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2017 IEEE International Conference on Image Processing (ICIP)最新文献

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Feature extraction using gaze of participants for classifying gender of pedestrians in images 基于参与者注视特征提取的图像行人性别分类
2017 IEEE International Conference on Image Processing (ICIP) Pub Date : 2017-09-18 DOI: 10.1109/ICIP.2017.8296942
Riku Matsumoto, Hiroki Yoshimura, Masashi Nishiyama, Y. Iwai
{"title":"Feature extraction using gaze of participants for classifying gender of pedestrians in images","authors":"Riku Matsumoto, Hiroki Yoshimura, Masashi Nishiyama, Y. Iwai","doi":"10.1109/ICIP.2017.8296942","DOIUrl":"https://doi.org/10.1109/ICIP.2017.8296942","url":null,"abstract":"Human participants look at informative regions when attempting to identify the gender of a pedestrian in images. In our preliminary experiment, participants mainly looked at the head and chest regions when classifying gender in these images. Thus, we hypothesized that the regions in which participants gaze locations were clustered would contain discriminative features for a gender classifier. In this paper, we discuss how to reveal and use gaze locations for the gender classification of pedestrian images. Our method acquired the distribution of gaze locations from various participants while they manually classified gender. We termed this distribution a gaze map. To extract discriminative features, we assigned large weights to regions with clusters of gaze locations in the gaze map. Our experiments show that this gaze-based feature extraction method significantly improved the performance of gender classification when combined with either a deep learning or a metric learning classifier.","PeriodicalId":229602,"journal":{"name":"2017 IEEE International Conference on Image Processing (ICIP)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121731095","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
Spectral pre-adaptation for two-step arbitrary-shape-support image restoration 两步任意形状支持图像恢复的光谱预适应
2017 IEEE International Conference on Image Processing (ICIP) Pub Date : 2017-09-18 DOI: 10.1109/ICIP.2017.8296936
Chaoqun Dong, J. Portilla
{"title":"Spectral pre-adaptation for two-step arbitrary-shape-support image restoration","authors":"Chaoqun Dong, J. Portilla","doi":"10.1109/ICIP.2017.8296936","DOIUrl":"https://doi.org/10.1109/ICIP.2017.8296936","url":null,"abstract":"In recent years, boundary problems associated to image restoration have attracted the attention of imaging scientists. We explore here the case of arbitrary boundary shapes (like blurred-background images), starting from a general discussion and advancing towards realist conditions, with simulations and real photographic images. We describe our Spectral-Pre-Adaptation (SPA) method, and compare it to the successful unconstrained boundary conditions Alternating Direction Method of Multipliers (UBC ADMM) method. Preliminary results indicate that SPA, combined with efficient restoration algorithms, such as Constrained Dynamic L2-relaxed L0, may set the new state-of-the-art, in performance and computational terms, for this kind of problems.","PeriodicalId":229602,"journal":{"name":"2017 IEEE International Conference on Image Processing (ICIP)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128876917","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
MVIRT, a toolbox for manifold-valued image restoration MVIRT，一个工具箱的流形值图像恢复
2017 IEEE International Conference on Image Processing (ICIP) Pub Date : 2017-09-18 DOI: 10.1109/ICIP.2017.8296271
Ronny Bergmann
{"title":"MVIRT, a toolbox for manifold-valued image restoration","authors":"Ronny Bergmann","doi":"10.1109/ICIP.2017.8296271","DOIUrl":"https://doi.org/10.1109/ICIP.2017.8296271","url":null,"abstract":"In many real life application measured data takes its values on Riemannian manifolds. For the special case of the Euclidean space this setting includes the classical grayscale and color images. Like these classical images, manifold-valued data might suffer from measurement errors in form of noise or missing data. In this paper we present the manifold-valued image restoration toolbox (MVIRT) that provides implementations of classical image processing tasks. Based on recent developments in variational methods for manifold-valued image processing methods, like total variation regularization, the toolbox provides easy access to work with these algorithms. The toolbox is implemented in Matlab, open source, and easily extendible, e.g. with own manifolds, noise models or further algorithms. This paper introduces the main mathematical methods as well as numerical examples.","PeriodicalId":229602,"journal":{"name":"2017 IEEE International Conference on Image Processing (ICIP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129585844","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: 11
Deep partial person re-identification via attention model 基于注意模型的深度局部人再识别
2017 IEEE International Conference on Image Processing (ICIP) Pub Date : 2017-09-18 DOI: 10.1109/ICIP.2017.8296918
Junyeong Kim, C. Yoo
