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Tensor based dictionary learning for compressive sensing MRI reconstruction 4th

Tensor based dictionary learning for compressive sensing MRI...

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4th International conference on computer vision and image processing, CVIP 2019

作者： Mubarak, Minha thomas, thomas James Sheeba Rani, J. Mishra, Deepak Department of Avionics Indian Institute of Space Science and Technology ThiruvananthapuramKerala695547 India

ISBN: (纸本)9789811540141

Compressed Sensing (CS) has heralded a new era in cardiac and body Magnetic Resonance (MR) imaging due to the unprecedented acceleration factors achieved within the ambit of existing scanning hardware, by exploiting the inherent sparsity of MR images in a known basis. Latter works have adopted dictionary learning strategies on vectorized image patches to enforce higher degrees of sparsity. However, the enormous computational complexity has confined them to learning from small patches, thereby foregoing any consideration of the expected structure of the MR images from prior knowledge. In order to efficiently exploit the multi-dimensional characteristic of MR data, this work learns 2D separable dictionaries possessing Kronecker structure at low computational costs by adapting the CANDECOMP/PARAFAC (CP) tensor decomposition method on stacked 2D under-sampled MR slices of similar scan type. the proposed method has been observed to obtain superior reconstruction quality in noiseless and noisy acquisition scenarios over the state-of-art. Furthermore, the learned dictionary can jointly reconstruct a stack of distinct 2D under-sampled slices of similar scan type, in significantly reduced running time. © Springer Nature Singapore Pte Ltd 2020.

关键词： Magnetic resonance

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Recognition of Adavus in Bharatanatyam Dance 1

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5th International conference on computer vision and image processing, CVIP 2020

作者： Bhuyan, Himadri Das, Partha Pratim Indian Institute of Technology Kharagpur Kharagpur West Bengal721302 India

ISBN: (数字)9789811611032

ISBN: (纸本)9789811611025

Digital heritage has been a challenging problem to preserve the cultural heritage like a dance. Digitization of dance requires systematic analyses and understanding the semantics. However, while several work has been reported in non-indian Classical Dance (ICD);only few has been done in the ICD because of its complexity. Bharatanatyam is an important ICD form, and Adavus constitute the primary collections of postures and movements in Bharatanatyam. there are 15 classes of Adavus having 52 sub-classes between them. Adavus are the critical building blocks for Bharatanatyam and critical for understanding its artefacts and semantics. Hence, this paper attempts to recognize Adavus based on the sequence of Key Postures (KPs) that define and constitute them. Kinect captures dance performances. the videos are then manually segmented into sequences of Key Frames (KFs) corresponding to the expected KPs. For recognizing KP, we follow two approaches;a) skeleton videos and angles of skeleton bones as features, b) the HOG features from the RGB frames. In both approaches, we train SVMs and recognize the KPs using them. the classifier generated by SVM predicts the sequence of KPs involved in a given Adavu. Since KPs are the string-like encoding symbols of an Adavu, we use the predicted sequence and master sequence of KPs in Edit Distance to recognize the matching Adavu. We use a pre-existing annotated data set of Natta (with eight variants) and Mettu (with four variants) Adavus to achieve over 99% accuracy. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Multiple Object Tracking by Bounding Boxes Without Using Texture Information and Optical Flow 29

Multiple Object Tracking by Bounding Boxes Without Using Tex...

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29th International conference in Central Europe on computer graphics, Visualization and computer vision, WSCG 2021

作者： Lipovits, Ágnes Czúni, László Tömördi, Katalin Vörösházi, Zsolt Image Processing Laboratory Faculty of Information Technology University of Pannonia Egyetem street 10 VeszprémH-8200 Hungary

ISBN: (纸本)9788086943343

Object tracking is a key task in many applications using video analytics. While there is a huge number of algorithms to track objects, there is still a need for new methods to solve the correspondence problem under certain circumstances. In our article, we assume a very typical but still open scenario: a still image object detector has already identified the objects to be tracked;thus, we have object labels, confidence values, and bounding boxes in each video frame captured at a low sampling rate. that is, optical flow methods difficult to be applied (also due to bad lighting conditions, cluttered or homogeneous areas and strong ego-motion), and moreover, many objects look similar (having the same category labels). Our proposed approach is based on the Hungarian method and incorporates the above information into the cost function evaluating the possible pairings of objects. To consider the uncertainty of the detector, the elements of the confusion matrix also contribute to the cost of pairs, as well as the probability of spatial translations based on prior observations. As a use case, we apply the algorithm to a data-set, where images were captured from onboard cameras and traffic signs were detected by RetinaNet. We analyze the performance with different parameter settings. © 2021 University of West Bohemia. All rights reserved.

