Islam MJ, Luo P, Sattar J (2020) Simultaneous enhancement and super-resolution of underwater imagery for improved visual perception. Mo J (2022) Towards a fast, robust and accurate visual-inertial simultaneous localization and mapping system. arXiv:2011.06252įulton M, Mehtaz M, Sattar J, Queeglay O (2022) Underwater robot-to-human communication via motion: implementation and full-loop human interface evaluation. In: Robotics: science and systems XVIII, robotics: science and systems foundation Islam MJ, Wang R, Sattar J (2020) Svam: saliency-guided visual attention modeling by autonomous underwater robots. Leonard JJ, Bahr A (2016) Autonomous underwater vehicle navigation. In: Springer handbook of ocean engineering, pp 341–358įulton M, Prabhu A, Sattar J (2022) HREyes: design, development, and evaluation of a novel method for AUVs to communicate information and gaze direction. Hu L, Li J, Peng X, Xiao J, Zhan B, Zu C et al (2022) Semi-supervised NPC segmentation with uncertainty and attention guided consistency. Knowl-Based Syst 239:108021 Qureshi I, Yan J, Abbas Q, Shaheed K, Riaz AB, Wahid A et al (2022) Medical image segmentation using deep semantic-based methods: a review of techniques, applications and emerging trends. Inf Fusion Müller D, Soto-Rey I, Kramer F (2022) Towards a guideline for evaluation metrics in medical image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 1290–1299 IEEE J Sel Top Appl Earth Observ Remote Sens 14:8909–8921Ĭheng B, Misra I, Schwing AG, Kirillov A, Girdhar R (2022) Masked-attention mask transformer for universal image segmentation. Xue B, Huang B, Chen G, Li H, Wei W (2021) Deep-sea debris identification using deep convolutional neural networks. Woock P, Frey C (2010) Deep-sea AUV navigation using side-scan sonar images and SLAM. With the proposed approach, the proposed work is tested and evaluated using the UFO-120 benchmark database, which results in better Jaccard, Dice, and accuracy values. In this study, a suitable scheme for analyzing underwater images with GA based on the optimally assigned gene and chromosome will be developed. Recently, GA has been applied to a new application known as Mayfly Algorithm (MA) which incorporates the best features of other successful heuristic methods along with GA to produce the Mayfly Algorithm (MA). There are four phases to the proposed approach (i) Image collection and resizing, (ii) Kapur’s image thresholding based on Genetic Algorithms (GAs) integrated with firefly and particle swarm optimization, (iii) Identifying the ROI and comparing it with the binary mask, and (iv) evaluating and validating its performance. In the proposed work, a scheme will be developed for obtaining an accurate ROI in underwater RGB scale pictures. In order to achieve a better result, a combination of image evaluation procedures is combined together to evaluate the ROI in complex and blurred images. Matlabroot\toolbox\matlab\strfun\deblank.In a variety of domains, image processing-supported Regions of Interest (RoIs) are widely used to evaluate specific areas of digital images. However, the function is overloaded by a second M-file (in the subdirectory) to handle cell array arguments as well. The default, s trfun\ deblank.m, handles most argument types. Examining the handle using functions(fhandle) reveals that it is bound to two M-files that implement the deblank function. In the next example, returns a function handle to variable, fhandle. The fminbnd function uses feval to evaluate the function handle that was passed in. The fhandle argument is a handle to the humps function. = following example passes a function handle, fhandle, in a call to fminbnd. The following two statements are equivalent. However, function handles offer the additional performance, reliability, and source file control benefits listed in the section Benefits of Using Function Handles. To support backward compatibility, feval also accepts a function name string as a first argument. The preferred means of evaluating a function by reference is to use a function handle. The function parameter must be a simple function name it cannot contain path information. If function is a quoted string containing the name of a function (usually defined by an M-file), then feval(function,x1.,xn) evaluates that function at the given arguments. If the function handle is bound to more than one built-in or M-file, (that is, it represents a set of overloaded functions), then the data type of the arguments x1 through xn, determines which function is dispatched to. Feval (MATLAB Functions) MATLAB Function ReferenceĮvaluates the function handle, fhandle, using arguments x1 through xn.
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