scholarly journals Performance evaluation of SIFT against common image deformations on iban plaited mat motif images

2021 ◽  
Vol 23 (3) ◽  
pp. 1470
Author(s):  
Silvia Joseph ◽  
Irwandi Hipiny ◽  
Hamimah Ujir ◽  
Sarah Flora Samson Juan ◽  
Jacey-Lynn Minoi
Keyword(s):  
Threshold Value ◽  
Scale Invariant ◽  
Traditional Beliefs ◽  
Geometric Patterns ◽  
Multiple Threshold ◽  
Sift Features ◽  
The Many ◽  
Rotation Set ◽  
Scale Invariant Feature ◽  
Special Meaning

Decorative plaited mat is one of the many examples of rich plait work often seen on Borneo handicraft products. The plaited mats are decorated with simple and complex motif designs; each has its own special meaning and taboos. The motif designs are used as a reflection of environment and the traditional beliefs in the Iban community. In line with efforts from UNESCO’s and Sarawak Government’s, digitization, and the use of IR4.0 technologies to preserve and promote this cultural heritage is encouraged. Towards this end goal, we present a novel image dataset containing 10 Iban plaited mat motif classes. The plaited mat motifs are made of diagonal and symmetrical shapes, as well as geometric and non-geometric patterns. Classification’s accuracy using scale-invariant feature transform (SIFT) features was evaluated against 6 common image deformations: zoom+rotation, viewpoint, image blur, JPEG compression, scale and illumination, across multiple threshold values. Varying degrees of each deformation were applied to a digitally cleaned (and cropped) image of each mat motif class. We used RANSAC to remove outliers from the noisy SIFT matching result. The optimal threshold value is 2.0e-2 with a reported 100.0% matching accuracy for the scale change and zoom+rotation set.

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 Hypoxia shapes coral reefs

2018 ◽  
Author(s):  
David Blakeway
Keyword(s):  
Three Dimensional ◽  
Distribution Data ◽  
Scale Invariant ◽  
Ecological Processes ◽  
Network Cell ◽  
Invariant Structure ◽  
Multi Scale ◽  
Geometric Patterns ◽  
Calm Weather ◽  
The Many

The three-dimensional form of a coral reef emerges from thousands of years of ecological interactions between reef-building organisms and their environment. Time integrates those interactions, such that the predominant ecological processes are distilled into reef form, often as striking geometric patterns. Several of these patterns have a fractal appearance, exhibiting nested, scale-invariant structure. Cellular reefs are one fractal reef morphotype, characterised by the presence of subcircular, bowl-shaped, depressions (‘cells’) within the reef network. Cell diameters range from approximately 10 metres to 1 kilometre, the larger cells being compound structures containing multiple smaller cells. The common attribute shared by cellular reefs of all scales is an abundance of staghorn Acropora. Staghorn’s fast growth, fuelled by a correspondingly fast metabolism, allows them to rapidly fill lagoons, but leaves them vulnerable to reduced water flow as their own growth begins to impede lagoonal circulation. This article outlines a conceptual model of multi-scale cellular reef development, based on water quality and coral distribution data from the cellular reefs of Western Australia’s Houtman Abrolhos Islands. The key process in the model is density-stratification of the water column during extended periods of warm, calm, weather. Warm water in the shallows traps stable pools of cooler and denser water at depth. The trapped water is rapidly depleted of oxygen, which causes extensive mortality among staghorn colonies. This initiates a negative feedback process in which ongoing growth of colonies above the stratification boundary further reduces water circulation at depth, such that subsequent stratification events kill increasingly larger areas of the reef, eventually producing massive, stagnant cells in which few corals can survive. Investigating the many other reef patterns may provide similar insights into the predominant natural ecological processes occurring on those reefs.

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 Hypoxia shapes coral reefs

2018 ◽  
Author(s):  
David Blakeway
Keyword(s):  
Three Dimensional ◽  
Distribution Data ◽  
Scale Invariant ◽  
Ecological Processes ◽  
Network Cell ◽  
Invariant Structure ◽  
Multi Scale ◽  
Geometric Patterns ◽  
Calm Weather ◽  
The Many

The three-dimensional form of a coral reef emerges from thousands of years of ecological interactions between reef-building organisms and their environment. Time integrates those interactions, such that the predominant ecological processes are distilled into reef form, often as striking geometric patterns. Several of these patterns have a fractal appearance, exhibiting nested, scale-invariant structure. Cellular reefs are one fractal reef morphotype, characterised by the presence of subcircular, bowl-shaped, depressions (‘cells’) within the reef network. Cell diameters range from approximately 10 metres to 1 kilometre, the larger cells being compound structures containing multiple smaller cells. The common attribute shared by cellular reefs of all scales is an abundance of staghorn Acropora. Staghorn’s fast growth, fuelled by a correspondingly fast metabolism, allows them to rapidly fill lagoons, but leaves them vulnerable to reduced water flow as their own growth begins to impede lagoonal circulation. This article outlines a conceptual model of multi-scale cellular reef development, based on water quality and coral distribution data from the cellular reefs of Western Australia’s Houtman Abrolhos Islands. The key process in the model is density-stratification of the water column during extended periods of warm, calm, weather. Warm water in the shallows traps stable pools of cooler and denser water at depth. The trapped water is rapidly depleted of oxygen, which causes extensive mortality among staghorn colonies. This initiates a negative feedback process in which ongoing growth of colonies above the stratification boundary further reduces water circulation at depth, such that subsequent stratification events kill increasingly larger areas of the reef, eventually producing massive, stagnant cells in which few corals can survive. Investigating the many other reef patterns may provide similar insights into the predominant natural ecological processes occurring on those reefs.

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.

Scene Categorization Through Combining LBP and SIFT Features Effectively

2015 ◽  
Vol 30 (01) ◽  
pp. 1655001 ◽  
Author(s):  
Shung Bai ◽  
Jianjun Hou ◽  
Noboru Ohnishi
Keyword(s):  
Input Image ◽  
Support Vector ◽  
Two Dimensional ◽  
Scale Invariant ◽  
Scene Categorization ◽  
Visual Words ◽  
Training Images ◽  
Sift Features ◽  
Image Representations ◽  
Scale Invariant Feature

In computer vision, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT) are two widely used local descriptors. In this paper, we propose to combine them effectively for scene categorization. First, LBP and SIFT features are regularly extracted from training images for constructing a LBP feature codebook and a SIFT feature codebook. Then, a two-dimensional table is created by combining the obtained codebooks. For creating a representation for an image, LBP and SIFT features extracted from the same positions of the image are encoded together based on sparse coding by using the two-dimensional table. After processing all features in the input image, we adopt spatial max pooling to determine its representation. Obtained image representations are forwarded to a Support Vector Machine classifier for categorization. In addition, in order to improve the scene categorization performance further, we propose a method to select correlated visual words from large codebooks for constructing the two-dimensional table. Finally, for evaluating the proposed method, extensive experiments are implemented on datasets Scene Categories 8, Scene Categories 15 and MIT 67 Indoor Scene. It is demonstrated that the proposed method is effective for scene categorization.

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.

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