A survey on image mosaicing techniques

stitching (Mosaicing) is considered as an active research area in computer vision and computer graphics. Image stitching is concerned with combining two or more images of the same scene into one high resolution image which is called panoramic image. Image stitching techniques can be categorized into two general approaches: direct and feature based techniques. Direct techniques compare all the pixel intensities of the images with each other, whereas feature based techniques aim to determine a relationship between the images through distinct features extracted from the processed images. The last approach has the advantage of being more robust against scene movement, faster, and has the ability to automatically discover the overlapping relationships among an unordered set of images. The purpose of this paper is to present a survey about the feature based image stitching. The main components of image stitching will be described. A framework of a complete image stitching system based on feature based approaches will be introduced. Finally, the current challenges of image stitching will be discussed. Keywordsstitching/mosaicing, panoramic image, features based detection, SIFT, SURF, image blending.

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Image Stitching based on Feature Extraction Techniques: A Survey

Image Mosaicing is a method of assembling multiple overlapping images of the same scene into a larger image. The output of the image mosaic will be the union of two input images. Image-mosaicing algorithms are used for obtaining a mosaiced image. There are five steps used in image mosaicing which includes; Feature extraction, Image registration, Computing homography, Warping and Blending. Feature extraction is an Image mosaicing technique which is done by using various corner detection algorithm. This corner detection algorithm produces an efficient and informative output mosaiced image. Importance of Image mosaicing can be seen in the field of medical imaging, computer vision, data from satellites, military automatic target recognition.

A Survey on Image Mosaicing Techniques

The direct registration problem for images of a deforming surface has been well studied. Parametric flexible warps based, for instance, on the Free-Form Deformation or a Radial Basis Function such as the Thin-Plate Spline, are often estimated using additive Gauss-Newton-like algorithms. The recently proposed compositional framework has been shown to be more efficient, but cannot be directly applied to such non-groupwise warps.Our main contribution in this paper is the Feature-Driven framework. It makes possible the use of compositional algorithms for most parametric warps such as those above mentioned. Two algorithms are proposed to demonstrate the relevance of our Feature-Driven framework: the Feature-Driven Inverse Compositional and the Feature-Driven Learning-based algorithms. As another contribution, a detailed derivation of the Feature-Driven warp parameterization is given for the Thin-Plate Spline and the Free-Form Deformation. We experimentally show that these two types of warps have a similar representational power. Experimental results show that our Feature-Driven registration algorithms are more efficient in terms of computational cost, without loss of accuracy, compared to existing methods.

Feature-Driven Direct Non-Rigid Image Registration

Image Stitching with Combined Moment Invariants and Sift Features

Image Mosaicing algorithm based on random corner method is proposed. An image mosaic is a method of assembling multiple overlapping images of same scene into a larger one. The output of image mosaic will be the union of two input images. In this paper we have to use three step automatic image mosaic methods. The first step is taking two input images and finding out the corners in both the images, second step is removing out the false corner in both the images and then by using homography we find its matched corner pair and we get final output mosaic. The experimental results show the proposed algorithm produces an improvement in mosaic accuracy, efficiency and robustness.

Image Mosaicing Using Corner Techniques

In this paper, the design of compact size Micro strip patch antenna is presented at resonant frequency 2 GHz using defected micro strip structure which has various wireless communication applications. The antenna is constructed using micro strip feed line technique and fabricated using dielectric FR4 substrate which has dimensions of (35.4 mm×45.6 mm). The frequency response gives a very sharp cut off at 2 GHz with return loss of-31.27dB. The simulation is performed using the commercial Electromagnetic simulation software IE3D 14.1.

Design of Microstrip Patch Antenna Using Defected Microstrip Structure

Atmospheric conditions induced by suspended particles such as fog, smog, rain, haze etc., severely affect the scene appearance and computer vision applications. In general, existing defogging algorithms use various constraints for fog removal. The efficiency of these algorithms depends on the accurate estimation of the depth models and the perfection of these models solely relies on pre-calculated coefficients through the training data. However, the depth model developed on the basis of these pre-calculated coefficients for dehazing may provide better accuracy for some kind of images but not equally well for every type of images. Therefore, training data-independent based depth model is required for a perfect haze removal algorithm. In this paper, an effective haze removal algorithm is reported for removing fog or haze from a single image. The proposed algorithm utilizes the atmospheric scattering model in fog removal. Apart from this, linearity in the depth model is achieved by the ratio of difference and sum of the intensity and saturation values of the input image. Besides, the proposed method also take care the well-known problems of edge preservation, white region handling and colour fidelity. Experimental results show that the proposed model is more efficient in comparison to the existing haze removal algorithms in terms of qualitative and quantitative analysis.

An efficient single image haze removal algorithm for computer vision applications

An image mosaic is a method of assembling multiple overlapping images of same scene into a larger one. The output of image mosaic will be the union of two input images. In this paper we have to use three step automatic image mosaic method. The first step is taking two input images and finding out the corners in both the images, second step is finding its matched corner and third step is its blending and we get final output mosaic. The experimental results show the proposed algorithm produces an improvement in mosaic accuracy, efficiency and robustness.

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Gaurav Saxena

Papers

Image Mosaicing Using Corner Techniques

Design of Microstrip Patch Antenna Using Defected Microstrip Structure

An efficient single image haze removal algorithm for computer vision applications

A novel still image mosaic algorithm construction using feature based method

Classification of Cardiac Signals with Automated R-Peak Detection Using Wavelet Transform Method