Image Blending Techniques and their Application in Underwater Mosaicing

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Image Blending Techniques and their Application in Underwater Mosaicing

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Image Blending Techniques and their Application in Underwater Mosaicing

Ricard Prados, Rafael Garcia, Lszl Neumann

02 May 2014-

TL;DR: This work proposes strategies and solutions to tackle the problem of building photo-mosaics of very large underwater optical surveys, presenting contributions to the image preprocessing, enhancing and blending steps, and resulting in an improved visual quality of the final photo- mosaic.

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Abstract: This work proposes strategies and solutions to tackle the problem of building photo-mosaics of very large underwater optical surveys, presenting contributions to the image preprocessing, enhancing and blending steps, and resulting in an improved visual quality of the final photo-mosaic. The text opens with a comprehensive review of mosaicing and blending techniques, before proposing an approach for large scale underwater image mosaicing and blending. In the image preprocessing step, a depth dependent illumination compensation function is used to solve the non-uniform illumination appearance due to light attenuation. For image enhancement, the image contrast variability due to different acquisition altitudes is compensated using an adaptive contrast enhancement based on an image quality reference selected through a total variation criterion. In the blending step, a graph-cut strategy operating in the image gradient domain over the overlapping regions is suggested. Next, an out-of-core blending strategy for very large scale photo-mosaics is presented and tested on real data. Finally, the performance of the approach is evaluated and compared with other approaches.

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Journal Article•DOI•

A survey on image mosaicing techniques

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Debabrata Ghosh¹, Naima Kaabouch¹•Institutions (1)

University of North Dakota¹

01 Jan 2016-Journal of Visual Communication and Image Representation

TL;DR: An in-depth survey of the existing image mosaicing algorithms by classifying them into several groups, and the fundamental concepts are first explained and then the modifications made to the basic concepts by different researchers are explained.

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140 citations

Journal Article•DOI•

An integrated view of the methane system in the pockmarks at Vestnesa Ridge, 79°N

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Giuliana Panieri, Stefan Bünz, Daniel J. Fornari¹, Javier Escartín², Pavel Serov, Pär Jansson, Marta E Torres³, Joel E. Johnson⁴, Wei-Li Hong, Simone Sauer, Rafael Garcia⁵, Nuno Gracias⁵ - Show less +8 more•Institutions (5)

Woods Hole Oceanographic Institution¹, IPG Photonics², Oregon State University³, University of New Hampshire⁴, University of Girona⁵

01 Aug 2017-Marine Geology

TL;DR: This paper presents a meta-analyses of the EMMARM, a probabilistic approach to estimating the number of mitochondria in the response of the immune system to EMTs.

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69 citations

Cites background from "Image Blending Techniques and their..."

...Details regarding image processing and mosaic construction are available 222 elsewhere (Prados et al., 2014)....
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Journal Article•DOI•

Automatic segmentation of fish using deep learning with application to fish size measurement

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Rafael Garcia¹, Ricard Prados¹, Josep Quintana¹, Alexander Tempelaar¹, Nuno Gracias¹, Shale Rosen, Håvard Vågstøl, Kristoffer Løvall - Show less +4 more•Institutions (1)

University of Girona¹

01 Jul 2020-Ices Journal of Marine Science

TL;DR: A novel image-based method for individual fish detection, targeted at drastically reducing catches of undersized fish in commercial trawling, based on the processing of stereo images acquired by the Deep Vision imaging system, directly placed in the trawl.

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Abstract: One of the leading causes of overfishing is the catch of unwanted fish and marine life in commercial fishing gears. Echosounders are nowadays routinely used to detect fish schools and make qualitative estimates of the amount of fish and species present. However, the problem of estimating sizes using acoustic systems is still largely unsolved, with only a few attempts at real-time operation and only at demonstration level. This paper proposes a novel image-based method for individual fish detection, targeted at drastically reducing catches of undersized fish in commercial trawling. The proposal is based on the processing of stereo images acquired by the Deep Vision imaging system, directly placed in the trawl. The images are pre-processed to correct for nonlinearities of the camera response. Then, a Mask R-CNN architecture is used to localize and segment each individual fish in the images. This segmentation is subsequently refined using local gradients to obtain an accurate estimate of the boundary of every fish. Testing was conducted with two representative datasets, containing in excess of 2600 manually annotated individual fish, and acquired using distinct artificial illumination setups. A distinctive advantage of this proposal is the ability to successfully deal with cluttered images containing overlapping fish.

