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3D convolutional neural network for object recognition: a review
Multimedia Tools and Applications, 2018Recognition of an object from an image or image sequences is an important task in computer vision. It is an important low-level image processing operation and plays a crucial role in many real-world applications. The challenges involved in object recognition are multi-model, multi-pose, complicated background, and depth variations.
Rahul Dev Singh +2 more
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LipsID Using 3D Convolutional Neural Networks
2018This paper presents a proposition for a method inspired by iVectors for improvement of visual speech recognition in the similar way iVectors are used to improve the recognition rate of audio speech recognition. A neural network for feature extraction is presented with training parameters and evaluation.
Miroslav Hlavác +3 more
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Deep 3D Convolution Neural Network for Alzheimer’s Detection
2020One of the most well-known and complex applications of artificial intelligence (AI) is Alzheimer’s detection, which lies in the field of medical imaging. The complexity in this task lies in the three-dimensional structure of the MRI scan images. In this paper, we propose to use 3D Convolutional Neural Networks (3D-CNN) for Alzheimer’s detection.
Charul Giri +2 more
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Classification of Ciliary Motion with 3D Convolutional Neural Networks
Proceedings of the SouthEast Conference, 2017Cilia are hair-like structures found on nearly every cell in the body. Motile cilia in the nasal and bronchial passages work to propel mucus and foreign matter from the respiratory system. Abnormal ciliary motion results in a wide spectrum of disorders.
Charles Lu 0001, Shannon Quinn
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Frequency Domain Compact 3D Convolutional Neural Networks
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020This paper studies the compression and acceleration of 3-dimensional convolutional neural networks (3D CNNs). To reduce the memory cost and computational complexity of deep neural networks, a number of algorithms have been explored by discovering redundant parameters in pre-trained networks. However, most of existing methods are designed for processing
Hanting Chen +8 more
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Laughter Classification Using 3D Convolutional Neural Networks
Proceedings of the 2019 3rd International Conference on Advances in Artificial Intelligence, 2019Social signals express the attitude of human being in social situations. Laughter has been determined as an important social signal that can predict emotional information of people. It conveys different emotions such as happiness, surprise, fear, anger, and anxiety. Therefore, identifying and extracting emotions in the laughter is useful for estimating
Faramarz Ataollahi +1 more
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A Geometric Convolutional Neural Network for 3D Object Detection
2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2019We propose a method for accurate 3D vehicle detection based on geometric deep neural networks. From only a single RGB image, the framework is able to recover the 3D positions and predict 3D bounding boxes. In particular, the algorithm leverages single image depth estimation and semantic segmentation to produce 3D point cloud for specific objects.
Yawen Lu, Qianyu Guo, Guoyu Lu 0001
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Psychiatric Disorders Classification with 3D Convolutional Neural Networks
2019Recently, the literature showed that psychiatric disorders, such as Schizophrenia and Bipolar disorder, cause abnormalities in some brain regions. Therefore, several automatic mechanisms based on classical Machine Learning techniques have been used to recognize these diseases by means of the study of neuroimaging. A serious drawback of these approaches
Campese, Stefano +4 more
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Video Generation Using 3D Convolutional Neural Network
Proceedings of the 24th ACM international conference on Multimedia, 2016Recently, content generation using neural network has been widely studied. Motivated by this recent progress, we studied the generation of videos using only a label as input. In our method, we iteratively minimize two objective functions at the same time : an objective function to evaluate how close the video is to the target class and another to ...
Shohei Yamamoto, Tatsuya Harada
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Hand gesture recognition with 3D convolutional neural networks
2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2015Touchless hand gesture recognition systems are becoming important in automotive user interfaces as they improve safety and comfort. Various computer vision algorithms have employed color and depth cameras for hand gesture recognition, but robust classification of gestures from different subjects performed under widely varying lighting conditions is ...
Pavlo Molchanov 0001 +3 more
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