Space Target Image Categorization Based on the Second Representation

2014 ◽  
Vol 35 (5) ◽  
pp. 1247-1251
Author(s):  
Fei-yun Jiang ◽  
Rui Sun ◽  
Xu-dong Zhang ◽  
Chao Li
2021 ◽  
Vol 41 (3) ◽  
pp. 0315002
Author(s):  
杨小姗 Yang Xiaoshan ◽  
潘雪峰 Pan Xuefeng ◽  
苏少杰 Su Shaojie ◽  
贾鹏 Jia Peng

2017 ◽  
Vol 56 (5) ◽  
pp. 053102 ◽  
Author(s):  
Zhisheng Gao ◽  
Miao Yang ◽  
Chunzhi Xie

2018 ◽  
Vol 313 ◽  
pp. 295-305 ◽  
Author(s):  
Zhisheng Gao ◽  
Chen Shen ◽  
Chunzhi Xie

2021 ◽  
Vol 15 ◽  
pp. 174830262110080
Author(s):  
Changjun Zha* ◽  
Qian Zhang* ◽  
Huimin Duan

Traditional single-pixel imaging systems are aimed mainly at relatively static or slowly changing targets. When there is relative motion between the imaging system and the target, sizable deviations between the measurement values and the real values can occur and result in poor image quality of the reconstructed target. To solve this problem, a novel dynamic compressive imaging system is proposed. In this system, a single-column digital micro-mirror device is used to modulate the target image, and the compressive measurement values are obtained for each column of the image. Based on analysis of the measurement values, a new recovery model of dynamic compressive imaging is given. Differing from traditional reconstruction results, the measurement values of any column of vectors in the target image can be used to reconstruct the vectors of two adjacent columns at the same time. Contingent upon characteristics of the results, a method of image quality enhancement based on an overlapping average algorithm is proposed. Simulation experiments and analysis show that the proposed dynamic compressive imaging can effectively reconstruct the target image; and that when the moving speed of the system changes within a certain range, the system reconstructs a better original image. The system overcomes the impact of dynamically changing speeds, and affords significantly better performance than traditional compressive imaging.


2021 ◽  
pp. 174702182110097
Author(s):  
Niamh Hunnisett ◽  
Simone Favelle

Unfamiliar face identification is concerningly error prone, especially across changes in viewing conditions. Within-person variability has been shown to improve matching performance for unfamiliar faces, but this has only been demonstrated using images of a front view. In this study, we test whether the advantage of within-person variability from front views extends to matching to target images of a face rotated in view. Participants completed either a simultaneous matching task (Experiment 1) or a sequential matching task (Experiment 2) in which they were tested on their ability to match the identity of a face shown in an array of either one or three ambient front-view images, with a target image shown in front, three-quarter, or profile view. While the effect was stronger in Experiment 2, we found a consistent pattern in match trials across both experiments in that there was a multiple image matching benefit for front, three-quarter, and profile-view targets. We found multiple image effects for match trials only, indicating that providing observers with multiple ambient images confers an advantage for recognising different images of the same identity but not for discriminating between images of different identities. Signal detection measures also indicate a multiple image advantage despite a more liberal response bias for multiple image trials. Our results show that within-person variability information for unfamiliar faces can be generalised across views and can provide insights into the initial processes involved in the representation of familiar faces.


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