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HazeNet: Does the image have haze? 21

HazeNet: Does the image have haze?

引用

12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Kalaivani, P. Rooomi, S. Mohamed Mansoor Nathan, D. Sabari Government College of Engineering Bodinayakanur Theni Tamilnadu India Thiagarajar College of Engineering Tamilnadu Madurai India Couger Inc Tokyo Japan

ISBN: (纸本)9781450391276

the visibility of scene is much important in applications of Intelligent Video surveillance systems, Autonomous Vehicle Navigation. the presence of haze limits the visibility, thereby hindering the proper operation of computer vision algorithms. there are lot of algorithms to remove haze unconditionally. But the haze removal on clear images or frames will cause adverse effects. Unnecessary dehazing results in the loss of original information and also delays the following process. To address these challenges, there is a need to recognize the image as hazy and haze-free before applying dehazing on it. Hence, this paper presents three light-weight deep neural network models to automatically classify image as hazy and haze-free in the name of HazeNet, R-HazeNet and RSE-HazeNet. the HazeNet is Convolutional Neural Network having five convolutional blocks and results in classification accuracy of 97.89%. In order to improve the performance of HazeNet further, the Residual blocks have been included after each convolutional layer. the model is called R-HazeNet. the residual blocks help in producing hierarchical features. To strengthen the useful convolutional channel features alone, Squeeze and Excitation blocks have been included with Residual units in RSE-HazeNet model. Both R-HazeNet and RSE-HazeNet produced classification accuracy of 99.98% which demonstrate superior performance compared to the stat-of-the-art methods. the proposed models are light-weight models due to the percentage improvement in number of parameters as a minimum of 93% has been achieved with respect to the the baseline method. © 2021 ACM.

关键词： Convolution

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A novel unsupervised thresholding technique for landsat image change detection 21

A novel unsupervised thresholding technique for landsat imag...

引用

12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Gupta, Neha Ari, Samit Mishra, Anoop Kumar VIT-AP University Andhra Pradesh Amaravati India National Institute of Technology Odisha Rourkela India

ISBN: (纸本)9781450391276

thresholding is the most widely used change detection technique for identifying the changes in remote sensing images. However, most of the thresholding methods would generate isolated spots in the final change map, which are reduced by applying postprocessing step. this paper proposes a novel thresholding technique to address the aforesaid problem without applying the postprocessisng operation. the proposed technique uses two thresholds simultaneously to generate the change map, which is the key point of this method. Above-stated thresholds are calculated by initial indicators that are generated by local neighborhood mutual information. In addition, this approach compares the local statistics of the interested pixel with the derived thresholds instead of the pixel itself, which enhances the robustness of this technique. Particularly, the method resides on three things: generation of initial indicators, use of two thresholds simultaneously, and comparison of local statistics with thresholds. Finally, the method is tested on multitemporal multispectral images of different sensors, and experimental results validate the effectiveness of the proposed method. © 2021 ACM.

关键词： K-means clustering

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Recognition technology of human body movement behavior in fitness exercise based on transfer learning 6

Recognition technology of human body movement behavior in fi...

引用

6th IEEE International conference on Intelligent Computing and Signal processing, ICSP 2021

作者： Ke, YiRan Zeng, CanNan Lu, XingHua Cui, YuHan Huali College Guangdong University of Technology Computer and Information Engineering Guangzhou China

ISBN: (纸本)9780738143705

By the detection of human body movements in fitness exercise, the normative correction of human body movements in fitness exercise is carried out. Aiming at the problem that the current empirical analysis method is not accurate in detecting the movements of complex movements, a method for detecting human body movements in fitness exercise based on multi-scale feature decomposition is proposed. Firstly, the image collection and feature analysis of the complex action link behavior of the body-building movement are carried out under the multimedia vision. then, Harris feature point detection and information screening method are used to enhance the collected image of the body-building movement, and the multi-scale morphological edge contour features of the image are extracted, thus realizing the feature point calibration and detection of the body-building movement behavior characteristics and improving the quantitative estimation accuracy of the movement behavior. Simulation results show that this method has a high probability of accurate detection and recognition, increases the extraction number of key information feature points of fitness human movement, and has a high guiding value for movement correction. © 2021 IEEE.

关键词： Health

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G3AN++: Exploring wide GANs with complementary feature learning for video generation 21

G3AN++: Exploring wide GANs with complementary feature learn...

