scholarly journals EMCM: A Novel Binary Edge-Feature-Based Maximum Clique Framework for Multispectral Image Matching

2019 ◽  
Vol 11 (24) ◽  
pp. 3026
Author(s):  
Bin Fang ◽  
Kun Yu ◽  
Jie Ma ◽  
Pei An
Keyword(s):  
Image Matching ◽  
Nearest Neighbor ◽  
Feature Matching ◽  
Maximum Clique ◽  
Feature Space ◽  
Multispectral Image ◽  
Feature Descriptor ◽  
Ranking Algorithm ◽  
Feature Based ◽  
Edge Feature

Seeking reliable correspondence between multispectral images is a fundamental and important task in computer vision. To overcome the nonlinearity problem occurring in multispectral image matching, a novel, edge-feature-based maximum clique-matching frame (EMCM) is proposed, which contains three main parts: (1) a novel strong edge binary feature descriptor, (2) a new correspondence-ranking algorithm based on keypoint distinctiveness analysis algorithms in the feature space of the graph, and (3) a false match removal algorithm based on maximum clique searching in the correspondence space of the graph considering both position and angle consistency. Extensive experiments are conducted on two standard multispectral image datasets with respect to the three parts. The feature-matching experiments suggest that the proposed feature descriptor is of high descriptiveness, robustness, and efficiency. The correspondence-ranking experiments validate the superiority of our correspondences-ranking algorithm over the nearest neighbor algorithm, and the coarse registration experiments show the robustness of EMCM with varied interferences.

Implementation of Verification and Matching E-KTP with Faster R-CNN and ORB

2021 ◽  
Vol 5 (4) ◽  
pp. 783-793
Author(s):  
Muhammad Muttabi Hudaya ◽  
Siti Saadah ◽  
Hendy Irawan
Keyword(s):  
Image Matching ◽  
Nearest Neighbor ◽  
Feature Matching ◽  
Image Feature ◽  
Brute Force ◽  
K Nearest Neighbor ◽  
Matching Method ◽  
Average Precision ◽  
Detection Model ◽  
Matching Process

needs a solid validation that has verification and matching uploaded images. To solve this problem, this paper implementing a detection model using Faster R-CNN and a matching method using ORB (Oriented FAST and Rotated BRIEF) and KNN-BFM (K-Nearest Neighbor Brute Force Matcher). The goal of the implementations is to reach both an 80% mark of accuracy and prove matching using ORB only can be a replaced OCR technique. The implementation accuracy results in the detection model reach mAP (Mean Average Precision) of 94%. But, the matching process only achieves an accuracy of 43,46%. The matching process using only image feature matching underperforms the previous OCR technique but improves processing time from 4510ms to 60m). Image matching accuracy has proven to increase by using a high-quality dan high quantity dataset, extracting features on the important area of EKTP card images.

scholarly journals Hybrid Invariant Local Features for Multiple Satellite Image Matching and Registration

2019 ◽  
Vol 9 (2) ◽  
pp. 309-314
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Local Features ◽  
Feature Descriptor ◽  
Matching Method ◽  
Multiple Images ◽  
Feature Sets ◽  
Precise Relationship

Automatic image registration (IR) is very challenging and very important in the field of hyperspectral remote sensing data. Efficient autonomous IR method is needed with high precision, fast, and robust. A key operation of IR is to align the multiple images in single co-ordinate system for extracting and identifying variation between images considered. In this paper, presented a feature descriptor by combining features from both Feature from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Key point (BRISK). The proposed hybrid invariant local features (HILF) descriptor extract useful and similar feature sets from reference and source images. The feature matching method allows finding precise relationship or matching among two feature sets. An experimental analysis described the outcome BRISK, FASK and proposed HILF in terms of inliers ratio and repeatability evaluation metrics.

Reference Point-Based SIFT Feature Matching

2014 ◽  
Vol 543-547 ◽  
pp. 2670-2673
Author(s):  
Lei Cao ◽  
Di Liao ◽  
Bin Dang Xue
Keyword(s):  
Reference Point ◽  
Image Matching ◽  
Euclidean Distance ◽  
Feature Matching ◽  
Experimental Results ◽  
Feature Descriptor ◽  
Matching Algorithm ◽  
Sift Feature ◽  
Sift Features ◽  
Suitable Reference

Aiming to solve the high computational and time consuming problem in SIFT feature matching, this paper presents an improved SIFT feature matching algorithm based on reference point. The algorithm starts from selecting a suitable reference point in the feature descriptor space when SIFT features are extracted. In the feature matching stage, this paper uses the Euclidean distance between descriptor vectors of the feature point to be matched and the reference point to make a fast filtration which removes most of the features that could not be matched. For the remaining SIFT features, Best-bin-first (BBF) algrithm is utilized to obtain precise matches. Experimental results demonstrate that the proposed matching algorithm achieves good effectiveness in image matching, and takes only about 60 percent of the time that the traditional matching algorithm takes.

