Performance Assessment of Sky Segmentation Approaches for UAVs

2019 ◽  
Vol 19 (04) ◽  
pp. 1950023
Author(s):  
Ahmed S. Mashaly
Keyword(s):  
Image Segmentation ◽  
Performance Assessment ◽  
General Purpose ◽  
Segmentation Method ◽  
Research Fields ◽  
Different Types ◽  
Segmentation Approach ◽  
Aerial Vehicle ◽  
Computer Vision Systems ◽  
Overall Performance

Image segmentation is one of the most challenging research fields for both image analysis and interpretation. The applications of image segmentation could be found as the primary step in various computer vision systems. Therefore, the choice of a reliable and accurate segmentation method represents a non-trivial task. Since the selected image segmentation method influences the overall performance of the remaining system steps, sky segmentation appears as a vital step for Unmanned Aerial Vehicle (UAV) autonomous obstacle avoidance missions. In this paper, we are going to introduce a comprehensive literature survey of the different types of image segmentation methodology followed by a detailed illustration of the general-purpose methods and the state-of-art sky segmentation approaches. In addition, we introduce an improved version of our previously published work for sky segmentation purpose. The performance of the proposed sky segmentation approach is compared with various image segmentation approaches using different parameters and datasets. For performance assessment, we test our approach under different situations and compare its performance with commonly used approaches in terms of several assessment indexes. From the experimental results, the proposed method gives promising results compared with the other image segmentation approaches.

scholarly journals A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-27
Author(s):  
Jinghua Zhang ◽  
Chen Li ◽  
Frank Kulwa ◽  
Xin Zhao ◽  
Changhao Sun ◽  
...  
Keyword(s):  
Image Segmentation ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Segmentation Method ◽  
Recall Accuracy ◽  
Evaluation Indexes ◽  
Segmentation Quality ◽  
Segmentation Approach ◽  
Overall Evaluation

To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, “mU-Net-B3”, with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel “buffer” strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field.

scholarly journals Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation

2019 ◽  
Vol 11 (9) ◽  
pp. 1046 ◽  
Author(s):  
Heming Jia ◽  
Zhikai Xing ◽  
Wenlong Song
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Optimization Algorithm ◽  
Oil Pollution ◽  
Three Dimensional ◽  
Oil Field ◽  
Segmentation Method ◽  
Pulse Coupled Neural Network ◽  
Aerial Vehicle ◽  
Pollution Area

This paper proposes a three dimensional pulse coupled neural network (3DPCNN) image segmentation method based on a hybrid seagull optimization algorithm (HSOA) to solve the oil pollution image. The image of oil pollution is taken by the unmanned aerial vehicle (UAV) in the oil field area. The UAV is good at shooting the ground area, but its ability to identify the oil pollution area is poor. In order to solve this problem, a 3DPCNN-HSOA algorithm is proposed to segment the oil pollution image, and the oil pollution area is segmented to identify the dirty oil area and improve the inspection of environmental pollution. The 3DPCNN image segmentation method has simple structure and good segmentation effect, but it has many parameters and poor segmentation effect for complex oil images. Therefore, we apply HSOA algorithm to optimize the parameters of 3DPCNN algorithm, so as to improve the segmentation accuracy and solve the segmentation of oil pollution images. The experimental results show that the 3DPCNN-HSOA model can separate the oil pollution area from the complex background.

A Crack Segmentation Approach Using the Combination of Gray Thresholds and Fractal Feature

2012 ◽  
Vol 487 ◽  
pp. 622-626 ◽  
Author(s):  
Song Yang ◽  
Long Tan Shao ◽  
Xiao Xia Guo ◽  
Xiao Liu ◽  
Bo Ya Zhao
Keyword(s):  
Image Segmentation ◽  
Fractal Geometry ◽  
Contour Detection ◽  
Crack Identification ◽  
Gray Level ◽  
Segmentation Method ◽  
Constraint Condition ◽  
Segmentation Approach ◽  
Initial Segmentation ◽  
Crack Contour

