Feature-Based GDLOH Deformable Registration for CT Lung Image

2013 ◽  
Vol 333-335 ◽  
pp. 969-973
Author(s):  
Yu Han Yang ◽  
Yao Qin Xie
Keyword(s):  
Feature Detection ◽  
Deformable Registration ◽  
Measurement Tool ◽  
Feature Descriptor ◽  
Feature Points ◽  
Scale Invariant ◽  
Clinical Images ◽  
Speeded Up Robust Features ◽  
Matching Process ◽  
Novel Approach

To improve the efficiency and accuracy of the conventional SIFT-TPS (Scale-invariant feature transform and Thin-Plate Spline) method in deformable registration for CT lung image, we develop a novel approach by using combining SURF(Speeded up Robust Features) and GDLOH(Gradient distance-location-orientation histogram) to detect matching feature points. First, we employ SURF as feature detection to find the stable feature points of the two CT images rapidly. Then GDLOH is taken as feature descriptor to describe each detected points characteristic, in order to supply measurement tool for matching process. In our experiment, five couples of clinical images are simulated using our algorithm above, result in an obvious improvement in run-time and registration quality, compared with the conventional methods. It is demonstrated that the proposed method may create a new window in performing a good robust and adaptively for deformable registration for CT lung tomography.

scholarly journals Retinal Mosaicking with Vascular Bifurcations Detected on Vessel Mask by a Convolutional Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Xiuxia Feng ◽  
Guangwei Cai ◽  
Xiaofang Gou ◽  
Zhaoqiang Yun ◽  
Wenhui Wang ◽  
...  
Keyword(s):  
Feature Detection ◽  
Recall Rate ◽  
Retinal Images ◽  
Feature Points ◽  
Scale Invariant ◽  
Convolutional Network ◽  
Speeded Up Robust Features ◽  
Vascular Structures ◽  
Scale Invariant Feature ◽  
Accurate Matching

Mosaicking of retinal images is potentially useful for ophthalmologists and computer-aided diagnostic schemes. Vascular bifurcations can be used as features for matching and stitching of retinal images. A fully convolutional network model is employed to segment vascular structures in retinal images to detect vascular bifurcations. Then, bifurcations are extracted as feature points on the vascular mask by a robust and efficient approach. Transformation parameters for stitching can be estimated from the correspondence of vascular bifurcations. The proposed feature detection and mosaic method is evaluated on retinal images of 14 different eyes, 62 retinal images. The proposed method achieves a considerably higher average recall rate of matching for paired images compared with speeded-up robust features and scale-invariant feature transform. The running time of our method was also lower than other methods. Results produced by the proposed method superior to that of AutoStitch, photomerge function in Photoshop cs6 and ICE, demonstrate that accurate matching of detected vascular bifurcations could lead to high-quality mosaic of retinal images.

PERSPECTIVE-INVARIANT IMAGE MATCHING FRAMEWORK WITH BINARY FEATURE DESCRIPTOR AND APSO

2014 ◽  
Vol 28 (08) ◽  
pp. 1455011
Author(s):  
LICHUAN GENG ◽  
SONGZHI SU ◽  
DONGLIN CAO ◽  
SHAOZI LI
Keyword(s):  
Image Matching ◽  
Feature Descriptor ◽  
Feature Points ◽  
Feature Location ◽  
Scale Invariant ◽  
Binary Descriptors ◽  
Binary Feature ◽  
Image Patches ◽  
Sample Points ◽  
Scale Invariant Feature

A novel perspective invariant image matching framework is proposed in this paper, noted as Perspective-Invariant Binary Robust Independent Elementary Features (PBRIEF). First, we use the homographic transformation to simulate the distortion between two corresponding patches around the feature points. Then, binary descriptors are constructed by comparing the intensity of sample points surrounding the feature location. We transform the location of the sample points with simulated homographic matrices. This operation is to ensure that the intensities which we compared are the realistic corresponding pixels between two image patches. Since the exact perspective transform matrix is unknown, an Adaptive Particle Swarm Optimization (APSO) algorithm-based iterative procedure is proposed to estimate the real transformation angles. Experimental results obtained on five different datasets show that PBRIEF outperforms significantly the existing methods on images with large viewpoint difference. Moreover, the efficiency of our framework is also improved comparing with Affine-Scale Invariant Feature Transform (ASIFT).