关键词： Object detection

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SynCGAN: Using Learnable Class Specific Priors to Generate Synthetic Data for Improving Classifier Performance on Cytological images 7th

SynCGAN: Using Learnable Class Specific Priors to Generate S...

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

作者： Dey, Soumyajyoti Das, Soham Ghosh, Swarnendu Mitra, Shyamali Chakrabarty, Sukanta Das, Nibaran Jadavpur University KolkataWB700032 India Theism Medical Diagnostics Centre Kolkata700030 India

ISBN: (纸本)9789811586965

One of the most challenging aspects of medical image analysis is the lack of a high quantity of annotated data. this makes it difficult for deep learning algorithms to perform well due to a lack of variations in the input space. While generative adversarial networks have shown promise in the field of synthetic data generation, but without a carefully designed prior the generation procedure can not be performed well. In the proposed approach we have demonstrated the use of automatically generated segmentation masks as learnable class-specific priors to guide a conditional GAN for the generation of patho-realistic samples for cytology image. We have observed that augmentation of data using the proposed pipeline called "SynCGAN" improves the performance of state of the art classifiers such as ResNet-152, DenseNet-161, Inception-V3 significantly. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image classification

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Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred thousand-Scale One-Shot Logo Identificationg 13

Contrastive Multi-View Textual-Visual Encoding: Towards One ...

引用

13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Sharma, Nakul Penamakuri, Abhirama Subramanyam V B Mishra, Anand Indian Institute of Technology Rajasthan Jodhpur India

ISBN: (纸本)9781450398237

In this paper, we study the problem of identifying logos of business brands in natural scenes in an open-set one-shot setting. this problem setup is significantly more challenging than traditionally-studied gclosed-set' and glarge-scale training samples per category' logo recognition settings. We propose a novel multi-view textual-visual encoding framework that encodes text appearing in the logos as well as the graphical design of the logos to learn robust contrastive representations. these representations are jointly learned for multiple views of logos over a batch and thereby they generalize well to unseen logos. We evaluate our proposed framework for cropped logo verification, cropped logo identification, and end-to-end logo identification in natural scene tasks;and compare it against state-of-the-art methods. Further, the literature lacks a gvery-large-scale' collection of reference logo images that can facilitate the study of one-hundred thousand-scale logo identification. To fill this gap in the literature, we introduce Wikidata Reference Logo Dataset (WiRLD), containing logos for 100K business brands harvested from Wikidata. Our proposed framework that achieves an area under the ROC curve of 91.3% on the QMUL-OpenLogo dataset for the verification task, outperforms state-of-the-art methods by 9.1% and 2.6% on the one-shot logo identification task on the Toplogos-10 and the FlickrLogos32 datasets, respectively. Further, we show that our method is more stable compared to other baselines even when the number of candidate logos is on a 100K scale. © 2022 ACM.

关键词： Encoding (symbols)

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image sequence evaluation: 30 years and still going strong

Image sequence evaluation: 30 years and still going strong

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International conference on Pattern Recognition

作者： H.-H. Nagel Institut für Algorithmen und Kognitive Systeme Fakultät für Informatik der Universität Karlsruhe (TH) Karlsruhe Germany

ISBN: (纸本)0769507506

Over the past thirty years, technical means to record, digitize, store, process, and present video image sequences have multiplied in capacity by several orders of magnitude. this large quantitative improvement is about to turn into a qualitative change regarding the research to be pursued within this area in the future. Whereas up to now an exploratory type of approach has prevailed, the increasingly available technical means for image sequence evaluation facilitate the investigation of large amounts of data. this requires to build encompassing systems and to study their behavior which in turn is expected to stimulate theoretical developments: even small effects of an assumption can be studied in detail within a system environment, thereby enabling stepwise improvements of theoretical approaches underlying the construction of system solutions. the following discussion will emphasize fundamental experimental and theoretical advances rather than details of particular approaches.

关键词： image sequences image sequence analysis computer graphics Vehicle dynamics Layout image analysis Signal processing computer vision Algorithm design and analysis Bandwidth

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Profession identification using handwritten text images 4th

Profession identification using handwritten text images

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4th International conference on computer vision and image processing, CVIP 2019

作者： Kumar, Parveen Gupta, Manu Gupta, Mayank Sharma, Ambalika Indian Institute of Technology Roorkee Roorkee India National Institute of Technology Uttarakhand Garhwal Srinagar India

ISBN: (纸本)9789811540172

A writer handwriting depicts various information and it gives the insights into the physical, mental and emotional state of the writer. this art of analyzing and studying handwriting is graphology. the prime features of handwriting such as margins, slanted, the baseline can tell the characteristics of a writer. the writer handwriting analysis reveals strokes and patterns through which identification and understanding the personality of a writer is possible. the writing of a person molds into various shapes and styles, starting from school until the struggle for his/her career. If we examine the writings of a person from different stages of his/her life then we will see that there are many differences in the shapes, styles, and sizes of the characters. the proposed work analyze the handwriting data written by the writer’s from different professions and classify them based on the top features that characterize their profession. In this paper, the profession of a writer is identified by analyzing the features of writer’s offline handwritten images. the previous work mostly includes determining various traits like honesty, emotional stability of a writer. the Proposed work uses the CNN based model for the feature extraction from the writer’s offline handwritten images. © Springer Nature Singapore Pte Ltd 2020.

