scholarly journals Visual Recognition Method Based on Hybrid KPCA Network

2020 ◽  
Vol E103.D (9) ◽  
pp. 2015-2018
Author(s):  
Feng YANG ◽  
Zheng MA ◽  
Mei XIE
2020 ◽  
Vol 17 (5) ◽  
pp. 172988142094872
Author(s):  
Chenlei Jiao ◽  
Binbin Lian ◽  
Zhe Wang ◽  
Yimin Song ◽  
Tao Sun

Object recognition is a prerequisite to control a soft gripper successfully grasping an unknown object. Visual and tactile recognitions are two commonly used methods in a grasping system. Visual recognition is limited if the size and weight of the objects are involved, whereas the efficiency of tactile recognition is a problem. A visual–tactile recognition method is proposed to overcome the disadvantages of both methods in this article. The design and fabrication of the soft gripper considering the visual and tactile sensors are implemented, where the Kinect v2 is adopted for visual information, bending and pressure sensors are embedded to the soft fingers for tactile information. The proposed method is divided into three steps: initial recognition by vision, detail recognition by touch, and a data fusion decision making. Experiments show that the visual–tactile recognition has the best results. The average recognition accuracy of the daily objects by the proposed method is also the highest. The feasibility of the visual–tactile recognition is verified.


2021 ◽  
Vol 13 (15) ◽  
pp. 8439
Author(s):  
Rui Zhang ◽  
Yuwei Zhao ◽  
Jianlei Kong ◽  
Chen Cheng ◽  
Xinyan Liu ◽  
...  

Decorative openwork windows (DO-Ws) in Suzhou traditional private gardens play a vital role in Chinese traditional garden art. Due to the delicate and elegant patterns, as well as their rich cultural meaning, DO-Ws have quite high protection and utilization value. In this study, we firstly visited 15 extant traditional gardens in Suzhou and took almost 3000 photos to establish the DO-W datasets. Then, we present an effective visual recognition method named CSV-Net to classify different DO-Ws’ patterns in Suzhou traditional gardens. On the basis of the backbone module of the cross stage partial network optimized with the Soft-VLAD architecture, the proposed CSV-Net achieves a preferable representation ability for distinguishing different DO-Ws in practical scenes. The comparative experimental results show that the CSV-Net model achieves a good balance between its performance, robustness and complexity for identifying DO-Ws, also having further potential for sustainable application in traditional gardens. Moreover, the Canglang Pavilion and the Humble Administrator’s Garden were selected as the cases to analyze the relation between identifying DO-W types and their locations in intelligent approaches, which further reveals the design rules of the sustainable culture contained in Chinese traditional gardens. This work ultimately promotes the sustainable application of artificial intelligence technology in the field of garden design and inheritance of the garden art.


Author(s):  
Nicolas Poirel ◽  
Claire Sara Krakowski ◽  
Sabrina Sayah ◽  
Arlette Pineau ◽  
Olivier Houdé ◽  
...  

The visual environment consists of global structures (e.g., a forest) made up of local parts (e.g., trees). When compound stimuli are presented (e.g., large global letters composed of arrangements of small local letters), the global unattended information slows responses to local targets. Using a negative priming paradigm, we investigated whether inhibition is required to process hierarchical stimuli when information at the local level is in conflict with the one at the global level. The results show that when local and global information is in conflict, global information must be inhibited to process local information, but that the reverse is not true. This finding has potential direct implications for brain models of visual recognition, by suggesting that when local information is conflicting with global information, inhibitory control reduces feedback activity from global information (e.g., inhibits the forest) which allows the visual system to process local information (e.g., to focus attention on a particular tree).


Author(s):  
Dean E. Stolldorf ◽  
Gordon M. Redding ◽  
Leon M. Manelis
Keyword(s):  

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