scholarly journals A Scale and Rotational Invariant Key-point Detector based on Sparse Coding

2021 ◽  
Vol 12 (3) ◽  
pp. 1-19
Author(s):  
Thanh Phuoc Hong ◽  
Ling Guan
Keyword(s):  
Sparse Coding ◽  
Intensity Change ◽  
Context Change ◽  
Geometric Transformations ◽  
Harris Corner ◽  
Key Points ◽  
Rotational Invariant ◽  
Public Datasets ◽  
Matching Score ◽  
Point Detector

Most popular hand-crafted key-point detectors such as Harris corner, SIFT, SURF aim to detect corners, blobs, junctions, or other human-defined structures in images. Though being robust with some geometric transformations, unintended scenarios or non-uniform lighting variations could significantly degrade their performance. Hence, a new detector that is flexible with context change and simultaneously robust with both geometric and non-uniform illumination variations is very desirable. In this article, we propose a solution to this challenging problem by incorporating Scale and Rotation Invariant design (named SRI-SCK) into a recently developed Sparse Coding based Key-point detector (SCK). The SCK detector is flexible in different scenarios and fully invariant to affine intensity change, yet it is not designed to handle images with drastic scale and rotation changes. In SRI-SCK, the scale invariance is implemented with an image pyramid technique, while the rotation invariance is realized by combining multiple rotated versions of the dictionary used in the sparse coding step of SCK. Techniques for calculation of key-points’ characteristic scales and their sub-pixel accuracy positions are also proposed. Experimental results on three public datasets demonstrate that significantly high repeatability and matching score are achieved.

scholarly journals CrossFuNet: RGB and Depth Cross-Fusion Network for Hand Pose Estimation

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6095
Author(s):  
Xiaojing Sun ◽  
Bin Wang ◽  
Longxiang Huang ◽  
Qian Zhang ◽  
Sulei Zhu ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Depth Map ◽  
Depth Information ◽  
Feature Maps ◽  
Hand Pose Estimation ◽  
Depth Sensors ◽  
Key Points ◽  
Rgb Images ◽  
Public Datasets ◽  
Hand Pose

Despite recent successes in hand pose estimation from RGB images or depth maps, inherent challenges remain. RGB-based methods suffer from heavy self-occlusions and depth ambiguity. Depth sensors rely heavily on distance and can only be used indoors, thus there are many limitations to the practical application of depth-based methods. The aforementioned challenges have inspired us to combine the two modalities to offset the shortcomings of the other. In this paper, we propose a novel RGB and depth information fusion network to improve the accuracy of 3D hand pose estimation, which is called CrossFuNet. Specifically, the RGB image and the paired depth map are input into two different subnetworks, respectively. The feature maps are fused in the fusion module in which we propose a completely new approach to combine the information from the two modalities. Then, the common method is used to regress the 3D key-points by heatmaps. We validate our model on two public datasets and the results reveal that our model outperforms the state-of-the-art methods.

scholarly journals Geometry Sharing Network for 3D Point Cloud Classification and Segmentation

2020 ◽  
Vol 34 (07) ◽  
pp. 12500-12507 ◽  
Author(s):  
Mingye Xu ◽  
Zhipeng Zhou ◽  
Yu Qiao
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Nearest Neighbors ◽  
Classification Performance ◽  
3D Point Cloud ◽  
Geometric Information ◽  
Geometric Transformations ◽  
Geometry Similarity ◽  
Public Datasets ◽  
Point Cloud Classification

In spite of the recent progresses on classifying 3D point cloud with deep CNNs, large geometric transformations like rotation and translation remain challenging problem and harm the final classification performance. To address this challenge, we propose Geometry Sharing Network (GS-Net) which effectively learns point descriptors with holistic context to enhance the robustness to geometric transformations. Compared with previous 3D point CNNs which perform convolution on nearby points, GS-Net can aggregate point features in a more global way. Specially, GS-Net consists of Geometry Similarity Connection (GSC) modules which exploit Eigen-Graph to group distant points with similar and relevant geometric information, and aggregate features from nearest neighbors in both Euclidean space and Eigenvalue space. This design allows GS-Net to efficiently capture both local and holistic geometric features such as symmetry, curvature, convexity and connectivity. Theoretically, we show the nearest neighbors of each point in Eigenvalue space are invariant to rotation and translation. We conduct extensive experiments on public datasets, ModelNet40, ShapeNet Part. Experiments demonstrate that GS-Net achieves the state-of-the-art performances on major datasets, 93.3% on ModelNet40, and are more robust to geometric transformations.

