scholarly journals A HIGH-ACCURACY METHOD TO MAKE MEASUREMENTS ALONG A LINE USING COMPUTER VISION AND LASER DISTANCE SENSORS

2017 ◽  
Vol 3 (2) ◽  
pp. 135-153
Author(s):  
Mariana Mascarenhas De Carvalho ◽  
Rodrigo Varejão Andreão ◽  
João Marques Salomão
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Image Plane ◽  
Industrial Applications ◽  
Distance Measures ◽  
Interest Points ◽  
Image Processing Techniques ◽  
Computer Vision Applications ◽  
Processing Techniques ◽  
Potential Use

This paper presents an efficient method to measure real-world distance between interest points of a 3-D objectusing a camera, two laser distance sensors and Image Processing techniques. This method consists of calculating the distance between interest points of an object in the image plane. Two laser distance sensors placed next to the camera measure accurately the distance between the camera and the object. Moreover, the laser dots generated by the distance sensors are visible in the image, acting as a reference along with the distance measures in the calculations. As a result, thecalculation of the interest point's coordinate on the camera 3-D space is transformed in a real-world distance between interest points. Through a set of experiments, we show that the proposed system measures distancesup to 70 mm with sub-mm accuracy. The results suggest a potential use of our method in many other Computer Vision applications, including industrial applications.

scholarly journals Clay-Based Brick Porosity Estimation Using Image Processing Techniques

2019 ◽  
Vol 29 (1) ◽  
pp. 1226-1234
Author(s):  
Safa Jida ◽  
Hassan Ouallal ◽  
Brahim Aksasse ◽  
Mohammed Ouanan ◽  
Mohamed El Amraoui ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Computer Vision ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Porosity Estimation ◽  
Microscopy Images ◽  
Scanning Electron

Abstract This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram-based porosity estimation.

Automated Design of Image Operators that Detect Interest Points

2008 ◽  
Vol 16 (4) ◽  
pp. 483-507 ◽  
Author(s):  
Leonardo Trujillo ◽  
Gustavo Olague
Keyword(s):  
Computer Vision ◽  
Search Space ◽  
Search Problem ◽  
Interest Points ◽  
Fitness Evaluation ◽  
Modern Computer ◽  
New Perspective ◽  
Processing Techniques ◽  
Point Detection ◽  
Optimization Search

This work describes how evolutionary computation can be used to synthesize low-level image operators that detect interesting points on digital images. Interest point detection is an essential part of many modern computer vision systems that solve tasks such as object recognition, stereo correspondence, and image indexing, to name but a few. The design of the specialized operators is posed as an optimization/search problem that is solved with genetic programming (GP), a strategy still mostly unexplored by the computer vision community. The proposed approach automatically synthesizes operators that are competitive with state-of-the-art designs, taking into account an operator's geometric stability and the global separability of detected points during fitness evaluation. The GP search space is defined using simple primitive operations that are commonly found in point detectors proposed by the vision community. The experiments described in this paper extend previous results (Trujillo and Olague, 2006a,b) by presenting 15 new operators that were synthesized through the GP-based search. Some of the synthesized operators can be regarded as improved manmade designs because they employ well-known image processing techniques and achieve highly competitive performance. On the other hand, since the GP search also generates what can be considered as unconventional operators for point detection, these results provide a new perspective to feature extraction research.

A Scientometric Analysis and a Review on Current Literature of Computer Vision Applications

2020 ◽  
pp. 104-117
Author(s):  
Osman Hürol Türkakın
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Current Literature ◽  
Machine Learning Techniques ◽  
Scientometric Analysis ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Computer Vision Applications ◽  
Processing Techniques

Computer vision methods are wide-spread techniques mostly used for detecting cracks on structural components, extracting information from traffic flows, and analyzing safety in construction processes. In recent years, with increasing usage of machine learning techniques, computer vision applications are supported by machine learning approaches. So, several studies were conducted using machine learning techniques to apply image processing. As a result, this chapter offers a scientometric analysis for investigating current literature of image processing studies for civil engineering field in order to track the scientometric relationship between machine learning and image processing techniques.

Evaluating deep semi-supervised learning methods for computer vision applications

2021 ◽  
Keyword(s):  
Computer Vision ◽  
International Symposium ◽  
Supervised Learning ◽  
Fast Track ◽  
Industrial Applications ◽  
Learning Methods ◽  
Electronic Imaging ◽  
Computer Vision Applications ◽  
Intelligent Robotics

Fast track article for IS&T International Symposium on Electronic Imaging 2021: Intelligent Robotics and Industrial Applications using Computer Vision 2021 proceedings.

scholarly journals Sistem Computer Vision Pengenalan Pola Angka dan Operator Matematika Pada Permainan Kartu Angka Berbasis Jaringan Syaraf Tiruan Perceptron

