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We consider the 3D object recognition problem from the perspective of the lack of labelled data. In this paper, we propose a novel progressive conditional generative adversarial network (PC-GAN) for 3D object recognition by conditioning the input with progressive learning strategies. PC-GAN is a powerful adversarial model whose generator automatically produces realistic 3D objects with annotations, and the discriminator distinguishes them from the training distribution and recognizes their categories. We train the discriminative classifier simultaneously with the generator to predict the class label by embedding a SoftMax classifier. Progressive learning uses input samples from lower to higher resolutions to increase the generator performance gradually and produce informative objects for a certain class of objects. The key idea of adopting progressing learning is to mitigate overshoots issues of the discriminator and increase variations in the generated objects by learning progressively. This strategy helps the generator to produce more realistic synthetic objects and improve the active classification performance of the discriminator. Our proposed PC-GAN is trained for object classification in a supervised manner and the performance is evaluated on two public datasets. Experimental results demonstrate that our adversarial PC-GAN outperforms the existing volumetric discriminative classifiers in term of classification accuracy.
A.A.M. Muzahid; Wan Wanggen; Ferdous Sohel; Mohammed Bennamoun; Li Hou; Hidayat Ullah. Progressive conditional GAN-based augmentation for 3D object recognition. Neurocomputing 2021, 460, 20 -30.
AMA StyleA.A.M. Muzahid, Wan Wanggen, Ferdous Sohel, Mohammed Bennamoun, Li Hou, Hidayat Ullah. Progressive conditional GAN-based augmentation for 3D object recognition. Neurocomputing. 2021; 460 ():20-30.
Chicago/Turabian StyleA.A.M. Muzahid; Wan Wanggen; Ferdous Sohel; Mohammed Bennamoun; Li Hou; Hidayat Ullah. 2021. "Progressive conditional GAN-based augmentation for 3D object recognition." Neurocomputing 460, no. : 20-30.
The main purpose of this research is to study the effect of various types of venues on the density distribution of residents and model check-in data from a Location-Based Social Network for the city of Shanghai, China by using combination of multiple temporal, spatial and visualization techniques by classifying users’ check-ins into different venue categories. This article investigates the use of Weibo for big data analysis and its efficiency in various categories instead of manually collected datasets, by exploring the relation between time, frequency, place and category of check-in based on location characteristics and their contributions. The data used in this research was acquired from a famous Chinese microblogs called Weibo, which was preprocessed to get the most significant and relevant attributes for the current study and transformed into Geographical Information Systems format, analyzed and, finally, presented with the help of graphs, tables and heat maps. The Kernel Density Estimation was used for spatial analysis. The venue categorization was based on nature of the physical locations within the city by comparing the name of venue extracted from Weibo dataset with the function such as education for schools or shopping for malls and so on. The results of usage patterns from hours to days, venue categories and frequency distribution into these categories as well as the density of check-in within the Shanghai and contribution of each venue category in its diversity are thoroughly demonstrated, uncovering interesting spatio-temporal patterns including frequency and density of users from different venues at different time intervals, and significance of using geo-data from Weibo to study human behavior in variety of studies like education, tourism and city dynamics based on location-based social networks. Our findings uncover various aspects of activity patterns in human behavior, the significance of venue classes and its effects in Shanghai, which can be applied in pattern analysis, recommendation systems and other interactive applications for these classes.
Naimat Ullah Khan; Wanggen Wan; Shui Yu; A. A. M. Muzahid; Sajid Khan; Li Hou. A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data. ISPRS International Journal of Geo-Information 2020, 9, 733 .
AMA StyleNaimat Ullah Khan, Wanggen Wan, Shui Yu, A. A. M. Muzahid, Sajid Khan, Li Hou. A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data. ISPRS International Journal of Geo-Information. 2020; 9 (12):733.
Chicago/Turabian StyleNaimat Ullah Khan; Wanggen Wan; Shui Yu; A. A. M. Muzahid; Sajid Khan; Li Hou. 2020. "A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data." ISPRS International Journal of Geo-Information 9, no. 12: 733.
Urban green spaces promote outdoor activities and social interaction, which make a significant contribution to the health and well-being of residents. This study presents an approach that focuses on the real spatial and temporal behavior of park visitors in different categories of green parks. We used the large dataset available from the Chinese micro-blog Sina Weibo (often simply referred to as “Weibo”) to analyze data samples, in order to describe the behavioral patterns of millions of people with access to green spaces. We select Shanghai as a case study because urban residential segregation has already taken place, which was expected to be followed by concerns of environmental sustainability. In this research, we utilized social media check-in data to measure and compare the number of visitations to different kinds of green parks. Furthermore, we divided the green spaces into different categories according to their characteristics, and our main findings were: (1) the most popular category based upon the check-in data; (2) changes in the number of visitors according to the time of day; (3) seasonal impacts on behavior in public in relation to the different categories of parks; and (4) gender-based differences. To the best of our knowledge, this is the first study carried out in Shanghai utilizing Weibo data to focus upon the categorization of green space. It is also the first to offer recommendations for planners regarding the type of facilities they should provide to residents in green spaces, and regarding the sustainability of urban environments and smart city architecture.
