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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

共 691 条记录，以下是121-130 订阅

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image MATCHING USING ENHANCEMENT OFFSETS WITH ADAPTIVE PARAMETER SELECTION VIA HISTOGRAM ANALYSIS

IMAGE MATCHING USING ENHANCEMENT OFFSETS WITH ADAPTIVE PARAM...

引用

IEEE international conference on image processing (ICIP)

作者： Psaila, Jonathan Thanh Hong-Phuoc Guan, Ling Ryerson Univ Toronto ON Canada

ISBN: (纸本)9781665441155

In this paper, an algorithm for improved image matching is presented using enhancement offsets that are created dynamically with adaptive parameter selection. The algorithm operates by analyzing an input image and building image offsets to improve colour contrast, non-uniform illumination and lack of detail which allows for additional keypoints to be detected by numerous different detectors. Keypoint detection testing on the enhanced images is conducted, as well as image matching using SIFT and SURF. It is quantitatively shown that the proposed algorithm results in visual improvements, as well as in additional, stronger keypoints being detected in all images irrespective of the detector used. Matching experiments are conducted using the Webcam and Oxford/EF datasets wherein the proposed algorithm consistently outputs more correct matches and achieves higher or comparable matching accuracy over other related enhancement algorithms using SIFT and SURF.

关键词： image matching keypoint enhancement local features feature extraction parameter optimization

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Evaluating Convolutional Neural Networks for No-Reference image Quality Assessment 4

Evaluating Convolutional Neural Networks for No-Reference Im...

引用

4th international conference on signal processing and Information Security (ICSPIS)

作者： Apostolidis, Kyriakos D. Polyzos, Theodore Grigoriadis, Ioannis Papakostas, George A. Int Hellen Univ Dept Comp Sci MLV Res Grp Kavala 65404 Greece

ISBN: (纸本)9781665437967

In the past years, deep learning evolution has helped the development of computer vision systems. However, the quality of images plays a significant role in the effectiveness of these systems and it would be useful to know the quality of the images that are imported into our systems. No-reference image quality assessment is a challenging procedure, which tries to predict the quality of an image without using any reference image. In this paper, we evaluate the performance of widely used deep learning models in no-reference image quality assessment. To that end, we used transfer learning on 8 pre-trained models which we fit into 3 datasets related to image quality assessment. The performance of these models was studied in terms of mean absolute percentage error (MAPE). Although most of the models performed reasonably well in a MAPE range of 15% to 40% depending on the dataset used, the best performing one in a single dataset was DenseNet201 with a MAPE of 9.8%, while the overall best performing model was ResNet50.

关键词： image quality assessment convolutional neural networks transfer learning

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image Denoising and Colorization Based on Plug and Play Framework 6

Image Denoising and Colorization Based on Plug and Play Fram...

引用

6th international conference on signal and image processing, ICSIP 2021

作者： Guo, Weicheng Chen, Fei Cheng, Hang Fuzhou University College of Mathematics and Computer Science Fuzhou China

ISBN: (纸本)9780738133737

A digital image has become an essential carrier of information transmission, but it will inevitably be destroyed during the transmission process. For image denoising or image colorization, there are already many excellent algorithms, and there have been promising developments in the field of deep learning. However, in these classic methods, few algorithms can accomplish image denoising and colorization simultaneously. This paper proposes a plug and play framework to solve this problem, which combines the advantages of generative adversarial networks and plug and play framework. Moreover, we design a lightweight conditional GAN to achieve the purpose of training with fewer computing resources. Experiments on CFP and Landscape datasets show that this method can effectively complete image denoising and image colorization tasks. © 2021 IEEE.

关键词： Generative adversarial networks

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Research and analysis of image enhancement algorithm in the classification of rock thin section images 3

Research and analysis of image enhancement algorithm in the ...

