An automatic method for image mosaic based on feature matching

In the paper, an image mosaic algorithm based on SIFT feature matching is proposed. For an image mosaic method based on feature matching, feature detection is needed to perform in each image. Thus a rapid detection operator is essential to the efficiency of the whole algorithm. In this paper, we use SIFT to extract features. The extracted features are matched by k-d tree and bidirectional matching strategy to enhance the accuracy of matching. Then, a RANSAC algorithm is applied to eliminate outliers to ensure effectiveness of the matching. Finally images are stitched by weighted average blending algorithm. The presented algorithm overcomes the disadvantages of the traditional image mosaic methods which are susceptible to different scale and moving objects, and can achieve sub-pixel accuracy of matching and algorithm is still available to the images at different scale. Experimental results show that the method with strong robustness performs effectively.

UAV remote sensing is low cost and convenient, but traditional methods for image registration and mosaic are low efficient, since it is hard to find ground control points for large quantity of images with small scale. In this study, adjacency transfer algorithm is used between every two adjacent images to automate the work flow: First SIFT(Scale-invariant feature transform) algorithm is employed to detect correspondence points, then polynomial algorithm is used for geometric correction, and finally all the digital orthophoto images are mosaiced into one image. Developed in VC program environment, this study builds its prototype system, which can produce digital orthophoto images with relative high efficiency. Through image overlay and visual interpretation, no image displacement was found. Adjacency transfer algorithm used in this study provides a new way for efficienct and automatic UAV image registration and mosaic.

Image mosaicing has been collecting considerable attention in the field of computer vision and photogrammetry. Feature based on image mosaics requires the integration of feature detection, feature matching, motion parameters estimation, and image stitching. In this paper, we present an automated mosaicing technique for generating mosaics from microscope images. And we introduce the SIFT feature detection into image mosaicing. Solve the problem that the microscope such as view smallness and being unable to observe the full pathological image. And this method is insensitive to the orientation, scale and illumination of the images.

Efficient detection and reliable matching of image features constitute a fundamental task in computer vision. When real-time operation is required, the solution to this problem becomes a real challenge, because of increased processing requirements. Scale Invariant Feature Transform (SIFT) is considered as a stable and robust algorithm for the extraction of invariant features, however special hardware is required for high frame-rate applications, in order to overcome its computational complexity in real-time. In this paper, a complete processor for SIFT feature matching in video sequences is presented. It performs SIFT feature extraction and matching, as well as rejection of false correspondences using the random sample consensus (RANSAC) algorithm. The processor was evaluated using metrics like response, repeatability and recall. It was found that the proposed fixed-point hardware implementation has a comparable performance with software implementations of SIFT. The processor architecture can process more than 36 fps with resolution 640×480, when it is implemented in Cyclone IV FPGA technology.

Road visual navigation relies on accurate road models. This study was aimed at proposing an improved scale-invariant feature transform (SIFT) algorithm for recovering depth information from farmland road images, which would provide a reliable path for visual navigation. The mean image of pixel value in five channels (R, G, B, S and V) were treated as the inspected image and the feature points of the inspected image were extracted by the Canny algorithm, for achieving precise location of the feature points and ensuring the uniformity and density of the feature points. The mean value of the pixels in 5×5 neighborhood around the feature point at an interval of 45o in eight directions was then treated as the feature vector, and the differences of the feature vectors were calculated for preliminary matching of the left and right image feature points. In order to achieve the depth information of farmland road images, the energy method of feature points was used for eliminating the mismatched points. Experiments with a binocular stereo vision system were conducted and the results showed that the matching accuracy and time consuming for depth recovery when using the improved SIFT algorithm were 96.48% and 5.6 s, respectively, with the accuracy for depth recovery of –7.17%-2.97% in a certain sight distance. The mean uniformity, time consuming and matching accuracy for all the 60 images under various climates and road conditions were 50%-70%, 5.0-6.5 s, and higher than 88%, respectively, indicating that performance for achieving the feature points (e.g., uniformity, matching accuracy, and algorithm real-time) of the improved SIFT algorithm were superior to that of conventional SIFT algorithm. This study provides an important reference for navigation technology of agricultural equipment based on machine vision. Keywords: scale-invariant feature transform (sift), feature matching, canny operator, energy method of feature point, farmland road, depth recovery, visual navigation DOI: 10.25165/j.ijabe.20191204.4821 Citation: Yao L J, Hu D, Yang Z D, Li H B, Qian M B. Depth recovery for unstructured farmland road image using an improved SIFT algorithm. Int J Agric & Biol Eng, 2019; 12(4): 141–147.