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Rian Rahmanda Putra ◽  
Fery Antony
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Image Processing Techniques ◽  
Perceptron Algorithm ◽  
Card Number ◽  
Training Stage ◽  
Processing Techniques ◽  
Real Time Application

<p align="center"><strong><em>Abstract <br /></em></strong></p><p><em>Computer vision is an image processing by a computer to obtain information from image captured through the camera generally used in real-time application. This paper reports on the results of research conducted on computer vision system designed to be able to recognize the image number (0-9) and mathematical operators (addition (+) and subtraction (-)) in a card number figures. Computer vision system designed in this study consists of a camera on the android phone that used to captured images on the card number and the computer that has artificial neural network perceptron algorithm in identifiying images. Both components of the computer vision system are connected wirelessly through the TCP/IP Protocol. At the training stage of Perceptron ANN, 10 samples for each number and mathematical operators are used. Computer vision system built in this study also have several image processing techniques such as greyscalling, thresholding, cropping and resizing. This techniques is used to filter the information from the images captured by camera in order to get the adequate and smaller image to be processed by ANN Perceptron. Stages of testing performed three times. First testing is given picture numbers 0-3, second testing is given picture number 4-7 and third testing is given number 8-9, addition symbol and subtraction symbol. Based on testing result, system built are able to recognize 10 from 12 image rendered with a success rate of 83.33%.</em></p><p><strong><em>Keywords</em></strong><em> : Computer vision, perceptron, card number</em></p><p><em> </em></p><p align="center"><strong><em>Abstrak <br /></em></strong></p><p><em>Computer vision merupakan proses pengolahan citra oleh computer untuk mendapatkan informasi dari citra yang ditangkap melalui kamera yang umumnya digunakan pada aplikasi waktu nyata. Tulisan ini melaporkan tentang hasil penelitian yang dilakukan tentang sistem computer vision yang dirancang untuk dapat mengenali gambar angka (0-9) dan operator matematika(penjumlahan (+) dan pengurangan (-)) pada permainan kartu angka. Sistem computer vision yang dirancang pada penelitian ini terdiri dari kamera pada ponsel android yang digunakan untuk menangkap gambar pada kartu angka dan komputer yang memiliki algoritama Jaringan Syaraf Tiruan Perceptron dalam melakukan identifikasi gambar. Kedua komponen sistem computer vision tersebut dihubungkan memlaui jaringan wireless melalui protocol TCP/IP. Pada tahapan pelatihan JST perceptron, digunakan 10 sample citra untuk masing – masing angka dan operator matematika yang akan dikenali oleh sistem. Pada penelitian ini juga dilakukan tahapan pemrosesan citra sebelum diolah oleh JST Perceptron baik dalam tahapan pelatihan maupun pada saat sistem dijalankan. Tahapan pengolahan citra yang digunakan pada penelitian ini adalah greyscalling, thresholding, cropping dan resizing. Hal ini dilakukan untuk menyaring informasi pada citra yang ditangkap oleh kamera agar didapatkan citra yang berukuran kecil dengan  informasi yang lengkap untuk diproses oleh JST Perceptron. Pada saat sistem diuji coba, diberikan 4 deret kartu angka di depan kamera. Pada pengujian pertama diberikan gambar angka 0-3, pengujian kedua diberikan gambar angka 4-7 dan pada pengujian ketiga diberikan angka 8-9 serta gambar operator penjumlahan dan pengurangan. Berdasarkan pengujian yang dilakukan, sistem computer vision yang dirancang mampu mengenali 10dari 12 gambar yang diberikan dengan tingkat keberhasilan sebesar 83.33%.</em></p><p><strong><em>Kata Kunci </em></strong><em>: computer vision, perceptron, kartu angka</em></p>

scholarly journals Fruit Quality Detection and Gradation

2021 ◽  
pp. 383-388
Author(s):  
Harshal S. Deshmukh ◽  
Dr. S. W. Mohod ◽  
Dr. N. N. Khalsa
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Support Vector ◽  
Color Classification ◽  
Image Processing Techniques ◽  
Background Removal ◽  
Quality Detection ◽  
Analysis Methods ◽  
Processing Techniques

Grading and classification of fruits is based on observations and through experiences. The system exerts image- processing techniques for classification and grading the quality of fruits. Two-dimensional fruit images are classified on shape and color-based analysis methods. However, different fruit images have different or same color and shape values. Hence, using color or shape analysis methods are still not that much effective enough to identify and distinguish fruits images. Therefore, computer vision and image processing techniques have been found increasingly useful in the food industry, especially for applications in quality detection. Research in this area indicates the feasibility of using computer vision systems to improve product quality, the use of computer vision for the inspection of food has increased during recent years. This proposed work presents food quality detection system. The system design considers some feature that includes fruit colors and size, which increases accuracy for detection of roots pixels. Histogram of oriented gradients is used for background removal, for color classification, support vector machine is used.

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