Detecting pipe feature points for sewer pipe system based on image information

2005 ◽  
Author(s):  
Alireza Ahrary ◽  
Masumi Ishikawa
Keyword(s):  
Feature Points ◽  
Sewer Pipe ◽  
Image Information ◽  
Pipe System

NAVIGATION OF AN AUTONOMOUS SEWER INSPECTION ROBOT BASED ON STEREO CAMERA IMAGES AND LASER SCANNER DATA

2007 ◽  
Vol 16 (04) ◽  
pp. 611-625 ◽  
Author(s):  
ALIREZA AHRARY ◽  
LI TIAN ◽  
SEI-ICHIRO KAMATA ◽  
MASUMI ISHIKAWA
Keyword(s):  
Laser Scanner ◽  
Distance Estimation ◽  
Test Field ◽  
Scanner Data ◽  
Sewer Pipe ◽  
Stereo Camera ◽  
Pipe System ◽  
Inspection Robot ◽  
Accurate Localization ◽  
Sewer Inspection

Sewer environment is composed of cylindrical pipes, in which only a few landmarks such as manholes, inlets and pipe joints are available for localization. This paper presents a method for navigation of an autonomous sewer inspection robot in a sewer pipe system based on detection of landmarks. In this method, location of an autonomous sewer inspection robot in the sewer pipe system is estimated from stereo camera images. The laser scanner data are also used to ensure accurate localization of the landmarks and reduce the error in distance estimation by image processing. The method is implemented and evaluated in a sewer pipe test field using a prototype robot, demonstrating its effectiveness.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

2009 ◽  
Vol 8 (3) ◽  
pp. 887-897
Author(s):  
Vishal Paika ◽  
Er. Pankaj Bhambri
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Facial Features ◽  
Facial Appearance ◽  
Feature Points ◽  
Inference System ◽  
Detection Techniques ◽  
The Face ◽  
Hair Style ◽  
Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

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