LEFT: LOCAL EDGE FEATURES TRANSFORM

2020 ◽  
pp. 11-18
Author(s):  
T. A. Tikhomirova ◽  
G. T. Fedorenko ◽  
K. M. Nazarenko ◽  
E. S. Nazarenko
Keyword(s):  
Large Scale ◽  
Vision System ◽  
Mutual Orientation ◽  
Feature Points ◽  
Scale Invariant ◽  
Actual Object ◽  
Speeded Up Robust Features ◽  
Localization And Mapping ◽  
Edge Features ◽  
Local Edge

To detect point correspondence between images or 3D scenes, local texture descriptors, such as SIFT (Scale Invariant Feature Transform), SURF (Speeded-Up Robust Features), BRIEF (Binary Robust Independent Elementary Features), and others, are usually used. Formally they provide invariance to image rotation and scale, but this properties are achieved only approximately due to discrete number of evaluable orientations and scales stored into the descriptor. Feature points preferable for such descriptors usually are not belong to actual object boundaries into 3D scenes and so are hard to be used into apipolar relationships. At the same time, linking the feature point to large-scale lines and edges is preferable for SLAM (Simultaneous Localization And Mapping) tasks, because their appearance are the most resistible to daily, seasonal and weather variations.In this paper, original feature points descriptor LEFT (Local Edge Features Transform) for edge images are proposed. LEFT accumulate directions and contrasts of alternative strait segments tangent to lines and edges in the vicinity of feature points. Due to this structure, mutual orientation of LEFT descriptors are evaluated and taken into account directly at the stage of their comparison. LEFT descriptors adapt to the shape of contours in the vicinity of feature points, so they can be used to analyze local and global geometric distortions of a various nature. The article presents the results of comparative testing of LEFT and common texture-based descriptors and considers alternative ways of representing them in a computer vision system.

Vehicle Logo Recognition Based on Local Feature Descriptor

2012 ◽  
Vol 263-266 ◽  
pp. 2418-2421
Author(s):  
Sheng Ke Wang ◽  
Lili Liu ◽  
Xiaowei Xu
Keyword(s):  
Feature Matching ◽  
Recognition Rate ◽  
Recognition System ◽  
Feature Descriptor ◽  
Training Set ◽  
Scale Invariant ◽  
Speeded Up Robust Features ◽  
Logo Recognition ◽  
Vehicle Logo Recognition ◽  
Scale Invariant Feature

In this paper, we present a comparison of the scale-invariant feature transforms (SIFT)-based feature-matching scheme and the speeded up robust features (SURF)-based feature-matching scheme in the field of vehicle logo recognition. We capture a set of logo images which are varied in illumination, blur, scale, and rotation. Six kinds of vehicle logo training set are formed using 25 images in average and the rest images are used to form the testing set. The Logo Recognition system that we programmed indicates a high recognition rate of the same kind of query images through adjusting different parameters.

scholarly journals PERFORMANCE ANALYSIS OF SELECTED FEATURE DESCRIPTORS USED FOR AUTOMATIC IMAGE REGISTRATION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 559-566
Author(s):  
O. G. Ajayi
Keyword(s):  
Image Registration ◽  
Feature Descriptor ◽  
Scale Invariant ◽  
Speeded Up Robust Features ◽  
Feature Descriptors ◽  
Single Feature ◽  
Image Registration Algorithm ◽  
Maximally Stable Extremal Region ◽  
Registration Scheme ◽  
Scale Invariant Feature

Abstract. Automatic detection and extraction of corresponding features is very crucial in the development of an automatic image registration algorithm. Different feature descriptors have been developed and implemented in image registration and other disciplines. These descriptors affect the speed of feature extraction and the measure of extracted conjugate features, which affects the processing speed and overall accuracy of the registration scheme. This article is aimed at reviewing the performance of most-widely implemented feature descriptors in an automatic image registration scheme. Ten (10) descriptors were selected and analysed under seven (7) conditions viz: Invariance to rotation, scale and zoom, their robustness, repeatability, localization and efficiency using UAV acquired images. The analysis shows that though four (4) descriptors performed better than the other Six (6), no single feature descriptor can be affirmed to be the best, as different descriptors perform differently under different conditions. The Modified Harris and Stephen Corner Detector (MHCD) proved to be invariant to scale and zoom while it is excellent in robustness, repeatability, localization and efficiency, but it is variant to rotation. Also, the Scale Invariant feature Transform (SIFT), Speeded Up Robust Features (SURF) and the Maximally Stable Extremal Region (MSER) algorithms proved to be invariant to scale, zoom and rotation, and very good in terms of repeatability, localization and efficiency, though MSER proved to be not as robust as SIFT and SURF. The implication of the findings of this research is that the choice of feature descriptors must be informed by the imaging conditions of the image registration analysts.