scholarly journals ROLE OF TIE-POINTS DISTRIBUTION IN AERIAL PHOTOGRAPHY

2016 ◽  
Vol XL-3/W4 ◽  
pp. 41-44
Author(s):  
S. Kerner ◽  
I. Kaufman ◽  
Y. Raizman
Keyword(s):  
Feature Extraction ◽  
Spatial Distribution ◽  
Aerial Photography ◽  
Image Matching ◽  
Feature Matching ◽  
Real Data ◽  
Matching Process ◽  
Feature Based ◽  
Extraction Stage

Automatic image matching algorithms, and especially feature-based methods, profoundly changed our understanding and requirements of tie points. The number of tie points has increased by orders of magnitude, yet the notions of accuracy and reliability of tie points remain equally important. The spatial distribution of tie points is less predictable, and is subject only to limited control. Feature-based methods also highlighted a conceptual division of the matching process into two separate stages – feature extraction and feature matching. <br><br> In this paper we discuss whether spatial distribution requirements, such as Von Gruber positions, are still relevant to modern matching methods. We argue that forcing such patterns might no longer be required in the feature extraction stage. However, we claim spatial distribution is important in the feature matching stage. <br><br> We will focus on terrains that are notorious for difficult matching, such as water bodies, with real data obtained by users of VisionMap’s A3 Edge camera and LightSpeed photogrammetric suite.

scholarly journals Feedback Unilateral Grid-Based Clustering Feature Matching for Remote Sensing Image Registration

2019 ◽  
Vol 11 (12) ◽  
pp. 1418
Author(s):  
Zhaohui Zheng ◽  
Hong Zheng ◽  
Yong Ma ◽  
Fan Fan ◽  
Jianping Ju ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Image Matching ◽  
Feature Matching ◽  
Remote Sensing Image ◽  
Registration Accuracy ◽  
Computation Efficiency ◽  
Feature Based ◽  
Local Grid ◽  
Grid Based

In feature-based image matching, implementing a fast and ultra-robust feature matching technique is a challenging task. To solve the problems that the traditional feature matching algorithm suffers from, such as long running time and low registration accuracy, an algorithm called feedback unilateral grid-based clustering (FUGC) is presented which is able to improve computation efficiency, accuracy and robustness of feature-based image matching while applying it to remote sensing image registration. First, the image is divided by using unilateral grids and then fast coarse screening of the initial matching feature points through local grid clustering is performed to eliminate a great deal of mismatches in milliseconds. To ensure that true matches are not erroneously screened, a local linear transformation is designed to take feedback verification further, thereby performing fine screening between true matching points deleted erroneously and undeleted false positives in and around this area. This strategy can not only extract high-accuracy matching from coarse baseline matching with low accuracy, but also preserves the true matching points to the greatest extent. The experimental results demonstrate the strong robustness of the FUGC algorithm on various real-world remote sensing images. The FUGC algorithm outperforms current state-of-the-art methods and meets the real-time requirement.

scholarly journals A Local Feature Descriptor Based on Oriented Structure Maps with Guided Filtering for Multispectral Remote Sensing Image Matching

2019 ◽  
Vol 11 (8) ◽  
pp. 951 ◽  
Author(s):  
Tao Ma ◽  
Jie Ma ◽  
Kun Yu
Keyword(s):  
Remote Sensing ◽  
Image Matching ◽  
Remote Sensing Image ◽  
Multispectral Image ◽  
Intensity Difference ◽  
Feature Descriptor ◽  
Multispectral Images ◽  
Oriented Structure ◽  
Guided Filtering ◽  
Local Feature Descriptor

Multispectral image matching plays a very important role in remote sensing image processing and can be applied for registering the complementary information captured by different sensors. Due to the nonlinear intensity difference in multispectral images, many classic descriptors designed for images of the same spectrum are unable to work well. To cope with this problem, this paper proposes a new local feature descriptor termed histogram of oriented structure maps (HOSM) for multispectral image matching tasks. This proposed method consists of three steps. First, we propose a new method based on local contrast to construct the structure guidance images from the multispectral images by transferring the significant contours from source images to results, respectively. Second, we calculate oriented structure maps with guided image filtering. In details, we first construct edge maps by the progressive Sobel filters to extract the common structure characteristics from the multispectral images, and then we compute the oriented structure maps by performing the guided filtering on the edge maps with the structure guidance images constructed in the first step. Finally, we build the HOSM descriptor by calculating the histogram of oriented structure maps in a local region of each interest point and normalize the feature vector. The proposed HOSM descriptor was evaluated on three commonly used datasets and was compared with several state-of-the-art methods. The experimental results demonstrate that the HOSM descriptor can be robust to the nonlinear intensity difference in multispectral images and outperforms other methods.

