scholarly journals Apparel Design and Development Based on 3D Scanning Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Guodong Zhang
Keyword(s):  
Design Thinking ◽  
3D Scanning ◽  
Image Feature ◽  
Apparel Design ◽  
Scale Invariant ◽  
Intelligent Modeling ◽  
Motion Recovery ◽  
Garment Design ◽  
Scale Invariant Feature ◽  
Feature Conversion

With the development of computer science, especially the application of 3D scanning technology in garment design, intelligent modeling is realized, which is impossible to achieve in traditional design methods. In this paper, we propose the 3D model construction of human garments based on the motion recovery structure method. The eigenmatrix is obtained from the camera parameters, and the transformation matrix is calculated by matching the image feature points with the help of scale-invariant feature conversion algorithm to realize the 3D reconstruction technology of human garments based on multiview image sequences. The effectiveness of this method is verified through experiments, and it has good robustness and accuracy. Through the form of style modeling, the design thinking and method can be extended to form a more reasonable garment structure and guide the innovation of garment production mode.

Medical Image Feature Matching Based on Wavelet Transform and SIFT Algorithm

2011 ◽  
Vol 65 ◽  
pp. 497-502
Author(s):  
Yan Wei Wang ◽  
Hui Li Yu
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Medical Image ◽  
Feature Matching ◽  
Image Feature ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Scale Invariant Feature

A feature matching algorithm based on wavelet transform and SIFT is proposed in this paper, Firstly, Biorthogonal wavelet transforms algorithm is used for medical image to delaminating, and restoration the processed image. Then the SIFT (Scale Invariant Feature Transform) applied in this paper to abstracting key point. Experimental results show that our algorithm compares favorably in high-compressive ratio, the rapid matching speed and low storage of the image, especially for the tilt and rotation conditions.

scholarly journals Annular Spatial Pyramid Mapping and Feature Fusion-Based Image Coding Representation and Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Mengxi Xu ◽  
Yingshu Lu ◽  
Xiaobin Wu
Keyword(s):  
Image Classification ◽  
Feature Fusion ◽  
Image Feature ◽  
Classification Model ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Spatial Pyramid

Conventional image classification models commonly adopt a single feature vector to represent informative contents. However, a single image feature system can hardly extract the entirety of the information contained in images, and traditional encoding methods have a large loss of feature information. Aiming to solve this problem, this paper proposes a feature fusion-based image classification model. This model combines the principal component analysis (PCA) algorithm, processed scale invariant feature transform (P-SIFT) and color naming (CN) features to generate mutually independent image representation factors. At the encoding stage of the scale-invariant feature transform (SIFT) feature, the bag-of-visual-word model (BOVW) is used for feature reconstruction. Simultaneously, in order to introduce the spatial information to our extracted features, the rotation invariant spatial pyramid mapping method is introduced for the P-SIFT and CN feature division and representation. At the stage of feature fusion, we adopt a support vector machine with two kernels (SVM-2K) algorithm, which divides the training process into two stages and finally learns the knowledge from the corresponding kernel matrix for the classification performance improvement. The experiments show that the proposed method can effectively improve the accuracy of image description and the precision of image classification.

An Improved Face Recognition Algorithm Based on SIFT and LBP

2013 ◽  
Vol 427-429 ◽  
pp. 1999-2004 ◽  
Author(s):  
Huai Ming Yang ◽  
Jin Guang Sun
Keyword(s):  
Recognition Rate ◽  
Recognition Algorithm ◽  
Image Feature ◽  
Local Similarity ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Face Images ◽  
Calculation Results ◽  
Scale Invariant Feature ◽  
Global And Local

A new face image feature extraction and recognition algorithm based on Scale Invariant Feature Transform (SIFT) and Local Linary Patterns (LBP) is proposed in this paper. Firstly, a set of keypoints are extracted from images by using the SIFT algorithm; Secondly, each keypoint is described by LBP patterns; Finally, a combination of the global and local similarity is adopted to calculate the matching results for face images. Calculation results show that the algorithm can reduce the matching dimension of feature points, improve the recognition rate and perspective; it has nice robustness against the interferences such as rotation, lighting and expression.

scholarly journals SIFT Image Feature Extraction for an Efficient Image Registration

2020 ◽  
Vol 8 (6) ◽  
pp. 4090-4094
Keyword(s):  
Image Registration ◽  
Image Feature ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Image Registration Algorithm ◽  
Key Points ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Ransac Algorithm ◽  
Unit Vectors

This paper presents an image registration algorithm based on SIFT (Scale Invariant Feature Transform).The obtained descriptors and key points by the SIFT confirms that, the algorithm is very robust to scaling, noise, translation and rotation. At the beginning, the key points are extracted from the image. Later to Match the obtained points, dot products between the unit vectors are calculated. Finally, transformation matrix is obtained by applying RANSAC algorithm. Experimental results shows that the algorithm extracts the better key points, which can be used for used for image registration applications.

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