A survey on image mosaicing techniques

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

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

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.

Optimal seamline detection for multiple image mosaicking via graph cuts

Image mosaicing: A deeper insight

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.

Lszl Neumann

Papers

Image Blending Techniques and their Application in Underwater Mosaicing