The extended marine underwater environment database and baseline evaluations

Jian, Muwei; Qi, Qiang; Yu, Hui; Dong, Junyu; Cui, Chaoran; Nie, Xiushan; Zhang, Huaxiang; Yin, Yilong; Lam, Kin Man

doi:10.1016/j.asoc.2019.04.025

Cited by 57 publications

(37 citation statements)

References 51 publications

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“…When the light transferring in the water, the energy will be attenuated or absorbed due to underwater dissolved organic matter and suspended particles, but some proposed method can produce reliable and promising results. [19][20][21] The result of GI under the underwater conditions for different turbidities has confirmed that GI can be a better alternative for underwater optical imaging. 22,23 The energy attenuation of light propagation through seawater means the low transmittance.…”

Section: Introductionmentioning

confidence: 64%

Ghost imaging enhancement for detections of the low-transmittance objects

Zhang

et al. 2020

International Journal of Advanced Robotic Systems

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The underwater environment is extremely complex and variable, which makes it difficult for underwater robots detecting or recognizing surroundings using images acquired with cameras. Ghost imaging as a new imaging technique has attracted much attention due to its special physical properties and potential for imaging of objects in optically harsh or noisy environments. In this work, we experimentally study three categories of image reconstruction methods of ghost imaging for objects of different transmittance. For high-transmittance objects, the differential ghost imaging is more efficient than traditional ghost imaging. However, for low-transmittance objects, the reconstructed images using traditional ghost imaging and differential ghost imaging algorithms are both exceedingly blurred and cannot be improved by increasing the number of measurements. A compressive sensing method named augmented Lagrangian and alternating direction algorithm (TVAL3) is proposed to reduce the background noise imposed by the low-transmittance. Experimental results show that compressive ghost imaging can dramatically subtract the background noise and enhance the contrast of the image. The relationship between the quality of the reconstructed image and the complexity of object itself is also discussed.

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Section: Introductionmentioning

confidence: 64%

Ghost imaging enhancement for detections of the low-transmittance objects

Zhang

et al. 2020

International Journal of Advanced Robotic Systems

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“…More recently, in Reference [87] and in its extension in Reference [88], a database specifically designed for the benchmarking of saliency estimation methods for underwater object detection is proposed. The database, named Marine Underwater Environment Database (MUED), is collected in an artificial pool mimicking the variabilities in illumination, background, and pose that are normally encountered in the real environment.…”

Section: Resources and Benchmarkingmentioning

confidence: 99%

“…A total of 8600 underwater images (resolution 648 × 486) of 430 distinct objects is contained in the database. Reference [88] presents also a baseline evaluation with a wide range of known general methods, including Graph Regularization [55], Patch-Distinctness [54], Dense and Sparse Reconstruction [89], Nonlinearity covariance [46], Multiscale Super-Pixel [90], Cellular Automata [91], QDWB [51], and PD and LC [53] (the last methods being proposed by the same team who published the database). Methods are compared with respect to the task of object detection computing the precision, recall, and F-score of detected salient object rectangle with respect to the ground-truth rectangle.…”

Section: Resources and Benchmarkingmentioning

confidence: 99%

The Use of Saliency in Underwater Computer Vision: A Review

Reggiannini

Moroni

2020

Remote Sensing

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Underwater survey and inspection are tasks of paramount relevance for a variety of applications. They are usually performed through the employment of optical and acoustic sensors installed aboard underwater vehicles, in order to capture details of the surrounding environment. The informative properties of the data are systematically affected by a number of disturbing factors, such as the signal energy absorbed by the propagation medium or diverse noise categories contaminating the resulting imagery. Restoring the signal properties in order to exploit the carried information is typically a tough challenge. Visual saliency refers to the computational modeling of the preliminary perceptual stages of human vision, where the presence of conspicuous targets within a surveyed scene activates neurons of the visual cortex, specifically sensitive to meaningful visual variations. In relatively recent years, visual saliency has been exploited in the field of automated underwater exploration. This work provides a comprehensive overview of the computational methods implemented and applied in underwater computer vision tasks, based on the extraction of visual saliency-related features.

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“…For most online systems, it is even more crucial to establish feature correspondence between consecutive frames accurately and robustly for proper functionality. However, in low-visibility conditions, the saliencies in the visual images can be highly impaired, where the pixel-based feature methods can fail easily, for example, in underwater environment [6]. Such failures can pose negative impacts on those computational tasks.…”

Section: Introductionmentioning

confidence: 99%

Augmented visual feature modeling for matching in low-visibility based on cycle-labeling of Superpixel Flow

Shu

Wang

et al. 2020

Knowledge-Based Systems

Self Cite

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There are many automation systems that are required to work under poor visual conditions, such as auto-navigation in foggy or underwater environment. The low-visibility poses challenges for traditional feature modelling methods, which commonly contribute as a key component to such autonomous systems. It can thus negatively impact the performance of those computing systems. For example, the matching precision (matching quality) and the successfully identified matches (matching quantity) can both drop dramatically in low-visibility. On the other hand, human vision system can robustly identify visual features correctly despite the variations in lighting conditions. Inspired by human knowledge of perceiving visual features, this paper presents a novel feature modelling solution under poor visual conditions. Based on a color constancy enhanced illumination alignment, a new concept called Superpixel Flow (SPF) is proposed to model the visual features in images. SPF is generated considering the content motions across frame pairs, which make it easier to track across frames compared with classic Superpixels. The matching is achieved by a cycle-labelling strategy using Markov Random Field (MRF) with energy functions composed according to human knowledge of compare visual features. An outlier removal follows to further improve the matching accuracy. Competitive performance is demonstrated in the experiments compared with state-of-the-art approaches.

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The extended marine underwater environment database and baseline evaluations

Cited by 57 publications

References 51 publications

Ghost imaging enhancement for detections of the low-transmittance objects

Ghost imaging enhancement for detections of the low-transmittance objects

The Use of Saliency in Underwater Computer Vision: A Review

Augmented visual feature modeling for matching in low-visibility based on cycle-labeling of Superpixel Flow

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