scholarly journals Pattern Recognition & Image Understanding based on Fuzzy Technology

1997 ◽  
Vol 1 (1) ◽  
pp. 71-78 ◽  
Author(s):  
Kaoru Hirota ◽  
◽  
Yoshinori Arai ◽  
Yukiko Nakagawa ◽  
Keyword(s):  
Pattern Recognition ◽  
Time Use ◽  
Additional Data ◽  
Fuzzy Inference ◽  
Vision System ◽  
Image Understanding ◽  
Robot Arm ◽  
Remote Sensing Images ◽  
Dynamic Image ◽  
Hierarchical Pattern

Four image recognition and understanding techniques based on fuzzy technology developed by the authors group have been surveyed. First topics is a fuzzy clustering with additional data applied to the remote sensing images. It is modified version of the well known FCM. A robot arm and vision system on assembling line is presented using fuzzy discriminant tree for a real time use. The repetition method is introduced into the construction of discriminant tree. Third is the pattern recognition for a models of cars which is applied a fuzzy hierarchical pattern recognition based on fixation feedback. Finally, a fuzzy dynamic image understanding system is presented using fuzzy knowledge base and fuzzy inference method to understand dynamic image understanding on general roads in Japan. These techniques are mentioned the algorithms, and some of them are with experimental results.

scholarly journals Computer Analysis of Architecture Using Automatic Image Understanding

2019 ◽  
Vol 2018 ◽  
Author(s):  
Fan Wei ◽  
Yuan Li ◽  
Lior Shamir
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Machine Vision ◽  
Computer Analysis ◽  
Vision System ◽  
Cognitive Task ◽  
Image Understanding ◽  
New Paradigm ◽  
Complex Cognitive Task ◽  
Architectural Styles

In the past few years, computer vision and pattern recognition systems have been becoming increasingly more powerful, expanding the range of automatic tasks enabled by machine vision. Here we show that computer analysis of building images can perform quantitative analysis of architecture, and quantify similarities between city architectural styles in a quantitative fashion. Images of buildings from 18 cities and three countries were acquired using Google StreetView, and were used to train a machine vision system to automatically identify the location of the imaged building based on the image visual content. Experimental results show that the automatic computer analysis can automatically identify the geographical location of the StreetView image. More importantly, the algorithm was able to group the cities and countries and provide a phylogeny of the similarities between architectural styles as captured by StreetView images. These results demonstrate that computer vision and pattern recognition algorithms can perform the complex cognitive task of analyzing images of buildings, and can be used to measure and quantify visual similarities and differences between different styles of architectures. This experiment provides a new paradigm for studying architecture, based on a quantitative approach that can enhance the traditional manual observation and analysis. The source code used for the analysis is open and publicly available.

DEVELOPMENT OF AN ARTICULATION ROBOT ARM

2017 ◽  
Vol 41 (3) ◽  
pp. 443-455
Author(s):  
Kuo-Lan Su ◽  
Jian-Fu Weng ◽  
Jr-Hung Guo ◽  
Kai-Lu Cai
Keyword(s):  
Control System ◽  
Vision System ◽  
Motion Trajectory ◽  
Robot Arm ◽  
Plc Control ◽  
Plc Control System

This article describes the design of an articulation robot arm with seven joints. The control core of the robot arm is the module-based system built using the Mitsubishi Q series programming logical controller (PLC). The robot arm contains seven AC servomotors, seven driver devices, a vision system and a PLC control system. The PLC-based controller programs the motion trajectory of the gripper to catch or hold the objects and finish the assigned tasks. Kinect system (Asus Xtion Pro-Live, or called RGB-D sensor) acts as the vision system to recognize shape and color of each object. During the experiments, we found that the robot arm recognizes the shape and color of each object, and catches each object moving to the assigned box with the same color.

scholarly journals Mapping the Unseen: Exploiting Super-Resolution for Semantic Segmentation in Low-Resolution Images

2020 ◽  
Author(s):  
Matheus B. Pereira ◽  
Jefersson Alex Dos Santos
Keyword(s):  
Remote Sensing ◽  
Pattern Recognition ◽  
Super Resolution ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
The Other ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Remote Sensing Images ◽  
Low Resolution

High-resolution aerial images are usually not accessible or affordable. On the other hand, low-resolution remote sensing data is easily found in public open repositories. The problem is that the low-resolution representation can compromise pattern recognition algorithms, especially semantic segmentation. In this M.Sc. dissertation1 , we design two frameworks in order to evaluate the effectiveness of super-resolution in the semantic segmentation of low-resolution remote sensing images. We carried out an extensive set of experiments on different remote sensing datasets. The results show that super-resolution is effective to improve semantic segmentation performance on low-resolution aerial imagery, outperforming unsupervised interpolation and achieving semantic segmentation results comparable to highresolution data.

