Image Mosaicing for Wide Angle Panorama

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.

Copy-move forgery is a general widespread type of digital image forgery, where a segment of an image is attached into a new portion of the similar image to hide or replicate the parts which are called forgered image. The forgered image appears original, as the objective region in spite of being forged, has attained the fundamental qualities of the similar image itself. The capability of the copy-move forgery detection (CMFD) technique is lacked due to some post-processing functions, like JPEG compression scaling, or rotation, etc. Therefore, this paper intends to develop a CMFD using Scale-invariant feature transform (SIFT), best-fin-first algorithm (BBF) and RANdom SAmple Consensus (RANSAC) directed by grey wolf optimization (GWO) algorithm. Initially, the keypoints are selected using SIFT principle, and BBF algorithm identifies the matched keypoints using keypoint threshold. Further, SIFT feature descriptor is determined, and the final extracted paired keypoints are given to RANSAC algorithm to remove all the mismatched keypoints. In this CMFD model, the parameters such as parameters keypoint threshold, maximum distance of inliers in RANSAC and distance threshold in SIFT features are optimized using GWO. The foremost purpose of this research work is maximizing the number of paired keypoints. Hence the proposed model is termed as GWO-based parameter optimization for CMFD (GWPO-CMD). The proposed model is compared over several other meta-heuristic-based keypoint threshold selections and proves its efficiency through diverse analysis.

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.

Pilot study of feature-based algorithm for breech face comparison

Image stitching is one of the branches of computer vision. It combines two or more images for a scene to acquire a high-resolution panoramic image. An invariant local function often uses to stitch two images together. Since the flat plate of a digital radiography (DR) system does not cover all parts of the body, the whole bone structure image cannot seize in a single scan. To solved this problem, image stitching is broadly utilized by medical systems to stitch DR images, which can be helpful for scoliosis or lower extremity deformities in the diagnosis, and pre-operative planning are of great importance. In this paper, the stitching and retrieval of medical images planned. To conquer the background noise in medical images, and improve the recovery of quality and stitching rapidity of medical images, a random sample consensus (RANSAC) algorithm is useful to stitching the images of Chest digital radiography by scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) feature extraction. Down-sampling utilizes to lessen the size of the images and reduction the measure of calculation. In the interim, the phase correlation is engaged to discover the overlapping region. After feature matching and perspective transformation, the stitched image is gotten dependent on the homography. At last, experimentation has finished showing the presentation.

In various applications, such as computer vision, medical imaging and robotics, three-dimensional (3D) image registration is a significant step. It enables the alignment of various datasets into a single coordinate system, consequently providing a consistent perspective that allows further analysis. By precisely aligning images we can compare, analyze, and combine data collected in different situations. This paper presents a novel approach for 3D or z-stack microscopy and medical image registration, utilizing a combination of conventional and deep learning techniques for feature extraction and adaptive likelihood-based methods for outlier detection. The proposed method uses the Scale-invariant Feature Transform (SIFT) and the Residual Network (ResNet50) deep neural learning network to extract effective features and obtain precise and exhaustive representations of image contents. The registration approach also employs the adaptive Maximum Likelihood Estimation SAmple Consensus (MLESAC) method that optimizes outlier detection and increases noise and distortion resistance to improve the efficacy of these combined extracted features. This integrated approach demonstrates robustness, flexibility, and adaptability across a variety of imaging modalities, enabling the registration of complex images with higher precision. Experimental results show that the proposed algorithm outperforms state-of-the-art image registration methods, including conventional SIFT, SIFT with Random Sample Consensus (RANSAC), and Oriented FAST and Rotated BRIEF (ORB) methods, as well as registration software packages such as bUnwrapJ and TurboReg, in terms of Mutual Information (MI), Phase Congruency-Based (PCB) metrics, and Gradiant-based metrics (GBM), using 3D MRI and 3D serial sections of multiplex microscopy images.

In the object recognition based on the bag of visual words, we focus on several main feature extraction algorithms. The Scale Invariant Feature Transform (SIFT) that based on local features with good significance and robustness has became a popular feature extraction method. But we take into consideration that the SIFT will generate a large number of high-dimensional feature vectors, which will increase the computational cost. In this paper, a novel feature extraction algorithm by means of SIFT to fuse region information entropy (SIFT-entropy) is proposed. The algorithm can improve the quality of the feature extracted from the image by cluster similar key points to removing the noisy key points. So a more discriminative high quality “visual word” codebook could be generated. We made a comprehensive comparison between the proposed method and the original SIFT method on Caltech-101 database. The experimental results show that this improvement can reduce the dimension of the feature space, and has higher classification accuracy.

