Effective Histogram Thresholding Techniques for Natural Images Using Segmentation

Thanammal, K. K.; Vijayalakshmi, R. R.; Arumugaperumal, S.; Jayasudha, J S

doi:10.12720/joig.2.2.113-116

Cited by 31 publications

(9 citation statements)

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“…There are multiple methods of performing the segmentation, from image processing techniques to Artificial Neural Networks [4]. Segmentation techniques have been used in multiple domains [5,6,7]. In this work, we will focus on the usage of these techniques in pavement distress detection.…”

Section: Related Workmentioning

confidence: 99%

Automatic UAV-based airport pavement inspection using mixed real and virtual scenarios

Alonso

Gordoa

Ortega

et al. 2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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Runway and taxiway pavements are exposed to high stress during their projected lifetime, which inevitably leads to a decrease in their condition over time. To make sure airport pavement condition ensure uninterrupted and resilient operations, it is of utmost importance to monitor their condition and conduct regular inspections. UAV-based inspection is recently gaining importance due to its wide range monitoring capabilities and reduced cost.In this work, we propose a vision-based approach to automatically identify pavement distress using images captured by UAVs. The proposed method is based on Deep Learning (DL) to segment defects in the image. The DL architecture leverages the low computational capacities of embedded systems in UAVs by using an optimised implementation of EfficientNet feature extraction and Feature Pyramid Network segmentation. To deal with the lack of annotated data for training we have developed a synthetic dataset generation methodology to extend available distress datasets. We demonstrate that the use of a mixed dataset composed of synthetic and real training images yields better results when testing the training models in real application scenarios.

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Section: Related Workmentioning

confidence: 99%

Automatic UAV-based airport pavement inspection using mixed real and virtual scenarios

Alonso

Gordoa

Ortega

et al. 2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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“…There are many studies about image segmentation [11][12][13][14][15]. Semantic image segmentation provides pixel-level crop area classification, which ensures high performance on multispectral remote sensing data [16].…”

Section: Introductionmentioning

confidence: 99%

Semantic segmentation of crop areas in remote sensing imagery using spectral indices and multiple channels

Ülkü¹,

Akagündüz

2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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This study focuses on pixel-wise semantic segmentation of crop production regions by using satellite remote sensing multispectral imagery. One of the principal aims of the study is to find out whether the raw multiple channel inputs are more effective in the training process of the semantic segmentation models or if the formularized counterparts as the spectral indices are more effective. For this purpose, the vegetation indices NDVI, ARVI and SAVI and the water indices NDWI, NDMI, and WRI are employed as inputs. Additionally, using 8, 10 and 16 channels, multiple channel inputs are utilized. Moreover, all spectral indices are taken as separate channels to form a multiple channel input. We conduct deep learning experiments using two semantic segmentation architectures, namely U-Net and DeepLabV3+. Our results show that, in general, feeding raw multiple channel inputs to semantic segmentation models performs much better than feeding the spectral indices. Hence, regarding crop production region segmentation, deep learning models are capable of encoding multispectral information. When the spectral indices are compared among themselves, ARVI, which reduces the atmospheric scattering effects, achieves better accuracy for both architectures. The results also reveal that spatial resolution of multispectral data has a significant effect on the semantic segmentation performance, and therefore the RGB band, which has the lowest ground sample distance (0.31 m) outperforms multispectral bands and shortwave infrared bands.

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“…In the image processing domain, pixels are unstructured as well, but numerous algorithms exist to create segmentation [15,16,17,18], edges [19], or pixel-pixel relationships such as connectivity [20,21]. Of particular interest to our work are image gradients.…”

Section: Introductionmentioning

confidence: 99%

City-scale ground segmentation of aerial LiDAR by image-analogous gradients

Azam

Jacoby

Coady

2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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LiDAR Point Cloud segmentation is a key input to downstream tasks such as object recognition and classification, obstacle avoidance, and even 3D reconstruction. A key challenge in the segmentation of large city-scale datasets is uneven distribution of points to specific classes and significant class imbalances. As highly detailed point cloud datasets of urban environments become available, neural networks have shown significant performance in recognizing large well-defined objects. However, data is fed into these networks in chunks and the scheme by which data is presented for training and evaluation can have a significant impact on performance. In this work, we establish a method analogous to gradients in image processing to segment the ground in point clouds, achieving an accuracy of 91.4% on the Sensaturban dataset. By isolating the ground, we reduce the quantity of classes that need to be segmented from structures in urban LiDAR and improve data partitioning schemes when combined with random/grid down-sampling techniques for neural network inputs.

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Effective Histogram Thresholding Techniques for Natural Images Using Segmentation

Cited by 31 publications

References 0 publications

Automatic UAV-based airport pavement inspection using mixed real and virtual scenarios

Automatic UAV-based airport pavement inspection using mixed real and virtual scenarios

Semantic segmentation of crop areas in remote sensing imagery using spectral indices and multiple channels

City-scale ground segmentation of aerial LiDAR by image-analogous gradients

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