2020, DOI:10.32604/cmc.2020.07730

Abstract Hashing technology has the advantages of reducing data storage and improving the efficiency of the learning system, making it more and more widely used in image retrieval. Multi-view data describes image information more comprehensively than traditional methods using a single-view. How to use hashing to combine multi-view data for image retrieval is still a challenge. In this paper, a multi-view fusion hashing method based on RKCCA (Random Kernel Canonical Correlation Analysis) is proposed. In order to describe image content more accurately, we use deep learning dense convolutional network feature DenseNet to construct multi-view by combining GIST feature or BoW_SIFT (Bag-of-Words… More >

Yiming Xue¹, Nan Wang², Yan Niu¹, Ping Zhong^{2, ∗}, Shaozhang Niu³, Yuntao Song⁴

CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 741-756, 2019, DOI:10.32604/cmc.2019.05611

Abstract In practical application, many objects are described by multi-view features because multiple views can provide a more informative representation than the single view. When dealing with the multi-view data, the high dimensionality is often an obstacle as it can bring the expensive time consumption and an increased chance of over-fitting. So how to identify the relevant views and features is an important issue. The matrix-based multi-view feature selection that can integrate multiple views to select relevant feature subset has aroused widely concern in recent years. The existing supervised multi-view feature selection methods usually concatenate all views into the long vectors… More >