Improved Segmentation Technique for Underwater Images Based on K-means and Local Adaptive Thresholding

Rajeev, Agrawal Avni; Hiranwal, Saroj; Sharma, Vijay K

doi:10.1007/978-981-10-3920-1_45

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…For underwater images, we set parameters (τ, ω) ∈ {(0, 0), (6, 0.8), (7, 0.9), (8, 1.0), (10, 1.0), (11, 1.0), (12, 1.0)}. For motion-blurred images, we set (l, a) ∈ {(0, 0), (15,15), (20,20), (25,25), (30,30), (35,35), (40,40)}. For fish-eye images, we set parameter e ∈ {1, 1.75, 2, 2.25, 2.5, 2.75, 3}.…”

Section: A Datasets and Experiments Setupmentioning

confidence: 99%

“…Moller et al [29] proposed a active learning method for the classification of species in underwater images from a fixed observatory. Rajeev et al [30] proposed a segmentation technique for underwater images based on K-means and local adaptive thresholding. Chen et al [31] proposed a underwater object segmentation method based on optical features.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification

Pei

Huang

Zou

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

183

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Just like many other topics in computer vision, image classification has achieved significant progress recently by using deep-learning neural networks, especially the Convolutional Neural Networks (CNN). Most of the existing works are focused on classifying very clear natural images, evidenced by the widely used image databases such as Caltech-256, PASCAL VOCs and ImageNet. However, in many real applications, the acquired images may contain certain degradations that lead to various kinds of blurring, noise, and distortions. One important and interesting problem is the effect of such degradations to the performance of CNN-based image classification. More specifically, we wonder whether image-classification performance drops with each kind of degradation, whether this drop can be avoided by including degraded images into training, and whether existing computer vision algorithms that attempt to remove such degradations can help improve the image-classification performance. In this paper, we empirically study this problem for four kinds of degraded images -hazy images, underwater images, motion-blurred images and fish-eye images. For this study, we synthesize a large number of such degraded images by applying respective physical models to the clear natural images and collect a new hazy image dataset from the Internet. We expect this work can draw more interests from the community to study the classification of degraded images.

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Section: A Datasets and Experiments Setupmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification

Pei

Huang

Zou

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

183

View full text Add to dashboard Cite

show abstract

“…Sun et al [10] and Li et al [11] used fuzzy C-means to segment underwater images. Rajeev et al [12] used the K-means algorithm to segment underwater images. However, the clustering algorithms mentioned above are greatly affected by the local gray unevenness of underwater images.…”

Section: Introductionmentioning

confidence: 99%

Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

Yang

Feng

2022

Applied Sciences

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Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is significant for target tracking and visual information mining. Aiming to resolve the problem that conventional active contour models cannot effectively extract the contours of salient targets in underwater images, we propose a dual-fusion active contour model with semantic information. First, the saliency images are introduced as semantic information and salient target contours are extracted by fusing Chan–Vese and local binary fitting models. Then, the original underwater images are used to supplement the missing contour information by using the local image fitting. Compared with state-of-the-art contour extraction methods, our dual-fusion active contour model can effectively filter out background information and accurately extract salient target contours. Moreover, the proposed model achieves the best results in the quantitative comparison of MAE (mean absolute error), ER (error rate), and DR (detection rate) indicators and provides reliable prior knowledge for target tracking and visual information mining.

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“…Sun et al [7] and Li et al [9] have used fuzzy C-means algorithm to segment underwater images. Rajeev et al [8] have used K-means algorithm to segment underwater images. However, the aforesaid clustering algorithms have been greatly affected by local gray unevenness of underwater images.…”

Section: Introductionmentioning

confidence: 99%

Dual-fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

Yang

Chen

et al. 2021

Preprint

View full text Add to dashboard Cite

Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is of great significance to target tracking and visual information mining. Aiming at the problem that conventional active contour models cannot effectively extract the contours of salient targets in underwater images, we propose a dual-fusion active contour model with semantic information. First, the saliency images are introduced as semantic information, and extract salient target contours by fusing Chan–Vese and local binary ﬁtting models. Then, the original underwater images are used to supplement the missing contour information by using the local image ﬁtting. Compared with state-of-the-art contour extraction methods, our dual-fusion active contour model can effectively filter out background information and accurately extract salient target contours.

show abstract

Improved Segmentation Technique for Underwater Images Based on K-means and Local Adaptive Thresholding

Cited by 5 publications

References 5 publications

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification

Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

Dual-fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

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