Eurasip Journal on Image and Video Processing最新文献_第6页

Weakly supervised spatial–temporal attention network driven by tracking and consistency loss for action detection 基于跟踪和一致性损失驱动的弱监督时空注意网络

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2022-07-18 DOI: 10.1186/s13640-022-00588-4

Jinlei Zhu, Houjin Chen, Pan Pan, Jia Sun

{"title":"Weakly supervised spatial–temporal attention network driven by tracking and consistency loss for action detection","authors":"Jinlei Zhu, Houjin Chen, Pan Pan, Jia Sun","doi":"10.1186/s13640-022-00588-4","DOIUrl":"https://doi.org/10.1186/s13640-022-00588-4","url":null,"abstract":"This study proposes a novel network model for video action tube detection. This model is based on a location-interactive weakly supervised spatial–temporal attention mechanism driven by multiple loss functions. It is especially costly and time consuming to annotate every target location in video frames. Thus, we first propose a cross-domain weakly supervised learning method with a spatial–temporal attention mechanism for action tube detection. In source domain, we trained a newly designed multi-loss spatial–temporal attention–convolution network on the source data set, which has both object location and classification annotations. In target domain, we introduced internal tracking loss and neighbor-consistency loss; we trained the network with the pre-trained model on the target data set, which only has inaccurate action temporal positions. Although this is a location-unsupervised method, its performance outperforms typical weakly supervised methods, and even shows comparable results with some recent fully supervised methods. We also visualize the activation maps, which reveal the intrinsic reason behind the higher performance of the proposed method.","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":"14 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Performance analysis of different DCNN models in remote sensing image object detection 不同DCNN模型在遥感图像目标检测中的性能分析

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2022-06-07 DOI: 10.1186/s13640-022-00586-6

Hua Liu, Jixiang Du, Yong Zhang, Hongbo Zhang

引用次数: 1

Multi-orientation local ternary pattern-based feature extraction for forensic dentistry 基于多方向局部三元模式的法医牙科特征提取

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2022-05-13 DOI: 10.1186/s13640-022-00584-8

Karunya Rajmohan, Askarunisa Abdul Khader

{"title":"Multi-orientation local ternary pattern-based feature extraction for forensic dentistry","authors":"Karunya Rajmohan, Askarunisa Abdul Khader","doi":"10.1186/s13640-022-00584-8","DOIUrl":"https://doi.org/10.1186/s13640-022-00584-8","url":null,"abstract":"Accurate and automated identification of the deceased victims with dental radiographs plays a significant role in forensic dentistry. The image processing techniques such as segmentation and feature extraction play a crucial role in image retrieval in accordance with the matching image. The raw image undergoes segmentation, feature extraction and distance-based image retrieval. The ultimate goal of the proposed work is the automated quality enhancement of the image by providing advanced enhancement techniques, segmentation techniques, feature extraction, and matching techniques. In this paper, multi-orientation local ternary pattern-based feature extraction is proposed for feature extraction. The grey level difference method (GLDM) is adopted to extract the texture and shape features that are considered for better results. The image retrieval is done by the computation of similarity score using distances such as Manhattan, Euclidean, vector cosine angle, and histogram intersection distance to obtain the optimal match from the database. The manually picked dataset of 200 images is considered for performance analysis. By extracting both the shape features and texture features, the proposed approach achieved maximum accuracy, precision, recall, F-measure, sensitivity, and specificity and lower false-positive and negative values.","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":"21 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Face image synthesis from facial parts 从面部部位合成人脸图像

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2022-05-10 DOI: 10.1186/s13640-022-00585-7

Qiushi Sun, Jingtao Guo, Yi Liu

{"title":"Face image synthesis from facial parts","authors":"Qiushi Sun, Jingtao Guo, Yi Liu","doi":"10.1186/s13640-022-00585-7","DOIUrl":"https://doi.org/10.1186/s13640-022-00585-7","url":null,"abstract":"Recently, inspired by the growing power of deep convolutional neural networks (CNNs) and generative adversarial networks (GANs), facial image editing has received increasing attention and has produced a series of wide-ranging applications. In this paper, we propose a new and effective approach to a challenging task: synthesizing face images based on key facial parts. The proposed approach is a novel deep generative network that can automatically align facial parts with the precise positions in a face image and then output an entire facial image conditioned on the well-aligned parts. Specifically, three loss functions are introduced in this approach, which are the key to making the synthesized realistic facial image: a reconstruction loss to generate image content in an unknown region, a perceptual loss to enhance the network's ability to model high-level semantic structures and an adversarial loss to ensure that the synthesized images are visually realistic. In this approach, the three components cooperate well to form an effective framework for parts-based high-quality facial image synthesis. Finally, extensive experiments demonstrate the superior performance of this method to existing solutions.","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":"27 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An image-guided network for depth edge enhancement 一种用于深度边缘增强的图像引导网络