Qi Liu; Hidayat Ullah; Wanggen Wan; Zhangyou Peng; Li Hou; Sanam Shahla Rizvi; Saqib Ali Haidery; Tong Qu; A. A. M. Muzahid. Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data. Electronics 2020, 9, 1028 .
AMA StyleQi Liu, Hidayat Ullah, Wanggen Wan, Zhangyou Peng, Li Hou, Sanam Shahla Rizvi, Saqib Ali Haidery, Tong Qu, A. A. M. Muzahid. Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data. Electronics. 2020; 9 (6):1028.
Chicago/Turabian StyleQi Liu; Hidayat Ullah; Wanggen Wan; Zhangyou Peng; Li Hou; Sanam Shahla Rizvi; Saqib Ali Haidery; Tong Qu; A. A. M. Muzahid. 2020. "Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data." Electronics 9, no. 6: 1028.
The advancement of low-cost RGB-D and LiDAR three-dimensional (3D) sensors has permitted the obtainment of the 3D model easier in real-time. However, making intricate 3D features is crucial for the advancement of 3D object classifications. The existing volumetric voxel-based CNN approaches have achieved remarkable progress, but they generate huge computational overhead that limits the extraction of global features at higher resolutions of 3D objects. In this paper, a low-cost 3D volumetric deep convolutional neural network is proposed for 3D object classification based on joint multiscale hierarchical and subvolume supervised learning strategies. Our proposed deep neural network inputs 3D data, which are preprocessed by implementing memory-efficient octree representation, and we propose to limit the full layer octree depth to a certain level based on the predefined input volume resolution for storing high-precision contour features. Multiscale features are concatenated from multilevel octree depths inside the network, aiming to adaptively generate high-level global features. The strategy of the subvolume supervision approach is to train the network on subparts of the 3D object in order to learn local features. Our framework has been evaluated with two publicly available 3D repositories. Experimental results demonstrate the effectiveness of our proposed method where the classification accuracy is improved in comparison to existing volumetric approaches, and the memory consumption ratio and run-time are significantly reduced.
A. A. M. Muzahid; Wanggen Wan; Li Hou. A New Volumetric CNN for 3D Object Classification Based on Joint Multiscale Feature and Subvolume Supervised Learning Approaches. Computational Intelligence and Neuroscience 2020, 2020, 1 -17.
AMA StyleA. A. M. Muzahid, Wanggen Wan, Li Hou. A New Volumetric CNN for 3D Object Classification Based on Joint Multiscale Feature and Subvolume Supervised Learning Approaches. Computational Intelligence and Neuroscience. 2020; 2020 ():1-17.
Chicago/Turabian StyleA. A. M. Muzahid; Wanggen Wan; Li Hou. 2020. "A New Volumetric CNN for 3D Object Classification Based on Joint Multiscale Feature and Subvolume Supervised Learning Approaches." Computational Intelligence and Neuroscience 2020, no. : 1-17.
Hidayat Ullah; Wanggen Wan; Saqib Ali Haidery; Naimat Ullah Khan; Zeinab Ebrahimpour; A. A. M. Muzahid. Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports. IEEE Access 2020, 8, 39197 -39211.
AMA StyleHidayat Ullah, Wanggen Wan, Saqib Ali Haidery, Naimat Ullah Khan, Zeinab Ebrahimpour, A. A. M. Muzahid. Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports. IEEE Access. 2020; 8 ():39197-39211.
Chicago/Turabian StyleHidayat Ullah; Wanggen Wan; Saqib Ali Haidery; Naimat Ullah Khan; Zeinab Ebrahimpour; A. A. M. Muzahid. 2020. "Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports." IEEE Access 8, no. : 39197-39211.
We consider the recent challenges of 3D shape analysis based on a volumetric CNN that requires a huge computational power. This high-cost approach forces to reduce the volume resolutions when applying 3D CNN on volumetric data. In this context, we propose a multiorientation volumetric deep neural network (MV-DNN) for 3D object classification with octree generating low-cost volumetric features. In comparison to conventional octree representations, we propose to limit the octree partition to a certain depth to reserve all leaf octants with sparsity features. This allows for improved learning of complex 3D features and increased prediction of object labels at both low and high resolutions. Our auxiliary learning approach predicts object classes based on the subvolume parts of a 3D object that improve the classification accuracy compared to other existing 3D volumetric CNN methods. In addition, the influence of views and depths of the 3D model on the classification performance is investigated through extensive experiments applied to the ModelNet40 database. Our deep learning framework runs significantly faster and consumes less memory than full voxel representations and demonstrate the effectiveness of our octree-based auxiliary learning approach for exploring high resolution 3D models. Experimental results reveal the superiority of our MV-DNN that achieves better classification accuracy compared to state-of-art methods on two public databases.
