An in Depth Review Paper on Numerous Image Mosaicing Approaches and Techniques

Image mosaicing is one of the most important subjects of research in computer vision at current. Image mocaicing requires the integration of direct techniques and feature based techniques. Direct techniques are found to be very useful for mosaicing large overlapping regions, small translations and rotations while feature based techniques are useful for small overlapping regions. Feature based image mosaicing is a combination of corner detection, corner matching, motion parameters estimation and image stitching.Furthermore, image mosaicing is considered the process of obtaining a wider field-of-view of a scene from a sequence of partial views, which has been an attractive research area because of its wide range of applications, including motion detection, resolution enhancement, monitoring global land usage, and medical imaging. Numerous algorithms for image mosaicing have been proposed over the last two decades.In this paper the authors present a review on different approaches for image mosaicing and the literature over the past few years in the field of image masaicing methodologies. The authors take an overview on the various methods for image mosaicing.This review paper also provides an in depth survey of the existing image mosaicing algorithms by classifying them into several groups. For each group, the fundamental concepts are first clearly explained. Finally this paper also reviews and discusses the strength and weaknesses of all the mosaicing groups

Development Of A High Performance Mosaicing And Super-Resolution Algorithm

In this dissertation, a high-performance mosaicing and super-resolution algorithm is described. The scale invariant feature transform (SIFT)-based mosaicing algorithm builds an initial mosaic which is iteratively updated by the robust super resolution algorithm to achieve the final high-resolution mosaic. Two different types of datasets are used for testing: high altitude balloon data and unmanned aerial vehicle data. To evaluate our algorithm, five performance metrics are employed: mean square error, peak signal to noise ratio, singular value decomposition, slope of reciprocal singular value curve, and cumulative probability of blur detection. Extensive testing shows that the proposed algorithm is effective in improving the captured aerial data and the performance metrics are accurate in quantifying the evaluation of the algorithm

Performance analysis on color image mosaicing techniques on FPGA

Today, the surveillance systems and other monitoring systems are considering the capturing of image sequences in a single frame. The captured images can be combined to get the mosaiced image or combined image sequence. But the captured image may have quality issues like brightness issue, alignment issue (correlation issue), resolution issue, manual image registration issue etc. The existing technique like cross correlation can offer better image mosaicing but faces brightness issue in mosaicing. Thus, this paper introduces two different methods for mosaicing i.e., (a) Sliding Window Module (SWM) based Color Image Mosaicing (CIM) and (b) Discrete Cosine Transform (DCT) based CIM on Field Programmable Gate Array (FPGA). The SWM based CIM adopted for corner detection of two images and perform the automatic image registration while DCT based CIM aligns both the local as well as global alignment of images by using phase correlation approach. Finally, these two methods performances are analyzed by comparing with parameters like PSNR, MSE, device utilization and execution time. From the analysis it is concluded that the DCT based CIM can offers significant results than SWM based CIM

Image Mosaicing Using Feature Detection Algorithms

In most recent couple of decades, image processing specialists has been using image mosaicing as a testing field in real time applications. It has wide utilization in the 3D picture reproduction, field of satellite imaging, computer vision fields and a few therapeutic fields also. Movement recognition & tracking, mosaic-based localisation, resolution enhancement, generating substantial FOV, augmented reality, and so forth are also some of its application fields. In this exploration work, feature based image mosaicing procedure has been proposed. There are five essential steps in feature based procedures: feature extraction, feature matching, transformation model estimation, image re-sampling and transformation, and image blending. The achievement of image mosaicing can be accounted by the feature identification algorithms such as Harris corner detector, SURF, FAST and FREAK. But each of these algorithms has their own particular impediments and preferences as indicated by the applications concerned. The proposed strategy first compares the above mentioned four feature extraction algorithm on the basis of accuracy and computational time and determines FREAK to be the most optimum one and then utilizes this FREAK descriptor algorithm for feature detection. All the distinctive features detected in an image and the feature descriptors are shaped around the corners. Matching between the feature descriptors from both the images is done to achieve best closeness and all the features other than the ones with higher degree of resemblance are rejected. Now, the features with higher degree of resemblance are used for computing the transformation model and correspondingly, the warping of the image is done. The warping of the picture is done on a typical mosaic plane after estimation. The removal of the intensity seam in the neighbourhood of the boundary of the images and to modify the image grey levels at the junction joint to obtain a smooth transition between the images is the final step. Alpha blending technique is utilized for the purpose of image blendin