A segmentation method of combining gray-level threshold and fractal feature for crack images is proposed, and the fractal law for the perimeter and area of the target is introduced as the constraint condition for the image segmentation of crack. At first, Otsu algorithm is used for the initial segmentation of the crack image, and then the edge of crack is optimized in accordance with fractal law. At last, boundary of crack is determined, and the final result of the image segmentation is obtained. This method makes full use of the fractal geometry law and image information, to effectively solve the problems such as crack contour detection, regional connection and cross crack identification. Several typical examples are analyzed, and the results show that this method has a good segmentation effect on crack images, and it can also be used to identify the other images which have fractal feature.

scholarly journals An Advanced Road Structure Image Segmentation Method using Swt in Cnn

2019 ◽  
Vol 9 (2S3) ◽  
pp. 114-118
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
High Speed ◽  
Learning Phase ◽  
Convolution Neural Network ◽  
Aerial Images ◽  
Aerial Image ◽  
Segmentation Method ◽  
Stroke Width ◽  
Segmentation Approach

In this paper, the design of advanced road structure image segmentation approach using stroke width transformation (SWT) in convolution neural network (CNN) is proposed. The main intent of the proposed system is to acquire the aerial images for the vehicle. Basically, this image segmentation performs its operation in two forms they are operating phase and learning phase. Here the aerial image has enhanced by using the SWT transformation. Hence the main advantage of this proposes system is that it processes the entire operation in simple way with high speed. The SWT will capture the images of road areas in effective way. Hence the propose system has various features which will determine the color, width and many other.

scholarly journals Hybrid Iris Segmentation Method Based on CNN and Principal Curvatures

2020 ◽  
pp. paper31-1-paper31-10
Author(s):  
Varvara Tikhonova ◽  
Elena Pavelyeva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
Principal Curvatures ◽  
Mathematical Methods ◽  
Iris Image ◽  
Segmentation Approach

In this article the new hybrid iris image segmentation method based on convolutional neural networks and mathematical methods is proposed. Iris boundaries are found using modified Daugman’s method. Two UNet-based convolutional neural networks are used for iris mask detection. The first one is used to predict the preliminary iris mask including the areas of the pupil, eyelids and some eyelashes. The second neural network is applied to the enlarged image to specify thin ends of eyelashes. Then the principal curvatures method is used to combine the predicted by neural networks masks and to detect eyelashes correctly. The pro- posed segmentation algorithm is tested using images from CASIA IrisV4 Interval database. The results of the proposed method are evaluated by the Intersection over Union, Recall and Precision metrics. The average metrics values are 0.922, 0.957 and 0.962, respectively. The proposed hy- brid iris image segmentation approach demonstrates an improvement in comparison with the methods that use only neural networks.

Self-Supervised Online Long-Range Road Estimation in Complicated Urban Environments

2012 ◽  
Vol 24 (1) ◽  
pp. 16-27 ◽  
Author(s):  
Yoji Kuroda ◽  
◽  
Masataka Suzuki ◽  
Teppei Saitoh ◽  
Eisuke Terada
Keyword(s):  
Image Segmentation ◽  
Long Range ◽  
Laser Scanner ◽  
Estimation Method ◽  
Urban Environments ◽  
Road Surface ◽  
University Campus ◽  
Segmentation Method ◽  
Segmentation Approach ◽  
Standard Classification

In this paper, we propose a long-range road estimation method for autonomousmobile robots in unstructured urban environments. Near-range road surface conditions are estimated by using remission value as reflectivity of a laser scanner. Graph cut algorithm is applied to estimate road region robustly also in complicated environments. Moreover, we propose a novel image segmentation method to estimate long-range road surface. A compact texture/color feature is integrated with level-set method to estimate precise road boundaries robustly. Our proposed image segmentation approach gives better performance compared with standard classification approach. Finally, we run our autonomous mobile robot in “Tsukuba Challenge 2009” and our university campus, and experimental results have shown a marked increase accuracy in road estimation over standard methods.

scholarly journals Image Segmentation Method Selection for Vehicle Detection Using Unmanned Aerial Vehicle

2015 ◽  
Vol 9 (5) ◽  
Author(s):  
Kirill Viktorovich Abramov ◽  
Pavel Vyacheclavovich Skribtsov ◽  
Pavel Alexandrovich Kazantsev
Keyword(s):  
Image Segmentation ◽  
Unmanned Aerial Vehicle ◽  
Vehicle Detection ◽  
Segmentation Method ◽  
Method Selection ◽  
Aerial Vehicle ◽  
Selection For

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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