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

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

Robust Techniques for Feature-based Image Mosaicing

Since the last few decades, image mosaicing in real time applications has been a challenging field for image processing experts. It has wide applications in the field of video conferencing, 3D image reconstruction, satellite imaging and several medical as well as computer vision fields. It can also be used for mosaic-based localization, motion detection & tracking, augmented reality, resolution enhancement, generating large FOV etc. In this research work, feature based image mosaicing technique using image fusion have been proposed. The image mosaicing algorithms can be categorized into two broad horizons. The first is the direct method and the second one is based on image features. The direct methods need an ambient initialization whereas, Feature based methods does not require initialization during registration. The feature-based techniques are primarily followed by the four steps: feature detection, feature matching, transformation model estimation, image resampling and transformation. SIFT and SURF are such algorithms which are based on the feature detection for the accomplishment of image mosaicing, but both the algorithms has their own limitations as well as advantages according to the applications concerned. The proposed method employs this two feature based image mosaicing techniques to generate an output image that works out the limitations of the both in terms of image quality The developed robust algorithm takes care of the combined effect of rotation, illumination, noise variation and other minor variation. Initially, the input images are stitched together using the popular stitching algorithms i.e. Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). To extract the best features from the stitching results, the blending process is done by means of Discrete Wavelet Transform (DWT) using the maximum selection rule for both approximate as well as detail-components

Blending techniques for underwater photomosaics

The creation of consistent underwater photomosaics is typically hampered by local misalignments and inhomogeneous illumination of the image frames, which introduce visible seams that complicate post processing of the mosaics for object recognition and shape extraction. In this thesis, methods are proposed to improve blending techniques for underwater photomosaics and the results are compared with traditional methods. Five specific techniques drawn from various areas of image processing, computer vision, and computer graphics have been tested: illumination correction based on the median mosaic, thin plate spline warping, perspective warping, graph-cut applied in the gradient domain and in the wavelet domain. A combination of the first two methods yields globally homogeneous underwater photomosaics with preserved continuous features. Further improvements are obtained with the graph-cut technique applied in the spatial domain

Development and Optimization of the Ratio Vegetation Index on the Visible and Infrared Spectrum

This study aims to find a suitable vegetation index model to analyze the distribution of clove vegetation in Buleleng regency, Bali. Vegetation index model Ratio Vegetation Index (RVI) extracted from Landsat 8 was developed in the visible spectrum (l = 0.450 - 0.680 ?m) and infrared (l = 0.845 - 2.300 ?m). Development methods are carried out on the basis of the spectral reflectance response characteristics of the dominant electromagnetic waves from the visible and infrared spectra of vegetation. Created a multiple regression relationship results from scattergram that links RVI vegetation index with band 3 = B3, band 5 = B5, band 6 = B6, and band 7 = B7. Optimization strategy is carried out by dividing the development of RVI with a variable number factor. There are 4 forms of RVI vegetation index models from the development and optimization of the visible and infrared spectra. Of the 4 new vegetation index forms, which provide optimal results and close to extensive data from the Forestry and Plantation Service, Buleleng regency, Bali is RVInew4 = 0.0022 + 0.00142 * B3 + 0.00028 * B5 + 0.00054 * B6 + 0.00096 * B7. The area produced by this vegetation index model in analyzing the distribution of clove vegetation is 7667.82 ha. Is this area 99.40% of the average data area? the Forestry and Plantation Service, Buleleng regency, Bali in 2014, which is 7622.32 ha. The dominant distribution of clove vegetation is in the rare category with an area of 7441.74 ha