scholarly journals PATTERN RECOGNITION IN THE VISION SYSTEM

2017 ◽  
pp. 4-7
Author(s):  
Pelevin E.E. ◽  
Balyasny S.V.
Keyword(s):  
Pattern Recognition ◽  
Vision System

New continuum surgical robot based on hybrid concentric tube-tendon driven mechanism

2021 ◽  
pp. 095440622110424
Author(s):  
Mohammed Abdel-Nasser ◽  
Omar Salah
Keyword(s):  
Inverse Kinematics ◽  
High Performance ◽  
Analytical Study ◽  
Fuzzy Inference ◽  
Closed Form Solution ◽  
Form Solution ◽  
Robot Arm ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Intelligent Approach

Robotics technology is used widely in minimally invasive surgery (MIS) which provides high performance and accuracy. The most famous robot arm mechanisms, which are used in MIS, are tendon-driven mechanism (TDM), and concentric tube mechanism (CTM). Unfortunately, these mechanisms until now have some limitations, i.e. making friction with the tissue during extracting and retracting and strain limits, for TDM and CTM respectively. A new hybrid concentric tube-tendon driven mechanism (HCTDM) is proposed to overcome these limitations. HCTDM enables the end-effector to get close to and get away from the surgical area during the operation without harming the tissue and with more flexibility. In addition to that, the workspace increases as a result of this combination, too. This benefit serves MIS, especially endoscopic surgeries (ESs). We did an analytical study of this idea and got the forward kinematics. In the inverse kinematics, an intelligent approach which is called an adaptive neuro-fuzzy inference system (ANFIS) is used because the closed-form solution is more complicated for such these mechanisms. Finally, HCTDM is analyzed and evaluated by using a computer simulation. The simulation results show that the workspace becomes wider and has more dexterity than use TDM or CTM individually. Furthermore, various trajectories are used to test the mechanism and the kinematic analysis, which show the mechanism can follow and track the trajectories with maximum mean error 1.279, 0.7027, and [Formula: see text] for X, Y, and Z axes respectively.

Tracking and Catching of an In-Flight Ring using a High-Speed Vision System and a Robot Arm

2021 ◽  
Author(s):  
Xiao Liang ◽  
Hairui Zhu ◽  
YanLong Chen ◽  
Yuji Yamakawa
Keyword(s):  
High Speed ◽  
Vision System ◽  
Robot Arm

Direct Perception and Action Decision for Unknown Object Grasping

2019 ◽  
pp. 1372-1387
Author(s):  
Hiroyuki Masuta ◽  
Tatsuo Motoyoshi ◽  
Kei Sawai ◽  
Ken'ichi Koyanagi ◽  
Toru Oshima ◽  
...  
Keyword(s):  
Relative Position ◽  
Fuzzy Inference ◽  
Perceptual System ◽  
Moment Of Inertia ◽  
Direct Perception ◽  
Physical Parameters ◽  
Robot Arm ◽  
Depth Sensor ◽  
Unknown Object ◽  
Action Decision

This paper discusses the direct perception of an unknown object and the action decision to grasp an unknown object using depth sensor for social robots. Conventional methods estimate the accurate physical parameters when a robot wants to grasp an unknown object. Therefore, we propose a perceptual system based on an invariant concept in ecological psychology, which perceives the information relevant to the action of the robot. Firstly, we proposed the plane detection based approach for perceiving an unknown object. In this paper, we propose the sensation of grasping which is expressed by using inertia tensor, and applied with fuzzy inference using the relation between principle moment of inertia. The sensation of grasping encourages the decision for the grasping action directly without inferring from physical value such as size, posture and shape. As experimental results, we show that the sensation of grasping expresses the relative position and posture between the robot and the object, and the embodiment of the robot arm by one parameter. And, we verify the validity of the action decision from the sensation of grasping.

scholarly journals Stereo Vision-Based Object Recognition and Manipulation by Regions with Convolutional Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 210 ◽  
Author(s):  
Yi-Chun Du ◽  
Muslikhin Muslikhin ◽  
Tsung-Han Hsieh ◽  
Ming-Shyan Wang
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Convolutional Neural Network ◽  
Stereo Vision ◽  
Fuzzy Inference ◽  
Target Object ◽  
Robot Arm ◽  
Segmentation Method ◽  
Inference System ◽  
Six Degree Of Freedom

This paper develops a hybrid algorithm of adaptive network-based fuzzy inference system (ANFIS) and regions with convolutional neural network (R-CNN) for stereo vision-based object recognition and manipulation. The stereo camera at an eye-to-hand configuration firstly captures the image of the target object. Then, the shape, features, and centroid of the object are estimated. Similar pixels are segmented by the image segmentation method, and similar regions are merged through selective search. The eye-to-hand calibration is based on ANFIS to reduce computing burden. A six-degree-of-freedom (6-DOF) robot arm with a gripper will conduct experiments to demonstrate the effectiveness of the proposed system.

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