Underwater object detection with efficient shadow-removal for side scan sonar images

Chang, Ruijie; Wang, Yaomin; Hou, Jiaru; Qiu, Shuqi; Nian, Rui; He, Bo; Lendasse, Amaury

doi:10.1109/oceansap.2016.7485696

Cited by 18 publications

(7 citation statements)

References 10 publications

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“…It is regarded as device independent and functions as a standard reference [11]. With all three stimulus X, Y and Z defined, it is possible to derive CIE xy chromatic coordinates from it with (4) and (5), as the two chromatic coordinates x and y are used to specify "pure" color under the absence of illumination [20].…”

Section: B Color Space Conversionmentioning

confidence: 99%

“…This greatly affects geometrical properties and appearance of the object to be detected [3]. Shadow can cause inaccuracy in some applications, such as the applications for plant leaves segmentation [4], license plate recognition [5], gait recognition [6,7], analysis of remote-sensing images [8,9], classification of objects from video surveillance system [10], underwater object detection [11] and clustering [12].…”

Section: Introductionmentioning

confidence: 99%

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Development of Algorithm to Reduce Shadow on Digital Image

Ooi¹,

Ibrahim²

2016

IJIGSP

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Abstract-In this paper, two shadow reduction algorithms have been proposed and implemented using CIE Lab color space. The task of performing shadow reduction is done by executing shadow detection, shadow removal and lastly shadow edge correction in a sequential order. The first proposed algorithm is implemented based on pixel illumination and color information meanwhile the second algorithm is carried out via thresholding of one or more CIE Lab color space channels. The outputs from both proposed algorithms are compared in terms of shadow detection accuracy and required processing period. The proposed methods shown some promising results.

show abstract

Section: B Color Space Conversionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of Algorithm to Reduce Shadow on Digital Image

Ooi¹,

Ibrahim²

2016

IJIGSP

View full text Add to dashboard Cite

show abstract

“…International Commission on Illumination (abbreviated as CIE) has created CIE XYZ color space in year 1931 by standardizing XYZ values as tri-stimulus values to describe levels of perceptible visual stimulus. It is regarded as device independent and functions as a standard reference [11]. With all three stimulus X, Y and Z defined, it is possible to derive CIE xy chromatic coordinates from it with (4) and (5), as the two chromatic coordinates x and y are used to specify "pure" color under the absence of illumination [20].…”

Section: B Color Space Conversionmentioning

confidence: 99%

Ultrasonic C-Scan Image Processing Using Multilevel Thresholding for Damage Evaluation in Aircraft Vertical Stabilizer

Ooi¹,

Ibrahim²

2015

IJIGSP

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In this paper, two shadow reduction algorithms have been proposed and implemented using CIE Lab color space. The task of performing shadow reduction is done by executing shadow detection, shadow removal and lastly shadow edge correction in a sequential order. The first proposed algorithm is implemented based on pixel illumination and color information meanwhile the second algorithm is carried out via thresholding of one or more CIE Lab color space channels. The outputs from both proposed algorithms are compared in terms of shadow detection accuracy and required processing period. The proposed methods shown some promising results.

show abstract

“…This algorithm can reduce error segmentation but is still sensitive to noise. The idea of a threshold is also introduced into the clustering algorithm [7]. The algorithm has certain robustness and effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Evaluation criterion of underwater object clustering segmentation with pulse‐coupled neural network

Wang

2020

IET image process

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The success of clustering algorithms in object segmentation depends on the quality of the evaluation criterion. However, sonar images are seriously affected by noise. Most of the existing evaluation criteria such as the Davies Bouldin (DB) criterion only considers their pixel value, and sonar image information extraction is not sufficient. As a result, they fail to achieve good underwater object segmentation results. To overcome this problem, this study proposes an improved DB criterion with pulse-coupled neural network (PCNN), which is called the DB-PCNN. In the calculation process of DB-PCNN, the role of internal activity items in PCNN is considered, which can make better use of pixel information in adjacent space on the sonar image. The experimental results show that DB-PCNN can further improve the accuracy of underwater object segmentation and has certain adaptability to different optimisation frameworks.

show abstract

Underwater object detection with efficient shadow-removal for side scan sonar images

Cited by 18 publications

References 10 publications

Development of Algorithm to Reduce Shadow on Digital Image

Development of Algorithm to Reduce Shadow on Digital Image

Ultrasonic C-Scan Image Processing Using Multilevel Thresholding for Damage Evaluation in Aircraft Vertical Stabilizer

Evaluation criterion of underwater object clustering segmentation with pulse‐coupled neural network

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