The OUC-vision large-scale underwater image database

Jian, Muwei; Qi, Qiang; Dong, Junyu; Yin, Yinlong; Zhang, Wenyin; Lam, Kin‐Man

doi:10.1109/icme.2017.8019324

Cited by 42 publications

(13 citation statements)

References 30 publications

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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%

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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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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“…Although being available in most situations, the average criterion may lead to potential problems in a few cases, and the following experiment indicates that such problems may happen. To validate that the proposed SCEA criterion is more competitive rather than the average criterion in a certain application scenario, we randomly select 200 images as the imageset D from a large-scale underwater imageset [27]. Then, we randomly select 50 images as the imageset C from the imageset D, and randomly select 5 images as the subset B from the imageset C. To verify the subset-guided enhancement, experiments are respectively performed on subset B, imageset C and imageset D.…”

Section: B the Robustness Of Sceamentioning

confidence: 99%

Subset-Guided Consistency Enhancement Assessment Criterion for an Imageset Without Reference

Líu

Wei

2019

IEEE Access

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In a new era for machine vision, some image enhancement algorithms can be used to improve the quality of an imageset without reference. To assess the performance of imageset enhancement, the existing average criterion utilizes a no-reference image quality metric to calculate the quality score of each enhanced image, and quantifies the performance of an enhancement algorithm by the mean value of all scores. If the quality scores of some images fluctuate greatly, their mean value difficultly reflects the degradation or worst cases during imageset enhancement. Therefore, this paper analyzes and illustrates the need and significance of consistency enhancement assessment, and then proposes a subsetguided consistency enhancement assessment criterion for an imageset without reference. By measuring the subset of an imageset, the proposed criterion firstly calculates the difference of quality scores of each image before and after enhancement and then filters the outlier data outside confidence interval, and finally quantifies the consistency enhancement performance of an enhancement algorithm according to its consistency enhancement degree. When a small subset is used to guide its large imageset, the average criterion judges a consistency or non-consistency enhancement algorithm with a 16.7% false identification ratio, and also makes one misjudgment about the optimal-consistency algorithm, while the proposed criterion always correctly judges the non-consistency or optimal-consistency enhancement algorithm. This paper can help the scientific community to select a robust enhancement algorithm in the degradation or worst cases. As compared with the average criterion, the proposed criterion is more robust in terms of subset-guided consistency enhancement assessment, which may effectively find an optimal-consistency or non-consistency enhancement algorithm for the rest of an imageset. INDEX TERMSImageset enhancement, no-reference image quality, consistency enhancement assessment, subset guided.

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“…Recently, underwater saliency detection becomes a hot issue. A large-scale underwater image database was constructed in [36]. Later, Jian et al [37] designed an underwater saliency detection model by integrating Quaternionic Distance Based Weber Descriptor (QDWD) with pattern distinctness and Local Contrast, which incorporated quaternion number system and principal components analysis simultaneously.…”

Section: Related Workmentioning

confidence: 99%

“…Currently, a plenty of saliency detection models have been designed, which have been extended to various computer-vision and multimedia applications. Among them, typical applications include object detection [4,5,8], traffic congestion analysis [7,8], facial analysis [9,10], underwater vision [36,37], health prediction [51,52,53], visual prediction [56 -61], activity recognition [62,63], image retrieval [64 -68] and so on.…”

Section: Introductionmentioning

confidence: 99%

Saliency detection based on directional patches extraction and principal local color contrast

Jian

Zhang

et al. 2018

Journal of Visual Communication and Image Representation

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Saliency detection has become an active topic in both computer vision and multimedia fields. In this paper, we propose a novel computational model for saliency detection by integrating the holistic centerdirectional map with the principal local color contrast (PLCC) map. In the proposed framework, perceptual directional patches are firstly detected based on discrete wavelet frame transform (DWFT) and sparsity criterion, then the center of the spatial distribution of the extracted directional patches are utilized to locate the salient object in an image. Meanwhile, we proposed an efficient local color contrast method, called principal local color contrast (PLCC), to compute the color contrast between the salient object and the image background, which is sufficient to highlight and separate salient objects from complex background while dramatically reduce the computational cost. Finally, by incorporating the complementary visual cues of the global center-directional map with the PLCC map, a final compounded saliency map can be generated. Extensive experiments performed on three publicly available image databases, verify that the proposed scheme is able to achieve satisfactory results compared to other stateof-the-art saliency-detection algorithms.

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The OUC-vision large-scale underwater image database

Cited by 42 publications

References 30 publications

The Use of Saliency in Underwater Computer Vision: A Review

The Use of Saliency in Underwater Computer Vision: A Review

Subset-Guided Consistency Enhancement Assessment Criterion for an Imageset Without Reference

Saliency detection based on directional patches extraction and principal local color contrast

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