Pub Date : 2016-09-01DOI: 10.1109/ICIP.2016.7533111
Kenta Takahashi, Yusuke Monno, Masayuki Tanaka, M. Okutomi
Color correction is an essential image processing operation that transforms a camera-dependent RGB color space to a standard color space, e.g., the XYZ or the sRGB color space. The color correction is typically performed by multiplying the camera RGB values by a color correction matrix, which often amplifies image noise. In this paper, we propose an effective color correction pipeline for a noisy image. The proposed pipeline consists of two parts; the color correction and denoising. In the color correction part, we utilize spatially varying color correction (SVCC) that adaptively calculates the color correction matrices for each local image block considering the noise effect. Although the SVCC can effectively suppress the noise amplification, the noise is still included in the color corrected image, where the noise levels spatially vary for each local block. In the denoising part, we propose an effective denoising framework for the color corrected image with spatially varying noise levels. Experimental results demonstrate that the proposed color correction pipeline outperforms existing algorithms for various noise levels.
{"title":"Effective color correction pipeline for a noisy image","authors":"Kenta Takahashi, Yusuke Monno, Masayuki Tanaka, M. Okutomi","doi":"10.1109/ICIP.2016.7533111","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7533111","url":null,"abstract":"Color correction is an essential image processing operation that transforms a camera-dependent RGB color space to a standard color space, e.g., the XYZ or the sRGB color space. The color correction is typically performed by multiplying the camera RGB values by a color correction matrix, which often amplifies image noise. In this paper, we propose an effective color correction pipeline for a noisy image. The proposed pipeline consists of two parts; the color correction and denoising. In the color correction part, we utilize spatially varying color correction (SVCC) that adaptively calculates the color correction matrices for each local image block considering the noise effect. Although the SVCC can effectively suppress the noise amplification, the noise is still included in the color corrected image, where the noise levels spatially vary for each local block. In the denoising part, we propose an effective denoising framework for the color corrected image with spatially varying noise levels. Experimental results demonstrate that the proposed color correction pipeline outperforms existing algorithms for various noise levels.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"92 1","pages":"4002-4006"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89966764","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}
Pub Date : 2016-09-01DOI: 10.1109/ICIP.2016.7532637
V. Mygdalis, Alexandros Iosifidis, A. Tefas, I. Pitas
In this paper, we apply One-Class Classification methods in facial image analysis problems. We consider the cases where the available training data information originates from one class, or one of the available classes is of high importance. We propose a novel extension of the One-Class Extreme Learning Machines algorithm aiming at minimizing both the training error and the data dispersion and consider solutions that generate decision functions in the ELM space, as well as in ELM spaces of arbitrary dimensionality. We evaluate the performance in publicly available datasets. The proposed method compares favourably to other state-of-the-art choices.
{"title":"One class classification applied in facial image analysis","authors":"V. Mygdalis, Alexandros Iosifidis, A. Tefas, I. Pitas","doi":"10.1109/ICIP.2016.7532637","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532637","url":null,"abstract":"In this paper, we apply One-Class Classification methods in facial image analysis problems. We consider the cases where the available training data information originates from one class, or one of the available classes is of high importance. We propose a novel extension of the One-Class Extreme Learning Machines algorithm aiming at minimizing both the training error and the data dispersion and consider solutions that generate decision functions in the ELM space, as well as in ELM spaces of arbitrary dimensionality. We evaluate the performance in publicly available datasets. The proposed method compares favourably to other state-of-the-art choices.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"1 1","pages":"1644-1648"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89972185","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}
Pub Date : 2016-09-01DOI: 10.1109/ICIP.2016.7532693
Mahdyar Ravanbakhsh, Hossein Mousavi, Moin Nabi, Mohammad Rastegari, C. Regazzoni
In this paper we introduce a novel method for general semantic segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and semantically coherent image segments. We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space. These binary codes are very robust against noise and non-semantic changes in the image. These binary encoding can be embedded into the CNN as an extra layer at the end of the network. This results in real-time segmentation. To the best of our knowledge our method is the first attempt on general semantic image segmentation using CNN. All the previous papers were limited to few number of category of the images (e.g. PASCAL VOC). Experiments show that our segmentation algorithm outperform the state-of-the-art non-semantic segmentation methods by large margin.