At present, dealing with the piracy and tampering of images has become a notable challenge, due to the presence of smart mobile gadgets. In this paper, we propose a novel watermarking algorithm based on non-subsampled contourlet transform (NSCT) for improving the security aspects of such images. Moreover, the fusion of feature searching approach with watermarking methods has gained prominence in the current years. The scale-invariant feature transform (SIFT) is a technique in computer vision for detecting and illustrating the local features in images. Nevertheless, the SIFT algorithm can extract feature points with high invariance that are resilient to several issues like rotation, compression, and scaling. Furthermore, the extracted feature points are embedded with watermark using the NSCT approach. Subsequently, the tree split, voting, rotation searching, and morphology techniques are employed for improving the robustness against the noise. The proposed watermarking algorithm offers superior capability, better capture quality, and tampering resistance, when compared with existing watermarking approaches.

Exploration on Data Collection and Analysis System Based on Integrated SIFT Algorithm

A Comparative Analysis of RootSIFT and SIFT Methods for Drowsy Features Extraction

This article is focused on the comprehensive evaluation of alleyways to scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) based multispectral (MS) image registration. In this paper, the idea is to extensively evaluate three such SIFT- and RANSAC-based registration approaches over a heterogenous mix containing Triticum aestivum crop and Raphanus raphanistrum weed. The first method is based on the application of a homography matrix, derived during the registration of MS images on spatial coordinates of individual annotations to achieve spatial realignment. The second method is based on the registration of binary masks derived from the ground truth of individual spectral channels. The third method is based on the registration of only the masked pixels of interest across the respective spectral channels. It was found that the MS image registration technique based on the registration of binary masks derived from the manually segmented images exhibited the highest accuracy, followed by the technique involving registration of masked pixels, and lastly, registration based on the spatial realignment of annotations. Among automatically segmented images, the technique based on the registration of automatically predicted mask instances exhibited higher accuracy than the technique based on the registration of masked pixels. In the ground truth images, the annotations performed through the near-infrared channel were found to have a higher accuracy, followed by green, blue, and red spectral channels. Among the automatically segmented images, the accuracy of the blue channel was observed to exhibit a higher accuracy, followed by the green, near-infrared, and red channels. At the individual instance level, the registration based on binary masks depicted the highest accuracy in the green channel, followed by the method based on the registration of masked pixels in the red channel, and lastly, the method based on the spatial realignment of annotations in the green channel. The instance detection of wild radish with YOLOv8l-seg was observed at a mAP@0.5 of 92.11% and a segmentation accuracy of 98% towards segmenting its binary mask instances.

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.

Abstract. Historical aerial photographs directly provide good evidences of past times. The Research Center for Humanities and Social Sciences (RCHSS) of Taiwan Academia Sinica has collected and scanned numerous historical maps and aerial images of Taiwan and China. Some maps or images have been geo-referenced manually, but most of historical aerial images have not been registered since there are no GPS or IMU data for orientation assisting in the past. In our research, we developed an automatic process of matching historical aerial images by SIFT (Scale Invariant Feature Transform) for handling the great quantity of images by computer vision. SIFT is one of the most popular method of image feature extracting and matching. This algorithm extracts extreme values in scale space into invariant image features, which are robust to changing in rotation scale, noise, and illumination. We also use RANSAC (Random sample consensus) to remove outliers, and obtain good conjugated points between photographs. Finally, we manually add control points for registration through least square adjustment based on collinear equation. In the future, we can use image feature points of more photographs to build control image database. Every new image will be treated as query image. If feature points of query image match the features in database, it means that the query image probably is overlapped with control images.With the updating of database, more and more query image can be matched and aligned automatically. Other research about multi-time period environmental changes can be investigated with those geo-referenced temporal spatial data.

At present, most of the existing seal image registration methods are based on shape, and most of the methods can't tolerate the image scale change. To overcome these problems, a general seal registration and identification approach is proposed in this paper. This method uses the classic scale invariant feature transform (SIFT) algorithm to extract the key points. To register the seal accurately, the Random Sample Consensus (RANSAC) algorithm and the Least Squares Method (LSM) are used to obtain the transformational matrix. Then the invariant features are extracted based on residual image. At last, the Normal Bayesian classifier (NBC), K-Nearest Neighbor classifier (K-NN) and the Support Vector Machine (SVM) are combined to classify the feature vector. The experiments demonstrate the identification ability of the proposed approach.

In this paper, an automatic image mosaic algorithm based on feature matching is presented. In the algorithm, a scale invariant feature transform (SIFT) is used to extract feature points. The initial matches are divided into inliers and outliers by robust RANSAC algorithm. Then LM algorithm is used to estimate the point transformation matrix between two images accurately. In the end, weighted average method is used to blend images. Experimental results show the image mosaic effect is wonderful and the algorithm is adaptive very much. It is high valuable in practice.