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2022-04-15 DOI: 10.1186/s13640-022-00583-9

Kuan-Ting Lee, Enyu Liu, J. Yang, Li Hong

引用次数: 0

Automatic kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for malignant potential analysis in complex renal cyst based on CT images 基于CT图像，采用2.5D ResUNet和2.5D DenseUNet自动分割复杂肾囊肿恶性潜能分析

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2022-03-22 DOI: 10.1186/s13640-022-00581-x

Parin Kittipongdaja, Thitirat Siriborvornratanakul

{"title":"Automatic kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for malignant potential analysis in complex renal cyst based on CT images","authors":"Parin Kittipongdaja, Thitirat Siriborvornratanakul","doi":"10.1186/s13640-022-00581-x","DOIUrl":"https://doi.org/10.1186/s13640-022-00581-x","url":null,"abstract":"Bosniak renal cyst classification has been widely used in determining the complexity of a renal cyst. However, it turns out that about half of patients undergoing surgery for Bosniak category III, take surgical risks that reward them with no clinical benefit at all. This is because their pathological results reveal that the cysts are actually benign not malignant. This problem inspires us to use recently popular deep learning techniques and study alternative analytics methods for precise binary classification (benign or malignant tumor) on Computerized Tomography (CT) images. To achieve our goal, two consecutive steps are required–segmenting kidney organs or lesions from CT images then classifying the segmented kidneys. In this paper, we propose a study of kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for efficiently extracting intra-slice and inter-slice features. Our models are trained and validated on the public data set from Kidney Tumor Segmentation (KiTS19) challenge in two different training environments. As a result, all experimental models achieve high mean kidney Dice scores of at least 95% on the KiTS19 validation set consisting of 60 patients. Apart from the KiTS19 data set, we also conduct separate experiments on abdomen CT images of four Thai patients. Based on the four Thai patients, our experimental models show a drop in performance, where the best mean kidney Dice score is 87.60%.","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":"21 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Adaptive response maps fusion of correlation filters with anti-occlusion mechanism for visual object tracking 基于抗遮挡机制的相关滤波器自适应响应映射融合视觉目标跟踪

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2022-03-18 DOI: 10.1186/s13640-022-00582-w

Jianming Zhang, Hehua Liu, Yaoqi He, Li-Dan Kuang, Xi Chen

{"title":"Adaptive response maps fusion of correlation filters with anti-occlusion mechanism for visual object tracking","authors":"Jianming Zhang, Hehua Liu, Yaoqi He, Li-Dan Kuang, Xi Chen","doi":"10.1186/s13640-022-00582-w","DOIUrl":"https://doi.org/10.1186/s13640-022-00582-w","url":null,"abstract":"Despite the impressive performance of correlation filter-based trackers in terms of robustness and accuracy, the trackers have room for improvement. The majority of existing trackers use a single feature or fixed fusion weights, which makes it possible for tracking to fail in the case of deformation or severe occlusion. In this paper, we propose a multi-feature response map adaptive fusion strategy based on the consistency of individual features and fused feature. It is able to improve the robustness and accuracy by building the better object appearance model. Moreover, since the response map has multiple local peaks when the target is occluded, we propose an anti-occlusion mechanism. Specifically, if the nonmaximal local peak is satisfied with our proposed conditions, we generate a new response map which is obtained by moving the center of the region of interest to the nonmaximal local peak position of the response map and re-extracting features. We then select the response map with the largest response value as the final response map. This proposed anti-occlusion mechanism can effectively cope with the problem of tracking failure caused by occlusion. Finally, by adjusting the learning rate in different scenes, we designed a high-confidence model update strategy to deal with the problem of model pollution. Besides, we conducted experiments on OTB2013, OTB2015, TC128 and UAV123 datasets and compared them with the current state-of-the-art algorithms, and the proposed algorithms have impressive advantages in terms of accuracy and robustness.","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":"12 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Random CNN structure: tool to increase generalization ability in deep learning 随机CNN结构：提高深度学习泛化能力的工具

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2022-02-08 DOI: 10.1186/s13640-022-00580-y

B. Świderski, S. Osowski, Grzegorz Gwardys, J. Kurek, M. Słowińska, I. Lugowska

引用次数: 4

Printing and scanning investigation for image counter forensics 用于图像反取证的打印和扫描调查

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2022-02-07 DOI: 10.1186/s13640-023-00610-3

Hailey James, O. Gupta, D. Raviv

引用次数: 0

Printing and scanning investigation for image counter forensics 图像反取证的印刷和扫描调查

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2022-02-07 DOI: 10.1186/s13640-022-00579-5

Hailey Joren, O. Gupta, D. Raviv

引用次数: 2