2020 IEEE International Conference on Image Processing (ICIP)最新文献_第2页

Block-Size Dependent Overlapped Block Motion Compensation 块大小相关的重叠块运动补偿

2020 IEEE International Conference on Image Processing (ICIP) Pub Date : 2020-10-01 DOI: 10.1109/ICIP40778.2020.9191338

Yoshitaka Kidani, Kei Kawamura, Kyohei Unno, S. Naito

引用次数: 0

Circular Shift: An Effective Data Augmentation Method For Convolutional Neural Network On Image Classification 圆移位:卷积神经网络图像分类的有效数据增强方法

2020 IEEE International Conference on Image Processing (ICIP) Pub Date : 2020-10-01 DOI: 10.1109/ICIP40778.2020.9191303

Kailai Zhang, Zheng Cao, Ji Wu

{"title":"Circular Shift: An Effective Data Augmentation Method For Convolutional Neural Network On Image Classification","authors":"Kailai Zhang, Zheng Cao, Ji Wu","doi":"10.1109/ICIP40778.2020.9191303","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9191303","url":null,"abstract":"In this paper, we present a novel and effective data augmentation method for convolutional neural network(CNN) on image classification tasks. CNN-based models such as VGG, Resnet and Densenet have achieved great success on image classification tasks. The common data augmentation methods such as rotation, crop and flip are always used for CNN, especially under the lack of data. However, in some cases such as small images and dispersed feature of objects, these methods have limitations and even can decrease the classification performance. In this case, an operation that has lower risk is important for the performance improvement. Addressing this problem, we design a data augmentation method named circular shift, which provides variations for the CNN-based models but does not lose too much information. Three commonly used image datasets are chosen for the evaluation of our proposed operation, and the experiment results show consistent improvement on different CNN-based models. What is more, our operation can be added to the current set of augmentation operation and achieves further performance improvement.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126839010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

An Image-based Method to Predict Surface Enhanced Raman Spectroscopy Sensor Quality 基于图像的表面增强拉曼光谱传感器质量预测方法

2020 IEEE International Conference on Image Processing (ICIP) Pub Date : 2020-10-01 DOI: 10.1109/ICIP40778.2020.9190905

Yiming Zuo, Yang Lei, S. Barcelo

引用次数: 1

Attention Unet++: A Nested Attention-Aware U-Net for Liver CT Image Segmentation 一种用于肝脏CT图像分割的嵌套注意力感知U-Net

2020 IEEE International Conference on Image Processing (ICIP) Pub Date : 2020-10-01 DOI: 10.1109/ICIP40778.2020.9190761

Chen Li, Yusong Tan, W. Chen, Xin Luo, Yuanming Gao, Xiaogang Jia, Zhiying Wang

引用次数: 69

Quaternion Harris For Multispectral Keypoint Detection Harris四元数用于多光谱关键点检测

2020 IEEE International Conference on Image Processing (ICIP) Pub Date : 2020-10-01 DOI: 10.1109/ICIP40778.2020.9191302

Giorgos Sfikas, D. Ioannidis, D. Tzovaras

引用次数: 2

Segmentation Algorithm of the Valid Region in Fisheye Images Using Edge and Region Information 基于边缘和区域信息的鱼眼图像有效区域分割算法

