scholarly journals A ROBUST METHOD FOR STEREO VISUAL ODOMETRY BASED ON MULTIPLE EUCLIDEAN DISTANCE CONSTRAINT AND RANSAC ALGORITHM

2017 ◽  
Vol XLII-3/W1 ◽  
pp. 219-224
Author(s):  
Q. Zhou ◽  
X. Tong ◽  
S. Liu ◽  
X. Lu ◽  
S. Liu ◽  
...  
Keyword(s):  
Euclidean Distance ◽  
Visual Odometry ◽  
Distance Constraint ◽  
Feature Points ◽  
Matching Process ◽  
Robotic Motion ◽  
Challenging Environment ◽  
Left Image ◽  
Key Steps ◽  
Ransac Algorithm

Visual Odometry (VO) is a critical component for planetary robot navigation and safety. It estimates the ego-motion using stereo images frame by frame. Feature points extraction and matching is one of the key steps for robotic motion estimation which largely influences the precision and robustness. In this work, we choose the Oriented FAST and Rotated BRIEF (ORB) features by considering both accuracy and speed issues. For more robustness in challenging environment e.g., rough terrain or planetary surface, this paper presents a robust outliers elimination method based on Euclidean Distance Constraint (EDC) and Random Sample Consensus (RANSAC) algorithm. In the matching process, a set of ORB feature points are extracted from the current left and right synchronous images and the Brute Force (BF) matcher is used to find the correspondences between the two images for the Space Intersection. Then the EDC and RANSAC algorithms are carried out to eliminate mismatches whose distances are beyond a predefined threshold. Similarly, when the left image of the next time matches the feature points with the current left images, the EDC and RANSAC are iteratively performed. After the above mentioned, there are exceptional remaining mismatched points in some cases, for which the third time RANSAC is applied to eliminate the effects of those outliers in the estimation of the ego-motion parameters (Interior Orientation and Exterior Orientation). The proposed approach has been tested on a real-world vehicle dataset and the result benefits from its high robustness.

Hierarchical Correspondence of 3D Faces Based on Thin Plate Spline Deformation (HCTD)

2012 ◽  
Vol 479-481 ◽  
pp. 2235-2241
Author(s):  
Yi Yue He ◽  
Guo Hua Geng ◽  
Ming Quan Zhou ◽  
Jie Qiong He ◽  
Jia Jia ◽  
...  
Keyword(s):  
Thin Plate ◽  
Euclidean Distance ◽  
Salient Feature ◽  
Thin Plate Spline ◽  
Geometric Constraints ◽  
Distance Constraint ◽  
Feature Points ◽  
Weighted Distance ◽  
Point Correspondence ◽  
Candidate Set

Aiming at establishing physiological consistent point correspondence between 3D faces, this paper proposes a new hierarchical correspondence method based on thin plate spline deformation called HCTD by introducing local geometric constraints. Firstly, mark feature points in unified Frankfurt Coordinate and the sample face deform based on thin plate spline function according to strict correspondences of feature points, so the sample face approximately coincide with the template; Secondly, build voxel models respectively and select vertexes with salient feature from the template as the current under-corresponding vertex, and the candidate set of the corresponding vertex on sample face is determined by local relative position geometric constraint and Euclidean Distance constraint. Finally, the optimal corresponding vertex is selected according to the weighted distance of local geometric features. Experimental results prove that HCTD can establish point correspondence of faces with higher precision than existing methods.

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 Robust surface matching by integrating edge segments

2014 ◽  
Vol II-5 ◽  
pp. 203-210
Author(s):  
N. Kochi ◽  
T. Sasaki ◽  
K. Kitamura ◽  
S. Kaneko
Keyword(s):  
High Precision ◽  
High Speed ◽  
Stereo Matching ◽  
Direct Consequence ◽  
Surface Matching ◽  
Matching Process ◽  
Edge Matching ◽  
Edge Surface ◽  
Image Area ◽  
Left Image

