检索结果-内蒙古大学图书馆

Frequency-aware camouflaged object detection

ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2023年第2期19卷 1-16页

作者： Lin, Jiaying Tan, Xin Xu, Ke Ma, Lizhuang Lau, Rynsonw. H. City Univ Hong Kong Hong Kong Peoples R China East China Normal Univ Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

camouflaged object detection (COD) is important as it has various potential applications. Unlike salient object detection (SOD), which tries to identify visually salient objects, COD tries to detect objects that are visually very similar to the surrounding background. We observe that recent COD methods try to fuse features from different levels using some context aggregation strategies originally developed for SOD. Such an approach, however, may not be appropriate for COD as these existing context aggregation strategies are good at detecting distinctive objects while weakening the features from less discriminative objects. To address this problem, we propose in this article to exploit frequency learning to suppress the confusing high-frequency texture information, to help separate camouflaged objects from their surrounding background, and a frequency-based method, called FBNet, for camouflaged object detection. Specifically, we design a frequency-aware context aggregation (FACA) module to suppress high-frequency information and aggregate multi-scale features from a frequency perspective, an adaptive frequency attention (AFA) module to enhance the features of the learned important frequency components, and a gradient-weighted loss function to guide the proposed method to pay more attention to contour details. Experimental results show that our model outperforms relevant state-of-the-art methods.

关键词： camouflaged object detection frequency learning

来源：评论

学校读者我要写书评

暂无评论

FindNet: Can You Find Me? Boundary-and-Texture Enhancement Network for camouflaged object detection

引用

IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 6396-6411页

作者： Li, Peng Yan, Xuefeng Zhu, Hongwei Wei, Mingqiang Zhang, Xiao-Ping Qin, Jing Nanjing Univ Aeronaut & Astronaut Sch Comp Sci & Technol Nanjing 210016 Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada Hong Kong Polytech Univ Sch Nursing Hong Kong Peoples R China

camouflaged objects share very similar colors but have different semantics with the surroundings. Cognitive scientists observe that both the global contour (i.e., boundary) and the local pattern (i.e., texture) of camouflaged objects are key cues to help humans find them successfully. Inspired by the cognitive scientist's observation, we propose a novel boundary-and-texture enhancement network (FindNet) for camouflaged object detection (COD) from single images. Different from most of existing COD methods, FindNet embeds both the boundary-and-texture information into the camouflaged object features. The boundary enhancement (BE) module is leveraged to focus on the global contour of the camouflaged object, and the texture enhancement (TE) module is utilized to focus on the local pattern. The enhanced features from BE and TE, which complement each other, are combined to obtain the final prediction. FindNet performs competently on various conditions of COD, including slightly clear boundaries but very similar textures, fuzzy boundaries but slightly differentiated textures, and simultaneous fuzzy boundaries and textures. Experimental results exhibit clear improvements of FindNet over fifteen state-of-the-art methods on four benchmark datasets, in terms of detection accuracy and boundary clearness. The code will be publicly released.

关键词： Codes Convolution Shape Image color analysis Fuses Image edge detection Semantics FindNet camouflaged object detection boundary enhancement module texture enhancement module

来源：评论

学校读者我要写书评

暂无评论

FRBNet: feature-iterative reinforcement and boundary-directed network for camouflaged object detection

引用

MULTIMEDIA SYSTEMS 2024年第5期30卷 1-19页

作者： Liu, Yitong Zhang, Jindong Wang, Yiming Jin, Jingyi Sun, Wenyue Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China

camouflaged object detection (COD) aims to segment objects blending into their surroundings. Existing studies are impeded by noise interference in shallow layers and insufficient exploration of features. This poses challenges in detecting highly camouflaged scenarios, frequently leading to vague and incomplete edges and shapes. In this paper, we propose a novel Feature-iterative Reinforcement and Boundary-directed Network (FRB-Net), a dual-supervised architecture designed to preserve more information while minimizing semantic noise. FRB-Net consists of two branches: one dedicated to edge inference and the other to context reasoning. We propose the Gateway Boundary Clue (GBC) module for edge inference, which utilizes gateway learning to reduce extraneous noise. For context-based reasoning, we have developed the Contextual Integration Exploration (CIE) module based on multiplication. This advanced operation enhances the understanding of adjacent features, strengthens semantic associations, and reduces unnecessary distractions. To seamlessly integrate these branches, we propose a Figure-Ground Reinforcement (FGR) module. The major innovation of FGR is the edge and non-edge attentive strategy. It enhances exploration by alternately focusing on boundaries and non-edge details via iterative feedback. Extensive experiments have been conducted on four benchmark datasets. Additionally, we have performed ablation studies and parameter analyses to further validate the effectiveness and efficiency of our approach. It demonstrates that FRB-Net significantly outperforms 24 state-of-the-art methods. Additionally, it surpasses the leading method by 3.6% in weighted F-measure on CAMO.

