Rosepreet Kaur Bhogal^1,*, V. Devendran²

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2759-2772, 2023, DOI:10.32604/csse.2023.034862

Abstract Human activity recognition is a recent area of research for researchers. Activity recognition has many applications in smart homes to observe and track toddlers or oldsters for their safety, monitor indoor and outdoor activities, develop Tele immersion systems, or detect abnormal activity recognition. Three dimensions (3D) skeleton data is robust and somehow view-invariant. Due to this, it is one of the popular choices for human action recognition. This paper proposed using a transversal tree from 3D skeleton data to represent videos in a sequence. Further proposed two neural networks: convolutional neural network recurrent neural network_1 (CNN_RNN_1), used to find the… More >

Jinxing Niu^1,*, Shaokui Gu¹, Junmin Du², Yongxing Hao¹

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2015-2031, 2023, DOI:10.32604/cmc.2023.040489

Abstract With the continuous development of the economy and society, plastic pollution in rivers, lakes, oceans, and other bodies of water is increasingly severe, posing a serious challenge to underwater ecosystems. Effective cleaning up of underwater litter by robots relies on accurately identifying and locating the plastic waste. However, it often causes significant challenges such as noise interference, low contrast, and blurred textures in underwater optical images. A weighted fusion-based algorithm for enhancing the quality of underwater images is proposed, which combines weighted logarithmic transformations, adaptive gamma correction, improved multi-scale Retinex (MSR) algorithm, and the contrast limited adaptive histogram equalization (CLAHE)… More >

Jing Xin^1,*, Kenan Du¹, Jiale Feng¹, Mao Shan²

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2621-2640, 2023, DOI:10.32604/cmes.2023.027467

Abstract This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images. The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance. To address these issues, we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model. Then, an indoor RGB-D image semantic segmentation network is proposed, which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud model. Finally, Bayesian updating is… More >

Jia-Yu Liang¹, Hong-Bo Zhang^1,*, Qing Lei², Ji-Xiang Du³, Tian-Liang Lin⁴

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3243-3256, 2023, DOI:10.32604/iasc.2023.035812

Abstract Monocular 6D pose estimation is a functional task in the field of computer vision and robotics. In recent years, 2D-3D correspondence-based methods have achieved improved performance in multiview and depth data-based scenes. However, for monocular 6D pose estimation, these methods are affected by the prediction results of the 2D-3D correspondences and the robustness of the perspective-n-point (PnP) algorithm. There is still a difference in the distance from the expected estimation effect. To obtain a more effective feature representation result, edge enhancement is proposed to increase the shape information of the object by analyzing the influence of inaccurate 2D-3D matching on… More >

Siddiqui Muhammad Yasir¹, Hyunsik Ahn^2,*

Journal on Artificial Intelligence, Vol.4, No.2, pp. 99-114, 2022, DOI:10.32604/jai.2022.031235

Abstract The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D segmentation. Segmentation is challenging with point cloud data due to substantial redundancy, fluctuating sample density and lack of apparent organization. The research area has a wide range of robotics applications, including intelligent vehicles, autonomous mapping and navigation. A number of researchers have introduced various methodologies and algorithms. Deep learning has been successfully used to a spectrum of 2D vision domains as a prevailing A.I. methods. However, due to the specific problems of processing point clouds… More >

Siddiqui Muhammad Yasir¹, Amin Muhammad Sadiq², Hyunsik Ahn^3,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5777-5791, 2022, DOI:10.32604/cmc.2022.025909

Abstract 3D object recognition is a challenging task for intelligent and robot systems in industrial and home indoor environments. It is critical for such systems to recognize and segment the 3D object instances that they encounter on a frequent basis. The computer vision, graphics, and machine learning fields have all given it a lot of attention. Traditionally, 3D segmentation was done with hand-crafted features and designed approaches that didn’t achieve acceptable performance and couldn’t be generalized to large-scale data. Deep learning approaches have lately become the preferred method for 3D segmentation challenges by their great success in 2D computer vision. However,… More >

Reena Thomas^1,*, M. Sucharitha²

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 879-889, 2022, DOI:10.32604/iasc.2022.024070

Abstract The major intention of this work is to introduce a novel hybrid image watermarking technique for RGB color images. This hybrid watermarking algorithm uses two transforms such as Contourlet and Gould transform. The Contourlet transform is used as first stage while the Gould transform is used as second stage. In the watermark embedding phase, the R, G and B channels are transformed using Contourlet transform. The bandpass directional sub band coefficients of Contourlet transformed image are then divided into sub-blocks. The sub-blocks are then transformed using Gould transform and the watermark information is embedded on the initial coefficients of each… More >

N. Shyla^*, K. Kalimuthu

Journal of Information Hiding and Privacy Protection, Vol.3, No.3, pp. 143-149, 2021, DOI:10.32604/jihpp.2021.026760

Abstract The term “steganography” is derived from the Greek words steganos, which means “verified, concealed, or guaranteed”, and graphein, which means “writing”. The primary motivation for considering steganography is to prevent unapproved individuals from obtaining disguised data. With the ultimate goal of comprehending the fundamental inspiration driving the steganography procedures, there should be no significant change in the example report. The Least Significant Bit (LSB) system, which is one of the methodologies for concealing propelled picture data, is examined in this assessment. In this evaluation, another procedure for data stowing indefinitely is proposed with the ultimate goal of limiting the progressions… More >

Javaria Amin¹ , Muhammad Almas Anjum² , Abida Sharif³ , Mudassar Raza⁴ , Seifedine Kadry⁵, Yunyoung Nam^6,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6023-6039, 2022, DOI:10.32604/cmc.2022.019115

Abstract

Malaria is a severe illness triggered by parasites that spreads via mosquito bites. In underdeveloped nations, malaria is one of the top causes of mortality, and it is mainly diagnosed through microscopy. Computer-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected groups. Therefore, in this study, we present a new idea based on the ensemble quantum-classical framework for malaria classification. The methods comprise three core steps: localization, segmentation, and classification. In the first core step, an improved FRCNN model is proposed for the localization of the infected malaria cells. Then, the… More >

Xinyue Zhang¹, Ting Jin^1,*, Mingjie Han¹, Jingsheng Lei², Zhichao Cao³

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 439-452, 2021, DOI:10.32604/iasc.2021.018643

Abstract Saliency prediction has recently gained a large number of attention for the sake of the rapid development of deep neural networks in computer vision tasks. However, there are still dilemmas that need to be addressed. In this paper, we design a visual saliency prediction model using attention-based cross-model integration strategies in RGB-D images. Unlike other symmetric feature extraction networks, we exploit asymmetric networks to effectively extract depth features as the complementary information of RGB information. Then we propose attention modules to integrate cross-modal feature information and emphasize the feature representation of salient regions, meanwhile neglect the surrounding unimportant pixels, so… More >