Iris feature extraction is the crucial stage of the whole iris recognition process. A brief introduction of iris recognition system is made firstly in this paper, then presented the method of iris feature extraction and matching using Scale Invariant Feature Transform (SIFT). Through analyzing iris feature extraction and matching method, we found in conventional iris recognition system, Iris features are not consistent because most feature extraction techniques are sensitive to the variations of captured iris image data, are impacted of natural illumination or other variant conditions easily. So this paper used SIFT feature which is invariant to image scaling and rotation, and partially invariant to change in illumination. They are also well localized in both the spatial and frequency domains. SIFT feature shows a higher feasibility in the iris feature extraction and matching process.

A novel digital image stabilization approach using Harris and Scale Invariant Feature Transform (SIFT) was presented in this article. Using SIFT in digital image stabilization, too many feature points and matches were extracted, but some of them were not so stable. Using these feature points and matches can not only increase the computational effort, but also enhance the wrong matching probability. We proposed to use SIFT to detect feature points and incorporate the Harris criterion to select the most stable feature points in the video sequence where image motion was happened due to vehicle or platform vibration. With these feature points, we use general feature descriptor and matching algorithm to achieve the image stabilization. Experimental results show that the proposed algorithm can not only bring down the probability of wrong matching and get more accurate matches, but also reduce the computation burden than SIFT effectively.

This paper examines the Kernel Principal Component Analysis (KPCA) feature detection and classification for underwater images. In Underwater images the numbers of distortion occurred are blurring of image, illumination of light and rotation of angle, noise etc. Features are normally extracted by the method called SIFT (Scale Invariant Feature Transform for underwater images). It is used for extracting distinctive invariant features from images that can be invariant to image scale and rotation. It is used in image mosaic, recognition, retrieval and etc. where PCA-SIFT (Principal Component Analysis-Scale Invariant Feature Transform) is also used for dimension reduction and feature detection for underwater images. In this paper we propose a method by combining KPCA and SIFT together called KPCA-SIFT feature detection for underwater images. It is well suited for blur, illumination change and rotation of the input image. When apply PCA to the normalized gradient patch it reduces the dimension of the feature extracted. The parameters used for evaluation are Precision Vs Recall curve and the parameter like elapsed time, sigma value and threshold value. PNN classification is used for underwater images. The proposed method gives desirable results with respect to parameters like Roc curve, Precision Vs Recall, Elapsed time and sigma value. It is more robust and distinctive image deformation and more compact. with increased accuracy and faster matching than the PCA SIFT and SIFT algorithms.

The SIFT algorithm (Scale Invariant Feature Transform) proposed by Lowe [1] is an approach for extracting distinctive invariant features from images. It has been successfully applied to a variety of computer vision problems based on feature matching including object recognition, pose estimation, image retrieval and many others. However, in real-world applications there is still a need for improvement of the algorithm’s robustness with respect to the correct matching of SIFT features. In this paper, an improvement of the original SIFT algorithm providing more reliable feature matching for the purpose of object recognition is proposed. The main idea is to divide the features extracted from both the test and the model object image into several sub-collections before they are matched. The features are divided into several sub-collections considering the features arising from different octaves, that is from different frequency domains. The SIFT algorithm (Scale Invariant Feature Transform) proposed by Lowe [1] is an approach for extracting distinctive invariant features from images. It has been successfully applied to a variety of computer vision problems based on feature matching including object recognition, pose estimation, image retrieval and many others. However, in real-world applications there is still a need for improvement of the algorithm’s robustness with respect to the correct matching of SIFT features. In this paper, an improvement of the original SIFT algorithm providing more reliable feature matching for the purpose of object recognition is proposed. The main idea is to divide the features extracted from both the test and the model object image into several sub-collections before they are matched. The features are divided into several sub-collections considering the features arising from different octaves, that is from different frequency domains.

