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.

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.

Digital Knowledge and Diagnostic Information

2000 ◽  
Vol 6 (S2) ◽  
pp. 1008-1009
Author(s):  
Peter H. Bartels ◽  
Deborah Thompson ◽  
Rodolfo Montironi ◽  
Peter W. Hamilton ◽  
Gian M. Mariuzzi ◽  
...  
Keyword(s):  
Machine Vision ◽  
Quantitative Measurement ◽  
Vision System ◽  
Objective Assessment ◽  
Image Understanding ◽  
Diagnostic Information ◽  
Machine Vision System ◽  
Digital Knowledge ◽  
Diagnostic Clue ◽  
Numeric Data

Digital representation of diagnostic imagery offers mensuration, numeric data for diagnostic clue expression, objective assessment, and the option to define standards. Quantitative measurement allows the detection and documentation of very small differences and of diagnostic information that is visually not perceived. The automated extraction of such information from microscopic imagery is beginning to yield to knowledge guided image processing and the development of image understanding systems for machine vision. In a machine vision system, knowledge guidance may be provided by an expert system that controls a top to bottom scene segmentation with constant checks on local bottom-up derived segmentation results for compliance with model specifications and final scene reconstruction. Knowledge guidance is based on a knowledge file for the fully autonomous processing of scenes from a given domain. The knowledge file includes all entities representing traditional diagnostic and histologic terms and concepts: epithelium, stroma, lumen, nucleus, secretory cell, basal cell, stroma cell, chromatin, to name a few.

Special Issue on Vision

1999 ◽  
Vol 11 (2) ◽  
pp. 87-87
Author(s):  
Shunichiro Oe ◽  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Visual Processing ◽  
Research Work ◽  
Image Understanding ◽  
Normal Vector ◽  
Automatic Welding ◽  
Special Issue ◽  
Image Sensing

The widely used term <B>Computer Vision</B> applies to when computers are substituted for human visual information processing. As Real-world objects, except for characters, symbols, figures and photographs created by people, are 3-dimensional (3-D), their two-dimensional (2-D) images obtained by camera are produced by compressing 3-D information to 2-D. Many methods of 2-D image processing and pattern recognition have been developed and widely applied to industrial and medical processing, etc. Research work enabling computers to recognize 3-D objects by 3-D information extracted from 2-D images has been carried out in artificial intelligent robotics. Many techniques have been developed and some applied practically in scene analysis or 3-D measurement. These practical applications are based on image sensing, image processing, pattern recognition, image measurement, extraction of 3-D information, and image understanding. New techniques are constantly appearing. The title of this special issue is <B>Vision</B>, and it features 8 papers from basic computer vision theory to industrial applications. These papers include the following: Kohji Kamejima proposes a method to detect self-similarity in random image fields - the basis of human visual processing. Akio Nagasaka et al. developed a way to identify a real scene in real time using run-length encoding of video feature sequences. This technique will become a basis for active video recording and new robotic machine vision. Toshifumi Honda presents a method for visual inspection of solder joint by 3-D image analysis - a very important issue in the inspection of printed circuit boards. Saburo Okada et al. contribute a new technique on simultaneous measurement of shape and normal vector for specular objects. These methods are all useful for obtaining 3-D information. Masato Nakajima presents a human face identification method for security monitoring using 3-D gray-level information. Kenji Terada et al. propose a method of automatic counting passing people using image sensing. These two technologies are very useful in access control. Yoji. Ogawa presents a new image processing method for automatic welding in turbid water under a non-preparatory environment. Liu Wei et al. develop a method for detection and management of cutting-tool wear using visual sensors. We are certain that all of these papers will contribute greatly to the development of vision systems in robotics and mechatronics.

scholarly journals 3-D Illusory Phenomena with Binocular Viewing and Computer Vision