scholarly journals Computational learning of features for automated colonic polyp classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kangkana Bora ◽  
M. K. Bhuyan ◽  
Kunio Kasugai ◽  
Saurav Mallik ◽  
Zhongming Zhao
Keyword(s):  
Cross Validation ◽  
Statistical Significance ◽  
Colonic Polyps ◽  
Colonic Polyp ◽  
Least Square ◽  
Support Vector ◽  
Feature Descriptor ◽  
Ranking Algorithm ◽  
Feature Based ◽  
Fold Cross Validation

AbstractShape, texture, and color are critical features for assessing the degree of dysplasia in colonic polyps. A comprehensive analysis of these features is presented in this paper. Shape features are extracted using generic Fourier descriptor. The nonsubsampled contourlet transform is used as texture and color feature descriptor, with different combinations of filters. Analysis of variance (ANOVA) is applied to measure statistical significance of the contribution of different descriptors between two colonic polyps: non-neoplastic and neoplastic. Final descriptors selected after ANOVA are optimized using the fuzzy entropy-based feature ranking algorithm. Finally, classification is performed using Least Square Support Vector Machine and Multi-layer Perceptron with five-fold cross-validation to avoid overfitting. Evaluation of our analytical approach using two datasets suggested that the feature descriptors could efficiently designate a colonic polyp, which subsequently can help the early detection of colorectal carcinoma. Based on the comparison with four deep learning models, we demonstrate that the proposed approach out-performs the existing feature-based methods of colonic polyp identification.

scholarly journals IFRAD: A Fast Feature Descriptor for Remote Sensing Images

2021 ◽  
Vol 13 (18) ◽  
pp. 3774
Author(s):  
Qinping Feng ◽  
Shuping Tao ◽  
Chunyu Liu ◽  
Hongsong Qu ◽  
Wei Xu
Keyword(s):  
Remote Sensing ◽  
Feature Matching ◽  
State Of The Art ◽  
Image Data ◽  
Feature Descriptor ◽  
Feature Description ◽  
Sensing Applications ◽  
Feature Similarity ◽  
Feature Based ◽  
Remote Sensing Applications

Feature description is a necessary process for implementing feature-based remote sensing applications. Due to the limited resources in satellite platforms and the considerable amount of image data, feature description—which is a process before feature matching—has to be fast and reliable. Currently, the state-of-the-art feature description methods are time-consuming as they need to quantitatively describe the detected features according to the surrounding gradients or pixels. Here, we propose a novel feature descriptor called Inter-Feature Relative Azimuth and Distance (IFRAD), which will describe a feature according to its relation to other features in an image. The IFRAD will be utilized after detecting some FAST-alike features: it first selects some stable features according to criteria, then calculates their relationships, such as their relative distances and azimuths, followed by describing the relationships according to some regulations, making them distinguishable while keeping affine-invariance to some extent. Finally, a special feature-similarity evaluator is designed to match features in two images. Compared with other state-of-the-art algorithms, the proposed method has significant improvements in computational efficiency at the expense of reasonable reductions in scale invariance.

scholarly journals ROLE OF TIE-POINTS DISTRIBUTION IN AERIAL PHOTOGRAPHY

2016 ◽  
Vol XL-3/W4 ◽  
pp. 41-44 ◽  
Author(s):  
S. Kerner ◽  
I. Kaufman ◽  
Y. Raizman
Keyword(s):  
Feature Extraction ◽  
Spatial Distribution ◽  
Aerial Photography ◽  
Image Matching ◽  
Feature Matching ◽  
Real Data ◽  
Matching Process ◽  
Feature Based ◽  
Extraction Stage

Automatic image matching algorithms, and especially feature-based methods, profoundly changed our understanding and requirements of tie points. The number of tie points has increased by orders of magnitude, yet the notions of accuracy and reliability of tie points remain equally important. The spatial distribution of tie points is less predictable, and is subject only to limited control. Feature-based methods also highlighted a conceptual division of the matching process into two separate stages – feature extraction and feature matching. &lt;br&gt;&lt;br&gt; In this paper we discuss whether spatial distribution requirements, such as Von Gruber positions, are still relevant to modern matching methods. We argue that forcing such patterns might no longer be required in the feature extraction stage. However, we claim spatial distribution is important in the feature matching stage. &lt;br&gt;&lt;br&gt; We will focus on terrains that are notorious for difficult matching, such as water bodies, with real data obtained by users of VisionMap’s A3 Edge camera and LightSpeed photogrammetric suite.

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