Quantitative Comparison of Flood Fill and Modified Flood Fill Algorithms

Law, George S.

doi:10.7763/ijcte.2013.v5.738

Cited by 19 publications

(9 citation statements)

References 2 publications

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“…A flood fill algorithm is used after eroding the binary image [ 79 ]. It fills a connected component with a specific color (i.e., black in our case) [ 80 ].…”

Section: The Proposed Hybrid Covid-19 Framework (Hmb-hcf)mentioning

confidence: 99%

Hybrid COVID-19 segmentation and recognition framework (HMB-HCF) using deep learning and genetic algorithms

Balaha

Ali

2021

Artificial Intelligence in Medicine

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COVID-19 (Coronavirus) went through a rapid escalation until it became a pandemic disease. The normal and manual medical infection discovery may take few days and therefore computer science engineers can share in the development of the automatic diagnosis for fast detection of that disease. The study suggests a hybrid COVID-19 framework (named HMB-HCF) based on deep learning (DL), genetic algorithm (GA), weighted sum (WS), and majority voting principles in nine phases. Its segmentation phase suggests a lung segmentation algorithm using X-Ray images (named HMB-LSAXI) for extracting lungs. Its classification phase is built from a hybrid convolutional neural network (CNN) architecture using an abstractly-designed CNN (named HMB1-COVID19) and transfer learning (TL) pre-trained models (VGG16, VGG19, ResNet50, ResNet101, Xception, DenseNet121, DenseNet169, MobileNet, and MobileNetV2). The hybrid CNN architecture is used for learning, classification, and parameters optimization while GA is used to optimize the hyperparameters. This hybrid working mechanism is combined in an overall algorithm named HMB-DLGA. The study experiments implemented the WS approach to evaluate the models' performance using the loss, accuracy, F1-score, precision, recall, and area under curve (AUC) metrics with different pre-defined ratios. A collected, combined, and unified X-Ray dataset from 8 different public datasets was used alongside the regularization, dropout, and data augmentation techniques to limit the overall overfitting. The applied experiments reported state-of-the-art metrics. VGG16 reported 100% WS metric (i.e., 0.0097, 99.78%, 0.9984, 99.89%, 99.78%, and 0.9996 for the loss, accuracy, F1, precision, recall, and AUC respectively) concerning the highest WS. It also reported a 99.92% WS metric (i.e., 0.0099, 99.84%, 0.9984, 99.84%, 99.84%, and 0.9996 for the loss, accuracy, F1, precision, recall, and AUC respectively) concerning the last reported WS result. HMB-HCF was validated on 13 different public datasets to verify its generalization. The best-achieved metrics were compared with 13 related studies. These extensive experiments' target was the applicability verification and generalization.

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“…A flood fill algorithm is used after eroding the binary image [ 79 ]. It fills a connected component with a specific color (i.e., black in our case) [ 80 ].…”

Section: The Proposed Hybrid Covid-19 Framework (Hmb-hcf)mentioning

confidence: 99%

Hybrid COVID-19 segmentation and recognition framework (HMB-HCF) using deep learning and genetic algorithms

Balaha

Ali

2021

Artificial Intelligence in Medicine

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“…In [2], the authors compared to the Flood Fill algorithm, the edited Flood-Fill algorithm offers an important reduction in cell distance updates.…”

Section: Related Workmentioning

confidence: 99%

Extracting a Bounded Region from a Map Using Flood Fill Algorithm

Shuaeb¹,

Kamruzzaman²,

Ali³

2021

AJRCoS

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Extracting the needed portion from a bounded region is an important task in image processing. Editing a map and extracting a region from the map is challenging. It is useful in some contexts to have a region in a separate sheet. In this image processing, we have used the Flood Fill algorithm to extract a region from the image map. To achieve that goal, we had worked in our study to separate a bounded region on a map. Usually, a scanned map may contain a lot of useless information. So we have to process the image to remove useless information from the map. We had quantized the image to a binary one. In the second phase, we have applied a gray color to separate the desired position from a map. Our main objective of the study to extract a bounded region from mapping an image that contains useless information and removes it. We have experimented with several maps and it works successfully.

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“…This uses a flood-fill algorithm approach (e.g. Law, 2013) to summarize the shape and intensity patterns exhibited by each target, and if the target is within the user-defined specifications it records basic information to *.txt files on timings (hh : mm : ss), locations (X-Y coordinates), ranges from the sonar (metres), and kinematic information (speed and trajectory in the X and Y planes) for all mobile targets detected (Parsons et al, 2017). Grey seals were imaged on the sonar appearing as distinct targets that were temporally persistent and had highly localized patterns of highintensity pixels in the sonar images.…”

Section: Sonar Systemmentioning

confidence: 99%

Automated detection and tracking of marine mammals: A novel sonar tool for monitoring effects of marine industry

Hastie

Moss

et al. 2019

Aquatic Conservation

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1. Many marine industries may pose acute risks to marine wildlife. For example, tidal turbines have the potential to injure or kill marine mammals through collisions with turbine blades. However, the quantification of collision risk is currently limited by a lack of suitable technologies to collect long-term data on marine mammal behaviour around tidal turbines.2. Sonar provides a potential means of tracking marine mammals around tidal turbines. However, its effectiveness for long-term data collection is hindered by the large data volumes and the need for manual validation of detections. Therefore, the aim here was to develop and test automated classification algorithms for marine mammals in sonar data.3. Data on the movements of harbour seals were collected in a tidally energetic environment using a high-frequency multibeam sonar on a custom designed seabedmounted platform. The study area was monitored by observers to provide visual validation of seals and other targets detected by the sonar.4. Sixty-five confirmed seals and 96 other targets were detected by the sonar. Movement and shape parameters associated with each target were extracted and used to develop a series of classification algorithms. Kernel support vector machines were used to classify targets (seal vs. nonseal) and cross-validation analyses were carried out to quantify classifier efficiency.5. The best-fit kernel support vector machine correctly classified all the confirmed seals but misclassified a small percentage of non-seal targets (~8%) as seals. Shape and non-spectral movement parameters were considered to be the most important in achieving successful classification.6. Results indicate that sonar is an effective method for detecting and tracking seals in tidal environments, and the automated classification approach developed here provides a key tool that could be applied to collecting long-term behavioural data around anthropogenic activities such as tidal turbines.

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Quantitative Comparison of Flood Fill and Modified Flood Fill Algorithms

Cited by 19 publications

References 2 publications

Hybrid COVID-19 segmentation and recognition framework (HMB-HCF) using deep learning and genetic algorithms

Hybrid COVID-19 segmentation and recognition framework (HMB-HCF) using deep learning and genetic algorithms

Extracting a Bounded Region from a Map Using Flood Fill Algorithm

Automated detection and tracking of marine mammals: A novel sonar tool for monitoring effects of marine industry

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