Enhanced Tracking Aerial Image by Applying Frame Extraction Technique

An image registration method is introduced that is capable of registering images from different views of a 3-D scene in the presence of occlusion. The proposed method is capable of withstanding considerable occlusion and homogeneous areas in images. The only requirement of the method is for the ground to be locally flat and sufficient ground cover be visible in the frames being registered. With help of fusion technique we solve the problem of blur images. In previous project sometime object recognition is not possible they do not show appropriate area, path and location. So with the help of object recognition we show the appropriate location, path and area. Then it captured the motion images, static images, video and CCTV footage also. Because of occlusion sometime result not get correct or sometime problems are occurred but with the help of techniques solve the problem of occlusion. This method is applicable for the various investigation departments. For the purpose of tracking such as smuggling or any unwanted operations which are apply or performed by illegally. Various types of technique are applied for performing the tracking operation. That technique return the correct result according to object tracking. Camera is not supported this type of operation because they do not return the clear image result. So apply the drone and aircraft for capturing the long distance or multiview images

Agile Multi-Scale Decompositions for Automatic Image Registration

In recent works, the first and third authors developed an automatic image registration algorithm based on a multiscale hybrid image decomposition with anisotropic shearlets and isotropic wavelets. This prototype showed strong performance, improving robustness over registration with wavelets alone. However, this method imposed a strict hierarchy on the order in which shearlet and wavelet features were used in the registration process, and also involved an unintegrated mixture of MATLAB and C code. In this paper, we introduce a more agile model for generating features, in which a flexible and user-guided mix of shearlet and wavelet features are computed. Compared to the previous prototype, this method introduces a flexibility to the order in which shearlet and wavelet features are used in the registration process. Moreover, the present algorithm is now fully coded in C, making it more efficient and portable than the MATLAB and C prototype. We demonstrate the versatility and computational efficiency of this approach by performing registration experiments with the fully-integrated C algorithm. In particular, meaningful timing studies can now be performed, to give a concrete analysis of the computational costs of the flexible feature extraction. Examples of synthetically warped and real multi-modal images are analyzed

Feature Based Multi View Image Registration by Detecting the Feature with Fuzzy Logic for Corner Detection

This paper aim to Present accurate feature base registration by detecting the feature with Fuzzy logic for corner detection. Image registration is process used to match two or more partially overlapping image taken for example at different times ,from different sensors, or from different viewpoints and stitch these image into one panoramic image comprising whole scene. It is a fundamental image processing technique very useful in integrating information from different sensors, finding changes in image taken at different time, inferring three-dimensional information from stereo images and recognizing model-based objects. The paper presents a corner detection algorithm for feature detection which employs such fuzzy reasoning. The robustness of the proposed algorithm is compared to well-known conventional Harris corner detectors and its performance is also tested over a noise image. DOI: 10.17762/ijritcc2321-8169.150616

An ASIFT-based local registration method for satellite imagery

Imagery registration is a fundamental step, which greatly affects later processes in image mosaic, multi-spectral image fusion, digital surface modelling, etc., where the final solution needs blending of pixel information from more than one images. It is highly desired to find a way to identify registration regions among input stereo image pairs with high accuracy, particularly in remote sensing applications in which ground control points (GCPs) are not always available, such as in selecting a landing zone on an outer space planet. In this paper, a framework for localization in image registration is developed. It strengthened the local registration accuracy from two aspects: less reprojection error and better feature point distribution. Affine scale-invariant feature transform (ASIFT) was used for acquiring feature points and correspondences on the input images. Then, a homography matrix was estimated as the transformation model by an improved random sample consensus (IM-RANSAC) algorithm. In order to identify a registration region with a better spatial distribution of feature points, the Euclidean distance between the feature points is applied (named the S criterion). Finally, the parameters of the homography matrix were optimized by the Levenberg–Marquardt (LM) algorithm with selective feature points from the chosen registration region. In the experiment section, the Chang’E-2 satellite remote sensing imagery was used for evaluating the performance of the proposed method. The experiment result demonstrates that the proposed method can automatically locate a specific region with high registration accuracy between input images by achieving lower root mean square error (RMSE) and better distribution of feature points