关键词： Feature extraction

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Estimation of discrete curvature based on chain-code pairing and digital straightness

Estimation of discrete curvature based on chain-code pairing...

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IEEE International conference on image processing

作者： Shyamosree Pal Partha Bhowmick Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

Estimation of discrete curvature is a challenging problem, since a mere replacement of functional derivatives by numerical differences fails to produce the desired result. Several algorithms have been proposed so far, which are mostly based on the concepts of real geometry and hence are computationally expensive. the existing measure of k-curvature, though easy to compute, is crippled with some unwanted syndromes arising out of improper consideration of chain codes. Hence, an improved algorithm for estimating k-curvature is proposed, which is marked by its inherent simplicity and computational attractiveness, and produces the expected estimate, whether the concerned point has an extreme (high or low) curvature or the concerned segment has a constant or changing curvature. Examples and experimental results demonstrate the fitness and effectiveness of the proposed technique for digital curves of arbitrary shapes.

关键词： Shape Application software image analysis Surface fitting computer science Computational geometry image processing computer graphics computer vision Embossing

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Extreme weather prediction using 2-phase deep learning pipeline 4th

Extreme weather prediction using 2-phase deep learning pipel...

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4th International conference on computer vision and image processing, CVIP 2019

作者： Oza, Vidhey thesia, Yash Rasalia, Dhananjay thakkar, Priyank Dube, Nitant Garg, Sanjay Computer Science and Engineering Department Institute of Technology Nirma University AhmedabadGujarat India Space Application Centre Indian Space Research Organisation AhmedabadGujarat India

ISBN: (纸本)9789811540172

Weather nowcasting is a problem pursued by scientists for a long time. Accurate short-term forecasting is helpful for detecting weather patterns leading to extreme weather events. Adding the dimension of nowcasting to extreme weather prediction increases the ability of models to look for preliminary patterns ahead in time. In this paper, we propose a two-stage deep learning pipeline that fuses the usability of nowcasting to the high value of extreme events prediction. Our experiments are performed on INSAT-3D satellite data from MOSDAC, SAC-ISRO. We show that our pipeline is modular, and many events can be predicted in the second phase based on the availability of the relevant data from the first phase. Testing for extreme events like the Chennai floods of 2015 and Mumbai floods of 2017 validates the efficacy of our approach. © Springer Nature Singapore Pte Ltd 2020.

关键词： Pipelines

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image and Video Retrieval 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Hari Sundaram Milind Naphade John R. Smith Yong Rui

ISBN: (数字)9783540360193

ISBN: (纸本)9783540360186

this volume contains the proceeding of the 5th International conference on - age and Video Retrieval (CIVR), July 13–15, 2006, Arizona State University, Tempe, AZ, USA: http://www. civr2006. org. image and video retrieval cont- ues to be one of the most exciting and fast-growing research areas in the ?eld of multimedia technology. However, opportunities for exchanging ideas between researchers and users of image and video retrieval systems are still limited. the InternationalconferenceonimageandVideo Retrieval(CIVR)hastakenonthe mission of bringing together these communities to allow researchers and prac- tioners around the world to share points of view on image and video retrieval. A uniquefeatureoftheconferenceistheemphasisonparticipationfrompractiti- ers. the objective is to illuminate critical issues and energize both communities for the continuing exploration of novel directions for image and video retrieval. We receivedover 90 submissions for the conference. Eachpaper wascarefully reviewed by three members of the program committee, and then checked by one of the program chairs and/or general chairs. the program committee consisted of more than 40 experts in image and video retrieval from Europe, Asia and North America, and we drew upon approximately 300 high-quality reviews to ensure a thorough and fair review process. the paper submission and review process was fully electronic, using the EDAS system. the quality of the submitted papers was very high, forcing the committee members to make some di?cult decisions. Due to time and space constraints, we could only accept 18 oral papers and 30 poster papers.

关键词： computer graphics Information Storage and Retrieval Database Management Information Systems Applications (incl. Internet) Multimedia Information Systems image processing and computer vision

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