{"title":"Deep partial person re-identification via attention model","authors":"Junyeong Kim, C. Yoo","doi":"10.1109/ICIP.2017.8296918","DOIUrl":"https://doi.org/10.1109/ICIP.2017.8296918","url":null,"abstract":"This paper considers a novel algorithm referred to as deep partial person re-identification (DPPR) for partial person re-identification where only a part of a person is observed and full body images are available for identification. The DPPR is based on an end-to-end deep model which make use of convolutional neural network (CNN), RoI Pooling layer and attention model. The RoI Pooling layer enables the extraction of feature vector corresponding to predefined part of input image. The attention model selects a subset of CNN feature vectors. For qualitative evaluation of proposed model, data from CUHK03 are randomly cropped in constructing p-CUHK03. Experimental results show that DPPR outperforms our baseline model on p-CUHK03.","PeriodicalId":229602,"journal":{"name":"2017 IEEE International Conference on Image Processing (ICIP)","volume":"55 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114130422","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: 6
Image quality assessment to enhance infrared face recognition 图像质量评估，增强红外人脸识别
2017 IEEE International Conference on Image Processing (ICIP) Pub Date : 2017-09-18 DOI: 10.1109/ICIP.2017.8296392
Camilo G. Rodriguez Pulecio, H. Benítez-Restrepo, A. Bovik
{"title":"Image quality assessment to enhance infrared face recognition","authors":"Camilo G. Rodriguez Pulecio, H. Benítez-Restrepo, A. Bovik","doi":"10.1109/ICIP.2017.8296392","DOIUrl":"https://doi.org/10.1109/ICIP.2017.8296392","url":null,"abstract":"Automatic quality evaluation of infrared images has not been researched as extensively as for images of the visible spectrum. Moreover, there is a lack of studies on the influence of degradation of image quality on the performance of computer vision tasks operating on thermal images. Here, we quantify the impact of common image distortions on infrared face recognition, and present a method for aggregating perceptual quality-aware features to improve the identification rates. We use Natural Scene Statistics (NSS) to detect degradation of infrared images, and to adapt the face recognition algorithm to the quality of the test image. The proposed approach applied to a face identification algorithm based on thermal signatures yielded an improvement of rank one recognition rates between 11% and 19%. These results confirm the relevance of image quality assessment for improving biometric identification systems that use thermal images.","PeriodicalId":229602,"journal":{"name":"2017 IEEE International Conference on Image Processing (ICIP)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127226079","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: 12
Complex nonseparable oversampled lapped transform for sparse representation of millimeter wave radar image 复不可分过采样重叠变换在毫米波雷达图像稀疏表示中的应用
2017 IEEE International Conference on Image Processing (ICIP) Pub Date : 2017-09-18 DOI: 10.1109/ICIP.2017.8296776
S. Nagayama, Shogo M. Ramats, Hiroyoshi Yamada, Yuuichi S. Giyama
{"title":"Complex nonseparable oversampled lapped transform for sparse representation of millimeter wave radar image","authors":"S. Nagayama, Shogo M. Ramats, Hiroyoshi Yamada, Yuuichi S. Giyama","doi":"10.1109/ICIP.2017.8296776","DOIUrl":"https://doi.org/10.1109/ICIP.2017.8296776","url":null,"abstract":"This work generalizes an existing framework of nonseparable oversampled lapped transforms (NSOLTs) to effectively represent complex-valued images. The original NSOLTs are lattice-structure-based redundant transforms, which satisfy the linear-phase, compact-supported and real-valued property. The lattice structure is able to constitute a Parseval tight frame with rational redundancy and to generate a dictionary with directional atomic images. In this study, a generalized structure of NSOLTs is proposed to cover complex-valued atomic images. The novel transform is referred to as a complex NSOLT (CNSOLT). The effectiveness of the structure is verified by evaluating the sparse approximation performance using the iterative hard thresholding (IHT) algorithm for a millimeter wave radar image.","PeriodicalId":229602,"journal":{"name":"2017 IEEE International Conference on Image Processing (ICIP)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122074876","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: 3
Lifting-based Illumination Adaptive Transform (LIAT) using mesh-based illumination modelling 基于网格照明建模的基于提升的照明自适应变换(LIAT)
2017 IEEE International Conference on Image Processing (ICIP) Pub Date : 2017-09-18 DOI: 10.1109/ICIP.2017.8296834
Maryam Haghighat, R. Mathew, A. Naman, D. Taubman
{"title":"Lifting-based Illumination Adaptive Transform (LIAT) using mesh-based illumination modelling","authors":"Maryam Haghighat, R. Mathew, A. Naman, D. Taubman","doi":"10.1109/ICIP.2017.8296834","DOIUrl":"https://doi.org/10.1109/ICIP.2017.8296834","url":null,"abstract":"State-of-the-art video coding techniques employ block-based illumination compensation to improve coding efficiency. In this work, we propose a Lifting-based Illumination Adaptive Transform (LIAT) to exploit temporal redundancy among frames that have illumination variations, such as the frames of low frame rate video or multi-view video. LIAT employs a mesh-based spatially affine model to represent illumination variations between two frames. In LIAT, transformed frames are jointly compressed, together with illumination information, into a layered rate-distortion optimal codestream, using the JPEG2000 format. We show that the LIAT framework significantly improves compression efficiency of temporal subband transforms for both predictive and more general transforms with predict and update steps.","PeriodicalId":229602,"journal":{"name":"2017 IEEE International Conference on Image Processing (ICIP)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122753977","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: 3