Images are integral part in our daily lives. With a normal camera it is not possible to get a wide angle panorama with high resolution. Image Mosaicing is one of the novel techniques, for combining two or more images of the same scene taken in different views into one image. In the dark areas, the obtained image is a panoramic image with high resolution without mask. But in the case of lighting areas, the resultant image is generating mask. In order to gets wide angle panorama, in the existing system, extracting feature points, finding the best stitching line, Cluster Analysis (CA) and Dynamic Programming (DP) methods are used. Also used Weighted Average (WA) method for smooth stitching results and also eliminate intensity seam effectively. In the proposed system, to get feature extraction and feature matching SIFT (Scaled Invariant Feature Transform) algorithm used. In this process, outliers can be generated. RANSAC (Random Sample Consensus) is used for detecting the outliers from the resultant image. Masking is significantly reduced by using Algebraic Reconstruction Techniques (ART).

The problems of high dimensional feature vectors lead to large data calculating and time consuming in Scale Invariant Feature Transform (SIFT) feature matching. This paper combines the SIFT and Locality Preserving Projection (LPP) as the LPP-SIFT algorithm. First, use the SIFT algorithm to extract feature point vectors, then by the linear LPP dimensionality reduction method, reduce the amount of data. Use the Euclidean distance similarity criteria to achieve the coarse matching feature point vectors. Then re-match each pair of the match points by the way of the Gaussian neighborhood weighted average to get rid of the mismatch points. The experimental results show that the improved LPP-SIFT algorithm can reduce the amount of data, enhance the real-time and has a high matching accuracy and matching efficiency.

Aiming at the problems of large calculating scale and high complexity in Scale Invariant Feature Transform (SIFT) feature matching algorithm, this paper presents an improved SIFT feature matching algorithm based on quasi Euclidean distance.The traditional Euclidean distance can only calculate out the variance of the two images to the corresponding pixel, so when a slight shift or distortion occurs in the image, it may produce a large deviation.The quasi Euclidean distance instead of Euclidean distance is as the similarity measure of feature descriptors to improve the SIFT feature matching.It reduces the dimensions of SIFT feature vector to improve the efficiency of feature matching.Experimental results show that under the condition of keeping the image matching rate and algorithm robust, the method can not only improve the matching accuracy but also shorten the matching time.The new algorithm has better performance than traditional algorithm, it is possible and valid, which are useful for the fields of image recognition, image reconstruction, etc.

Feature point extraction and matching are the key modules in the front-end of visual simultaneous localization and mapping (SLAM), which determines the accuracy of locating and map Nbuilding robots. To obtain a good performance, the image extraction algorithm being applied should have high efficiency and robustness to the changes of the surrounding environment. In this paper, three widely used feature extraction algorithms, namely Scale-invariant feature transform (SIFT), speed up robust features (SURF) and oriented FAST, and rotated BRIEF (ORB), are studied. The basic concepts and details of the three algorithms are introduced. Then the execution time of the three algorithms is compared to evaluate their efficiency. Meanwhile, the key points matching rate of the three algorithms are compared under various transformations and distortions. The experiment results show that ORB has higher efficiency than SIFT and SURF. However, SIFT has better robustness to image rotation, illumination change, scaling transformation, Gaussian noise and salt-and-pepper noise. In addition, the performance of the ORB-SLAM system is tested on different datasets. It has great ability in camera trajectory estimation and map building and therefore is suitable for deploying in industry.

A novel scale-invariant feature transform (SIFT) algorithm is proposed for soccer target recognition application in a robot soccer game. First, the method of generating scale space is given, extreme points are detected. This gives the precise positioning of the extraction step and the SIFT feature points. Based on the gradient and direction of the feature point neighboring pixels, a description of the key points of the vector is generated. Finally, the matching method based on feature vectors is extracted from SIFT feature points and implemented on the image of the football in a soccer game. By employing the proposed SIFT algorithm for football and stadium key feature points extraction and matching, significant increase can be achieved in the robot soccer ability to identify and locate the football.

This paper presents a framework for evaluating the performance of the Scale Invariant Feature Transform (SIFT) algorithm in matching features within synthetic multi-view X-ray images. The research supports the development of the kinetic depth X-ray imaging (KDEX) technique, which enhances luggage scan interpretation at airport security checkpoints. Two experiments were conducted with identical input imagery. The first experiment applied SIFT directly to color-coded X-ray views, while the second experiment involved segmenting the imagery into material classes before applying SIFT to each class. The matching results from both experiments will be analyzed to assess the algorithm's effectiveness as the angular separation between X-ray views increased. This study lays the groundwork for further research into automated feature matching in security screening applications, offering a promising approach to improving X-ray image interpretation.