1992 ◽  
Vol 4 (3) ◽  
pp. 249-255
Author(s):  
Masanori Idesawa ◽  
Keyword(s):  
Computer Vision ◽  
Visual System ◽  
Human Visual System ◽  
Visual Function ◽  
Vision System ◽  
Visual Stimuli ◽  
Space Perception ◽  
Binocular Viewing ◽  
Depth Information ◽  
New Paradigm

The human visual system can perceive 3-D information of an object by using disparity between two eyes, gradient of illumination (shading), occlusion, textures and their perspective and so on. Consequently, the disparity and the occlusion observed with binocular viewing seems to be the most important cues to get 3-D information. For the artificial realization of the visual function such as in computer vision or robot vision system, it seems to be a clever way to learn from the human visual mechanism. Recently, the author found a new type of illusion. When the visual stimuli of disparity are given only partially along the contour of an object, human visual system can perceive the 3-D surface (not only plane but also curved) of the object where there are no physical visual stimuli to get depth information. The interactions between the perceived illusory surface (occlusion, intersection and transparency) can be recognized. These newly found illusory phenomena have close relations with the visual function of 3-D space perception and can provide a new paradigm in the field of computer vision and human interface.

Pattern recognition with moment invariants on a machine vision system

1989 ◽  
Vol 9 (3) ◽  
pp. 175-180 ◽  
Author(s):  
Zhu Mingfa ◽  
Santosh Hasani ◽  
Surendra Bhattarai ◽  
Harpreet Singh
Keyword(s):  
Pattern Recognition ◽  
Machine Vision ◽  
Vision System ◽  
Moment Invariants ◽  
Machine Vision System

Ensuring the Safety of UAV Flights by Means of Intellectualization of Control Systems

2019 ◽  
pp. 287-310 ◽  
Author(s):  
Konstantin Dergachov ◽  
Anatolii Kulik
Keyword(s):  
Computer Vision ◽  
Control System ◽  
Machine Vision ◽  
Unmanned Aerial Vehicles ◽  
Vision System ◽  
Visual Estimation ◽  
Computer Vision System ◽  
Estimation Algorithms ◽  
Aerial Vehicles ◽  
Rational Control

In this chapter, the authors present analysis of reasons for deficient safety of unmanned aerial vehicles (UAV) and further ground an approach to improve the safety by intellectualizing operation of the control system. Intellectualization results from the rational control owing to machine vision means used. A conception of building algorithms for visual evaluating position of the UAV that is equipped with a computer vision system is suggested. Algorithms are illustrated by related investigation of an adapted UAV. Both hardware and software means for realizing the visual estimation algorithms are presented.

Real-time inspection of fruit by computer vision on a mobile harvesting platform under field conditions

2010 ◽  
Vol 6 (1) ◽  
pp. 1-16 ◽  
Author(s):  
S. Cubero ◽  
E. Moltó ◽  
A. Gutiérrez ◽  
N. Aleixos ◽  
O. García-Navarrete ◽  
...  
Keyword(s):  
Computer Vision ◽  
Machine Vision ◽  
Fruits And Vegetables ◽  
Vision System ◽  
Production Costs ◽  
Field Conditions ◽  
Fresh Market ◽  
Citrus Production ◽  
Surface Damages ◽  
Marketable Fruit

The best alternative for reducing citrus production costs is mechanization. Machine vision is a reliable technology for the automatic inspection of fresh fruits and vegetables that can be adapted to harvesting machines. In these, fruits can be inspected before sending them to the packinghouse and machine vision provides important information for subsequent processing and avoids spending further resources in non-marketable fruit. The present work describes a computer vision system installed on a harvesting machine developed jointly by IVIA and a Spanish enterprise. In this machine, hand pickers directly drop the fruit as they collect it, which results in an important increase of productivity. The machine vision system is placed over rollers in order to inspect the produce, and separate those that can be directly sent to the fresh market from those that do not meet minimal quality requirements but can be used by the processing industry, based on color, size and the presence of surface damages. The system was tested under field conditions.

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

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