scholarly journals A Comparative Study of Semantic Segmentation Models for Identification of Grape with Different Varieties

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 997
Author(s):  
Yun Peng ◽  
Aichen Wang ◽  
Jizhan Liu ◽  
Muhammad Faheem
Keyword(s):  
Image Enhancement ◽  
Precision Agriculture ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Histogram Equalization ◽  
Performance Difference ◽  
Convolutional Network ◽  
Enhancement Method ◽  
The Impact ◽  
Segmentation Models

Accurate fruit segmentation in images is the prerequisite and key step for precision agriculture. In this article, aiming at the segmentation of grape cluster with different varieties, 3 state-of-the-art semantic segmentation networks, i.e., Fully Convolutional Network (FCN), U-Net, and DeepLabv3+ applied on six different datasets were studied. We investigated: (1) the segmentation performance difference of the 3 studied networks; (2) The impact of different input representations on segmentation performance; (3) The effect of image enhancement method to improve the poor illumination of images and further improve the segmentation performance; (4) The impact of the distance between grape clusters and camera on segmentation performance. The experiment results show that compared with FCN and U-Net the DeepLabv3+ combined with transfer learning is more suitable for the task with an intersection over union (IoU) of 84.26%. Five different input representations, namely RGB, HSV, L*a*b, HHH, and YCrCb obtained different IoU, ranging from 81.5% to 88.44%. Among them, the L*a*b got the highest IoU. Besides, the adopted Histogram Equalization (HE) image enhancement method could improve the model’s robustness against poor illumination conditions. Through the HE preprocessing, the IoU of the enhanced dataset increased by 3.88%, from 84.26% to 88.14%. The distance between the target and camera also affects the segmentation performance, no matter in which dataset, the closer the distance, the better the segmentation performance was. In a word, the conclusion of this research provides some meaningful suggestions for the study of grape or other fruit segmentation.

scholarly journals Low-Light Image Enhancement Based on Quasi-Symmetric Correction Functions by Fusion

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1561
Author(s):  
Changli Li ◽  
Shiqiang Tang ◽  
Jingwen Yan ◽  
Teng Zhou
Keyword(s):  
Image Enhancement ◽  
State Of The Art ◽  
Histogram Equalization ◽  
Local Contrast ◽  
Low Light ◽  
Adaptive Histogram Equalization ◽  
Light Image ◽  
Image Capturing ◽  
Enhancement Method ◽  
Image Details

Sometimes it is very difficult to obtain high-quality images because of the limitations of image-capturing devices and the environment. Gamma correction (GC) is widely used for image enhancement. However, traditional GC perhaps cannot preserve image details and may even reduce local contrast within high-illuminance regions. Therefore, we first define two couples of quasi-symmetric correction functions (QCFs) to solve these problems. Moreover, we propose a novel low-light image enhancement method based on proposed QCFs by fusion, which combines a globally-enhanced image by QCFs and a locally-enhanced image by contrast-limited adaptive histogram equalization (CLAHE). A large number of experimental results showed that our method could significantly enhance the detail and improve the contrast of low-light images. Our method also has a better performance than other state-of-the-art methods in both subjective and objective assessments.

scholarly journals Image Enhancement via Subimage Histogram Equalization Based on Mean and Variance

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Liyun Zhuang ◽  
Yepeng Guan
Keyword(s):  
Image Enhancement ◽  
State Of The Art ◽  
Image Database ◽  
Histogram Equalization ◽  
Input Image ◽  
Public Image ◽  
Enhancement Method ◽  
Mean And Variance ◽  
The Mean ◽  
Histogram Bins

This paper puts forward a novel image enhancement method via Mean and Variance based Subimage Histogram Equalization (MVSIHE), which effectively increases the contrast of the input image with brightness and details well preserved compared with some other methods based on histogram equalization (HE). Firstly, the histogram of input image is divided into four segments based on the mean and variance of luminance component, and the histogram bins of each segment are modified and equalized, respectively. Secondly, the result is obtained via the concatenation of the processed subhistograms. Lastly, the normalization method is deployed on intensity levels, and the integration of the processed image with the input image is performed. 100 benchmark images from a public image database named CVG-UGR-Database are used for comparison with other state-of-the-art methods. The experiment results show that the algorithm can not only enhance image information effectively but also well preserve brightness and details of the original image.

Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods

2017 ◽  
Vol 7 (8) ◽  
pp. 56
Author(s):  
Ashish Dwivedi ◽  
Nirupma Tiwari
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Human Perception ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Visual Appearance ◽  
Enhancement Method ◽  
Fuzzy Histogram ◽  
Rgb Image

Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.

scholarly journals Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation

2021 ◽  
Vol 11 (15) ◽  
pp. 7046
Author(s):  
Jorge Francisco Ciprián-Sánchez ◽  
Gilberto Ochoa-Ruiz ◽  
Lucile Rossi ◽  
Frédéric Morandini
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
State Of The Art ◽  
Fire Detection ◽  
Loss Functions ◽  
Wildfire Spread ◽  
Combine Information ◽  
The Impact ◽  
Image Type ◽  
Segmentation Models

Wildfires stand as one of the most relevant natural disasters worldwide, particularly more so due to the effect of climate change and its impact on various societal and environmental levels. In this regard, a significant amount of research has been done in order to address this issue, deploying a wide variety of technologies and following a multi-disciplinary approach. Notably, computer vision has played a fundamental role in this regard. It can be used to extract and combine information from several imaging modalities in regard to fire detection, characterization and wildfire spread forecasting. In recent years, there has been work pertaining to Deep Learning (DL)-based fire segmentation, showing very promising results. However, it is currently unclear whether the architecture of a model, its loss function, or the image type employed (visible, infrared, or fused) has the most impact on the fire segmentation results. In the present work, we evaluate different combinations of state-of-the-art (SOTA) DL architectures, loss functions, and types of images to identify the parameters most relevant to improve the segmentation results. We benchmark them to identify the top-performing ones and compare them to traditional fire segmentation techniques. Finally, we evaluate if the addition of attention modules on the best performing architecture can further improve the segmentation results. To the best of our knowledge, this is the first work that evaluates the impact of the architecture, loss function, and image type in the performance of DL-based wildfire segmentation models.

scholarly journals Attention-Based Multi-Modal Fusion Network for Semantic Scene Completion

2020 ◽  
Vol 34 (07) ◽  
pp. 11402-11409
Author(s):  
Siqi Li ◽  
Changqing Zou ◽  
Yipeng Li ◽  
Xibin Zhao ◽  
Yue Gao
Keyword(s):  
State Of The Art ◽  
Semantic Segmentation ◽  
Spatial Dimension ◽  
Semantic Features ◽  
Convolutional Network ◽  
The Real ◽  
Single View ◽  
Depth Cues ◽  
Semantic Scene ◽  
3D Scene

This paper presents an end-to-end 3D convolutional network named attention-based multi-modal fusion network (AMFNet) for the semantic scene completion (SSC) task of inferring the occupancy and semantic labels of a volumetric 3D scene from single-view RGB-D images. Compared with previous methods which use only the semantic features extracted from RGB-D images, the proposed AMFNet learns to perform effective 3D scene completion and semantic segmentation simultaneously via leveraging the experience of inferring 2D semantic segmentation from RGB-D images as well as the reliable depth cues in spatial dimension. It is achieved by employing a multi-modal fusion architecture boosted from 2D semantic segmentation and a 3D semantic completion network empowered by residual attention blocks. We validate our method on both the synthetic SUNCG-RGBD dataset and the real NYUv2 dataset and the results show that our method respectively achieves the gains of 2.5% and 2.6% on the synthetic SUNCG-RGBD dataset and the real NYUv2 dataset against the state-of-the-art method.

scholarly journals New Combined Technique for Fingerprint Image Enhancement

2016 ◽  
Vol 11 (1) ◽  
pp. 222 ◽  
Author(s):  
Alaa Ahmed Abbood ◽  
Mohammed Sabbih Hamoud Al-Tamimi ◽  
Sabine U. Peters ◽  
Ghazali Sulong
Keyword(s):  
Image Enhancement ◽  
Histogram Equalization ◽  
Fingerprint Image ◽  
Pixel Intensity ◽  
Improve Image Quality ◽  
Enhancement Method ◽  
Equalization Technique ◽  
Fingerprint Image Enhancement ◽  
Normalization Process ◽  
Combined Technique

This paper presents a combination of enhancement techniques for fingerprint images affected by different type of noise. These techniques were applied to improve image quality and come up with an acceptable image contrast. The proposed method included five different enhancement techniques: Normalization, Histogram Equalization, Binarization, Skeletonization and Fusion. The Normalization process standardized the pixel intensity which facilitated the processing of subsequent image enhancement stages. Subsequently, the Histogram Equalization technique increased the contrast of the images. Furthermore, the Binarization and Skeletonization techniques were implemented to differentiate between the ridge and valley structures and to obtain one pixel-wide lines. Finally, the Fusion technique was used to merge the results of the Histogram Equalization process with the Skeletonization process to obtain the new high contrast images. The proposed method was tested in different quality images from National Institute of Standard and Technology (NIST) special database 14. The experimental results are very encouraging and the current enhancement method appeared to be effective by improving different quality images.