引用

3rd international conference on Intelligent Control, Measurement and signal processing and Intelligent Oil Field, ICMSP 2021

作者： Huaizhou, Yang Danyang, Xu School of Computer Science Xi'An Shiyou University Xi'an China

ISBN: (纸本)9781665437158

The structure of rock flakes is complex and difficult to be classified accurately. The proposed method to solve the problem is to use an image enhancement algorithm to enhance the rock slice image. In the study, the neural network ResNet50, which has a significant effect on fine-grained classification, was used to construct the rock cast thin section image classifier, and three image enhancement algorithms, CutOut, MixUp, and CutMix, were used to enhance the rock thin section image. The rock slice images used in the data set are from Ordos, and are divided into five categories according to the size of the rock. The experimental result obtained was that the CutOut algorithm performs well on the data set, and the accuracy of the classifier was as high as 93.39%, which is 1.3% higher than the result of only using ResNet50 for classification. The experimental results show the effectiveness of the image enhancement algorithm in the classification of rock slice images. © 2021 IEEE.

关键词： image classification

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GIID-NET: GENERALIZABLE image INPAINTING DETECTION NETWORK

GIID-NET: GENERALIZABLE IMAGE INPAINTING DETECTION NETWORK

引用

IEEE international conference on image processing (ICIP)

作者： Wu, Haiwei Zhou, Jiantao Univ Macau Dept Comp & Informat Sci State Key Lab Internet Things Smart City Macau Peoples R China

ISBN: (纸本)9781665441155

Deep learning (DL) has demonstrated its powerful capabilities in the field of image inpainting, which could produce visually plausible results. Meanwhile, the malicious use of advanced image inpainting tools (e.g. removing key objects to report fake news) has led to increasing threats to the reliability of image data. To fight against the inpainting forgeries, in this work, we propose a novel end-to-end Generalizable image Inpainting Detection Network (GIID-Net), to detect the inpainted regions at pixel accuracy. Extensive experimental results are presented to validate the superiority of the proposed GIID-Net, compared with the state-of-the-art competitors. Our results would suggest that common artifacts are shared across diverse image inpainting methods.

关键词： Inpainting forensics generalizability deep neural networks

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Multimodal medical image fusion in DWT domain using Otsu's Thresholding 5

Multimodal medical image fusion in DWT domain using Otsu's T...

引用

5th international conference on Computer, Communication, and signal processing, ICCCSP 2021

作者： Rajangam, Vijayarajan Sangeetha, N. Gnanasivam, P. Innovation and Research Vellore Institute of Technology Centre for Healthcare Advancement Chennai India Saveetha Institute of Medical and Applied Sciences Saveetha School of Engineering Department of Cse Chennai India Jerusalem College of Engineering Department of Ece Chennai India

ISBN: (纸本)9781665432771

image fusion algorithms have an important role in the integration of multimodal medical images or slices of the same modality to eliminate redundancy in clinical image analysis. This paper presents a threshold-based fusion scheme for DWT coefficients with the help of Otsu's method. This threshold-based fusion rule is applied alternatively to both approximate and detail coefficients of the source images and the results are analyzed for fusion performance using the metrics such as peak signal to noise ratio, fusion factor, average quality index and average fusion factor. The experiments conducted on the fusion of multimodal and same modality images effectively demonstrate the performance of the proposed threshold based fusion scheme over other fusion methods. © 2021 IEEE.

关键词： image analysis

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image steganography algorithm based on image colorization

Image steganography algorithm based on image colorization

引用

2021 international conference on signal image processing and Communication, ICSIPC 2021

作者： Bi, Xinliang Yao, Wujun Zhang, Zhuo Huang, Siyuan Liu, Jia Chen, Bowei Engineering University of PAP College of Cryptographic Engineering Xi'an710086 China Network and Information Security Key Laboratory of PAP Xi'an710086 China

ISBN: (数字)9781510645417

ISBN: (纸本)9781510645400

Aiming at the problems of stego images with modification traces and unnatural image generation in the image steganography algorithm based on deep learning, this paper proposes an image steganography algorithm based on image coloring. It first embeds secret information in a gray image using traditional image steganography algorithms, and then uses image coloring algorithms to color the gray image, disguising the modification of the gray image as the result of image embedding, highlighting the coloring features of the algorithm, reducing the steganographic characteristics of the algorithm, and guide the enemy to mistake the algorithm for the image coloring algorithm, thereby reducing the probability of steganography being discovered. Experimental results show that this algorithm has good results in steganographic capacity and generated images. © 2021 SPIE.

