A two-stage adaptive thresholding segmentation for noisy low-contrast images

Song, Junting; Jiao, Wenbin; Lankowicz, Katie; Cai, Zhonghua; Bi, Hongsheng

doi:10.1016/j.ecoinf.2022.101632

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…Image segmentation, dividing an image into distinct regions or segments based on certain characteristics, is a long‐standing problem in computer vision and most existing techniques are not suitable for noisy environments (Pal and Pal 1993; Song and Yan 2017). Recent efforts on developing segmentation techniques, specifically for crowded underwater images, partially alleviate this issue (Cheng et al 2020; Song et al 2022); but given the complexity and uncertainty in underwater images, unsupervised deep learning approaches like region‐based CNN (R‐CNN) offer a more promising solution (Minaee et al 2021). The R‐CNN models combine a region proposal network (RPN) to locate RoIs, a CNN model to describe features of RoIs generated from RPN proposals, and a classification layer to predict final bounding boxes and classes.…”

Section: Figmentioning

confidence: 99%

Taming the data deluge: A novel end‐to‐end deep learning system for classifying marine biological and environmental images

Bi,

Cheng,

Cheng

et al. 2023

Limnology & Ocean Methods

Self Cite

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Underwater imaging enables nondestructive plankton sampling at frequencies, durations, and resolutions unattainable by traditional methods. These systems necessitate automated processes to identify organisms efficiently. Early underwater image processing used a standard approach: binarizing images to segment targets, then integrating deep learning models for classification. While intuitive, this infrastructure has limitations in handling high concentrations of biotic and abiotic particles, rapid changes in dominant taxa, and highly variable target sizes. To address these challenges, we introduce a new framework that starts with a scene classifier to capture large within‐image variation, such as disparities in the layout of particles and dominant taxa. After scene classification, scene‐specific Mask regional convolutional neural network (Mask R‐CNN) models are trained to separate target objects into different groups. The procedure allows information to be extracted from different image types, while minimizing potential bias for commonly occurring features. Using in situ coastal plankton images, we compared the scene‐specific models to the Mask R‐CNN model encompassing all scene categories as a single full model. Results showed that the scene‐specific approach outperformed the full model by achieving a 20% accuracy improvement in complex noisy images. The full model yielded counts that were up to 78% lower than those enumerated by the scene‐specific model for some small‐sized plankton groups. We further tested the framework on images from a benthic video camera and an imaging sonar system with good results. The integration of scene classification, which groups similar images together, can improve the accuracy of detection and classification for complex marine biological images.

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Section: Figmentioning

confidence: 99%

Taming the data deluge: A novel end‐to‐end deep learning system for classifying marine biological and environmental images

Bi,

Cheng,

Cheng

et al. 2023

Limnology & Ocean Methods

Self Cite

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“…Results revealed that the method, when compared to conventional binarization techniques, increased the quality of extracted jellyfish and increased hardware resource consumption and computational efficiency. Results revealed that the method, when compared to conventional binarization techniques, increased the quality of extracted jellyfish and increased hardware resource consumption [118].…”

Section: Environmental Sciencementioning

confidence: 99%

Image Segmentation Using Hybrid Optimization Algorithms: Review

Hemeida¹,

Yahya

Al-Sanary

2022

Aswan University Journal of Sciences and Technology

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Image segmentation, often based on the properties of image pixels, is a widely used method in digital image processing and analysis to divide an image into multiple parts or areas. The main goal of image segmentation is to simplify the image for easier analysis. It would be very difficult to implement computer vision without performing image segmentation. An important component of computer vision is image segmentation, which has numerous commercial applications. Google and others Image-based search engines use image segmentation algorithms to identify the objects in your image and match their results to the relevant images they find to present you with search results.

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“…The result of image segmentation can segment a food processing scene image into target regions, thus providing the location of the target in the image. Algorithms based on grey-scale threshold segmentation 2 , edge segmentation 3 and region segmentation 4 are widely used in image segmentation. The threshold segmentation method is particularly suitable for images where the target and background occupy different grey level ranges and has been applied in many fields, where the selection of threshold values is a key technique in image threshold 5 , 6 .…”

Section: Introductionmentioning

confidence: 99%

A clustering-optimized segmentation algorithm and application on food quality detection

Chen

et al. 2023

Sci Rep

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For solving the problem of quality detection in the production and processing of stuffed food, this paper suggests a small neighborhood clustering algorithm to segment the frozen dumpling image on the conveyor belt, which can effectively improve the qualified rate of food quality. This method builds feature vectors by obtaining the image's attribute parameters. The image is segmented by a distance function between categories using a small neighborhood clustering algorithm based on sample feature vectors to calculate the cluster centers. Moreover, this paper gives the selection of optimal segmentation points and sampling rate, calculates the optimal sampling rate, suggests a search method for optimal sampling rate, as well as a validity judgment function for segmentation. Optimized small neighborhood clustering (OSNC) algorithm uses the fast frozen dumpling image as a sample for continuous image target segmentation experiments. The experimental results show the accuracy of defect detection of OSNC algorithm is 95.9%. Compared with other existing segmentation algorithms, OSNC algorithm has stronger anti-interference ability, faster segmentation speed as well as more efficiently saves key information ability. It can effectively improve some disadvantages of other segmentation algorithms.

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A two-stage adaptive thresholding segmentation for noisy low-contrast images

Cited by 9 publications

References 17 publications

Taming the data deluge: A novel end‐to‐end deep learning system for classifying marine biological and environmental images

Taming the data deluge: A novel end‐to‐end deep learning system for classifying marine biological and environmental images

Image Segmentation Using Hybrid Optimization Algorithms: Review

A clustering-optimized segmentation algorithm and application on food quality detection

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