Comparison and Study of Classic Feature Point Detection Algorithm

Jiang, Daguang; Yi, Junkai

doi:10.1109/csss.2012.572

Cited by 15 publications

(5 citation statements)

References 4 publications

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“…Wang, Panda et al design an improved SIFT (Scale Invariant Feature Transform) algorithm to realize object segmentation and localization [7,8]. Although these methods are feasible in certain cases, they depend on image features and are sensitive to image noise [9]. To satisfy the requirements of these methods, the robot operating workspace needs to be strictly controlled, hence these methods are not generally applicable.…”

Section: Object Classification and Detectionmentioning

confidence: 99%

Robot Grasping Based on Stacked Object Classification Network and Grasping Order Planning

et al. 2022

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In this paper, the robot grasping for stacked objects is studied based on object detection and grasping order planning. Firstly, a novel stacked object classification network (SOCN) is proposed to realize stacked object recognition. The network takes into account the visible volume of the objects to further adjust its inverse density parameters, which makes the training process faster and smoother. At the same time, SOCN adopts the transformer architecture and has a self-attention mechanism for feature learning. Subsequently, a grasping order planning method is investigated, which depends on the security score and extracts the geometric relations and dependencies between stacked objects, it calculates the security score based on object relation, classification, and size. The proposed method is evaluated by using a depth camera and a UR-10 robot to complete grasping tasks. The results show that our method has high accuracy for stacked object classification, and the grasping order effectively and successfully executes safely.

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Section: Object Classification and Detectionmentioning

confidence: 99%

Robot Grasping Based on Stacked Object Classification Network and Grasping Order Planning

et al. 2022

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“…(4) Extract edge feature points. the feature points in the edge are determined by the characteristics of the the Hessian matrix's Eigen values ,which are proportional to the main curvature values of the Gaussian difference function D. The Hessian matrix is as shown in Equation (5).…”

Section: D2og Feature Detection Stepsmentioning

confidence: 99%

“…Although, the algorithm can detect corners in a single scale, the positioning accuracy of detection is poor [4]. According to different scenarios, Mikolajczyk etc take a test for a variety of the most representative descriptors [5]. The SIFT descriptor performance is good, but the complexity of the algorithm is high, and the calculation of the algorithm is large.…”

Section: Introductionmentioning

confidence: 99%

Image Mosaic Method Based on Gaussian Second-order Difference Feature Operator

Chen¹,

Hao²,

Di³

2015

TIJEE

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<p><em>To compose the wide visual angle and high resolution image from the sequence of images which have overlapping region in the same scene quickly and correctly, an improved SIFT algorithm which is based on D2oG interest point detector was proposed. It extracted the image feature points and generated corresponding feature descriptors by improved SIFT algorithm. Then, using the random consistency (RANSAC) algorithm purified feature point matching pairs and calculating the transformation matrix H. Last, complete the seamless mosaic of images by using the image fusion algorithm of slipping into and out. It respectively process the images which had the four typical transformations with the traditional SIFT and the proposed method. The result indicated that the number of feature pairs is fewer than SIFT algorithm and the mosaic time is shorter, and then the matching efficiency is higher than the later. This proposed method reduces the complexity of operation and improves real-time of image mosaic simultaneously.</em></p>

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“…It is not suitable for images with detailed information. Scale-Invariant Feature Transform (SIFT) algorithm has been widely used as the mainstream algorithm for image matching because of its strong robustness to illumination and scale rotation [9,14]. However, the cost is an increase in computation time [8,22].…”

Section: Introductionmentioning

confidence: 99%

Mismatch Removal Based on Gaussian Mixture Model for Aircraft Surface Texture Mapping

Niu

Wang

Tan

2020

ITC

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Aiming at the fact of lower efficiency and higher time cost for feature matching in aircraft surface texture mapping process, a novel mismatch removal method based on Gaussian mixture model is proposed to increase correct corresponding feature matching point pairs. The detection and initial point sets for corresponding pairs are carried out, and a vector field is interpolated between the two matching of ORB feature points. The Gaussian mixture model(GMM) is introduced and a prior is taken to force the smoothness of the field, which is based on the Tikhonov regularization in vector-valued reproducing kernel Hilbert space(RKHS). In order to obtain the optimal estimation, the MAP solution of a Bayesian model with latent variables, which could be performed by Expectation Maximization (EM) algorithm, is utilized to determine the correct correspondence. The experimental results show that the algorithm could remove mismatches effectively and the classification for feature points is excellent. The calculation time is greatly reduced, which enhanced real-time performance of aircraft surface texture mapping process.

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Comparison and Study of Classic Feature Point Detection Algorithm

Cited by 15 publications

References 4 publications

Robot Grasping Based on Stacked Object Classification Network and Grasping Order Planning

Robot Grasping Based on Stacked Object Classification Network and Grasping Order Planning

Image Mosaic Method Based on Gaussian Second-order Difference Feature Operator

Mismatch Removal Based on Gaussian Mixture Model for Aircraft Surface Texture Mapping

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