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Unsupervised domain adaptation without source domain training samples - a maximum margin clustering based approach 16

Unsupervised domain adaptation without source domain trainin...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Saha, Sudipan Banerjee, Biplab Merchant, Shabbir N. Indian Inst Technol Dept Elect Engn Bombay Maharashtra India Indian Inst Technol Dept Comp Sci Roorkee Uttar Pradesh India

ISBN: (纸本)9781450347532

Unsupervised domain adaptation (DA) techniques inherently assume the presence of ample amount of source domain training samples in addition to the target domain test data. the domains are characterized by domain-specific probability distributions governing the data which are substantially different from each other. the goal is to build a task oriented classifier model that performs proportionately in both the domains. In contrary to the standard unsupervised DA setup, we propose a maximum-margin clustering (MMC) based framework for the same which does not consider source domain labeled samples. Instead we formulate it as a joint clustering problem of all the samples from both the domains in a common feature subspace. the Geodesic Flow Kernel (GFK) based subspace projection technique in the Grassmannian manifold is adopted to cast the samples in a domain invariant space. Further, the MMC stage is followed to simultaneously group the data based on the maximization of margins and a classifier is learned for each group. the data overlapping problem is taken care of by specifically learning a SVM-KNN classifier for the potentially unreliable samples per group. We validate the framework on a pair of remote sensing images of different modalities for the purpose of land-cover classification and a generic object dataset for recognition. We observe that the proposed method exhibits performances at par with the fully supervised case for both the tasks but without the requirement of costly annotations.

关键词： Domain adaptation Max-margin clustering Geodesic Flow Kernel

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computer vision – ECCV 2018 Workshops 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Laura Leal-Taixé Stefan Roth

ISBN: (数字)9783030110154

ISBN: (纸本)9783030110147

the six-volume set comprising the LNCS volumes 11129-11134 constitutes the refereed proceedings of the workshops that took place in conjunction with the 15th European conference on computer vision, ECCV 2018, held in Munich, Germany, in September 2018.43 workshops from 74 workshops proposals were selected for inclusion in the proceedings. the workshop topics present a good orchestration of new trends and traditional issues, built bridges into neighboring fields, and discuss fundamental technologies and novel applications.

关键词： image processing and computer vision Pattern Recognition Artificial Intelligence computer graphics Special Purpose and Application-Based Systems

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Single image augmented reality using planar structures in urban environments

Single image augmented reality using planar structures in ur...

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2011 15th Irish Machine vision and image processing conference, IMVIP 2011

作者： McClean, Eric Cao, Yanpeng McDonald, John Department of Computer Science National University of Ireland Maynooth Maynooth CO Kildare Ireland

ISBN: (纸本)9780769546292

In this paper, we present an effective method for integrating 3D augmented reality graphics into single images taken in urban environments. Building facades usually contain a large number of parallel lines aligned along several principal directions. We make use of the images of these 3D parallel lines and their corresponding vanishing points to recover a number of 3D planes from single 2D images of urban environments and then use them to represent the spatial layout of a scene. 3D objects are then aligned with respect to these recovered planes to achieve realistic augmented reality effects. In experiments we applied the proposed method to implement augmented reality in images from a benchmark image dataset of urban environments. © 2011 IEEE.

关键词： Augmented reality

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Discrete Geometry for computer imagery 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Srečko Brlek Christophe Reutenauer Xavier Provençal

ISBN: (数字)9783642043970

ISBN: (纸本)9783642043963

this book constitutes the refereed proceedings of the 15th IAPR International conference on Discrete Geometry for computer imagery, DGCI 2009, held in Montréal, Canada, in September/October 2009. the 42 revised full papers were carefully reviewed and selected from numerous submissions. the papers are organized in topical sections on discrete shape, representation, recognition and analysis; discrete and combinatorial tools for image segmentation and analysis; discrete and combinatorial Topology; models for discrete geometry; geometric transforms; and discrete tomography.

