Catching Data from Displayers by Machine Vision

2012 ◽  
Vol 566 ◽  
pp. 124-129 ◽  
Author(s):  
Li Feng Yao ◽  
Jian Fei Ouyang
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Health Assessment ◽  
Measuring System ◽  
Health Measurement ◽  
Scale Invariant ◽  
Individual Values ◽  
Segmented Images ◽  
Sift Features ◽  
Scale Invariant Feature

With the emergence of eHealth, the importance of keeping digital personal health statistics is quickly rising in demand. Many current health assessment devices output values to the user without a method of digitally saving the data. This paper presents a method to directly translate the numeric displays of the devices into digital records using machine vision. A wireless-based machine vision system is designed to image the display and a tracking algorithm based on SIFT (Scale Invariant Feature Transform) is developed to recognize the numerals from the captured images. First, a local camera captures an image of the display and transfers it wirelessly to a remote computer, which generates the gray-scale and binary figures of the images for further processing. Next, the computer applies the watershed segmentation algorithm to divide the image into regions of individual values. Finally, the SIFT features of the segmented images are picked up in sequence and matched with the SIFT features of the ten standard digits from 0 to 9 one by one to recognize the digital numbers of the device’s display. The proposed approach can obtain the data directly from the display quickly and accurately with high environmental tolerance. The numeric recognition converts with over 99.2% accuracy, and processes an image in less than one second. The proposed method has been applied in the E-health Station, a physiological parameters measuring system that integrates a variety of commercial instruments, such as OMRON digital thermometer, oximeter, sphygmomanometer, glucometer, and fat monitor, to give a more complete physiological health measurement.

scholarly journals The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Supakorn Harnsoongnoen ◽  
Nuananong Jaroensuk
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Low Cost ◽  
Measuring System ◽  
Coefficient Of Determination ◽  
Water Displacement ◽  
Non Invasive ◽  
Future Potential ◽  
Non Destructive ◽  
Egg Freshness

AbstractThe water displacement and flotation are two of the most accurate and rapid methods for grading and assessing freshness of agricultural products based on density determination. However, these techniques are still not suitable for use in agricultural inspections of products such as eggs that absorb water which can be considered intrusive or destructive and can affect the result of measurements. Here we present a novel proposal for a method of non-destructive, non-invasive, low cost, simple and real—time monitoring of the grading and freshness assessment of eggs based on density detection using machine vision and a weighing sensor. This is the first proposal that divides egg freshness into intervals through density measurements. The machine vision system was developed for the measurement of external physical characteristics (length and breadth) of eggs for evaluating their volume. The weighing system was developed for the measurement of the weight of the egg. Egg weight and volume were used to calculate density for grading and egg freshness assessment. The proposed system could measure the weight, volume and density with an accuracy of 99.88%, 98.26% and 99.02%, respectively. The results showed that the weight and freshness of eggs stored at room temperature decreased with storage time. The relationship between density and percentage of freshness was linear for the all sizes of eggs, the coefficient of determination (R2) of 0.9982, 0.9999, 0.9996, 0.9996 and 0.9994 for classified egg size classified 0, 1, 2, 3 and 4, respectively. This study shows that egg freshness can be determined through density without using water to test for water displacement or egg flotation which has future potential as a measuring system important for the poultry industry.

scholarly journals Effective Approach to Classify and Segment Retinal Hemorrhage Using ANFIS and Particle Swarm Optimization

2018 ◽  
Vol 27 (4) ◽  
pp. 681-697
Author(s):  
Lawrence Livingston Godlin Atlas ◽  
Kumar Parasuraman
Keyword(s):  
Particle Swarm Optimization ◽  
Median Filter ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Retinal Hemorrhage ◽  
Scale Invariant ◽  
Swarm Optimization ◽  
Segmented Images ◽  
Occurrence Matrix ◽  
Scale Invariant Feature

