International Conference on Digital Image Processing最新文献_第2页

Deep learning techniques for image recognition of counterfeited luxury handbags materials 基于深度学习技术的奢侈品手袋仿冒材料图像识别

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644669

P. Apipawinwongsa, Y. Limpiyakorn

引用次数: 0

Stochastic recursive gradient descent optimization-based on foreground features of Fisher vector 基于Fisher向量前景特征的随机递归梯度下降优化

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644640

Mohamed Gamal M. Kamaleldin, S. Abu-Bakar, U. U. Sheikh

{"title":"Stochastic recursive gradient descent optimization-based on foreground features of Fisher vector","authors":"Mohamed Gamal M. Kamaleldin, S. Abu-Bakar, U. U. Sheikh","doi":"10.1117/12.2644640","DOIUrl":"https://doi.org/10.1117/12.2644640","url":null,"abstract":"Human action recognition has been one of the hot topics in computer vision both from the handcrafted and deep learning approaches. In the handcrafted approach, the extracted features are encoded for reducing the size of these features. Amonsgt the state-of-the-art approaches is to encode these visual features using the Gaussian mixture model. However, the size of the codebook is an issue in terms of the computation complexity, especially for large-scale data as it requires encoding using a large codebook. In this paper, we introduced the use of different optimizers to reduce the codebook size while boosting its accuracy. To illustrate the performance , first we use the improved dense trajectories (IDT) to extract the handcrafted features. This is followed with encoding the descriptor using Fisher kernel-based codebook using the Gaussian mixture model. Next, the support vector machine is used to classify the categories. We then use and compare five different Stochastic gradient descent optimization techniques to modify the number of Gaussian components. In this manner we are able to select the discriminative foreground features (as represented by the final number of Gaussian components), and omit the background features. Finally, to show the performance improvement of the proposed method, we implement this technique to two datasets UCF101 and HMDB51.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131472787","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: 0

PointIt3D: a benchmark dataset and baseline for pointed object detection task PointIt3D:点目标检测任务的基准数据集和基线

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2645330

Chun-Tse Lin, Hongxin Zhang, Hao Zheng

引用次数: 0

A new zero-watermarking algorithm based on deep learning 一种新的基于深度学习的零水印算法

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643017

Jing Liu, Qian Li, Hui Yang

引用次数: 0

Multi-modal transformer for video retrieval using improved sentence embeddings 基于改进句子嵌入的多模态视频检索变压器

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643741

Zhi Liu, Fangyuan Zhao, Mengmeng Zhang

{"title":"Multi-modal transformer for video retrieval using improved sentence embeddings","authors":"Zhi Liu, Fangyuan Zhao, Mengmeng Zhang","doi":"10.1117/12.2643741","DOIUrl":"https://doi.org/10.1117/12.2643741","url":null,"abstract":"With the explosive growth of the number of online videos, video retrieval becomes increasingly difficult. Multi-modal visual and language understanding based video-text retrieval is one of the mainstream framework to solve this problem. Among them, MMT (Multi-modal Transformer) is a novel and mainstream model. On the language side, BERT (Bidirectional Encoder Representation for Transformers) is used to encode text, where the pretrained BERT will be fine tuned during training. However, there exists a mismatch in this stage. The pre-training tasks of BERT is based on NSP (Next Sentence Prediction) and MLM(masked language model) which have weak correlation with video retrieval. For text encoder will encode text into semantic embeddings. On the visual side, Transformer is used to aggregate multimodal experts of videos. We find that the output of visual transformer is not fully utilized. In this paper, a sentence- BERT model is introduced to substitute BERT model in MMT to improve sentence embeddings efficiency. In addition, a max-pooling layer is adopted after Transformer to improve the utilization efficiency of the output of the model. Experiment results show that the proposed model outperforms MMT.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124678369","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: 1

Real-time ranging of traffic signs for smart car environment perception 面向智能汽车环境感知的交通标志实时测距

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643117

Peng Liu, Wen-hui Pei, Zhi-jia Zhang, Jin-zhi Du, Xiu-tian Wang

引用次数: 0

Infrared and visible images fusion method based on unsupervised learning 基于无监督学习的红外与可见光图像融合方法