Восстановление полей дрейфа морского льда по последовательным спутниковым радиолокационным изображениям методом прослеживания особых точек

The state of the art methods for sea ice drift retrieval from sequential SAR images are described. An original algorithm based on scale-spaced image representation that effi cient both to noise suppression and signal preserving is proposed. The validation of the algorithm against the manual-derived reference data presented. Its advantages demonstrated in comparison with previously developed algorithms using Sentinel-1a data.Описываются подходы к автоматизированному расчету дрейфа морского льда по последовательным изображениям на основе данных активной радиолокации РСА-SAR (радиолокаторы с синтезированной апертурой). Предлагается оригинальный алгоритм на основе применения многомасштабного представления изображений с использованием адаптивного подавления шумов. Приводятся оценки качества расчетов с использованием данных спутника Sentinel-1А, демонстрируются преимущества разработанного алгоритма на основе результатов сопоставления с существующими мировыми аналогами

Automated Quantitative Analysis of Blood Flow in Extracranial–Intracranial Arterial Bypass Based on Indocyanine Green Angiography

Microvascular imaging based on indocyanine green is an important tool for surgeons who carry out extracranial–intracranial arterial bypass surgery. In terms of blood perfusion, indocyanine green images contain abundant information, which cannot be effectively interpreted by humans or currently available commercial software. In this paper, an automatic processing framework for perfusion assessments based on indocyanine green videos is proposed and consists of three stages, namely, vessel segmentation based on the UNet deep neural network, preoperative and postoperative image registrations based on scale-invariant transform features, and blood flow evaluation based on the Horn–Schunck optical flow method. This automatic processing flow can reveal the blood flow direction and intensity curve of any vessel, as well as the blood perfusion changes before and after an operation. Commercial software embedded in a microscope is used as a reference to evaluate the effectiveness of the algorithm in this study. A total of 120 patients from multiple centers were sampled for the study. For blood vessel segmentation, a Dice coefficient of 0.80 and a Jaccard coefficient of 0.73 were obtained. For image registration, the success rate was 81%. In preoperative and postoperative video processing, the coincidence rates between the automatic processing method and commercial software were 89 and 87%, respectively. The proposed framework not only achieves blood perfusion analysis similar to that of commercial software but also automatically detects and matches blood vessels before and after an operation, thus quantifying the flow direction and enabling surgeons to intuitively evaluate the perfusion changes caused by bypass surgery

Enhanced phase congruency feature-based image registration for multimodal remote sensing imagery

Multimodal image registration is an essential image processing task in remote sensing. Basically, multimodal image registration searches for optimal alignment between images captured by different sensors for the same scene to provide better visualization and more informative images. Manual image registration is a tedious task and requires more effort, hence developing an automated image registration is very crucial to provide a faster and reliable solution. However, image registration faces many challenges from the nature of remote sensing image, the environment, and the technical shortcoming of the current methods that cause three issues, namely intensive processing power, local intensity variation, and rotational distortion. Since not all image details are significant, relying on the salient features will be more efficient in terms of processing power. Thus, the feature-based registration method was adopted as an efficient method to avoid intensive processing. The proposed method resolves rotation distortion issue using Oriented FAST and Rotated BRIEF (ORB) to produce invariant rotation features. However, since it is not intensity invariant, it cannot support multimodal data. To overcome the intensity variations issue, Phase Congruence (PC) was integrated with ORB to introduce ORB-PC feature extraction to generate feature invariance to rotation distortion and local intensity variation. However, the solution is not complete since the ORB-PC matching rate is below the expectation. Enhanced ORB-PC was proposed to solve the matching issue by modifying the feature descriptor. While better feature matches were achieved, a high number of outliers from multimodal data makes the common outlier removal methods unsuccessful. Therefore, the Normalized Barycentric Coordinate System (NBCS) outlier removal was utilized to find precise matches even with a high number of outliers. The experiments were conducted to verify the registration qualitatively and quantitatively. The qualitative experiment shows the proposed method has a broader and better features distribution, while the quantitative evaluation indicates improved performance in terms of registration accuracy by 18% compared to the related works