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Front Matter: Volume 10615

Hui Yu, Junyu Dong
2018 Ninth International Conference on Graphic and Image Processing (ICGIP 2017)  
using a Base 36 numbering system employing both numerals and letters.  ...  The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee.  ...  10615 06 A speeded-up saliency region-based contrast detection method for small targets 10615 07 Fabric defect detection based on visual saliency using deep feature and low-rank recovery 10615 08 Lane  ... 
doi:10.1117/12.2316542 fatcat:tdaw76jq6nehpnttiga2lcuhna

A fusion of salient and convolutional features applying healthy templates for MRI brain tumor segmentation

Petra Takács, Levente Kovács, Andrea Manno-Kovacs
2020 Multimedia tools and applications  
Based on a saliency map built using the pseudo-color templates, combination models are proposed, fusing the saliency map with convolutional neural networks' prediction maps to improve predictions and to  ...  The proposed method introduces a novel combination of multiple MRI modalities used as pseudo-color channels for highlighting the potential tumors.  ...  To view a copy of this licence, visit http://creativecommonshorg/licenses/by/4.0/.  ... 
doi:10.1007/s11042-020-09871-w fatcat:7l4i3cno7vda3cufcu37raswly

LCNN: Low-level Feature Embedded CNN for Salient Object Detection [article]

Hongyang Li, Huchuan Lu, Zhe Lin, Xiaohui Shen, Brian Price
2015 arXiv   pre-print
In this paper, we propose a novel deep neural network framework embedded with low-level features (LCNN) for salient object detection in complex images.  ...  We utilise the advantage of convolutional neural networks to automatically learn the high-level features that capture the structured information and semantic context in the image.  ...  To address this issue, [35] use a recurrent convolutional neural network to consider large contexts.  ... 
arXiv:1508.03928v1 fatcat:wbmvm5eqije4tm4h5s2a2usjhm

Research on Salient Object Detection using Deep Learning and Segmentation Methods

2019 International journal of recent technology and engineering  
While many models have been proposed and several applications have emerged, yet a deep understanding of achievements and issues is lacking.  ...  Detecting and segmenting salient objects in natural scenes, often referred to as salient object detection has attracted a lot of interest in computer vision and recently various heuristic computational  ...  First, integrate the public Indoor dataset and the private Frames of Videos (FoVs) dataset to train a Convolutional Neural Network (CNN).  ... 
doi:10.35940/ijrte.b1046.0982s1119 fatcat:6ofq53vb7zhx7boq4ndpraphs4

2019 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 29

2019 IEEE transactions on circuits and systems for video technology (Print)  
., +, TCSVT Dec. 2019 3487-3500 A Novel Patch Variance Biased Convolutional Neural Network for No-Reference Image Quality Assessment.  ...  ., +, TCSVT Nov. 2019 3444-3453 A Novel Patch Variance Biased Convolutional Neural Network for No-Ref- erence Image Quality Assessment.  ... 
doi:10.1109/tcsvt.2019.2959179 fatcat:2bdmsygnonfjnmnvmb72c63tja

Multi-layer attention for person re-identification

Yuele Zhang, Jie Guo, Zheng Huang, Weidong Qiu, Hexiaohui Fan, W. Anggono
2019 MATEC Web of Conferences  
In this paper, we introduce a novel attention network which explores spatial attention in a convolutional neural network. Our algorithm learns the visual attention in multi-layer feature maps.  ...  However, most of those algorithms treat different areas without distinction.  ...  Our contributions are: (1) We propose a spatial attention-based convolutional neural network for person re-identification task. Existing models generally ignore the selection of attentive regions.  ... 
doi:10.1051/matecconf/201927702025 fatcat:ehczc2xdajgdbow4jgjjlmb6v4

2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30

2020 IEEE transactions on circuits and systems for video technology (Print)  
Zhang, X., TCSVT Jan. 2020 217-231 Hu, X., see Zhu, L., TCSVT Oct. 2020 3358-3371 Hu, Y., Lu, M., Xie, C., and Lu, X  ...  ., see Sepas-Moghaddam, A., TCSVT Dec. 2020 4496-4512 Hassanpour, H., see Khosravi, M.H., TCSVT Jan. 2020 48-58 Hatzinakos, D., see 2900-2916 Hayat, M., see 2900-2916 He, C., Hu, Y., Chen, Y., Fan  ...  Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network.  ... 
doi:10.1109/tcsvt.2020.3043861 fatcat:s6z4wzp45vfflphgfcxh6x7npu