引用

12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Gupta, Sonam Keshari, Arti Das, Sukhendu Indian Institute of Technology Madras Tamil Nadu Chennai India

ISBN: (纸本)9781450391276

Video generation task is a challenging problem which involves the modelling of complex real-world dynamics. Most of the existing methods have designed deep networks to tackle high-dimensional video data distributions. However, the utilization of wide networks is still under explored. Inspired by the success of wide networks in the image recognition literature, we present G3AN++, a three-stream generative adversarial network for video. the three streams are spatial, temporal and spatio-temporal processing branches. In pursuit of improving the quality of video generation, we make our network wider by splitting the spatial stream into two parallel identical branches learning complementary feature representations. We further introduce a novel adaptive masking layer to impose the complementary constraint. the masking layer encourages the parallel branches to learn distinct and richer visual features. Extensive quantitative and qualitative analysis demonstrates that our model outperforms the existing state-of-the-art methods by a significant margin on Weizmann Action, UvA-Nemo Smile and UCF101 Action datasets. Additional exploration reveals that G3AN++ is capable of disentangling the appearance and motion. We also show that the proposed method can be easily extended to solve the hard task of text-to-video generation. © 2021 ACM.

关键词： Generative adversarial networks

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HDR-cGAN: Single LDR to HDR image translation using conditional GAN 21

HDR-cGAN: Single LDR to HDR image translation using conditio...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Raipurkar, Prarabdh Pal, Rohil Raman, Shanmuganathan CVIG Lab IIT Gandhinagar Gujarat India

ISBN: (纸本)9781450391276

the prime goal of digital imaging techniques is to reproduce the realistic appearance of a scene. Low Dynamic Range (LDR) cameras are incapable of representing the wide dynamic range of the real-world scene. the captured images turn out to be either too dark (underexposed) or too bright (overexposed). Specifically, saturation in overexposed regions makes the task of reconstructing a High Dynamic Range (HDR) image from single LDR image challenging. In this paper, we propose a deep learning based approach to recover details in the saturated areas while reconstructing the HDR image. We formulate this problem as an image-to-image (I2I) translation task. To this end, we present a novel conditional GAN (cGAN) based framework trained in an end-to-end fashion over the HDR-REAL and HDR-SYNth datasets. Our framework uses an overexposed mask obtained from a pre-trained segmentation model to facilitate the hallucination task of adding details in the saturated regions. We demonstrate the effectiveness of the proposed method by performing an extensive quantitative and qualitative comparison with several state-of-the-art single-image HDR reconstruction techniques. © 2021 ACM.

关键词： Generative adversarial networks

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Camouflaged Object Segmentation with Transformer 6th

Camouflaged Object Segmentation with Transformer

引用

6th International conference on Cognitive Systems and Signal processing, ICCSIP 2021

作者： Wang, Haiwen Wang, Xinzhou Sun, Fuchun Song, Yixu State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China School of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9789811692468

the vision Transformer (ViT) [6] directly applies a Transformer architecture to image classification and achieves an impressive result compared with convolutional networks. this paper presents a new ViT-base camouflaged object segmentation method, called COS Transformer, which aims to identify and segment objects concealed in a complex environment. the high intrinsic similarities between object and surrounding makes the task challenging than salient object detection. Most recent camouflaged object segmentation methods(e.g., EGNet [29], PraNet [10] and SINet [9]) adopt convolutional network with an encoder-decoder architecture and focused on increasing the receptive field, which is limited by the depth of the network. In camouflaged object segmentation (COS) task, the camouflage is mainly relied on contrast of the whole surrounding instead of the local information. We introduce transformer with global context awareness in this paper, for self-attention allowing COS Transformer to aggregate features globally even in the lowest layers. Specifically, the architecture is composed of a transformer-based encoder and a multi-layers feature aggregation refinement module. After training on the COD10K [9] dataset, COS Transformer attains excellent results compared to state-of-the-art convolutional networks, e.g. 11.7% improvement of E [8] on the COD10K contrasted to SINet. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Multiple Feature Extraction of image using 2D CA 6

Multiple Feature Extraction of Image using 2D CA

引用

6th International conference on image Information processing, ICIIP 2021

作者： Chakraborty, Shramona Roy Chowdhury, Dipanwita Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur India

ISBN: (纸本)9781665433617

Feature Extraction is a long active topic in image processing, and it attracts lots of attention over the last five decades due to its uses in real-life applications. Edge detection method allows users to discover the features of an image for a notable change in the gray level. this texture indicates an ending of one region and the beginning of another in the image. Likewise, over the last few decades, researchers have been exploring to exploit the simple computing model of Cellular Automata (CA) with local neighborhood structure in image processing techniques. the usage of CA in applications of medical image-processing is identified by Kendall Preston [1] in 1979. It is found that edge detection using CA has a potential advantage over traditional approaches for its lightweight nature and computational efficiency. this paper introduces a new technique for image edge detection using two-dimensional CA. the method can use specific rule sets of CA using the Von Neumann neighborhood for edge detection with null boundary conditions. the performance analysis of the scheme is done and compared with some existing standard edge detection techniques. the results obtained from the proposed technique is very promising for feature extraction. © 2021 IEEE.