This paper describes a novel area-based stereo-matching method which aims at reconstructing the shape of objects robustly, correctly, with high precision and with high density. Our goal is to reconstruct correctly the shape of the object by comprising also edges as part of the resulting surface. For this purpose, we need to overcome the problem of how to reconstruct and describe shapes with steep and sharp edges. Area-based matching methods set an image area as a template and search the corresponding match. As a direct consequence of this approach, it becomes not possible to correctly reconstruct the shape around steep edges. Moreover, in the same regions, discontinuities and discrepancies of the shape between the left and right stereo-images increase the difficulties for the matching process. In order to overcome these problems, we propose in this paper the approach of reconstructing the shape of objects by embedding reliable edge line segments into the area-based matching process with parallax estimation. We propose a robust stereo-matching (the extended Edge TIN-LSM) method which integrates edges and which is able to cope with differences in right and left image shape, brightness changes and occlusions. The method consists of the following three steps: (1) parallax estimation, (2) edge-matching, (3) edge-surface matching. In this paper, we describe and explain in detail the process of parallax estimation and the area-based surface-matching with integrated edges; the performance of the proposed method is also validated. The main advantage of this new method is its ability to reconstruct with high precision a 3D model of an object from only two images (for ex. measurement of a tire with 0.14 mm accuracy), thus without the need of a large number of images. For this reason, this approach is intrinsically simple and high-speed.

scholarly journals Pose detection of parallel robot based on improved RANSAC algorithm

2019 ◽  
Vol 52 (7-8) ◽  
pp. 855-868 ◽  
Author(s):  
Guo-Qin Gao ◽  
Qian Zhang ◽  
Shu Zhang
Keyword(s):  
Real Time ◽  
Parallel Robot ◽  
Feature Points ◽  
Scale Invariant ◽  
Time Performance ◽  
Invariant Feature ◽  
Pose Detection ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Ransac Algorithm

For the factors of complex image background, unobvious end-effector characteristics and uneven illumination in the pose detection of parallel robot based on binocular vision, the detection speed, and accuracy cannot meet the requirement of the closed-loop control. So a pose detection method based on improved RANSAC algorithm is presented. First, considering that the image of parallel robot is rigid and has multiple corner points, the Harris–Scale Invariant Feature Transform algorithm is adopted to realize image prematching. The feature points are extracted by Harris and matched by Scale Invariant Feature Transform to realize good accuracy and real-time performance. Second, for the mismatching from prematching, an improved RANSAC algorithm is proposed to refine the prematching results. This improved algorithm can overcome the disadvantages of mismatching and time-consuming of the conventional RANSAC algorithm by selecting feature points in separated grids of the images and predetecting to validate provisional model. The improved RANSAC algorithm was applied to a self-developed novel 3-degrees of freedom parallel robot to verify the validity. The experiment results show that, compared with the conventional algorithm, the average matching time decreases by 63.45%, the average matching accuracy increases by 15.66%, the average deviations of pose detection in Y direction, Z direction, and roll angle [Formula: see text] decrease by 0.871 mm, 0.82 mm, and 0.704°, respectively, using improved algorithm to refine the prematching results. The real-time performance and accuracy of pose detection of parallel robot can be improved.

A New Algorithm of Fast-Generated Panoramic Images

2011 ◽  
Vol 393-395 ◽  
pp. 539-542
Author(s):  
Wei Cong Na
Keyword(s):  
Random Sampling ◽  
Euclidean Distance ◽  
Feature Matching ◽  
Correction Coefficient ◽  
Feature Points ◽  
Generation Efficiency ◽  
Sift Algorithm ◽  
Panoramic Images ◽  
Space Image ◽  
Quality Guarantee

The new algorithm of fast-generated panoramic images this paper puts forward is to extract the feature points of images by the improved SIFT algorithm, and use Euclidean distance combining the K-D tree structure to realize the rapid initial feature matching. Then, based on these initial matching points and the theory of random sampling consistent algorithm, the purification of feature points is realized. At last, the introduction of correction coefficient makes it possible to eliminate fusion ghosts, and HIS space image fusion is applied in order to eliminate the brightness differences. It is verified by the experiments that on the premise of generation of quality guarantee, the new algorithm greatly improves the generation efficiency of panorama images.