Deep Progressive Hashing for Image Retrieval

Jiale Bai, Bingbing Ni, Minsi Wang, Yang Shen, Hanjiang Lai, Chongyang Zhang, Lin Mei, Chuanping Hu, Chen Yao
2017 Proceedings of the 2017 ACM on Multimedia Conference - MM '17  
This paper proposes a novel recursive hashing scheme, in contrast to conventional one-off based hashing algorithms.  ...  Inspired by human's nonsalient-to-salient perception path, the proposed hashing scheme generates a series of binary codes based on progressively expanded salient regions.  ...  Second, each region in the sequence is connected to deep convolutional neural networks based hashing unit, forming basic building blocks of the recursive hashing scheme.  ... 
doi:10.1145/3123266.3123280 dblp:conf/mm/BaiNWSLZMHY17 fatcat:zmcavxe6bzcu5dtr4kkyqetnxu

Pooling Methods in Deep Neural Networks, a Review [article]

Hossein Gholamalinezhad, Hossein Khosravi
2020 arXiv   pre-print
Convolutional Neural Network is a special type of DNN consisting of several convolution layers, each followed by an activation function and a pooling layer.  ...  There are a lot of methods for the implementation of pooling operation in Deep Neural Networks. In this paper, we reviewed some of the famous and useful pooling methods.  ...  Acknowledgment This work is supported by the research unit of the Shahaab company ( The authors would like to thank the staff of this company for their help.  ... 
arXiv:2009.07485v1 fatcat:ufskh27dpbap7hf7kcewdidavi

Front Matter: Volume 10033

2016 Eighth International Conference on Digital Image Processing (ICDIP 2016)  
using a Base 36 numbering system employing both numerals and letters.  ...  Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  novel biometric image encryption algorithm based on compressed sensing and dualtree complex wavelet transform [10033-165] 10033 2U A SIFT-based robust watermarking scheme in DWT-SVD domain using majority  ... 
doi:10.1117/12.2257252 fatcat:v2ipfp2mp5gedjypzpecahpo7e

Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art

Chao Li, Jun Li, Yafei Li, Lingmin He, Xiaokang Fu, Jingjing Chen, Xiaokang Zhou
2021 Security and Communication Networks  
model-based algorithms.  ...  Second, defect detection methods are categorized into traditional algorithms and learning-based algorithms, and traditional algorithms are further categorized into statistical, structural, spectral, and  ...  Inspired by the successful use of deep convolutional neural networks (DCNN) for target detection, we propose a wide-and-light network structure called WALNet.  ... 
doi:10.1155/2021/9948808 fatcat:l2bt7y7mlnhq3iololodyhg5j4

Exploiting inter-image similarity and ensemble of extreme learners for fixation prediction using deep features

Hamed R. Tavakoli, Ali Borji, Jorma Laaksonen, Esa Rahtu
2017 Neurocomputing  
This paper presents a novel fixation prediction and saliency modeling framework based on inter-image similarities and ensemble of Extreme Learning Machines (ELM).  ...  That is, after retrieving a set of similar images for a given image, a saliency predictor is learnt from each of the images in the retrieved image set using an ELM, resulting in an ensemble.  ...  -Tavakoli and Jorma Laaksonen were supported by the Finnish Center of Excellence in Computational Inference Research (COIN).  ... 
doi:10.1016/j.neucom.2017.03.018 fatcat:mmq47eo4ebdgjpxxduxbgl2tqy

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  ., +, TMM 2021 3264-3277 An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks.  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

Image Hashing for Tamper Detection with Multiview Embedding and Perceptual Saliency

Ling Du, Zhen Chen, Yongzhen Ke
2018 Advances in Multimedia  
In this paper, we propose a Multi-View Semi-supervised Hashing algorithm with Perceptual Saliency (MV-SHPS), which explores supervised information and multiple features into hashing learning simultaneously  ...  Our method calculates the image hashing distance by taking into account the perceptual saliency rather than directly considering the distance value between total images.  ...  [16] propose a median filtering detection and steganalysis based on convolutional neural networks (CNNs). Bayar et al.  ... 
doi:10.1155/2018/4235268 fatcat:lrh6tquth5dn7ndiwbakncyfnm

Deep Embedding Features for Salient Object Detection

Yunzhi Zhuge, Yu Zeng, Huchuan Lu
Benefiting from the rapid development of Convolutional Neural Networks (CNNs), some salient object detection methods have achieved remarkable results by utilizing multi-level convolutional features.  ...  In this paper, we propose a novel approach that transforms prior information into an embedding space to select attentive features and filter out outliers for salient object detection.  ...  Acknowledgement This work was supported by the Natural Science Foundation of China under Grant 61725202, 61751212, 61771088, 61632006 and 91538201.  ... 
doi:10.1609/aaai.v33i01.33019340 fatcat:5k6g6uauavbnpnhy3o4jqworda
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