{"title":"CNN-aware binary MAP for general semantic segmentation","authors":"Mahdyar Ravanbakhsh, Hossein Mousavi, Moin Nabi, Mohammad Rastegari, C. Regazzoni","doi":"10.1109/ICIP.2016.7532693","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532693","url":null,"abstract":"In this paper we introduce a novel method for general semantic segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and semantically coherent image segments. We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space. These binary codes are very robust against noise and non-semantic changes in the image. These binary encoding can be embedded into the CNN as an extra layer at the end of the network. This results in real-time segmentation. To the best of our knowledge our method is the first attempt on general semantic image segmentation using CNN. All the previous papers were limited to few number of category of the images (e.g. PASCAL VOC). Experiments show that our segmentation algorithm outperform the state-of-the-art non-semantic segmentation methods by large margin.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"63 1","pages":"1923-1927"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91001204","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}
Pub Date : 2016-09-01DOI: 10.1109/ICIP.2016.7532836
Eliezer Bernart, J. Scharcanski, S. Bampi
This work presents a novel approach for detecting and classifying melanocytic skin lesions on macroscopic images. We oversegment the skin lesions using superpixels, and classify independently each superpixel as a benign or malignant using local and contextual information. The overall superpixel classification results allow to calculate an index of malignancy or benignity for the skin lesion. Using the proposed approach it is possible to discriminate a malignant from a benign skin lesion by recognizing early signs of malignancy in parts of the segmented skin lesion. The experimental results are promising, and show a potential accuracy of 99.34% on a popular data set, outperforming the current state-of-art methods.
{"title":"Segmentation and classification of melanocytic skin lesions using local and contextual features","authors":"Eliezer Bernart, J. Scharcanski, S. Bampi","doi":"10.1109/ICIP.2016.7532836","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532836","url":null,"abstract":"This work presents a novel approach for detecting and classifying melanocytic skin lesions on macroscopic images. We oversegment the skin lesions using superpixels, and classify independently each superpixel as a benign or malignant using local and contextual information. The overall superpixel classification results allow to calculate an index of malignancy or benignity for the skin lesion. Using the proposed approach it is possible to discriminate a malignant from a benign skin lesion by recognizing early signs of malignancy in parts of the segmented skin lesion. The experimental results are promising, and show a potential accuracy of 99.34% on a popular data set, outperforming the current state-of-art methods.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"19 16","pages":"2633-2637"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91446880","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}
Pub Date : 2016-09-01DOI: 10.1109/ICIP.2016.7532510
Yu Zhou, Leida Li, Ke Gu, Yuming Fang, Weisi Lin
Depth-Image-Based-Rendering (DIBR) is fundamental in free-viewpoint 3D video, which has been widely used to generate synthesized views from multi-view images. The majority of DIBR algorithms cause disoccluded regions, which are the areas invisible in original views but emerge in synthesized views. The quality of synthesized images is mainly contaminated by distortions in these disoccluded regions. Unfortunately, traditional image quality metrics are not effective for these synthesized images because they are sensitive to geometric distortions. To solve the problem, this paper proposes an objective quality evaluation method for 3D Synthesized images via Disoccluded Region Discovery (SDRD). A self-adaptive scale transform model is first adopted to preprocess the images on account of the impacts of view distance. Then disoccluded regions are detected by comparing the absolute difference between the preprocessed synthesized image and the warped image of preprocessed reference image. Furthermore, the disoccluded regions are weighted by a weighting function proposed to account for the varying sensitivities of human eyes to the size of disoccluded regions. Experiments conducted on IRCCyN/IVC DIBR image database demonstrate that the proposed SDRD method remarkably outperforms traditional 2D and existing DIBR-related quality metrics.