Research on Method of Items Recognition Based on SURF Algorithm

2011 ◽  
Vol 121-126 ◽  
pp. 4630-4634
Author(s):  
Wen Yu Chen ◽  
Wen Zhi Xie ◽  
Yan Li Zhao ◽  
Zhong Bo Hao
Keyword(s):  
Euclidean Distance ◽  
Item Recognition ◽  
Image Features ◽  
Feature Points ◽  
Interest Points ◽  
Distance Method ◽  
Sift Algorithm ◽  
Fast Running ◽  
Long Time ◽  
Detection And Recognition

Items detection and recognition have become one of hotspots in the field of computer vision research. Based on image features method has the advantage of low amount of information, fast running speed, high precision, and SIFT algorithm is one of them. But traditional SIFI algorithm have large amount of calculation data and spend long time to compute in terms of items recognition. Therefore, this paper come up with a method of items recognition based on SURF. This article elaborates the basic principle of SURF algorithm that firstly use SURF algorithm to extract feature points of item image, secondly adopt Euclidean distance method to find corresponding interest points of image, and finally get the image after items recognition combination with mapping relation of item image using RANSAC(Random Sample Consesus). Experimental results show that the system of item recognition based on SURF algorithm have better effect on matching recognition, higher instantaneity, better robustness.

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 Passive Forensics for Region Duplication Image Forgery Based on Harris Feature Points and Local Binary Patterns

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Jie Zhao ◽  
Weifeng Zhao
Keyword(s):  
Efficient Method ◽  
Local Binary Pattern ◽  
Local Binary Patterns ◽  
Image Editing ◽  
Feature Points ◽  
Feature Vectors ◽  
Region Duplication ◽  
Software Packages ◽  
Passive Forensics ◽  
Ransac Algorithm

Nowadays the demand for identifying the authenticity of an image is much increased since advanced image editing software packages are widely used. Region duplication forgery is one of the most common and immediate tampering attacks which are frequently used. Several methods to expose this forgery have been developed to detect and locate the tampered region, while most methods do fail when the duplicated region undergoes rotation or flipping before being pasted. In this paper, an efficient method based on Harris feature points and local binary patterns is proposed. First, the image is filtered with a pixelwise adaptive Wiener method, and then dense Harris feature points are employed in order to obtain a sufficient number of feature points with approximately uniform distribution. Feature vectors for a circle patch around each feature point are extracted using local binary pattern operators, and the similar Harris points are matched based on their representation feature vectors using the BBF algorithm. Finally, RANSAC algorithm is employed to eliminate the possible erroneous matches. Experiment results demonstrate that the proposed method can effectively detect region duplication forgery, even when an image was distorted by rotation, flipping, blurring, AWGN, JPEG compression, and their mixed operations, especially resistant to the forgery with the flat area of little visual structures.

scholarly journals Grasp Detection under Occlusions Using SIFT Features

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhaojun Ye ◽  
Yi Guo ◽  
Chengguang Wang ◽  
Haohui Huang ◽  
Genke Yang
Keyword(s):  
Neural Network ◽  
Target Object ◽  
New Thought ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Sift Features ◽  
Effectiveness And Efficiency ◽  
Key Steps ◽  
Ransac Algorithm

Distinguishing target object under occlusions has become the forefront of research to cope with grasping study in general. In this paper, a novel framework which is able to be utilized for a parallel robotic gripper is proposed. There are two key steps for the proposed method in the process of grasping occluded object: generating template information and grasp detection using the matching algorithm. A neural network, trained by the RGB-D data from the Cornell Grasp Dataset, predicts multiple grasp rectangles on template images. A proposed matching algorithm is utilized to eliminate the influence caused by occluded parts on scene images and generates multiple grasp rectangles for objects under occlusions using the grasp information of matched template images. In order to improve the quality of matching result, the proposed matching algorithm improves the SIFT algorithm and combines it with the improved RANSAC algorithm. In this way, this paper obtains suitable grasp rectangles on scene images and offers a new thought about grasping detection under occlusions. The validation results show the effectiveness and efficiency of this approach.

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