关键词： camouflaged object detection Feature-iterative reinforcement Gateway mechanism Edge and non-edge focused

来源：评论

学校读者我要写书评

暂无评论

A Universal Multi-View Guided Network for Salient object and camouflaged object detection

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第11期34卷 11184-11197页

作者： Song, Xiaogang Zhang, Pengfei Lu, Xiaofeng Hei, Xinhong Liu, Rongrong Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Univ Shaanxi Prov Engn Res Ctr Human Machine Integrat Intelligent R Xian 710048 Peoples R China Fourth Mil Med Univ Dept Dermatol Xijing Hosp Xian 710032 Peoples R China

Salient object detection and camouflaged object detection have attracted increasing attention due to their significant practical applications. While these two domains share similarities in recognition methods and object characteristics, they also exhibit distinctions. In this paper, we propose a novel multi-view guided network for camouflaged and salient object detection, utilizing the Transformer as the backbone network for feature extraction. Capitalizing on shared characteristics, we introduce a CNN-based multi-view encoder and a multi-view fusion module, enhancing the acquisition of multi-perspective information while minimizing the increase in computational cost. Moreover, recognizing domain differences, we incorporate an attention exploration module, seamlessly integrating multi-view features with globally extracted features from the backbone network. This integration involves simultaneous exploration from both positional and color perspectives, unearthing valuable information to identify salient and camouflaged objects. Our approach maximizes shared characteristics between the two tasks while effectively addressing their differences, leading to precise object identification-be it for camouflaged or salient objects. Extensive experiments on nine challenging benchmark datasets demonstrate the superior performance of our method across four widely used evaluation metrics, outperforming 34 state-of-the-art methods. Furthermore, we applied our method to other visually-related tasks, such as polyp segmentation and defect detection. The results further demonstrate the versatility of our model. The source code and results of our method are available at https://***/1900zpf/MVGNet.

关键词： Salient object detection camouflaged object detection instance segmentation

来源：评论

学校读者我要写书评

暂无评论

Boundary enhancement and refinement network for camouflaged object detection

引用

MACHINE VISION AND APPLICATIONS 2024年第5期35卷 107-107页

作者： Xia, Chenxing Cao, Huizhen Gao, Xiuju Ge, Bin Li, Kuan-Ching Fang, Xianjin Zhang, Yan Liang, Xingzhu Anhui Univ Sci & Technol Coll Comp Sci & Engn Huainan 232001 Peoples R China Anhui Univ Sci & Technol Affiliated Hosp 1 Huainan Peoples Hosp 1 Huainan Anhui Peoples R China Anhui Purvar Bigdata Technol Co Ltd Huainan 232001 Peoples R China Anhui Univ Sci & Technol Coll Elect & Informat Engn Huainan Anhui Peoples R China Providence Univ Dept Comp Sci & Informat Engn Taiwan Peoples R China Anhui Univ Sch Elect & Informat Engn Hefei Anhui Peoples R China Anhui Univ Sci & Technol Inst Environm Friendly Mat & Occupat Hlth Wuhu Anhui Peoples R China

camouflaged object detection aims to locate and segment objects accurately that conceal themselves well in the environment. Despite the advancements in deep learning methods, prevalent issues persist, including coarse boundary identification in complex scenes and the ineffective integration of multi-source features. To this end, we propose a novel boundary enhancement and refinement network named BERNet, which mainly consists of three modules for enhancing and refining boundary information: an asymmetric edge module (AEM) with multi-groups dilated convolution block (GDCB), a residual mixed pooling enhanced module (RPEM), and a multivariate information interaction refiner module (M2IRM). AEM with GDCB is designed to obtain rich boundary clues, where different dilation rates are used to expand the receptive field. RPEM is capable of enhancing boundary features under the guidance of boundary cues to improve the detection accuracy of small and multiple camouflaged objects. M2IRM is introduced to refine the side-out prediction maps progressively under the supervision of the ground truth by the fusion of multi-source information. Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of our BERNet with competitive state-of-the-art methods under the most evaluation metrics.