关键词： Steganography

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IMPROVED MULTICLASS ADABOOST FOR image CLASSIFICATION: THE ROLE OF TREE OPTIMIZATION

IMPROVED MULTICLASS ADABOOST FOR IMAGE CLASSIFICATION: THE R...

引用

IEEE international conference on image processing (ICIP)

作者： Zharmagambetov, Arman Gabidolla, Magzhan Carreira-Perpinan, Miguel A. Univ Calif Merced Dept Comp Sci & Engn Merced CA 95343 USA

ISBN: (纸本)9781665441155

Decision tree boosting is considered as an important and widely recognized method in image classification, despite dominance of the deep learning based approaches in this area. Provided with good image features, it can produce a powerful model with unique properties, such as strong predictive power, scalability, interpretability, etc. In this paper, we propose a novel tree boosting framework which capitalizes on the idea of using shallow, sparse and yet powerful oblique decision trees (trained with recently proposed Tree Alternating Optimization algorithm) as the base learners. We empirically show that the resulting model achieves better or comparable performance (both in terms of accuracy and model size) against established boosting algorithms such as gradient boosting or AdaBoost in number of benchmarks. Further, we show that such trees can directly and efficiently handle multiclass problems without using one-vs-all strategy employed by most of the practical boosting implementations.

关键词： image classification adaboost oblique trees tree optimization

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Pothole Detection Using Machine Learning algorithms 15

Pothole Detection Using Machine Learning Algorithms

引用

15th international conference on signal processing and Communication Systems (ICSPCS)

作者： Al Masud, A. K. M. Jobayer Sharin, Saraban Tasnim Shawon, Khandokar Farhan Tanvir Zaman, Zakia Daffodil Int Univ Dept Comp Sci & Engn Dhaka Bangladesh Univ New South Wales Sydney NSW Australia

ISBN: (纸本)9781665436991

Potholes are one of the greatest problems on the roads of Bangladesh. Stuck water on the roads and overloaded vehicles are mainly responsible for surface decay and erosion of rocks under the road surface which creates potholes that cause a lot of accidents and risks for general people. A system is needed that will detect potholes not only to alert the drivers but also to alert the authorities. In this paper, we emphasized on detecting the potholes using pothole image data and normal road image data. At first, we collected the data and then preprocessed them by resizing and rescaling. We used MobileNetV2 to extract the features and we reduced the dimension of the features using PCA, LDA, and t-SNE. Finally, for training, we applied five Machine Learning classification algorithms which are Support Vector Machine (SVM), Logistic Regression, Random Forest, Elastic Net, and Decision Tree. The results from Logistic Regression, Elastic Net, and Support Vector Machine (SVM) are relatively better than the other two. After setting up the three of them side by side we observed that Support Vector Machine (SVM) works best for our system and gave an accuracy of 99%.

关键词： Pothole Detection image processing Support Vector Machine

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SEMASUPERPIXEL: A MULTI-CHANNEL PROBABILITY-DRIVEN SUPERPIXEL SEGMENTATION METHOD

SEMASUPERPIXEL: A MULTI-CHANNEL PROBABILITY-DRIVEN SUPERPIXE...

引用

IEEE international conference on image processing (ICIP)

作者： Wang, Xuehui Zhao, Qingyun Fan, Lei Zhao, Yuzhi Wang, Tiantian Yan, Qiong Chen, Long Sun Yat Sen Univ Guangzhou Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Northwestern Univ Evanston IL 60208 USA City Univ Hong Kong Hong Kong Peoples R China SenseTime Res Beijing Peoples R China

ISBN: (纸本)9781665441155

Superpixel, an efficient image segmentation approach, aggregates a group of similar pixels into the same cluster. Existing superpixel algorithms still mainly focus on the color information while ignoring the semantic distribution knowledge. In this paper, we propose a semantic information-driven method that adopts multi-channel semantic probabilities for superpixel segmentation. By conducting statistical analysis on the semantic output and then formulating the distance measure, the prior knowledge of the semantic with a dynamic confidence value could be utilized by our method during the global update effectively. Extensive experimental evaluations show that our method achieves a leading segmentation quality and convergence speed, compared to other five state-of-the-art algorithms, as measured by boundary recall, undersegmentation error, and explained variation.

关键词： Superpixel segmentation image segmentation image processing

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