关键词： computer graphics Pattern Recognition image processing and computer vision computer Imaging, vision, Pattern Recognition and graphics Discrete Mathematics in computer Science Algorithm Analysis and Problem Complexity

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A Fourier Transform-based Approach to Fusion High Spatial Resolution Remote Sensing images

A Fourier Transform-based Approach to Fusion High Spatial Re...

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6th indian conference on computer vision, graphics and image processing

作者： Denipote, Juliana G. Paiva, Maria Stela V. Univ Sao Paulo EESC BR-05508 Sao Paulo Brazil

ISBN: (纸本)9781424442195

the modern remote sensing imaging sensors, like those in the IKONOS and QuickBird satellites, are capable of generating panchromatic images with one meter spatial resolution and multiespectral images with good spectral information. the principal objective of fusion in remote sensing is to obtain high-resolution multispectral images that can combine the spectral characteristic of the low-resolution multispectral images with the spatial information of the high-resolution panchromatic images. Traditional fusion methods, such as IHS, PCA and Brovey, can reach good spatial resolution results, but often cause spectral distortion problems. In the literature, it is possible to find some image fusion methods using frequency domain processing, like wavelet-based fusion methods. Although they preserve good spectral information, their spatial visual effects are not satisfactory. IHS fusion method enhanced by Fourier transform presents good spectral and spatial resolution results, but limits the number of spectral bands used in the fusion process to three. In this paper, a method based on Fourier transform is proposed in order to obtain good spatial and spectral resolutions, without limiting the number of bands. In order to compare the spatial and spectral effects of this new method with those of IHS, IHS enhanced by Fourier transform and wavelet-based methods, IKONOS panchromatic and multispectral images were used as the test data. Quantitative measurements such as correlation coefficient, discrepancy and Mean Structural Similarity index were applied to evaluate the quality of the fused images. the results have shown that the new method can keep almost the same spatial resolution as the panchromatic images, and its spectral effect is well preserved.

关键词： remote sensing IKONOS image fusion IHS wavelet transform Fourier transform frequency domain

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A Fuzzy approach to Region-of-interest coding in JPEG-2000 for ATR Applications from High Resolution Satellite images

A Fuzzy approach to Region-of-interest coding in JPEG-2000 f...

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6th indian conference on computer vision, graphics and image processing

作者： Raj, K. C. Emmanuel Sanjay Venkatraman, S. varadan, Geeta Systems Engineering Division Advanced Data Processing Research Institute(ADRIN) Dept. of Space. India

ISBN: (纸本)9781424442195

Automatic Target Recognition (ATR) from satellite imagery involves detection of Foreground (FG) objects from the Background (BG). ATR demands higher fidelity, which in turn requires more bitrate, hence a conventional compression, which does not discriminate targets with the background results in poor detection rate. Here we propose a mechanism to achieve lower bitrate without compromising the detection efficiency. By allowing the background to be coded with lower fidelity than the regions-of-interest (ROIs), significant gains can be achieved in terms of compression and hence in storage space and transmission times. One interesting feature of the new JPEG-2000 image coding standard is support of ROI coding using maximum shift (MaxShift) method, which allows for arbitrarily shaped ROI image compression without shape coding or explicitly transmitting any shape information to the decoder. We propose a Fuzzy C-Means clustering approach to generating arbitrary shape mask so as to cluster the images into regions of varying homogeneity. Homogenous ROIs can be coded at a lower bitrate than the high detail regions. this ensures that the target recognition process is not affected by the compression process. A validation benchmark using 'Fuzzy Feature Vectors' is proposed which checks the foreground objects for features like Rectangularity, Circularity. Elongatedness, Symmetry, Area etc., as compared to its uncompressed equivalent. the validation is done on Standard JPEG, JPEG-2000 with ROI coding and JPEG-2000 without ROI coding at preset bitrates and compared.