scholarly journals Digital Image Falsification Detection System for Effective Data Communication

2019 ◽  
Vol 9 (2) ◽  
pp. 4081-4086
Keyword(s):  
Digital Image ◽  
Detection System ◽  
Detection Algorithm ◽  
Feature Points ◽  
Scale Invariant ◽  
Speeded Up Robust Features ◽  
Extraction Algorithm ◽  
Region Extraction ◽  
Block Based ◽  
Scale Invariant Feature

In this proposed system a digital imagefalsification can be identified using the combination of both adaptive over block based segmentation, feature keypointbased feature extraction algorithms(Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF)) and forgery region extraction algorithm. The proposed falsification detection algorithm comprises both block based falsification detection algorithm (adaptive over block based segmentation and block feature matching algorithm) and the keypoint based falsification detection algorithm(forgery region extraction algorithm). Adaptive over block based Segmentation algorithm adaptively segments the input digital image into separate(non overlapped) blocks in irregular manner. Scale Invariant Feature Transform (SIFT) algorithm and Speeded Up Robust Features (SURF) algorithms are used to draw out features from the segmentedblocks as a block features. Then the extracted features are matched with the feature points of other segmented block. If the feature key points are matched with any other feature point presents in the segmented blocks, then the matched feature points are marked as Labeled key Points (LKP), which can be doubted as a forged regions. Finally, the Forgery Region Extraction algorithm can be used to detect the forged region from the input digital image based on the extracted labeled feature points. The experimental outcomesdisplay that the novelfalsification detection system can accomplished the requirements compared with the existing digital imagefalsification detection methods

scholarly journals FPGA Design of Enhanced Scale-Invariant Feature Transform with Finite-Area Parallel Feature Matching for Stereo Vision

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1632
Author(s):  
Chien-Hung Kuo ◽  
Erh-Hsu Huang ◽  
Chiang-Heng Chien ◽  
Chen-Chien Hsu
Keyword(s):  
Stereo Vision ◽  
Feature Detection ◽  
Feature Matching ◽  
Threshold Value ◽  
Frame Rate ◽  
Feature Descriptor ◽  
Fpga Design ◽  
Scale Invariant ◽  
Finite Area ◽  
Time Required

In this paper, we propose an FPGA-based enhanced-SIFT with feature matching for stereo vision. Gaussian blur and difference of Gaussian pyramids are realized in parallel to accelerate the processing time required for multiple convolutions. As for the feature descriptor, a simple triangular identification approach with a look-up table is proposed to efficiently determine the direction and gradient of the feature points. Thus, the dimension of the feature descriptor in this paper is reduced by half compared to conventional approaches. As far as feature detection is concerned, the condition for high-contrast detection is simplified by moderately changing a threshold value, which also benefits the reduction of the resulting hardware in realization. The proposed enhanced-SIFT not only accelerates the operational speed but also reduces the hardware cost. The experiment results show that the proposed enhanced-SIFT reaches a frame rate of 205 fps for 640 × 480 images. Integrated with two enhanced-SIFT, a finite-area parallel checking is also proposed without the aid of external memory to improve the efficiency of feature matching. The resulting frame rate by the proposed stereo vision matching can be as high as 181 fps with good matching accuracy as demonstrated in the experimental results.

scholarly journals Image Mosaic Research and Realization Based on LoFTR Algorithm

2021 ◽  
Author(s):  
Aikui Tian ◽  
Kangtao Wang ◽  
liye zhang ◽  
Bingcai Wei
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
The Self ◽  
Image Feature ◽  
Feature Descriptor ◽  
Image Mosaic ◽  
Feature Points ◽  
Matching Method ◽  
Fusion Algorithm ◽  
The Matrix