Scalable light field compression scheme using sparse reconstruction and restoration 基于稀疏重建和恢复的可扩展光场压缩方案
2017 IEEE International Conference on Image Processing (ICIP) Pub Date : 2017-09-18 DOI: 10.1109/ICIP.2017.8296883
F. Hawary, C. Guillemot, D. Thoreau, Guillaume Boisson
{"title":"Scalable light field compression scheme using sparse reconstruction and restoration","authors":"F. Hawary, C. Guillemot, D. Thoreau, Guillaume Boisson","doi":"10.1109/ICIP.2017.8296883","DOIUrl":"https://doi.org/10.1109/ICIP.2017.8296883","url":null,"abstract":"This paper describes a light field scalable compression scheme based on the sparsity of the angular Fourier transform of the light field. A subset of sub-aperture images (or views) is compressed using HEVC as a base layer and transmitted to the decoder. An entire light field is reconstructed from this view subset using a method exploiting the sparsity of the light field in the continuous Fourier domain. The reconstructed light field is enhanced using a patch-based restoration method. Then, restored samples are used to predict original ones, in a SHVC-based SNR-scalable scheme. Experiments with different datasets show a significant bit rate reduction of up to 24% in favor of the proposed compression method compared with a direct encoding of all the views with HEVC. The impact of the compression on the quality of the all-in-focus images is also analyzed showing the advantage of the proposed scheme.","PeriodicalId":229602,"journal":{"name":"2017 IEEE International Conference on Image Processing (ICIP)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116102852","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: 26
Principal curvature of point cloud for 3D shape recognition 用于三维形状识别的点云主曲率
2017 IEEE International Conference on Image Processing (ICIP) Pub Date : 2017-09-18 DOI: 10.1109/ICIP.2017.8296353
J. Lev, Joo-Hwee Lim, Nizar Ouarti
{"title":"Principal curvature of point cloud for 3D shape recognition","authors":"J. Lev, Joo-Hwee Lim, Nizar Ouarti","doi":"10.1109/ICIP.2017.8296353","DOIUrl":"https://doi.org/10.1109/ICIP.2017.8296353","url":null,"abstract":"In the recent years, we experienced the proliferation of sensors for retrieving depth information on a scene, such as LIDAR or RGBD sensors (Kinect). However, it is still a challenge to identify the meaning of a specific point cloud to recognize the underlying object. Here, we wonder if it is possible to define a global feature for an object that is robust to noise, sampling and occlusion. We propose a local measure based on curvature. We called it Principal Curvature because rather than using the Gaussian curvature we keep the information of the two principal curvatures. In our approach, this local information is then aggregated as histograms that are compared with a Chi-2 metric. Results show the robustness of the method particularly when only few points are available. This means that our approach can be very suitable to match objects even with a limited resolution and possible occlusions. It could be particularly adapted to recognize objects with LIDAR inputs.","PeriodicalId":229602,"journal":{"name":"2017 IEEE International Conference on Image Processing (ICIP)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125930678","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: 2
Adaptive thresholding hosvd algorithm with iterative regularization for image denoising 基于迭代正则化的自适应阈值hosvd图像去噪算法
2017 IEEE International Conference on Image Processing (ICIP) Pub Date : 2017-09-18 DOI: 10.1109/ICIP.2017.8296831
Rodion Movchan, Zhengwei Shen
{"title":"Adaptive thresholding hosvd algorithm with iterative regularization for image denoising","authors":"Rodion Movchan, Zhengwei Shen","doi":"10.1109/ICIP.2017.8296831","DOIUrl":"https://doi.org/10.1109/ICIP.2017.8296831","url":null,"abstract":"In this paper, we propose a very simple 3D patch stack based image denoising method by Higher Order Singular Value Decomposition (HOSVD). We used the idea of iterative regularization from spatially adaptive iterative singular-value thresholding(SAIST) to design our algorithm, which indicates more faster convergence speed than some other methods. By using the parallel computing technique for implementing the algorithm, the computational complexity is highly reduced. The experiments also show good PNSR result with different noise levels.","PeriodicalId":229602,"journal":{"name":"2017 IEEE International Conference on Image Processing (ICIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128630951","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: 2
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