Open Access
Applications and Datasets for Superpixel Techniques A Survey
Abdelhameed Ibrahim,El-Sayed M. El-kenawy +1 more
- Vol. 15
Reads0
Chats0
TLDR
This work aims to survey the recent applications and the most common datasets that can be used based on superpixel techniques, and to evaluate the superpixel algorithms used in these applications.Abstract:
The use of superpixels instead of pixels can significantly improve the speed of the current pixel-based algorithms, and can even produce better results in many applications such as robotics, remote sensing, industrial inspection, and medical diagnosis. Two main tasks related to vision could benefit from superpixels, named object class segmentation and medical image segmentation. In both cases, superpixels can increase performance significantly and also reduce the computational cost. In addition to superpixel applications, various datasets were employed for the evaluation of the superpixel algorithms. This work aims to survey the recent applications and the most common datasets that can be used based on superpixel techniques.read more
Citations
More filters
Journal ArticleDOI
Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification
El-Sayed M. El-kenawy,Seyedali Mirjalili,Abdelhameed Ibrahim,Mohammed Alrahmawy,Mohammed El-Said,Rokaia M. Zaki,Marwa M. Eid +6 more
TL;DR: In this article, the authors proposed a classification method with two stages to classify different cases from the chest X-ray images based on a proposed Advanced Squirrel Search Optimization Algorithm (ASSOA).
Proceedings ArticleDOI
A binary Sine Cosine-Modified Whale Optimization Algorithm for Feature Selection
TL;DR: In this article, a new wrapper feature selection binary formula is intended based upon the Sine Cosine Algorithm (SCA) and a modified Whale Optimization Algorithm(MWOA).
Journal ArticleDOI
EEG Channel Selection Using A Modified Grey Wolf Optimizer
TL;DR: A modified grey wolf optimizer (MGWO) that can select optimal EEG channels to be used in (BCIs), the way that identifies main features and the immaterial ones from that dataset and the complexity to be removed is proposed.
Journal ArticleDOI
Efficient Pneumonia Detection for Chest Radiography Using ResNet-Based SVM
Marwa M. Eid,Yasser H. Elawady +1 more
TL;DR: An innovative approach for distinguishing the residence of pneumonia by embedding computational techniques to chest x-rays images which eliminating the demands for single-image investigation and significantly decrease the total costs is presented.
References
More filters
Journal ArticleDOI
Contour Detection and Hierarchical Image Segmentation
TL;DR: This paper investigates two fundamental problems in computer vision: contour detection and image segmentation and presents state-of-the-art algorithms for both of these tasks.
Book ChapterDOI
Indoor segmentation and support inference from RGBD images
TL;DR: The goal is to parse typical, often messy, indoor scenes into floor, walls, supporting surfaces, and object regions, and to recover support relationships, to better understand how 3D cues can best inform a structured 3D interpretation.
Proceedings ArticleDOI
Learning a classification model for segmentation
TL;DR: A two-class classification model for grouping is proposed that defines a variety of features derived from the classical Gestalt cues, including contour, texture, brightness and good continuation, and trains a linear classifier to combine these features.
Proceedings ArticleDOI
SUN RGB-D: A RGB-D scene understanding benchmark suite
TL;DR: This paper introduces an RGB-D benchmark suite for the goal of advancing the state-of-the-arts in all major scene understanding tasks, and presents a dataset that enables the train data-hungry algorithms for scene-understanding tasks, evaluate them using meaningful 3D metrics, avoid overfitting to a small testing set, and study cross-sensor bias.
Proceedings ArticleDOI
Decomposing a scene into geometric and semantically consistent regions
TL;DR: A region-based model which combines appearance and scene geometry to automatically decompose a scene into semantically meaningful regions and which achieves state-of-the-art performance on the tasks of both multi-class image segmentation and geometric reasoning.