关键词： camouflaged object detection Boundary clues Feature aggregation

来源：评论

学校读者我要写书评

暂无评论

Deep Texton-Coherence Network for camouflaged object detection

引用

IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 5155-5165页

作者： Zhai, Wei Cao, Yang Xie, HaiYong Zha, Zheng-Jun Univ Sci & Technol China Dept Informat Sci & Technol Hefei 230000 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230022 Anhui Peoples R China

camouflaged object detection is a challenging visual task since the appearance and morphology of foreground objects and background regions are highly similar in nature. Recent CNN-based studies gradually integrated the high-level semantic information and the low-level local features of images through hierarchical and progressive structures to achieve camouflaged object detection. However, these methods ignore the spatial statistical properties of the local context, which is a critical cue for distinguishing and describing camouflaged objects. To address this problem, we propose a novel Deep Texton-Coherence Network (DTC-Net) that leverages the spatial organization of textons in the foreground and background regions as discriminative cues for camouflaged object detection. Specifically, a Local Bilinear module (LB) is devised to obtain the robust representation of texton to trivial details and illumination changes, by replacing the classic first-order linearization operations with bilinear second-order statistical operations in the convolution process. Next, these texton representations are associated with a Spatial Coherence Organization module (SCO) to capture irregular spatial coherence via a deformable convolutional strategy, and then the descriptions of the textons extracted by the LB module are used as weights to suppress features that are spatially adjacent but have different representations. Finally, the texton-coherence representation is integrated with the original features at different levels to achieve camouflaged object detection. Evaluation on the three most challenging camouflaged object detection datasets demonstrats the superiority of the proposed model when compared to the state-of-the-art methods. Furthermore, our ablation studies and performance analyses demonstrate the effectiveness of the texton-coherence module.

关键词： camouflaged object detection texture representation deep learning

来源：评论

学校读者我要写书评

暂无评论

SDRNet: camouflaged object detection with independent reconstruction of structure and detail

引用

KNOWLEDGE-BASED SYSTEMS 2024年 299卷

作者： Guan, Juwei Fang, Xiaolin Zhu, Tongxin Qian, Weiqi Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Jiangsu Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Minist Educ Dhaka Peoples R China

The simultaneous reconstruction of structure and detail is a prevalent strategy in camouflaged object detection. However, the reconstruction features required for structure and detail exhibit disparities, a facet overlooked in existing methods. Therefore, we present a novel methodology, termed SDRNet, which employs a dual -branch approach for the independent reconstruction of structure and detail, aiming to discern camouflaged targets and their edges. Specifically, we propose a decomposition block to segregate encoded features into distinct structure and detail components. Furthermore, structure enhancement block and detail enhancement block are proposed as feature enhancement methods to boost the capacity of structure and detail information. Subsequently, the introduced structure fusion block and detail fusion block progressively amalgamate the enhanced features. Additionally, the shared feature block is designed to serve as a bridge for the interaction between structure and detail information. Experimental results demonstrate that SDRNet outperforms existing state-of-the-art methods significantly on benchmark datasets. Our code is available at https://***/whyandbecause/SDRNet/.

关键词： camouflaged object detection Structure and detail reconstruction Feature enhancement Feature fusion Feature decomposition

来源：评论

学校读者我要写书评

暂无评论

Rethinking camouflaged object detection: Models and Datasets

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第9期32卷 5708-5724页

作者： Bi, Hongbo Zhang, Cong Wang, Kang Tong, Jinghui Zheng, Feng Northeast Petr Univ Dept Elect Informat Engn Daqing 163318 Peoples R China Northeast Petr Univ Elect Engn Informat Dept Daqing 163318 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