A. A. M. Muzahid; Wanggen Wan; Ferdous Sohel; Naimat Ullah Khan; Ofelia Delfina Cervantes Villagomez; Hidayat Ullah. 3D Object Classification Using a Volumetric Deep Neural Network: An Efficient Octree Guided Auxiliary Learning Approach. IEEE Access 2020, 8, 23802 -23816.
AMA StyleA. A. M. Muzahid, Wanggen Wan, Ferdous Sohel, Naimat Ullah Khan, Ofelia Delfina Cervantes Villagomez, Hidayat Ullah. 3D Object Classification Using a Volumetric Deep Neural Network: An Efficient Octree Guided Auxiliary Learning Approach. IEEE Access. 2020; 8 (99):23802-23816.
Chicago/Turabian StyleA. A. M. Muzahid; Wanggen Wan; Ferdous Sohel; Naimat Ullah Khan; Ofelia Delfina Cervantes Villagomez; Hidayat Ullah. 2020. "3D Object Classification Using a Volumetric Deep Neural Network: An Efficient Octree Guided Auxiliary Learning Approach." IEEE Access 8, no. 99: 23802-23816.
During mesh processing operations (e.g. simplifications, compression, and watermarking), a 3D triangle mesh is subject to various visible distortions on mesh surface which result in a need to estimate visual quality. The necessity of perceptual quality evaluation is already established, as in most cases, human beings are the end users of 3D meshes. To measure such kinds of distortions, the metrics that consider geometric measures integrating human visual system (HVS) is called perceptual quality metrics. In this paper, we direct an expansive study on 3D mesh quality evaluation mostly focusing on recently proposed perceptual based metrics. We limit our study on greyscale static mesh evaluation and attempt to figure out the most workable method for real-time evaluation by making a quantitative comparison. This paper also discusses in detail how to evaluate objective metric's performance with existing subjective databases. In this work, we likewise research the utilization of the psychometric function to expel non-linearity between subjective and objective values. Finally, we draw a comparison among some selected quality metrics and it shows that curvature tensor based quality metrics predicts consistent result in terms of correlation.
A A M Muzahid; Wanggen Wan; Xiang Feng. Perceptual Quality Evaluation of 3D Triangle Mesh: A Technical Review. 2018 International Conference on Audio, Language and Image Processing (ICALIP) 2018, 266 -272.
AMA StyleA A M Muzahid, Wanggen Wan, Xiang Feng. Perceptual Quality Evaluation of 3D Triangle Mesh: A Technical Review. 2018 International Conference on Audio, Language and Image Processing (ICALIP). 2018; ():266-272.
Chicago/Turabian StyleA A M Muzahid; Wanggen Wan; Xiang Feng. 2018. "Perceptual Quality Evaluation of 3D Triangle Mesh: A Technical Review." 2018 International Conference on Audio, Language and Image Processing (ICALIP) , no. : 266-272.
Acoustic Echo Cancellation (AEC) is employed in hands-free communication systems to enhance communication experience. Double-Talk Detection (DTD) plays a vital role in AEC to distinguish the far-end and near-end signals and its failure will cause the system divergence. An efficient new DTD algorithm based on the joint signal energy and cross-correlation estimation techniques is proposed in this paper to reduce the DTD faults. The proposed algorithm can provide fast DTD and enhanced system performance without increasing much more computational complexity over the conventional techniques. Simulation results show the performance improvement achieved by the proposed technique.
A A M Muzahid; K. M. R. Ingrid; S. I. M. M. Raton Mondol; Y. Zhou. Advanced double-talk detection algorithm based on joint signal energy and cross-correlation estimation. 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN) 2016, 303 -306.
AMA StyleA A M Muzahid, K. M. R. Ingrid, S. I. M. M. Raton Mondol, Y. Zhou. Advanced double-talk detection algorithm based on joint signal energy and cross-correlation estimation. 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN). 2016; ():303-306.
Chicago/Turabian StyleA A M Muzahid; K. M. R. Ingrid; S. I. M. M. Raton Mondol; Y. Zhou. 2016. "Advanced double-talk detection algorithm based on joint signal energy and cross-correlation estimation." 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN) , no. : 303-306.
A A M Muzahid; S.I.M.M. Raton Mondol; K.M.R. Ingrid; Y. Zhou. An Efficient Switching Mechanism Using Voice Activity Detection for Acoustic Echo Cancellation. Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology 2016, 1 .
AMA StyleA A M Muzahid, S.I.M.M. Raton Mondol, K.M.R. Ingrid, Y. Zhou. An Efficient Switching Mechanism Using Voice Activity Detection for Acoustic Echo Cancellation. Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology. 2016; ():1.
Chicago/Turabian StyleA A M Muzahid; S.I.M.M. Raton Mondol; K.M.R. Ingrid; Y. Zhou. 2016. "An Efficient Switching Mechanism Using Voice Activity Detection for Acoustic Echo Cancellation." Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology , no. : 1.