深度图像渲染(deep - image - based rendering, DIBR)是自由视点三维视频的基础，它被广泛用于从多视点图像生成合成视图。大多数DIBR算法都会产生闭塞区域，即在原始视图中不可见但在合成视图中出现的区域。合成图像的质量主要受到这些去遮挡区域畸变的影响。遗憾的是，传统的图像质量指标对这些合成图像并不有效，因为它们对几何畸变很敏感。为了解决这一问题，本文提出了一种基于去遮挡区域发现(Disoccluded Region Discovery, SDRD)的三维合成图像质量客观评价方法。考虑到视距的影响，首先采用自适应尺度变换模型对图像进行预处理。然后通过比较预处理后的合成图像与预处理后的参考图像的扭曲图像的绝对差值来检测去闭塞区域。此外，利用加权函数对解除遮挡区域进行加权，以考虑人眼对解除遮挡区域大小的不同敏感性。在IRCCyN/IVC DIBR图像数据库上进行的实验表明，提出的SDRD方法显著优于传统的2D和现有的DIBR相关质量指标。
{"title":"Quality assessment of 3D synthesized images via disoccluded region discovery","authors":"Yu Zhou, Leida Li, Ke Gu, Yuming Fang, Weisi Lin","doi":"10.1109/ICIP.2016.7532510","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532510","url":null,"abstract":"Depth-Image-Based-Rendering (DIBR) is fundamental in free-viewpoint 3D video, which has been widely used to generate synthesized views from multi-view images. The majority of DIBR algorithms cause disoccluded regions, which are the areas invisible in original views but emerge in synthesized views. The quality of synthesized images is mainly contaminated by distortions in these disoccluded regions. Unfortunately, traditional image quality metrics are not effective for these synthesized images because they are sensitive to geometric distortions. To solve the problem, this paper proposes an objective quality evaluation method for 3D Synthesized images via Disoccluded Region Discovery (SDRD). A self-adaptive scale transform model is first adopted to preprocess the images on account of the impacts of view distance. Then disoccluded regions are detected by comparing the absolute difference between the preprocessed synthesized image and the warped image of preprocessed reference image. Furthermore, the disoccluded regions are weighted by a weighting function proposed to account for the varying sensitivities of human eyes to the size of disoccluded regions. Experiments conducted on IRCCyN/IVC DIBR image database demonstrate that the proposed SDRD method remarkably outperforms traditional 2D and existing DIBR-related quality metrics.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"12 1","pages":"1012-1016"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84817468","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}
Pub Date : 2016-09-01DOI: 10.1109/ICIP.2016.7532722
Yilin Wang, S. Kum, Chao Chen, A. Kokaram
Banding is a common video artifact caused by compressing low texture regions with coarse quantization. Relatively few previous attempts exist to address banding and none incorporate subjective testing for calibrating the measurement. In this paper, we propose a novel metric that incorporates both edge length and contrast across the edge to measure video banding. We further introduce both reference and non-reference metrics. Our results demonstrate that the new metrics have a very high correlation with subjective assessment and certainly outperforms PSNR, SSIM, and VQM.
