Mosaicing of microscope images based on SURF

Wen, Rong; Chen, Hui; Lü, Jiaju; Xu, Yanyi; Haeusler, Ralf

doi:10.1109/ivcnz.2009.5378399

Cited by 17 publications

(8 citation statements)

References 13 publications

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“…Feature based methods for obtaining mosaic of microscopic images are presented in literatures [10,11,12,13,14,15]. Harris corner detection method is used in [10,11] for detecting and identifying the features from images followed by geometric and radiometric corrections for alignment of images and compensation of intensity differences respectively.…”

Section: Microscopic Image Mosaicingmentioning

confidence: 99%

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µ-Mosaicing and Thermo-Mosaicing using Image In-painting

Bendale¹,

Patil²,

Rane³

2014

IJCA

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Microscopy has enormous applications in both medical and non-medical fields. This has gained interest of many researchers in the field of digital image processing to develop algorithms and applications helping in improving their observation and analysis. Here, novel method called 'µ-Mosaicing' (read as Micro-Mosaicing) is introduced to create mosaics of microscopic images from a video sequence to obtain large view, high resolution images of specimen. A thermal camera forms images by sensing the infrared radiations from an object. High resolution thermal images can be used to determine temperatures in detailed thermal analysis. However, the cost of thermal camera increases with their resolution. While, thermo-graphy plays an important role in the diagnosis of various diseases, high resolution large field of view images will be certainly helpful in diagnosis and treatment. So a new approach to create mosaics of thermal images from video sequence, called 'Thermo-Mosaicing', is proposed which can find wide applications in medical field. Both, µ-Mosaicing and Thermo-Mosaicing employ

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Section: Microscopic Image Mosaicingmentioning

confidence: 99%

“…A SURF based mosaicing technique [14] is also implemented with the images from microscope system which has Automatically Controlled Object Stage and Image Capturing Unit. A new method to separate descriptor windows is implemented to increase the speed.…”

Section: Microscopic Image Mosaicingmentioning

confidence: 99%

µ-Mosaicing and Thermo-Mosaicing using Image In-painting

Bendale¹,

Patil²,

Rane³

2014

IJCA

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“…h 13 and h 23 are translations in the x and y direction, respectively. Rotation and scaling factors are h 11 , h 12 , h 21 and h 22 , whereas h 31 , h 32 are perspective transformation factors (Rong et al, 2009). Furthermore, homographies are endowed with a group structure so that their composition is still a homography.…”

Section: Warping Model Estimationmentioning

confidence: 99%

Automated image mosaics by non‐automated light microscopes: theMicroMossoftware tool

Piccinini

Bevilacqua

Lucarelli

2013

Journal of Microscopy

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Summary Light widefield microscopes and digital imaging are the basis for most of the analyses performed in every biological laboratory. In particular, the microscope's user is typically interested in acquiring high‐detailed images for analysing observed cells and tissues, meanwhile being representative of a wide area to have reliable statistics. The microscopist has to choose between higher magnification factor and extension of the observed area, due to the finite size of the camera's field of view. To overcome the need of arrangement, mosaicing techniques have been developed in the past decades for increasing the camera's field of view by stitching together more images. Nevertheless, these approaches typically work in batch mode and rely on motorized microscopes. Or alternatively, the methods are conceived just to provide visually pleasant mosaics not suitable for quantitative analyses. This work presents a tool for building mosaics of images acquired with nonautomated light microscopes. The method proposed is based on visual information only and the mosaics are built by incrementally stitching couples of images, making the approach available also for online applications. Seams in the stitching regions as well as tonal inhomogeneities are corrected by compensating the vignetting effect. In the experiments performed, we tested different registration approaches, confirming that the translation model is not always the best, despite the fact that the motion of the sample holder of the microscope is apparently translational and typically considered as such. The method's implementation is freely distributed as an open source tool called MicroMos. Its usability makes building mosaics of microscope images at subpixel accuracy easier. Furthermore, optional parameters for building mosaics according to different strategies make MicroMos an easy and reliable tool to compare different registration approaches, warping models and tonal corrections.

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“…Recently, the use of feature-based matching techniques has dominated the proposed mosaicing algorithms. Different image features have been successfully utilized in image mosaicing such as SIFT (Brown and Lowe, 2007;Jia et al, 2015;Liqian and Yuehui, 2010), Harris points (Zagrouba et al, 2009), and Speeded Up Robust Features (SURF) (Geng et al, 2012;Rong et al, 2009;Wang and Watada, 2015;Xingteng et al, 2015). According to the flight time, image acquisition frequency, and the required overlap, a typical UAV system acquires hundreds to thousands of images in one flight.…”

Section: Introductionmentioning

confidence: 99%

A Fast Approach for Stitching of Aerial Images

Moussa¹,

El-Sheimy²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The last few years have witnessed an increasing volume of aerial image data because of the extensive improvements of the Unmanned Aerial Vehicles (UAVs). These newly developed UAVs have led to a wide variety of applications. A fast assessment of the achieved coverage and overlap of the acquired images of a UAV flight mission is of great help to save the time and cost of the further steps. A fast automatic stitching of the acquired images can help to visually assess the achieved coverage and overlap during the flight mission. This paper proposes an automatic image stitching approach that creates a single overview stitched image using the acquired images during a UAV flight mission along with a coverage image that represents the count of overlaps between the acquired images. The main challenge of such task is the huge number of images that are typically involved in such scenarios. A short flight mission with image acquisition frequency of one second can capture hundreds to thousands of images. The main focus of the proposed approach is to reduce the processing time of the image stitching procedure by exploiting the initial knowledge about the images positions provided by the navigation sensors. The proposed approach also avoids solving for all the transformation parameters of all the photos together to save the expected long computation time if all the parameters were considered simultaneously. After extracting the points of interest of all the involved images using Scale-Invariant Feature Transform (SIFT) algorithm, the proposed approach uses the initial image's coordinates to build an incremental constrained Delaunay triangulation that represents the neighborhood of each image. This triangulation helps to match only the neighbor images and therefore reduces the time-consuming features matching step. The estimated relative orientation between the matched images is used to find a candidate seed image for the stitching process. The pre-estimated transformation parameters of the images are employed successively in a growing fashion to create the stitched image and the coverage image. The proposed approach is implemented and tested using the images acquired through a UAV flight mission and the achieved results are presented and discussed.

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Mosaicing of microscope images based on SURF

Cited by 17 publications

References 13 publications

µ-Mosaicing and Thermo-Mosaicing using Image In-painting

µ-Mosaicing and Thermo-Mosaicing using Image In-painting

Automated image mosaics by non‐automated light microscopes: theMicroMossoftware tool

A Fast Approach for Stitching of Aerial Images

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