A Parallel Analysis on Scale Invariant Feature Transform (SIFT) Algorithm

2011 ◽  
pp. 98-111 ◽  
Author(s):  
Donglei Yang ◽  
Lili Liu ◽  
Feiwen Zhu ◽  
Weihua Zhang
Keyword(s):  
Scale Invariant Feature Transform ◽  
Parallel Analysis ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

scholarly journals MRI Images under the Optimized Registration Algorithm for Primary Open Angle Glaucoma Visual Path Damage

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bin Zhou ◽  
Min Chen
Keyword(s):  
Primary Open Angle Glaucoma ◽  
Open Angle Glaucoma ◽  
Scale Invariant Feature Transform ◽  
Open Angle ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
The Impact

To explore the impact of different image registration algorithms on the diagnosis of visual path damage in patients with primary open angle glaucoma (POAG), 60 cases of suspected POAG patients were selected as the research objects. Shape-preserving scale invariant feature transform (SP-SIFT) algorithm, scale invariant feature transform (SIFT) algorithm, and Kanade-Lucas-Tomasi (KLT) algorithm were compared and applied to MRI images of 60 POAG patients. It was found that the SP-SIFT algorithm converged after 33 iterations, which had a higher registration speed than the SIFT algorithm and the KLT algorithm. The mean errors of the SP-SIFT algorithm in the rotation angle, X-direction translation, and Y-direction translation were 2.11%, 4.56%, and 4.31%, respectively. Those of the SIFT algorithm were 5.55%, 9.98%, and 7.01%, respectively. Those of the KLT algorithm were 7.45%, 11.31%, and 8.56%, respectively, and the difference among algorithms was significant ( P < 0.05 ). The diagnostic sensitivity and accuracy of the SP-SIFT algorithm for POAG were 96.15% and 94.34%, respectively. Those of the SIFT algorithm were 94.68% and 90.74%, respectively. Those of the KLT algorithm were 94.21% and 90.57%, respectively, and the three algorithms had significant differences ( P < 0.05 ). The results of MRI images based on the SP-SIFT algorithm showed that the average thickness of the cortex of the patient’s left talar sulcus, right talar sulcus, left middle temporal gyrus, and left fusiform gyrus were 2.49 ± 0.15 mm, 2.62 ± 0.13 mm, 3.00 ± 0.10 mm, and 2.99 ± 0.17 mm, respectively. Those of the SIFT algorithm were 2.51 ± 0.17 mm, 2.69 ± 0.12 mm, 3.11 ± 0.13 mm, and 3.09 ± 0.14 mm, respectively. Those of the KLT algorithm were 2.35 ± 0.12 mm, 2.52 ± 0.16 mm, 2.77 ± 0.11 mm, and 2.87 ± 0.17 mm, respectively, and the three algorithms had significant differences ( P < 0.05 ). In summary, the SP-SIFT algorithm was ideal for POAG visual pathway diagnosis and was of great adoption potential in clinical diagnosis.

scholarly journals ANALISIS PENDETEKSI KECOCOKAN OBJEK PADA CITRA DIGITAL DENGAN METODE ALGORITMA SIFT DAN HISTOGRAM COLOR RGB

2018 ◽  
Vol 1 (1) ◽  
pp. 20-27
Author(s):  
Rosidin Al Caruban ◽  
Bambang Sugiantoro ◽  
Yudi Prayudi
Keyword(s):  
Digital Image ◽  
Digital Images ◽  
Scale Invariant Feature Transform ◽  
Digital Image Forensics ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Scientific Methods ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

Through using tools of image processing on digital images just like gimp and adobe photoshop applications, an image on digital images can be a source of information for anyone who observes it. On one hand, those applications can easily change or manipulate the authenticity of the image. On the other hand, they can be misused to undermine the credibility of the authenticity of the image in various aspects. Thus, they can be considered as a crime. The implementation of the SIFT Algorithm (Scale Invariant feature transform) and RGB color histogram in Matlab can detect object fitness in digital images and perform accurate test. This study discusses the implementation of getting object fitness on digital image that has been manipulated by SIFT Algorithm method on the Matlab source. It is done by comparing the original image with the manipulated one. The object fitness in digital images can be obtained from a number of key points and other additional parameters through comparing number of pixels on the analyzed image and on the changed histogram in RGB color on each analyzed image. The digital image forensics which is known as one of the scientific methods commonly used in researches is aimed to obtain evidences or facts in determining the authenticity of the image on digital images. The use of the SIFT algorithm is chosen as an extraction method because it is invariant to scale, rotation, translation, and illumination changes. SIFT is used to obtain characteristics of the pattern of the gained key point. The tested result of the SIFT Algorithm method (Scale Invariant feature transform) is expected to produce a better image analysis.