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644227

Depeng Zhu, Weida Zhan, Yichun Jiang, Xiaoyu Xu, Renzhong Guo, Yu Chen

{"title":"Infrared and visible images fusion method based on unsupervised learning","authors":"Depeng Zhu, Weida Zhan, Yichun Jiang, Xiaoyu Xu, Renzhong Guo, Yu Chen","doi":"10.1117/12.2644227","DOIUrl":"https://doi.org/10.1117/12.2644227","url":null,"abstract":"Aiming at the problem that the current infrared and visible image fusion based on deep learning has no labels, this paper proposes an infrared and visible image fusion algorithm based on unsupervised learning. This method utilizes the characteristics of unsupervised learning, and introduces infrared image information with high gray value into the visible image to obtain the fusion image. The deep learning network proposed in this paper is composed of 6 layers of convolution blocks, and a dual attention module is also designed to make the fusion image pay more attention to the high gray value area in the infrared image. By introducing skip connections, the shallow features are fused with the deep features, so that the details of the entire fused image are richer and the appearance of halos is reduced. A large number of experimental results show that the fusion method proposed in this paper can accurately highlight the target object while maintaining the visible texture details, enhance the visual effect of the human eye, and improve the target recognition. At the same time, the quantitative experimental results show that the fusion algorithm proposed in this paper has obvious advantages in multiple indicators.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130579981","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: 0

An efficient image encryption scheme based on variable row-columns scrambling and selective block diffusion 一种基于可变行列置乱和选择性块扩散的高效图像加密方案

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644699

W. Huang, Lyu Xin, C. Zhang, Xin Li, Yifei Su, Tao Zeng, Xinyu Wang, Zhennan Xu, Yucong Huang

{"title":"An efficient image encryption scheme based on variable row-columns scrambling and selective block diffusion","authors":"W. Huang, Lyu Xin, C. Zhang, Xin Li, Yifei Su, Tao Zeng, Xinyu Wang, Zhennan Xu, Yucong Huang","doi":"10.1117/12.2644699","DOIUrl":"https://doi.org/10.1117/12.2644699","url":null,"abstract":"With the continuous development of multimedia technology and shooting hardware, more and more high-quality images appear in life and production. At present, there are many image encryption methods to ensure image security, but most of them cannot meet the real-time requirements of large-size image encryption, which causes great obstacles to the application and popularization of image encryption. In this paper, an efficient image encryption method based on variable row-columns scrambling and dynamic threshold selective block diffusion is designed. The pixels are operated in batches row by column and block, and the length of chaotic sequence required is reduced without reducing the security, thus reducing the time consuming of the encryption system. In addition, the modified five-point sampling is adopted to select chaotic sequence, which improves the utilization rate of chaotic sequence and further improves the encryption efficiency. Experimental results show that the proposed method has a higher encryption efficiency than the existing methods with similar security, and has strong practicability.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132422538","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: 0

An algorithm for facial mask area repair based upon deep learning 一种基于深度学习的人脸区域修复算法

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644299

Haoyu Zhang

引用次数: 0

Fusion of infrared and visible sensor images based on anisotropic diffusion and fast guided filter 基于各向异性扩散和快速制导滤波的红外和可见光传感器图像融合

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644537

Jingwen Nan, Zongxi Song, Hao Lei, W. Li

{"title":"Fusion of infrared and visible sensor images based on anisotropic diffusion and fast guided filter","authors":"Jingwen Nan, Zongxi Song, Hao Lei, W. Li","doi":"10.1117/12.2644537","DOIUrl":"https://doi.org/10.1117/12.2644537","url":null,"abstract":"Infrared images and visible images can obtain different image information in the same scene, especially in low-light scenes, infrared images can obtain image information that cannot be obtained by visible images. In order to obtain more useful information in the environment such as glimmer, infrared and visible images can be fused. In this paper, an image fusion method based on anisotropic diffusion and fast guided filter is proposed. Firstly, the source images are decomposed into base layers and detail layers by anisotropic dispersion. Secondly, the visible images and the infrared images are passed through the side window Gaussian filter to obtain the saliency map, and then the saliency map is passed through fast guided filter to obtain the fusion weight. Thirdly, the fused base layers and the fused detail layers are reconstructed to obtain the final fusion image. The application of the side window Gaussian filter helps to reduce the artifact information of the fused image. The results of the proposed algorithm are compared with similar algorithms. The fusion results reveal that the proposed method are outstanding in subjective evaluation and objective evaluation, and are better than other algorithms in standard deviation(STD) and entropy(EN), and other quality metrics are close to the optimal comparison algorithm.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130838315","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: 1