A pattern analysis based underwater video segmentation system for target object detection

Rajasekar, M.; Kavida, A. Celine; Bennet, M. Anto

doi:10.1007/s11045-020-00721-4

Cited by 12 publications

(3 citation statements)

References 23 publications

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“…The main challenge in obtaining such images is the haziness caused by light that reflects off the ocean's surface and is subsequently scattered by water molecules. Additionally, because different wavelengths of light absorb light differently, there are color variances [4][5][6]. Due to light scattering and color alterations, images captured underwater suffer from contrast loss and color divergence.…”

Section: Introductionmentioning

confidence: 99%

Low Intricacy‎ Multistage Algorithm for Underwater Image Enhancement

Al-Ameen,

Ahmed

2024

protk

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Humanity currently lives in a technological era that witnesses rapid progress in multiple fields. Digital image processing is one of the modern technologies that has provided practical answers to many challenges including image enhancement, analysis, reconstruction, recovery, compression, processing, and understanding. One of these notable challenges relates to underwater photography. Underwater images are always exposed to less-than-ideal conditions due to environmental and physical factors. These include refraction of light in water, scattering of particles and dust in the aquatic medium, lack of illumination in deep water, and poor contrast. These challenges make it extremely difficult to analyze and extract valuable information without advanced processing. In this study, an improved color balance-fusion algorithm is provided by improving the image visuality and modifying some equations to obtain sharper and clearer images. The proposed algorithm begins by finding the white balance of the input RGB color image, after that, it improves the intensity. Next, the edges are improved using Gamma separately. The weights are then found for each image and combined to find naive fusion. The resulting image is processed using a color retrieval algorithm to produce the final image. along with comparisons to eleven other algorithms with various processing methods. Experimental results showed that this algorithm can significantly improve underwater images, increasing image clarity and making colors clearer. The improvement rates reached 5.8389 and 2.6778 for UISM and UICM metrics, respectively.

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Section: Introductionmentioning

confidence: 99%

Low Intricacy‎ Multistage Algorithm for Underwater Image Enhancement

Al-Ameen,

Ahmed

2024

protk

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“…In the same direction, a saliency-based workflow was implemented to approximate background regions of the image to detect underwater ‘foreground’ objects 32 , whereas contrast stretching and adaptive thresholding has been used to segment and subsequently detect underwater objects 33 . Further, a combination of Laplacian filtering, histogram equalization and blob detection has also been used to detect underwater objects 34 . Regarding the detection of objects and human artifacts on the seafloor, a region-based approach was used to detect marine litter in Greek waters 24 , whereas geometric reasoning was employed for the detection of pipelines on the seabed 35 .…”

Section: Introductionmentioning

confidence: 99%

An automated image-based workflow for detecting megabenthic fauna in optical images with examples from the Clarion–Clipperton Zone

Mbani

Buck

Greinert

2023

Sci Rep

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Recent advances in optical underwater imaging technologies enable the acquisition of huge numbers of high-resolution seafloor images during scientific expeditions. While these images contain valuable information for non-invasive monitoring of megabenthic fauna, flora and the marine ecosystem, traditional labor-intensive manual approaches for analyzing them are neither feasible nor scalable. Therefore, machine learning has been proposed as a solution, but training the respective models still requires substantial manual annotation. Here, we present an automated image-based workflow for Megabenthic Fauna Detection with Faster R-CNN (FaunD-Fast). The workflow significantly reduces the required annotation effort by automating the detection of anomalous superpixels, which are regions in underwater images that have unusual properties relative to the background seafloor. The bounding box coordinates of the detected anomalous superpixels are proposed as a set of weak annotations, which are then assigned semantic morphotype labels and used to train a Faster R-CNN object detection model. We applied this workflow to example underwater images recorded during cruise SO268 to the German and Belgian contract areas for Manganese-nodule exploration, within the Clarion–Clipperton Zone (CCZ). A performance assessment of our FaunD-Fast model showed a mean average precision of 78.1% at an intersection-over-union threshold of 0.5, which is on a par with competing models that use costly-to-acquire annotations. In more detail, the analysis of the megafauna detection results revealed that ophiuroids and xenophyophores were among the most abundant morphotypes, accounting for 62% of all the detections within the surveyed area. Investigating the regional differences between the two contract areas further revealed that both megafaunal abundance and diversity was higher in the shallower German area, which might be explainable by the higher food availability in form of sinking organic material that decreases from east-to-west across the CCZ. Since these findings are consistent with studies based on conventional image-based methods, we conclude that our automated workflow significantly reduces the required human effort, while still providing accurate estimates of megafaunal abundance and their spatial distribution. The workflow is thus useful for a quick but objective generation of baseline information to enable monitoring of remote benthic ecosystems.

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“…In scientific endeavors like observing marine life, counting populations, and analyzing geological or biological conditions, the quality of underwater photos is crucial. The difficulty of taking pictures underwater is primarily due to the murkiness that results from light that is reflected from a surface, deflected, and scattered by water particles, as well as color changes brought on by the differing levels of light attenuation for different wavelengths [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Underwater Image Enhancement: A Review of Modern Utilized Concepts

A. Ahmed,

Al-Ameen

2023

IRJIET

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The revolution in current technology imposes on most systems of daily life in processing a wide range of applications through digital image processing techniques that have produced practical answers to many challenges such as image optimization, analysis, reconstruction, recovery, compression, processing, and so forth. One application of interest in underwater image enhancement has attracted the attention of experts in recent years due to its importance. In this work, a review of modern processing concepts for underwater images is given to get the basic knowledge on what are the used methodologies, what are the processed degradations, and what processing concepts have been used in the past years. Hence, different methods are reviewed and implemented on underwater images to know their advantages and disadvantages. The results of each method are demonstrated, the processing concept is summarized, and a synopsis that includes the main notion, complexity, pros, and cons is given to get a generalized idea about this topic.

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A pattern analysis based underwater video segmentation system for target object detection

Cited by 12 publications

References 23 publications

Low Intricacy‎ Multistage Algorithm for Underwater Image Enhancement

Low Intricacy‎ Multistage Algorithm for Underwater Image Enhancement

An automated image-based workflow for detecting megabenthic fauna in optical images with examples from the Clarion–Clipperton Zone

Underwater Image Enhancement: A Review of Modern Utilized Concepts

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