关键词： Feature extraction

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Simulation research on microscopic remaining oil distribution in high water cut oilfield 6

Simulation research on microscopic remaining oil distributio...

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6th International conference on Advances in Energy Resources and Environment Engineering (ICAESEE)

作者： Pan, Shaowei Wang, Zhaoyang Luo, Haining Xian Shiyou Univ Sch Comp Sci Xian 710065 Shaanxi Peoples R China PetroChina Res Inst Explorat & Dev Tarim Oilfield Co Korla 841000 Xinjiang Peoples R China

Most of the oilfields in China have entered the stage of high water cut nowadays, and it is very difficult to enhance oil recovery in these oilfields. In this paper, combined with the image processing technology, the visualization technology of 3D data field and the seepage mechanics theory, a simulation model on microscopic remaining oil distribution has been constructed. After that, the remaining oil distribution of a high water cut oilfield in south China in three different microscopic pore throat networks, namely big pore and coarse throat, medium pore and medium throat and small pore and fine throat, was simulated. through the simulation results, the conclusions are drawn: compared with the small pore and fine throat network, the development resistance of oil in the big pore and coarse throat network and the medium pore and medium throat network is smaller under the same water-flooding development conditions;compared with the big pore and coarse throat network, the development resistance of oil in the medium pore and medium throat network is larger, which leads to more remaining oil left. the simulation on microscopic remaining oil distribution is of great significance for enhancing oil recovery in high water cut oilfields of China.

关键词： three dimensional computer graphics

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Laplacian Pyramid Network for Transferring Picture into Van Gogh's Style 22

Laplacian Pyramid Network for Transferring Picture into Van ...

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Proceedings of the 2022 6th International conference on Electronic Information Technology and computer Engineering

作者： Youpeng Cheng School of Information Science and Engineering East China University of Science and Technology China

ISBN: (纸本)9781450397148

Artistic style transfer has become a heated field for machine learning research, where many methods exist to solve the problem. However, transferring a picture into another style is still a complicated image processing task. Each method has its advantages and disadvantages. Laplacian Pyramid Network (Lapstyle), is a novel feed-forward method that could provide artistic style transfer with high efficiency. In this paper, several of Van Gogh's paintings and various pictures are set as style images and content images, including portraits, scenery, and so on, to look at their performance in different situations. the result is that Lapstyle performs well when the style image is a distant view with simple color. At the same time, the stylized picture is awful when the style image is a portrait. Even though the style is paintings of the same artist, the performance varies significantly. In conclusion, the style image for Lapstyle is paintings with simple colors and few details, and portraits are unsuitable.

关键词： Machine Learning Lapstyle Style transfer Van Gogh computer vision

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image Enhancement Using HSV Color Space, DWT, and BiHE Techniques

Image Enhancement Using HSV Color Space, DWT, and BiHE Techn...

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International conference on Engineering Technology and their Applications (IICETA)

作者： Tameem Hameed Obaida Salman Abd Kadum Fallah H. Najjar Hassan M. Al-Jawahry Technical Institute of Najaf Al-Furat Al-Awsat Technical University Najaf Iraq Faculty of Engineering School of Computing University Technology of Malaysia Johar Bahru Malaysia Faculty of Engineering The Islamic University Najaf Iraq

image enhancement is vital in computer vision and image processing applications. this study introduces an innovative approach combining HSV color space, Discrete Wavelet Transforms (DWT), and Bi Histogram Equalization (BiHE) techniques. By converting the image from RGB to HSV, we separate color and intensity information, allowing us to enhance the intensity component for improved visual quality. the DWT is then applied to decompose the image into frequency sub bands, enabling targeted manipulation of specific frequency components to enhance image details and overall appearance. Additionally, the BiHE technique enhances contrast while preserving local image details. the proposed method was evaluated on various images, including tissue, football, Lena, and yellow lily. the results show that the proposed method achieved high Peak Signal-to-Noise Ratio (PSNR) values, with tissue achieving 32.1853 dB, football achieving 30.5452 dB, Lena achieving 27.1462 dB, and yellow lily achieving 25.2015 dB. the Mean Squared Error (MSE) values were also low, with tissue having an MSE of 0.0006, football having an MSE of 0.0009, Lena having an MSE of 0.0022, and yellow lily having an MSE of 0.0031. these results demonstrate the effectiveness of the proposed method in enhancing the visual quality, details, and contrast of the images while maintaining naturalness and avoiding artifacts. Experimental results demonstrate that our method outperforms traditional techniques, significantly enhancing visual quality, details, and contrast while maintaining naturalness and avoiding artifacts.

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