{"title":"A perceptual visibility metric for banding artifacts","authors":"Yilin Wang, S. Kum, Chao Chen, A. Kokaram","doi":"10.1109/ICIP.2016.7532722","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532722","url":null,"abstract":"Banding is a common video artifact caused by compressing low texture regions with coarse quantization. Relatively few previous attempts exist to address banding and none incorporate subjective testing for calibrating the measurement. In this paper, we propose a novel metric that incorporates both edge length and contrast across the edge to measure video banding. We further introduce both reference and non-reference metrics. Our results demonstrate that the new metrics have a very high correlation with subjective assessment and certainly outperforms PSNR, SSIM, and VQM.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"31 1","pages":"2067-2071"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77183986","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}
Pub Date : 2016-09-01DOI: 10.1109/ICIP.2016.7533169
Dan Wang, Canxiang Yan, S. Shan, Xilin Chen
The difficult acquisition of labeled data and the misalignment of local matching are major obstacles to apply person re-identification in real scenarios. To alleviate these problems, we propose an unsupervised method, called locality-constrained Earth Mover's Distance (LC-EMD), to learn the optimal measure between image pairs. Specifically, Gaussian mixture models (GMMs) are learned as signatures. By imposing locality constraints, LC-EMD can naturally achieve partial matching between Gaussian components. Moreover, LC-EMD has the analytical solution which can be efficiently computed. Experiments on two public datasets demonstrate LC-EMD is robust to misalignment and performs better than other unsupervised methods.
{"title":"Unsupervised person re-identification with locality-constrained Earth Mover's distance","authors":"Dan Wang, Canxiang Yan, S. Shan, Xilin Chen","doi":"10.1109/ICIP.2016.7533169","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7533169","url":null,"abstract":"The difficult acquisition of labeled data and the misalignment of local matching are major obstacles to apply person re-identification in real scenarios. To alleviate these problems, we propose an unsupervised method, called locality-constrained Earth Mover's Distance (LC-EMD), to learn the optimal measure between image pairs. Specifically, Gaussian mixture models (GMMs) are learned as signatures. By imposing locality constraints, LC-EMD can naturally achieve partial matching between Gaussian components. Moreover, LC-EMD has the analytical solution which can be efficiently computed. Experiments on two public datasets demonstrate LC-EMD is robust to misalignment and performs better than other unsupervised methods.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"115 1","pages":"4289-4293"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77926931","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}
Pub Date : 2016-09-01DOI: 10.1109/ICIP.2016.7532775
Y. Matviychuk, B. Mailhé, Xiao Chen, Qiu Wang, A. Kiraly, N. Strobel, M. Nadar
Denoising is an indispensable step in processing low-dose X-ray fluoroscopic images that requires development of specialized high-quality algorithms able to operate in near real-time. We address this problem with an efficient deep learning approach based on the process-centric view of traditional iterative thresholding methods. We develop a novel trainable patch-based multiscale framework for sparse image representation. In a computationally efficient way, it allows us to accurately reconstruct important image features on multiple levels of decomposition with patch dictionaries of reduced size and complexity. The flexibility of the chosen machine learning approach allows us to tailor the learned basis for preserving important structural information in the image and noticeably minimize the amount of artifacts. Our denoising results obtained with real clinical data demonstrate significant quality improvement and are computed much faster in comparison with the BM3D algorithm.
{"title":"Learning a multiscale patch-based representation for image denoising in X-RAY fluoroscopy","authors":"Y. Matviychuk, B. Mailhé, Xiao Chen, Qiu Wang, A. Kiraly, N. Strobel, M. Nadar","doi":"10.1109/ICIP.2016.7532775","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532775","url":null,"abstract":"Denoising is an indispensable step in processing low-dose X-ray fluoroscopic images that requires development of specialized high-quality algorithms able to operate in near real-time. We address this problem with an efficient deep learning approach based on the process-centric view of traditional iterative thresholding methods. We develop a novel trainable patch-based multiscale framework for sparse image representation. In a computationally efficient way, it allows us to accurately reconstruct important image features on multiple levels of decomposition with patch dictionaries of reduced size and complexity. The flexibility of the chosen machine learning approach allows us to tailor the learned basis for preserving important structural information in the image and noticeably minimize the amount of artifacts. Our denoising results obtained with real clinical data demonstrate significant quality improvement and are computed much faster in comparison with the BM3D algorithm.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"6 2 1","pages":"2330-2334"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78370389","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}
Pub Date : 2016-09-01DOI: 10.1109/ICIP.2016.7533162
Yan Huang, Hao Sheng, Z. Xiong
This work proposes a novel person re-identification method based on Hierarchical Bipartite Graph Matching. Because human eyes observe person appearance roughly first and then goes further into the details gradually, our method abstracts person image from coarse to fine granularity, and finally into a three layer tree structure. Then, three bipartite graph matching methods are proposed for the matching of each layer between the trees. At the bottom layer Non-complete Bipartite Graph matching is proposed to collect matching pairs among small local regions. At the middle layer Semi-complete Bipartite Graph matching is used to deal with the problem of spatial misalignment between two person bodies. Complete Bipartite Graph matching is presented to refine the ranking result at the top layer. The effectiveness of our method is validated on the CAVIAR4REID and VIPeR datasets, and competitive results are achieved on both datasets.