Research and Improvement of Content-Based Image Retrieval Framework

2018 ◽  
Vol 32 (12) ◽  
pp. 1850043 ◽  
Author(s):  
Yong Hou ◽  
Qingjun Wang
Keyword(s):  
Image Retrieval ◽  
High Performance ◽  
Gaussian Mixture ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Match Rate ◽  
Feature Transform ◽  
Scale Invariant Feature

This paper proposed a high-performance image retrieval framework, which combines the improved feature extraction algorithm SIFT (Scale Invariant Feature Transform), improved feature matching, improved feature coding Fisher and improved Gaussian Mixture Model (GMM) for image retrieval. Aiming at the problem of slow convergence of traditional GMM algorithm, an improved GMM is proposed. This algorithm initializes the GMM by using on-line [Formula: see text]-means clustering method, which improves the convergence speed of the algorithm. At the same time, when the model is updated, the storage space is saved through the improvement of the criteria for matching rules and generating new Gaussian distributions. Aiming at the problem that the dimension of SIFT (Scale Invariant Feature Transform) algorithm is too high, the matching speed is too slow and the matching rate is low, an improved SIFT algorithm is proposed, which preserves the advantages of SIFT algorithm in fuzzy, compression, rotation and scaling invariance advantages, and improves the matching speed, the correct match rate is increased by an average of 40% to 55%. Experiments on a recently released VOC 2012 database and a database of 20 category objects containing 230,800 images showed that the framework had high precision and recall rates and less query time. Compared with the standard image retrieval framework, the improved image retrieval framework can detect the moving target quickly and effectively and has better robustness.

The Improved Algorithm of Scale Invariant Feature Transform on Palmprint Recognition

2011 ◽  
Vol 186 ◽  
pp. 565-569
Author(s):  
Zhong Qu ◽  
Zheng Yong Wang
Keyword(s):  
Euclidean Distance ◽  
Recognition Rate ◽  
Scale Invariant Feature Transform ◽  
Palmprint Recognition ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
Simulation Results ◽  
Scale Invariant Feature

This paper presents a new method of palmprint recognition based on improved scale invariant feature transform (SIFT) algorithm which combines the Euclidean distance and weighted sub-region. It has the scale, rotation, affine, perspective, illumination invariance, and also has good robustness to the target's motion, occlusion, noise and other factors. Simulation results show that the recognition rate of the improved SIFT algorithm is higher than the recognition rate of SIFT algorithm.

Research on Image Matching Combining on Fractional Differential With Scale Invariant Feature Transform

2015 ◽  
Author(s):  
Guimei Zhang ◽  
Binbin Chen ◽  
YangQuan Chen
Keyword(s):  
Image Matching ◽  
Scale Invariant Feature Transform ◽  
Fractional Differentiation ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Self Similar ◽  
Fractional Differential ◽  
Feature Transform ◽  
Scale Invariant Feature

Image matching is one of the most important problems in computer vision. Scale Invariant Feature Transform (SIFT) algorithm has been proved to be effective for detecting features for image matching. However SIFT algorithm has limitation to extract features in textile image or self-similar construction image. Fortunately fractional differentiation has advantage to strengthen and extract textural features of digital images. Aiming at the problem, this paper proposes a new method for image matching based on fractional differentiation and SIFT. The method calculates the image pyramid combining the Riemann-Liouville (R-L) fractional differentiation and the derivative of the Gaussian function. Thus image feature has been enhanced, and more feature points can be extracted. As a result the matching accuracy is improved. Additionally, a new feature detection mask based on fractional differential is constructed. The proposed method is a significant extension of SIFT algorithm. The experiments carried out with images in database and real images indicate that the proposed method can obtain good matching results. It can be used for matching textile image or some self-similar construct image.

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