Real-time continuous feature extraction in large size satellite images

Rathore, M. Mazhar; Ahmad, Awais; Paul, Anand; Wu, Jiaji

doi:10.1016/j.sysarc.2015.11.006

Cited by 19 publications

(7 citation statements)

References 18 publications

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“…Low-levels feature are also known as hand-crafted features. Many researchers employ low-level feature-based algorithms to address facial expression problems, which achieves stateof-the-art performance in areas of pattern recognition and image understanding [22]- [27].…”

Section: Related Work and Motivation A Low-level Feature-based Methodsmentioning

confidence: 99%

A Deep Spatial and Temporal Aggregation Framework for Video-Based Facial Expression Recognition

et al. 2019

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Video-based facial expression recognition is a long-standing problem owing to a gap between visual features and emotions, difficulties in tracking the subtle movement of muscles and limited datasets.The key to solving this problem is to exploit effective features characterizing facial expression to perform facial expression recognition. We propose an effective framework to solve these problems. In our work, both spatial information and temporal information are utilized through the aggregation layer of a framework that fuses two state-of-the-art stream networks. We investigate different strategies for pooling across spatial information and temporal information. We find that it is effective to pool jointly across spatial information and temporal information for video-based facial expression recognition. Our framework is end-to-end trainable for whole-video recognition. In addressing the problem of facial recognition, the main contribution of this project is the design of a novel, trainable deep neural network framework that fuses spatial information and temporal information of video according to CNNs and LSTMs for pattern recognition. The experimental results on two public datasets, i.e., the RML and eNTERFACE05 databases, show that our framework outperforms previous state-of-the-art frameworks.INDEX TERMS Video-based facial expression recognition, CNNs, deep temporal-spatial features, optical flow, LSTM.

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Section: Related Work and Motivation A Low-level Feature-based Methodsmentioning

confidence: 99%

A Deep Spatial and Temporal Aggregation Framework for Video-Based Facial Expression Recognition

et al. 2019

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“…Besides, some works [39] try to utilize convolutional neural network (CNN) network to extract hyper-level feature from the original signal. In addition, there are many different feature extraction methods in areas of gesture tracking and understanding fields [40][41][42][43][44][45][46].…”

Section: Related Workmentioning

confidence: 99%

Towards Location Independent Gesture Recognition with Commodity WiFi Devices

Wang

2019

Electronics

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Recently, WiFi-based gesture recognition has attracted increasing attention. Due to the sensitivity of WiFi signals to environments, an activity recognition model trained at a specific place can hardly work well for other places. To tackle this challenge, we propose WiHand, a location independent gesture recognition system based on commodity WiFi devices. Leveraging the low rank and sparse decomposition, WiHand separates gesture signal from background information, thus making it resilient to location variation. Extensive evaluations showed that WiHand can achieve an average accuracy of 93% for various locations. In addition, WiHand works well under through the wall scenario.

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“…The state of the art features and algorithms used in different areas of object tracking and understanding are demonstrated in Ref. [50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68].…”

Section: Related Workmentioning

confidence: 99%

MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning

Yuan

Li²,

Jiang

et al. 2019

Future Internet

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In the context of Industry 4.0, the most popular way to identify and track objects is to add tags, and currently most companies still use cheap quick response (QR) tags, which can be positioned by computer vision (CV) technology. In CV, instance segmentation (IS) can detect the position of tags while also segmenting each instance. Currently, the mask region-based convolutional neural network (Mask R-CNN) method is used to realize IS, but the completeness of the instance mask cannot be guaranteed. Furthermore, due to the rich texture of QR tags, low-quality images can lower intersection-over-union (IoU) significantly, disabling it from accurately measuring the completeness of the instance mask. In order to optimize the IoU of the instance mask, a QR tag IS method named the mask UNet region-based convolutional neural network (MU R-CNN) is proposed. We utilize the UNet branch to reduce the impact of low image quality on IoU through texture segmentation. The UNet branch does not depend on the features of the Mask R-CNN branch so its training process can be carried out independently. The pre-trained optimal UNet model can ensure that the loss of MU R-CNN is accurate from the beginning of the end-to-end training. Experimental results show that the proposed MU R-CNN is applicable to both high-and low-quality images, and thus more suitable for Industry 4.0.

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Real-time continuous feature extraction in large size satellite images

Cited by 19 publications

References 18 publications

A Deep Spatial and Temporal Aggregation Framework for Video-Based Facial Expression Recognition

A Deep Spatial and Temporal Aggregation Framework for Video-Based Facial Expression Recognition

Towards Location Independent Gesture Recognition with Commodity WiFi Devices

MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning

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