2020 IEEE International Conference on Image Processing (ICIP) Pub Date : 2020-10-01 DOI: 10.1109/ICIP40778.2020.9191294

Tongxin Du, Bin Fang, Mingliang Zhou, Henjun Zhao, Weizhi Xian, X. Wu

引用次数: 1

Open-Set Metric Learning For Person Re-Identification In The Wild 野外人物再识别的开集度量学习

2020 IEEE International Conference on Image Processing (ICIP) Pub Date : 2020-10-01 DOI: 10.1109/ICIP40778.2020.9190744

Arindam Sikdar, Dibyadip Chatterjee, Arpan Bhowmik, A. Chowdhury

引用次数: 3

Super-Resolution by Image Enhancement Using Texture Transfer 使用纹理转移图像增强的超分辨率

2020 IEEE International Conference on Image Processing (ICIP) Pub Date : 2020-10-01 DOI: 10.1109/ICIP40778.2020.9190844

Jose Jaena Mari Ople, Daniel Stanley Tan, A. Azcarraga, Chao-Lung Yang, K. Hua

{"title":"Super-Resolution by Image Enhancement Using Texture Transfer","authors":"Jose Jaena Mari Ople, Daniel Stanley Tan, A. Azcarraga, Chao-Lung Yang, K. Hua","doi":"10.1109/ICIP40778.2020.9190844","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9190844","url":null,"abstract":"Recent deep learning approaches in single image super-resolution (SISR) can generate high-definition textures for super-resolved (SR) images. However, they tend to hallucinate fake textures and even produce artifacts. An alternative to SISR, reference-based SR (RefSR) approaches use high-resolution (HR) reference (Ref) images to provide HR details that are missing in the low-resolution (LR) input image. We propose a novel framework that leverages existing SISR approaches and enhances them with RefSR. Specifically, we refine the output of SISR methods using neural texture transfer, where HR features are queried from the Ref images. The query is conducted by computing the similarity of textural and semantic features between the input image and the Ref images. The most similar HR features, patch-wise, to the LR image is used to augment the SR image through an augmentation network. In the case of dissimilar Ref images from the LR input image, we prevent performance degradation by including the similarity scores in the input features of the network. Furthermore, we use random texture patches during the training to condition our augmentation network to not always trust the queried texture features. Different from past RefSR approaches, our method can use arbitrary Ref images and its lower-bound performance is based on the SR image. We showcase that our method drastically improves the performance of the base SISR approach.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123023252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Semantic-Preserving Image Compression 保持语义的图像压缩

2020 IEEE International Conference on Image Processing (ICIP) Pub Date : 2020-10-01 DOI: 10.1109/ICIP40778.2020.9191247

Neel Patwa, Nilesh A. Ahuja, Srinivasa Somayazulu, Omesh Tickoo, S. Varadarajan, S. Koolagudi

{"title":"Semantic-Preserving Image Compression","authors":"Neel Patwa, Nilesh A. Ahuja, Srinivasa Somayazulu, Omesh Tickoo, S. Varadarajan, S. Koolagudi","doi":"10.1109/ICIP40778.2020.9191247","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9191247","url":null,"abstract":"Video traffic comprises a large majority of the total traffic on the internet today. Uncompressed visual data requires a very large data rate; lossy compression techniques are employed in order to keep the data-rate manageable. Increasingly, a significant amount of visual data being generated is consumed by analytics (such as classification, detection, etc.) residing in the cloud. Image and video compression can produce visual artifacts, especially at lower data-rates, which can result in a significant drop in performance on such analytic tasks. Moreover, standard image and video compression techniques aim to optimize perceptual quality for human consumption by allocating more bits to perceptually significant features of the scene. However, these features may not necessarily be the most suitable ones for semantic tasks. We present here an approach to compress visual data in order to maximize performance on a given analytic task. We train a deep auto-encoder using a multi-task loss to learn the relevant embeddings. An approximate differentiable model of the quantizer is used during training which helps boost the accuracy during inference. We apply our approach on an image classification problem and show that for a given level of compression, it achieves higher classification accuracy than that obtained by performing classification on images compressed using JPEG. Our approach also outperforms the relevant state-of-the-art approach by a significant margin.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114570679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Deep Person Identification Using Spatiotemporal Facial Motion Amplification 基于时空面部运动放大的深度人识别

2020 IEEE International Conference on Image Processing (ICIP) Pub Date : 2020-10-01 DOI: 10.1109/ICIP40778.2020.9191281

K. Gkentsidis, Theodora Pistola, N. Mitianoudis, N. Boulgouris

引用次数: 0