Abstract The main objective of this study is to progress the structure and segment the images from hemorrhage recognition in retinal fundus images in ostensible. The abnormal bleeding of blood vessels in the retina which is the membrane in the back of the eye is called retinal hemorrhage. The image folders are deliberated, and the filter technique is utilized to decrease the images specifically adaptive median filter in our suggested proposal. Gray level co-occurrence matrix (GLCM), grey level run length matrix (GLRLM) and Scale invariant feature transform (SIFT) feature skills are present after filtrating the feature withdrawal. After this, the organization technique is performed, specifically artificial neural network with fuzzy interface system (ANFIS) method; with the help of this organization, exaggerated and non-affected images are categorized. Affected hemorrhage images are transpired for segmentation procedure, and in this exertion, threshold optimization is measured with numerous optimization methods; on the basis of this, particle swarm optimization is accomplished in improved manner. Consequently, the segmented images are projected, and the sensitivity is great when associating with accurateness and specificity in the MATLAB platform.

scholarly journals THE LOW BACKSCATTERING OBJECTS CLASSIFICATION IN POLSAR IMAGE BASED ON BAG OF WORDS MODEL USING SUPPORT VECTOR MACHINE

2018 ◽  
Vol XLII-3 ◽  
pp. 2089-2091
Author(s):  
L. Yang ◽  
L. Shi ◽  
P. Li ◽  
J. Yang ◽  
L. Zhao ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Bare Soil ◽  
Radar Signal ◽  
Support Vector ◽  
Data Sets ◽  
Bag Of Words ◽  
Scale Invariant ◽  
Polarimetric Synthetic Aperture Radar ◽  
Sift Features ◽  
Scale Invariant Feature

Due to the forward scattering and block of radar signal, the water, bare soil, shadow, named low backscattering objects (LBOs), often present low backscattering intensity in polarimetric synthetic aperture radar (PolSAR) image. Because the LBOs rise similar backscattering intensity and polarimetric responses, the spectral-based classifiers are inefficient to deal with LBO classification, such as Wishart method. Although some polarimetric features had been exploited to relieve the confusion phenomenon, the backscattering features are still found unstable when the system noise floor varies in the range direction. This paper will introduce a simple but effective scene classification method based on Bag of Words (BoW) model using Support Vector Machine (SVM) to discriminate the LBOs, without relying on any polarimetric features. In the proposed approach, square windows are firstly opened around the LBOs adaptively to determine the scene images, and then the Scale-Invariant Feature Transform (SIFT) points are detected in training and test scenes. The several SIFT features detected are clustered using K-means to obtain certain cluster centers as the visual word lists and scene images are represented using word frequency. At last, the SVM is selected for training and predicting new scenes as some kind of LBOs. The proposed method is executed over two AIRSAR data sets at C band and L band, including water, bare soil and shadow scenes. The experimental results illustrate the effectiveness of the scene method in distinguishing LBOs.

scholarly journals Three Landmark Optimization Strategies for Mobile Robot Visual Homing

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3180 ◽  
Author(s):  
Xun Ji ◽  
Qidan Zhu ◽  
Junda Ma ◽  
Peng Lu ◽  
Tianhao Yan
Keyword(s):  
Mobile Robot ◽  
Target Location ◽  
Mobile Robot Navigation ◽  
Scale Invariant ◽  
Visual Homing ◽  
Vision Sensors ◽  
Homing Performance ◽  
Sift Features ◽  
Scale Invariant Feature ◽  
Input Form

Visual homing is an attractive autonomous mobile robot navigation technique, which only uses vision sensors to guide the robot to the specified target location. Landmark is the only input form of the visual homing approaches, which is usually represented by scale-invariant features. However, the landmark distribution has a great impact on the homing performance of the robot, as irregularly distributed landmarks will significantly reduce the navigation precision. In this paper, we propose three strategies to solve this problem. We use scale-invariant feature transform (SIFT) features as natural landmarks, and the proposed strategies can optimize the landmark distribution without over-eliminating landmarks or increasing calculation amount. Experiments on both panoramic image databases and a real mobile robot have verified the effectiveness and feasibility of the proposed strategies.

scholarly journals Liquid Level Detection in Standard Capacity Measures with Machine Vision

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2676
Author(s):  
Gregor Bobovnik ◽  
Tim Mušič ◽  
Jože Kutin
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Liquid Level ◽  
Measuring System ◽  
Legal Metrology ◽  
System A ◽  
Measurement Results ◽  
Capacity Measure ◽  
Measuring Scale ◽  
Liquid Level Detection

Capacity measures are commonly used volume standards for testing measuring systems for liquids other than water. Manual readings from the measuring scale can often be difficult due to the location of the capacity measure or to the nature of the measured liquid. This article focuses on the automation of this procedure by using a single camera machine vision system. A camera positioned perpendicular to the transparent neck captures the image of the liquid meniscus and the measuring scale. The volume reading is determined with the user-defined software in the LabVIEW programming environment, which carries out the image preprocessing, detection of the scale marks and the liquid level, correction of lens distortion and parallax effects and final unit conversions. The realized measuring system for liquid level detection in standard capacity measures is tested and validated by comparing the automated measurement results with those taken by the operators. The results confirm the appropriateness of the presented measuring system for the field of legal metrology.