关键词： JPEG ROI JPEG-2000 Fuzzy Feature Vectors Object Profile Graphs

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computer vision – ECCV 2018 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Vittorio Ferrari Cristian Sminchisescu Martial Hebert Yair Weiss

ISBN: (数字)9783030012250

ISBN: (纸本)9783030012243

the sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European conference on computer vision, ECCV 2018, held in Munich, Germany, in September 2018.;the 776 revised papers presented were carefully reviewed and selected from 2439 submissions. the papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

关键词： image processing and computer vision Pattern Recognition Artificial Intelligence computer graphics computer Communication Networks

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CHAPVIDMR: Chapter-based Video Moment Retrieval using Natural Language Queries 24

CHAPVIDMR: Chapter-based Video Moment Retrieval using Natura...

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15th indian conference on computer vision graphics and image processing

作者： Agarwal, Uday Kumar, Yogesh Shahid, Abu Gatti, Prajwal Gupta, Manish Mishra, Anand IIT Jodhpur Jodhpur Rajasthan India Univ Bristol Bristol Avon England MicrosoftIndia Hyderabad Andhra Pradesh India Indian Inst Technol Jodhpur CSE Jodhpur Rajasthan India

ISBN: (纸本)9798400710759

Video Moment Retrieval (VMR) is the task of linking a query with a relevant moment from a video. Although, recently, there has been work on the VMR task where a query is linked to a single moment, the corresponding task where the query needs to be linked to multiple moments has been understudied. In this paper, we aim to work on the VMR task primarily by leveraging chapters of YouTube videos, i.e., video segments. YouTube chapters provide a meaningful segmentation of videos annotated by content authors. these annotated segmented regions help the viewer to navigate to a specific segment of the long video in which the user is interested. We present the CHAPVIDMR (Chapter-based Video Moment Retrieval) dataset, containing 10.8K user queries (obtained using GPT4) formed using multiple chapter names and other metadata extracted from videos using the YouTube API. Furthermore, we benchmark the proposed dataset on two VMR tasks: chapter classification-based VMR and segmentation-based VMR. In the chapter classification-based VMR task, the model classifies which of the chapters associated with a video are most relevant to the query. We represent a chapter by exploiting text (subtitles), audio, and visual (captions, video) modalities using state-of-the-art feature representation techniques and experiment with an exhaustive ablation for each modality. In segmentation-based VMR, the video is divided into various segments, and the segments most likely to answer the query are identified and returned. We benchmark our dataset on the state-of-the-art methods for segmentation tasks. We find that for the chapter classification-based VMR task, Sentence-BERT with subtitles and visual captions leads to the best results, while for the segmentation-based VMR, UniVTG is the most accurate. We make our code and data publicly available(1).

关键词： video moment retrieval CHAPVIDMR VMR chapter-based retrieval

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computer vision – ECCV 2018 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Vittorio Ferrari Cristian Sminchisescu Martial Hebert Yair Weiss

ISBN: (数字)9783030012700

ISBN: (纸本)9783030012694

关键词： image processing and computer vision computer graphics Artificial Intelligence Special Purpose and Application-Based Systems computer Communication Networks

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U-RME: Underwater Refined Motion Estimation in Hazy, Cluttered and Dynamic Environments 7th

U-RME: Underwater Refined Motion Estimation in Hazy, Clutter...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Gupta, Shilpi Mukherjee, Prerana Chaudhury, Santanu Lall, Brejesh Indian Institute of Technology Delhi India Indian Institute of Information Technology Sri CityAndhra Pradesh India Indian Institute of Technology Jodhpur India

ISBN: (纸本)9789811586965

Optical Flow is a popular method of computer vision for motion estimation. In this paper, we present a refined optical flow estimation method. Central to our approach is exploiting contour information as most of the motion lies on the edges. Further, we have formulated it as sparse to dense motion estimation. Proposed method has been evaluated on challenging real life image sequences of KITTI and Fish4Knowledge database. Results demonstrate that method performs well in case of low contrast, highly cluttered background, dynamic background, occlusion and illumination change. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Optical flows

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