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66 citations

Journal Article•DOI•

Optimal seamline detection for multiple image mosaicking via graph cuts

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Li Li¹, Jian Yao¹, Xiaohu Lu¹, Jinge Tu¹, Jie Shan² - Show less +1 more•Institutions (2)

Wuhan University¹, Purdue University²

01 Mar 2016-Isprs Journal of Photogrammetry and Remote Sensing

TL;DR: Experimental results on a large set of aerial, oblique and street-view panoramic images show that the proposed algorithm is capable of creating high-quality seamlines for multiple image mosaicking, while not crossing majority of visually obvious foreground objects and most of overlap regions with low image similarity.

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Abstract: While mosaicking images, especially captured from the scenes of large depth differences with respective to cameras at varying locations, the detection of seamlines within overlap regions is a key issue for creating seamless and pleasant image mosaics. In this paper, we propose a novel algorithm to efficiently detect optimal seamlines for mosaicking aerial images captured from different viewpoints and for mosaicking street-view panoramic images without a precisely common center in a graph cuts energy minimization framework. To effectively ensure that the seamlines are optimally detected in the laterally continuous regions with high image similarity and low object dislocation to magnificently conceal the parallax between images, we fuse the information of image color, gradient magnitude, and texture complexity into the data and smooth energy terms in graph cuts. Different from the traditional frame-to-frame optimization for sequentially detecting seamlines for mosaicking multiple images, our method applies a novel multi-frame joint optimization strategy to find seamlines within multi-overlapped images at one time. In addition, we propose simple but effective strategies to semi-automatically guide the seamlines by exploiting simple human–computer interaction strongly constraining the image regions that the seamlines will or won’t pass through, which is often ignored by many existing seamline detection methods. Experimental results on a large set of aerial, oblique and street-view panoramic images show that the proposed method is capable of creating high-quality seamlines for multiple image mosaicking, while not crossing majority of visually obvious foreground objects and most of overlap regions with low image similarity to effectively conceal the image parallax at different extents.

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45 citations

Cites background from "Image Blending Techniques and their..."

..., 2004; Xiong and Pulli, 2009) and image blending (Perez et al., 2003; Prados et al., 2014; Szeliski et al., 2011; Allene et al., 2008) techniques trying to conceal stitching artifacts by smoothing color differences between input images....
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...…very well by a series of color correction, smoothing transition (Levin et al., 2004; Xiong and Pulli, 2009) and image blending (Perez et al., 2003; Prados et al., 2014; Szeliski et al., 2011; Allene et al., 2008) techniques trying to conceal stitching artifacts by smoothing color differences…...
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Journal Article•DOI•

Image mosaicing: A deeper insight

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Achala Pandey¹, Umesh C. Pati¹•Institutions (1)

National Institute of Technology, Rourkela¹

01 Sep 2019-Image and Vision Computing

TL;DR: An insight is provided into the existing mosaicing algorithms, along with their merits and shortcomings, and a comparison among various mosaicing methods is presented to find out which algorithm works best for a particular application and image type.

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32 citations

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References

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Journal Article•DOI•

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

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Martin A. Fischler¹, Robert C. Bolles¹•Institutions (1)

SRI International¹

01 Jun 1981-Communications of The ACM

TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.

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Abstract: A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing

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23,396 citations

"Image Blending Techniques and their..." refers methods or result in this paper

...There are two main strategies to reject outliers widely used in the bibliography [60]: Random Sample Consensus (RANSAC) [41] and Least Median of Squares (LMedS) [109]....
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...When there is enough data, RANSAC can use a smoothing technique, such as least squares, to compute an improved estimate for the parameters of the model with the mutually consistent data which has been identified....
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...As stated in [41], contrary to other smoothing...
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...As stated in [38], contrary to other smoothing techniques, 24 2 Underwater 2D Mosaicing instead of using as much data as possible to obtain an initial solution and then attempting to eliminate the invalid data, RANSAC uses a small set of data as a point of departure and enlarges this set with consistent data when possible....
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...There are two main strategies to reject outliers widely used in the bibliography [37]: Random Sample Consensus (RANSAC) [38] andLeastMedian of Squares (LMedS) [39]....
[...]

Journal Article•DOI•

A note on two problems in connexion with graphs

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Edsger W. Dijkstra

01 Dec 1959-Numerische Mathematik

TL;DR: A tree is a graph with one and only one path between every two nodes, where at least one path exists between any two nodes and the length of each branch is given.