Merging grid maps of different resolutions by scaling registration

Robotica ◽  
2015 ◽  
Vol 34 (11) ◽  
pp. 2516-2531 ◽  
Author(s):  
Liang Ma ◽  
Jihua Zhu ◽  
Li Zhu ◽  
Shaoyi Du ◽  
Jingru Cui
Keyword(s):  
Objective Function ◽  
Superior Performance ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Consistent Feature ◽  
Initial Transformation ◽  
Random Sample Consensus ◽  
Sift Features ◽  
Scale Invariant Feature ◽  
Ransac Algorithm

SUMMARYThis paper considers the problem of merging grid maps that have different resolutions. Because the goal of map merging is to find the optimal transformation between two partially overlapping grid maps, it can be viewed as a special image registration issue. To address this special issue, the solution considers the non-common areas and designs an objective function based on the trimmed mean-square error (MSE). The trimmed and scaling iterative closest point (TsICP) algorithm is then proposed to solve this well-designed objective function. As the TsICP algorithm can be proven to be locally convergent in theory, a good initial transformation should be provided. Accordingly, scale-invariant feature transform (SIFT) features are extracted for the maps to be potentially merged, and the random sample consensus (RANSAC) algorithm is employed to find the geometrically consistent feature matches that are used to estimate the initial transformation for the TsICP algorithm. In addition, this paper presents the rules for the fusion of the grid maps based on the estimated transformation. Experimental results carried out with publicly available datasets illustrate the superior performance of this approach at merging grid maps with respect to robustness and accuracy.

Implementation of Image Matching Algorithm Based on SIFT Features

2014 ◽  
Vol 602-605 ◽  
pp. 3181-3184 ◽  
Author(s):  
Mu Yi Yin ◽  
Fei Guan ◽  
Peng Ding ◽  
Zhong Feng Liu
Keyword(s):  
Image Matching ◽  
Implementation Process ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Source Codes ◽  
Invariant Feature ◽  
Viewpoint Change ◽  
Sift Features ◽  
Scale Invariant Feature

With the aim to solve the implement problem in scale invariant feature transform (SIFT) algorithm, the theory and the implementation process was analyzed in detail. The characteristics of the SIFT method were analyzed by theory, combined with the explanation of the Rob Hess SIFT source codes. The effect of the SIFT method was validated by matching two different real images. The matching result shows that the features extracted by SIFT method have excellent adaptive and accurate characteristics to image scale, viewpoint change, which are useful for the fields of image recognition, image reconstruction, etc.

scholarly journals Hybrid Image-Retrieval Method for Image-Splicing Validation

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 83 ◽  
Author(s):  
Nam Pham ◽  
Jong-Weon Lee ◽  
Goo-Rak Kwon ◽  
Chun-Su Park
Keyword(s):  
Image Retrieval ◽  
Zernike Moment ◽  
Invariant Property ◽  
Image Splicing ◽  
Scale Invariant ◽  
Retrieval Method ◽  
One Step ◽  
Sift Features ◽  
Scaling Invariant ◽  
Scale Invariant Feature

Recently, the task of validating the authenticity of images and the localization of tampered regions has been actively studied. In this paper, we go one step further by providing solid evidence for image manipulation. If a certain image is proved to be the spliced image, we try to retrieve the original authentic images that were used to generate the spliced image. Especially for the image retrieval of spliced images, we propose a hybrid image-retrieval method exploiting Zernike moment and Scale Invariant Feature Transform (SIFT) features. Due to the symmetry and antisymmetry properties of the Zernike moment, the scaling invariant property of SIFT and their common rotation invariant property, the proposed hybrid image-retrieval method is efficient in matching regions with different manipulation operations. Our simulation shows that the proposed method significantly increases the retrieval accuracy of the spliced images.

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