Underwater Visual Computing: The Grand Challenge Just around the Corner

Lukas, Uwe Freiherr von

doi:10.1109/mcg.2016.24

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Computer vision applications in underwater settings are particularly affected by the optical properties of the surrounding medium [50]. In the 3D underwater reconstruction process, the image enhancement is a necessary pre-processing step that is usually tackled with two different approaches.…”

Section: Benchmarking Based On the Results Of 3d Reconstructionmentioning

confidence: 99%

Guidelines for Underwater Image Enhancement Based on Benchmarking of Different Methods

Mangeruga

Bruno

Cozza³

et al. 2018

Remote Sensing

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Images obtained in an underwater environment are often affected by colour casting and suffer from poor visibility and lack of contrast. In the literature, there are many enhancement algorithms that improve different aspects of the underwater imagery. Each paper, when presenting a new algorithm or method, usually compares the proposed technique with some alternatives present in the current state of the art. There are no studies on the reliability of benchmarking methods, as the comparisons are based on various subjective and objective metrics. This paper would pave the way towards the definition of an effective methodology for the performance evaluation of the underwater image enhancement techniques. Moreover, this work could orientate the underwater community towards choosing which method can lead to the best results for a given task in different underwater conditions. In particular, we selected five well-known methods from the state of the art and used them to enhance a dataset of images produced in various underwater sites with different conditions of depth, turbidity, and lighting. These enhanced images were evaluated by means of three different approaches: objective metrics often adopted in the related literature, a panel of experts in the underwater field, and an evaluation based on the results of 3D reconstructions.

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Section: Benchmarking Based On the Results Of 3d Reconstructionmentioning

confidence: 99%

Guidelines for Underwater Image Enhancement Based on Benchmarking of Different Methods

Mangeruga

Bruno

Cozza³

et al. 2018

Remote Sensing

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“…Unfortunately, the situation is different under water. In the sea, GPS, Wi-Fi, or Bluetooth signals are absorbed in water after 25 cm [5], which prevents the devices from obtaining their location, and also due to worsening visibility conditions, objects that are further away than a dozen meters cannot be distinguished. If water is very turbid, the visibility drops to a few meters.…”

Section: Introductionmentioning

confidence: 99%

Detecting Square Markers in Underwater Environments

et al. 2019

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Augmented reality can be deployed in various application domains, such as enhancing human vision, manufacturing, medicine, military, entertainment, and archeology. One of the least explored areas is the underwater environment. The main benefit of augmented reality in these environments is that it can help divers navigate to points of interest or present interesting information about archaeological and touristic sites (e.g., ruins of buildings, shipwrecks). However, the harsh sea environment affects computer vision algorithms and complicates the detection of objects, which is essential for augmented reality. This paper presents a new algorithm for the detection of fiducial markers that is tailored to underwater environments. It also proposes a method that generates synthetic images with such markers in these environments. This new detector is compared with existing solutions using synthetic images and images taken in the real world, showing that it performs better than other detectors: it finds more markers than faster algorithms and runs faster than robust algorithms that detect the same amount of markers.

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