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Abstract: We consider n points (nodes), some or all pairs of which are connected by a branch; the length of each branch is given. We restrict ourselves to the case where at least one path exists between any two nodes. We now consider two problems. Problem 1. Constrnct the tree of minimum total length between the n nodes. (A tree is a graph with one and only one path between every two nodes.) In the course of the construction that we present here, the branches are subdivided into three sets: I. the branches definitely assignec~ to the tree under construction (they will form a subtree) ; II. the branches from which the next branch to be added to set I, will be selected ; III. the remaining branches (rejected or not yet considered). The nodes are subdivided into two sets: A. the nodes connected by the branches of set I, B. the remaining nodes (one and only one branch of set II will lead to each of these nodes), We start the construction by choosing an arbitrary node as the only member of set A, and by placing all branches that end in this node in set II. To start with, set I is empty. From then onwards we perform the following two steps repeatedly. Step 1. The shortest branch of set II is removed from this set and added to

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22,704 citations

"Image Blending Techniques and their..." refers methods in this paper

...The problem of non-static objects in the overlapping regions was addressed by Davis [21] in 1998, who found an optimal seam using Dijkstra’s algorithm [23] through the photometric differences computed between two registered images....
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...This graph-cut is computed, similarly to Davis [21], using Disjkstra’s [23] algorithm....
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Proceedings Article•DOI•

Object recognition from local scale-invariant features

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David G. Lowe¹•Institutions (1)

University of British Columbia¹

20 Sep 1999

TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.

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Abstract: An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low residual least squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.

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16,989 citations

"Image Blending Techniques and their..." refers methods in this paper

...107 x Contents Acronyms AUV Autonomous Underwater Vehicle BA Bundle Adjustment BCM Brightness Constancy Model CLAHE Contrast Limited Adaptive Histogram Equalization DOF Degree Of Freedom DVL Doppler Velocity Log EKF Extended Kalman Filter GA Global Alignment GDIM Generalized Dynamic Image Model GPS Global Positioning System HDR High Dynamic Range HOG Histogram of Gradients LBL Long Baseline LMedS Least Median of Squares MEX Matlab EXecutable MST Minimum Spanning Tree RANSAC Random Sample Consensus ROD Region of Difference ROV Remotely Operated Vehicle SEF Seam-Eliminating Function SIFT Scale Invariant Feature Transform SNR Signal-to-Noise Ratio SSD Sum of Squared Differences SURF Speeded Up Robust Features...
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...The SIFT descriptor is based on Histograms of Gradient (HOGs) computed in the area surrounding the detected interest points, while SURF describes a distribution of Haar wavelet [32] responses within the neighborhood of the interest point....
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...2.2 Image Registration 21 The second strategy is based on the detection of features in both images using invariant feature descriptors, such as SIFT [30], its faster variant SURF [31] (which uses an approximation of the Laplacian andHessian detectors respectively) or others, and performing the matching, comparing their descriptor vectors....
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...The second strategy is based on the detection of features in both images using invariant feature descriptors, such as SIFT [79], its faster variant SURF [5] (which uses an approximation of the Laplacian and Hessian detectors respectively) or others, and performing the matching, comparing their descriptor vectors....
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...The second group of methods rely on the computation of a transformation between images using a sparse set of points [54, 99, 75, 79, 5] and correspondences....
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Multiple View Geometry in Computer Vision.

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Bernhard P. Wrobel

01 Jan 2001

TL;DR: This book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts and it will show the best book collections and completed collections.

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Abstract: Downloading the book in this website lists can give you more advantages. It will show you the best book collections and completed collections. So many books can be found in this website. So, this is not only this multiple view geometry in computer vision. However, this book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts. This is simple, read the soft file of the book and you get it.

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14,282 citations

"Image Blending Techniques and their..." refers background in this paper

...The planar transformation between two different views of the same flat scene can be described by means of a planar homography matrix [55, 83]....
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...The two projections x1,x2 of p in images I1 and I2 satisfy the epipolar constraint [55]...
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Proceedings Article•DOI•

A Combined Corner and Edge Detector

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Chris Harris, Mike Stephens

01 Jan 1988

TL;DR: The problem the authors are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work.

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Abstract: The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work. For example, we desire to obtain an understanding of natural scenes, containing roads, buildings, trees, bushes, etc., as typified by the two frames from a sequence illustrated in Figure 1. The solution to this problem that we are pursuing is to use a computer vision system based upon motion analysis of a monocular image sequence from a mobile camera. By extraction and tracking of image features, representations of the 3D analogues of these features can be constructed.

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13,993 citations

"Image Blending Techniques and their..." refers methods in this paper

...The first strategy consists of locating the interest points in one image of the pair using some feature detector, such as Harris [54], Laplacian [99] or Hessian [75], and identifying these in the other....
[...]
...The second group of methods rely on the computation of a transformation between images using a sparse set of points [54, 99, 75, 79, 5] and correspondences....
[...]
...Pair-wise registration can be performed using a featurebased approach, involving the well known image feature detectors and descriptors of Harris [54], SIFT [80] and SURF [5], among others....
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...The first strategy consists of locating the interest points in one image of 20 2 Underwater 2D Mosaicing the pair using some feature detector, such as Harris and Stephens [27], Beaudet [28] or Lindeberg [29], and identifying these in the other....
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