Abstract Aiming at the problem of inaccurate extraction of feature points by the traditional image matching method, low robustness, and problems such as diffculty in inentifying feature points in area with poor texture. This paper proposes a new local image feature matching method, which replaces the traditional sequential image feature detection, description and matching steps. First, extract the coarse features with a resolution of 1/8 from the original image, then tile to a one-dimensional vector plus the positional encoding, feed them to the self-attention layer and cross-attention layer in the Transformer module, and finally get through the Differentiable Matching Layer and confidence matrix, after setting the threshold and the mutual closest standard, a Coarse-Level matching prediction is obtained. Secondly the fine matching is refined at the Fine-level match, after the Fine-level match is established, the image overlapped area is aligned by transforming the matrix to a unified coordinate, and finally the image is fused by the weighted fusion algorithm to realize the unification of seamless mosaic of images. This paper uses the self-attention layer and cross-attention layer in Transformers to obtain the feature descriptor of the image. Finally, experiments show that in terms of feature point extraction, LoFTR algorithm is more accurate than the traditional SIFT algorithm in both low-texture regions and regions with rich textures. At the same time, the image mosaic effect obtained by this method is more accurate than that of the traditional classic algorithms, the experimental effect is more ideal.

scholarly journals FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1839
Author(s):  
Yutong Zhang ◽  
Jianmei Song ◽  
Yan Ding ◽  
Yating Yuan ◽  
Hua-Liang Wei
Keyword(s):  
Feature Matching ◽  
Field Of View ◽  
Image Feature ◽  
Feature Points ◽  
Matching Process ◽  
Feature Descriptors ◽  
Coordinate Mapping ◽  
Matching Performance ◽  
Perspective Model ◽  
Attitude Matrix

Fisheye images with a far larger Field of View (FOV) have severe radial distortion, with the result that the associated image feature matching process cannot achieve the best performance if the traditional feature descriptors are used. To address this challenge, this paper reports a novel distorted Binary Robust Independent Elementary Feature (BRIEF) descriptor for fisheye images based on a spherical perspective model. Firstly, the 3D gray centroid of feature points is designed, and the position and direction of the feature points on the spherical image are described by a constructed feature point attitude matrix. Then, based on the attitude matrix of feature points, the coordinate mapping relationship between the BRIEF descriptor template and the fisheye image is established to realize the computation associated with the distorted BRIEF descriptor. Four experiments are provided to test and verify the invariance and matching performance of the proposed descriptor for a fisheye image. The experimental results show that the proposed descriptor works well for distortion invariance and can significantly improve the matching performance in fisheye images.

scholarly journals Research on Remote Sensing Image Matching with Special Texture Background

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1380
Author(s):  
Sen Wang ◽  
Xiaoming Sun ◽  
Pengfei Liu ◽  
Kaige Xu ◽  
Weifeng Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Geometric Distortion ◽  
Reference Image ◽  
Remote Sensing Images ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Speeded Up Robust Features ◽  
Adaptive Quantization ◽  
Texture Background

The purpose of image registration is to find the symmetry between the reference image and the image to be registered. In order to improve the registration effect of unmanned aerial vehicle (UAV) remote sensing imagery with a special texture background, this paper proposes an improved scale-invariant feature transform (SIFT) algorithm by combining image color and exposure information based on adaptive quantization strategy (AQCE-SIFT). By using the color and exposure information of the image, this method can enhance the contrast between the textures of the image with a special texture background, which allows easier feature extraction. The algorithm descriptor was constructed through an adaptive quantization strategy, so that remote sensing images with large geometric distortion or affine changes have a higher correct matching rate during registration. The experimental results showed that the AQCE-SIFT algorithm proposed in this paper was more reasonable in the distribution of the extracted feature points compared with the traditional SIFT algorithm. In the case of 0 degree, 30 degree, and 60 degree image geometric distortion, when the remote sensing image had a texture scarcity region, the number of matching points increased by 21.3%, 45.5%, and 28.6%, respectively and the correct matching rate increased by 0%, 6.0%, and 52.4%, respectively. When the remote sensing image had a large number of similar repetitive regions of texture, the number of matching points increased by 30.4%, 30.9%, and −11.1%, respectively and the correct matching rate increased by 1.2%, 0.8%, and 20.8% respectively. When processing remote sensing images with special texture backgrounds, the AQCE-SIFT algorithm also has more advantages than the existing common algorithms such as color SIFT (CSIFT), gradient location and orientation histogram (GLOH), and speeded-up robust features (SURF) in searching for the symmetry of features between images.

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