IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision最新文献_第7页

Real-time video decolorization using bilateral filtering 实时视频脱色使用双边滤波

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836106

Yibing Song, Linchao Bao, Qingxiong Yang

{"title":"Real-time video decolorization using bilateral filtering","authors":"Yibing Song, Linchao Bao, Qingxiong Yang","doi":"10.1109/WACV.2014.6836106","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836106","url":null,"abstract":"This paper presents a real-time decolorization method. Given the human visual systems preference for luminance information, the luminance should be preserved as much as possible during decolorization. As a result, the proposed decolorization method measures the amount of color contrast/detail lost when converting color to luminance. The detail loss is estimated by computing the difference between two intermediate images: one obtained by applying bilateral filter to the original color image, and the other obtained by applying joint bilateral filter to the original color image with its luminance as the guidance image. The estimated detail loss is then mapped to a grayscale image named residual image by minimizing the difference between the image gradients of the input color image and the objective grayscale image that is the sum of the residual image and the luminance. Apparently, the residual image will contain pixels with all zero values (that is the two intermediate images will be the same) only when no visual detail is missing in the luminance. Unlike most previous methods, the proposed decolorization method preserves both contrast in the color image and the luminance. Quantitative evaluation shows that it is the top performer on the standard test suite. Meanwhile it is very robust and can be directly used to convert videos while maintaining the temporal coherence. Specifically it can convert a high-resolution video (1280 × 720) in real time (about 28 Hz) on a 3.4 GHz i7 CPU.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"55 1","pages":"159-166"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90052446","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: 16

Joint hierarchical learning for efficient multi-class object detection 高效多类目标检测的联合分层学习

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836090

Hamidreza Odabai Fard, M. Chaouch, Q. Pham, A. Vacavant, T. Chateau

引用次数: 2

Mining discriminative 3D Poselet for cross-view action recognition 面向交叉视角动作识别的判别性三维波selet挖掘

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836043

Jiang Wang, Xiaohan Nie, Yin Xia, Ying Wu

引用次数: 2

Transfer learning via attributes for improved on-the-fly classification 通过属性迁移学习改进动态分类

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836097

Praveen Kulkarni, Gaurav Sharma, J. Zepeda, Louis Chevallier

{"title":"Transfer learning via attributes for improved on-the-fly classification","authors":"Praveen Kulkarni, Gaurav Sharma, J. Zepeda, Louis Chevallier","doi":"10.1109/WACV.2014.6836097","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836097","url":null,"abstract":"Retrieving images for an arbitrary user query, provided in textual form, is a challenging problem. A recently proposed method addresses this by constructing a visual classifier with images returned by an internet image search engine, based on the user query, as positive images while using a fixed pool of negative images. However, in practice, not all the images obtained from internet image search are always pertinent to the query; some might contain abstract or artistic representation of the content and some might have artifacts. Such images degrade the performance of on-the-fly constructed classifier. We propose a method for improving the performance of on-the-fly classifiers by using transfer learning via attributes. We first map the textual query to a set of known attributes and then use those attributes to prune the set of images downloaded from the internet. This pruning step can be seen as zero-shot learning of the visual classifier for the textual user query, which transfers knowledge from the attribute domain to the query domain. We also use the attributes along with the on-the-fly classifier to score the database images and obtain a hybrid ranking. We show interesting qualitative results and demonstrate by experiments with standard datasets that the proposed method improves upon the baseline on-the-fly classification system.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"168 1","pages":"220-226"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86887252","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: 8

Optical filter selection for automatic visual inspection 光学滤光片选择自动目视检查

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836110

Matthias Richter, J. Beyerer

{"title":"Optical filter selection for automatic visual inspection","authors":"Matthias Richter, J. Beyerer","doi":"10.1109/WACV.2014.6836110","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836110","url":null,"abstract":"The color of a material is one of the most frequently used features in automated visual inspection systems. While this is sufficient for many “easy” tasks, mixed and organic materials usually require more complex features. Spectral signatures, especially in the near infrared range, have been proven useful in many cases. However, hyperspectral imaging devices are still very costly and too slow to use them in practice. As a work-around, off-the-shelve cameras and optical filters are used to extract few characteristic features from the spectra. Often, these filters are selected by a human expert in a time consuming and error prone process; surprisingly few works are concerned with automatic selection of suitable filters. We approach this problem by stating filter selection as feature selection problem. In contrast to existing techniques that are mainly concerned with filter design, our approach explicitly selects the best out of a large set of given filters. Our method becomes most appealing for use in an industrial setting, when this selection represents (physically) available filters. We show the application of our technique by implementing six different selection strategies and applying each to two real-world sorting problems.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"2021 1","pages":"123-128"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87954008","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