An Image Enhancement Method Based on the Coal Mine Monitoring System

2012 ◽  
Vol 468-471 ◽  
pp. 204-207
Author(s):  
Zhen Chong Wang ◽  
Yan Qin Zhao
Keyword(s):  
Image Enhancement ◽  
Coal Mine ◽  
Impulse Noise ◽  
Histogram Equalization ◽  
Video Monitor ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Special Environment

For the low illumination and low contrast in the coal mine, images captured from the video monitor system sometimes are not so clear to help the related personal monitoring the production and safety of the mine. According to the special environment of coal mine, an image enhancement method was presented. In this method the impulse noise which is the mainly noise in the coal mine was first reduced with median filtering, then the low contrast and illumination was greatly improved with the improved adaptive histogram equalization. Experiments show that this method can improve the quality of images underground effectively.

scholarly journals Image Enhancement of Cross-Border E-Commerce Logistics Video Surveillance Based on Partial Differential Equations

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiaosheng Yu ◽  
Peili Wu
Keyword(s):  
Partial Differential Equations ◽  
Differential Equations ◽  
Fractional Derivative ◽  
Image Enhancement ◽  
Video Surveillance ◽  
Empirical Formula ◽  
Histogram Equalization ◽  
Partial Differential ◽  
Cross Border ◽  
Enhancement Method

Although the development time of cross-border e-commerce in China is very short, the scale of its transactions and the speed of development are amazing, and as a supporting foundation for promoting economic and trade globalization, cross-border e-commerce has an extremely important strategy for this guiding role. This not only brings new opportunities to cross-border e-commerce companies but also excavates a huge potential market for the logistics industry. Cross-border e-commerce not only breaks through the trade barriers between countries; it makes trade move towards borderlessness and at the same time triggers major changes in international trade. This paper introduces partial differential equations into the video surveillance image enhancement system of cross-border e-commerce logistics. Aiming at the shortcomings of the contrast enhancement method based on gradient field equalization, this paper proposes a partial differential enhancement method based on histogram equalization. By proposing a gradient transformation function, the edges and textures with relatively small gradient values are enhanced to make the original weaker texture details clearer. In order to better adjust the brightness and contrast of the image, combined with histogram equalization, we propose an inverse equalization transformation. When the histogram equalization and the inverse equalization transform are combined reasonably, the brightness and contrast of the image can be adjusted very well. In this paper, the finite difference method is used for discretization when solving partial differential equations, and Euler’s equation is obtained by applying the principle of least squares. By introducing the heat equation, the direct solution of Euler’s equation is converted into an iterative form, which greatly reduces the amount of calculation. This article uses statistical methods to obtain the empirical formula of the fractional differential order. This empirical formula makes the calculation of the order of the fractional derivative easy and can be extended to other fractional image enhancement models and overcomes the shortcomings of the traditional fractional derivative order obtained through experience or a large number of experiments. Experiments show that the proposed algorithm not only enhances detailed texture information but also improves image clarity, overall brightness, and contrast without color distortion. The objective evaluation indicators also show the superiority of the algorithm.

scholarly journals An image enhancement method based on gabor filtering in wavelet domain and adaptive histogram equalization

2021 ◽  
Vol 21 (1) ◽  
pp. 146
Author(s):  
Jeevan K M ◽  
Anne Gowda A B ◽  
Padmaja Vijay Kumar
Keyword(s):  
Image Enhancement ◽  
Histogram Equalization ◽  
Median Filtering ◽  
Wavelet Domain ◽  
Gabor Filtering ◽  
Low Contrast ◽  
Enhancement Technique ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Two Parameters

<p><span>The images are not always good enough to convey the proper information. The image may be very bright or very dark sometime or it may be low contrast or high contrast. Because of these reasons image enhancement plays important role in digital image processing. In this paper we proposed an image enhancement technique in which Gabor and median filtering is performed in wavelet domain and Adaptive Histogram Equalization is performed in spatial domain. Brightness and contrast are the two parameters used for analyzing the performance of the proposed method</span></p>

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