{"title":"Person re-identification based on hierarchical bipartite graph matching","authors":"Yan Huang, Hao Sheng, Z. Xiong","doi":"10.1109/ICIP.2016.7533162","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7533162","url":null,"abstract":"This work proposes a novel person re-identification method based on Hierarchical Bipartite Graph Matching. Because human eyes observe person appearance roughly first and then goes further into the details gradually, our method abstracts person image from coarse to fine granularity, and finally into a three layer tree structure. Then, three bipartite graph matching methods are proposed for the matching of each layer between the trees. At the bottom layer Non-complete Bipartite Graph matching is proposed to collect matching pairs among small local regions. At the middle layer Semi-complete Bipartite Graph matching is used to deal with the problem of spatial misalignment between two person bodies. Complete Bipartite Graph matching is presented to refine the ranking result at the top layer. The effectiveness of our method is validated on the CAVIAR4REID and VIPeR datasets, and competitive results are achieved on both datasets.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"13 1","pages":"4255-4259"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78841906","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}
Pub Date : 2016-09-01DOI: 10.1109/ICIP.2016.7532496
R. Hebbalaguppe, Kevin McGuinness, J. Kuklyte, Rami Albatal, C. Direkoğlu, N. O’Connor
The percentage of false alarms caused by spiders in automated surveillance can range from 20-50%. False alarms increase the workload of surveillance personnel validating the alarms and the maintenance labor cost associated with regular cleaning of webs. We propose a novel, cost effective method to detect false alarms triggered by spiders/webs in surveillance camera networks. This is accomplished by building a spider classifier intended to be a part of the surveillance video processing pipeline. The proposed method uses a feature descriptor obtained by early fusion of blur and texture. The approach is sufficiently efficient for real-time processing and yet comparable in performance with more computationally costly approaches like SIFT with bag of visual words aggregation. The proposed method can eliminate 98.5% of false alarms caused by spiders in a data set supplied by an industry partner, with a false positive rate of less than 1%.
{"title":"Reduction of false alarms triggered by spiders/cobwebs in surveillance camera networks","authors":"R. Hebbalaguppe, Kevin McGuinness, J. Kuklyte, Rami Albatal, C. Direkoğlu, N. O’Connor","doi":"10.1109/ICIP.2016.7532496","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532496","url":null,"abstract":"The percentage of false alarms caused by spiders in automated surveillance can range from 20-50%. False alarms increase the workload of surveillance personnel validating the alarms and the maintenance labor cost associated with regular cleaning of webs. We propose a novel, cost effective method to detect false alarms triggered by spiders/webs in surveillance camera networks. This is accomplished by building a spider classifier intended to be a part of the surveillance video processing pipeline. The proposed method uses a feature descriptor obtained by early fusion of blur and texture. The approach is sufficiently efficient for real-time processing and yet comparable in performance with more computationally costly approaches like SIFT with bag of visual words aggregation. The proposed method can eliminate 98.5% of false alarms caused by spiders in a data set supplied by an industry partner, with a false positive rate of less than 1%.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"1 1","pages":"943-947"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79484024","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}
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