Understanding the 3D layout of a cluttered room from multiple images 从多个图像中理解杂乱房间的3D布局

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836035

Sid Ying-Ze Bao, A. Furlan, Li Fei-Fei, S. Savarese

引用次数: 31

Robust optical flow estimation for continuous blurred scenes using RGB-motion imaging and directional filtering 基于rgb运动成像和方向滤波的连续模糊场景鲁棒光流估计

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836022

Wenbin Li, Yang Chen, JeeHang Lee, Gang Ren, D. Cosker

引用次数: 17

Benchmarking large-scale Fine-Grained Categorization 大规模细粒度分类的基准测试

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836056

A. Angelova, Philip M. Long

{"title":"Benchmarking large-scale Fine-Grained Categorization","authors":"A. Angelova, Philip M. Long","doi":"10.1109/WACV.2014.6836056","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836056","url":null,"abstract":"This paper presents a systematic evaluation of recent methods in the fine-grained categorization domain, which have shown significant promise. More specifically, we investigate an automatic segmentation algorithm, a region pooling algorithm which is akin to pose-normalized pooling [31] [28], and a multi-class optimization method. We considered the largest and most popular datasets for fine-grained categorization available in the field: the Caltech-UCSD 200 Birds dataset [27], the Oxford 102 Flowers dataset [19], the Stanford 120 Dogs dataset [16], and the Oxford 37 Cats and Dogs dataset [21]. We view this work from a practitioner's perspective, answering the question: what are the methods that can create the best possible fine-grained recognition system which can be applied in practice? Our experiments provide insights of the relative merit of these methods. More importantly, after combining the methods, we achieve the top results in the field, outperforming the state-of-the-art methods by 4.8% and 10.3% for birds and dogs datasets, respectively. Additionally, our method achieves a mAP of 37.92 on the of 2012 Imagenet Fine-Grained Categorization Challenge [1], which outperforms the winner of this challenge by 5.7 points.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"83 1","pages":"532-539"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89952993","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: 10

Fast dense 3D reconstruction using an adaptive multiscale discrete-continuous variational method 基于自适应多尺度离散连续变分方法的快速密集三维重建

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836118

Z. Kang, G. Medioni

{"title":"Fast dense 3D reconstruction using an adaptive multiscale discrete-continuous variational method","authors":"Z. Kang, G. Medioni","doi":"10.1109/WACV.2014.6836118","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836118","url":null,"abstract":"We present a system for fast dense 3D reconstruction with a hand-held camera. Walking around a target object, we shoot sequential images using continuous shooting mode. High-quality camera poses are obtained offline using structure-from-motion (SfM) algorithm with Bundle Adjustment. Multi-view stereo is solved using a new, efficient adaptive multiscale discrete-continuous variational method to generate depth maps with sub-pixel accuracy. Depth maps are then fused into a 3D model using volumetric integration with truncated signed distance function (TSDF). Our system is accurate, efficient and flexible: accurate depth maps are estimated with sub-pixel accuracy in stereo matching; dense models can be achieved within minutes as major algorithms parallelized on multi-core processor and GPU; various tasks can be handled (e.g. reconstruction of objects in both indoor and outdoor environment with different scales) without specific hand-tuning parameters. We evaluate our system quantitatively and qualitatively on Middlebury benchmark and another dataset collected with a smartphone camera.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"5 9 1","pages":"53-60"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80468986","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

Segmentation and tracking of partial planar templates 部分平面模板的分割与跟踪

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6835731

Abdelsalam Masoud, W. Hoff

引用次数: 2