camouflaged object detection (COD) is an emerging visual detection task, which aims to locate and distinguish the disguised target in complex backgrounds by imitating the human visual detection system. Recently, COD has attracted increasing attention in computer vision, and a few models of camouflaged object detection have been successfully explored. However, most existing works primarily focus on modeling camouflaged object detection over in-depth analyzing existing COD structures. To the best of our knowledge, a systematic review for COD has not been publicly reported, especially for recently proposed deep learning-based COD models. To make up this vacancy, we firstly proposed a comprehensive review on both COD models and public benchmark datasets and provide potential directions for future COD studies. Specifically, we conduct a comprehensive summary of 39 existing COD models from 1998 to 2021. And then, to facilitate subsequent research on COD, we classify the existing structures into two categories, 27 traditional handcrafted feature-based structures and 12 structures based on deep learning. In addition, we further group traditional handcrafted feature-based structures into six sub-classes based on the detection mechanism: texture, color, motion, intensity, optical flow, and multi-modal fusion. Furthermore, we take an in-depth analysis of the deep learning-based structure based on both detection motivation and detection performance and evaluate the performance of each structure. Moreover, we sum up four widely used COD datasets and describe the details of each one. Finally, we also discuss the limitations of COD and the corresponding solutions to improve detection accuracy. We still mention the relevant applications of camouflaged object detection and its future research directions to promote the development of camouflaged object detection.

关键词： object detection Feature extraction Visualization Image color analysis Task analysis Optical imaging Image segmentation camouflaged object detection benchmark handcrafted feature-based deep learning

来源：评论

学校读者我要写书评

暂无评论

Integrating Part-object Relationship and Contrast for camouflaged object detection

引用

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2021年 16卷 5154-5166页

作者： Liu, Yi Zhang, Dingwen Zhang, Qiang Han, Jungong Changzhou Univ Sch Comp Sci & Artificial Intelligence Aliyun Sch Big Data Changzhou 213164 Jiangsu Peoples R China Changzhou Univ Sch Software Changzhou 213164 Jiangsu Peoples R China Northwestern Polytech Univ Sch Automat Xian 710071 Shaanxi Peoples R China Xidian Univ Sch Mechanoelect Engn Xian 710071 Shaanxi Peoples R China Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales

object detectors that solely rely on image contrast are struggling to detect camouflaged objects in images because of the high similarity between camouflaged objects and their surroundings. To address this issue, in this paper, we investigate the role of the part-object relationship for camouflaged object detection. Specifically, we propose a Part-object relationship and Contrast Integrated Network (POCINet) covering both search and identification stages, where each stage adopts an appropriate scheme to engage the contrast information and part-object relational knowledge for camouflaged pattern decoding. Besides, we bridge these two stages via a Search-to-Identification Guidance (SIG) module, in which the search result, as well as decoded semantic knowledge, jointly enhances the features encoding ability of the identification stage. Experimental results demonstrate the superiority of our algorithm on three datasets. Notably, our algorithm raises $F_\beta $ of the best existing method by approximately 17 points on the CPD1K dataset. The source code will be released soon.

关键词： object detection Search problems Feature extraction Decoding Pipelines Semantics Image segmentation camouflaged object detection contrast part-object relationships encoder-decoder multi-stage

来源：评论

学校读者我要写书评

暂无评论

Depth context aggregation network for camouflaged object detection

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2024年第31期83卷 75689-75708页

作者： Liu, Xiaogang Song, Shuang Nanjing Tech Univ Coll Comp & Informat Engn Nanjing 211800 Peoples R China

camouflaged object detection (COD) intends to find concealed objects hidden in the surroundings. COD is challenging for it has to discriminate the minor difference between foreground and background. In most existing methods, convolutional neural network (CNN)-based approaches are proposed to overcome this challenge. However, they have limitations in extracting semantic features of input images and learning global contexts. This study presents a novel Transformer and CNN-based Depth Context Aggregation network (call DCA-Net) for concealed object detection and segmentation. This network uses Swin Transformer as backbone to extract globalized semantic features. Dilated Reception (DR) module is designed to connect the encoder and decoder. A hybrid loss function is used for optimizing the model. In particular, to supplement the intersection over union (IOU) loss, Complementary (C) loss is introduced. By analyzing different metrics, comprehensive comparisons with previous methods are conducted on four public datasets, such as CAMO, CHAMELEON, COD10K, and NC4K. Experimental analysis proves that the proposed DCA-Net achieves state-of-the-art performance.

关键词： camouflaged object detection Deep learning Concealed object detection Convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：