scholarly journals Image Mosaic Method Based on SIFT Features of Line Segment

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jun Zhu ◽  
Mingwu Ren
Keyword(s):  
Line Segment ◽  
Corner Detection ◽  
Image Mosaic ◽  
Scale Invariant ◽  
Point Matching ◽  
Lighting Condition ◽  
Harris Corner ◽  
Key Points ◽  
Scale Invariant Feature ◽  
Harris Corner Detection

This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform) feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling.

A Novel Key-Point Detector Based on Sparse Coding

2020 ◽  
Vol 29 ◽  
pp. 747-756 ◽  
Author(s):  
Thanh Hong-Phuoc ◽  
Ling Guan
Keyword(s):  
Sparse Coding ◽  
Point Detector

A Modified Corner Detector for SAR Images Registration

2021 ◽  
Vol 53 ◽  
pp. 123-156
Author(s):  
Abdelhameed S. Eltanany ◽  
Ahmed S. Amein ◽  
Mohammed S. Elwan
Keyword(s):  
Image Registration ◽  
Feature Matching ◽  
Signal To Noise Ratio ◽  
Phase Congruency ◽  
Sar Images ◽  
Image Mosaicking ◽  
Harris Corner ◽  
Key Points ◽  
Frequency Properties ◽  
Simulated Images

As a first step for image processing operations, detection of corners is a vital procedure where it can be applied for many applications as feature matching, image registration, image mosaicking, image fusion, and change detection. Image registration can be defined as process of getting the misalignment of pixel's position between two or more images. In this paper, a modified corner detector named Synthetic Aperture Radar-Phase Congruency Harris (SAR-PCH) based on a combination between both phase congruency, named later PC, and Harris corner detector is proposed where PC image can supply fundamental and significative features although the complex changes of intensities. Also, the proposed approach overcomes the Harris limitation concerning the noise since the Harris is more sensitive to the noise. The performance was similitude with Shi-Tomasi, FAST, and Harris corner detectors where experiments are conducted first with simulated images and second with real ones. Mean square error (MSE) and peak signal-to-noise ratio (PSNR) are used for the simile. Experimental results, carried out in a standard computer, verify its effectiveness where it utilizes the privileges of image constitutional depicting, allowing extraction of the most powerful key points since it preserves robustness of co-registration process using image frequency properties which are not variant to illumination. Reasonable results compared to the state of art method as Shi-Tomasi, FAST, and Harris algorithms were achieved on the expense of high computational processing time that can be recovered using hardware having high capabilities.

scholarly journals Extracting Corresponding Point Based on Texture Synthesis for Nearly Flat Textureless Object Surface

2015 ◽  
Vol 2015 ◽  
pp. 1-16
Author(s):  
Min Mao ◽  
Kuang-Rong Hao ◽  
Yong-Sheng Ding
Keyword(s):  
Texture Synthesis ◽  
Image Feature ◽  
Intensity Change ◽  
Interest Point ◽  
Geometric Transformations ◽  
Interest Point Detectors ◽  
Significant Intensity ◽  
Matching Mechanism ◽  
Image Pairs ◽  
The Stability

Since the image feature points are always gathered at the range with significant intensity change, such as textured portions or edges of an image, which can be detected by the state-of-the-art intensity based point-detectors, there is nearly no point in the areas of low textured detected by classical interest-point detectors. In this paper we describe a novel algorithm based on affine transform and graph cut for interest point detecting and matching from wide baseline image pairs with weakly textured object. The detection and matching mechanism can be separated into three steps: firstly, the information on the large textureless areas will be enhanced by adding textures through the proposed texture synthesis algorithm TSIQ. Secondly, the initial interest-point set is detected by classical interest-point detectors. Finally, graph cuts are used to find the globally optimal set of matching points on stereo pairs. The efficacy of the proposed algorithm is verified by three kinds of experiments, that is, the influence of point detecting from synthetic texture with different texture sample, the stability under the different geometric transformations, and the performance to improve the quasi-dense matching algorithm, respectively.

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