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检索条件"任意字段=Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing"

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High precision automated face localization in thermal images: Oral cancer dataset as test case

High precision automated face localization in thermal images...

引用

conference on Medical Imaging - image processing

作者： Chakraborty, M. Raman, S. K. Mukhopadhyay, S. Patsa, S. Anjum, N. Ray, J. G. Indian Inst Technol Kharagpur W Bengal India Natl Inst Technol Tiruchirappalli Tamil Nadu India Dr R Ahmed Dent Coll & Hosp Kolkata India

ISBN: (数字)9781510607125

ISBN: (纸本)9781510607118;9781510607125

Automated face detection is the pivotal step in computer vision aided facial medical diagnosis and biometrics. This paper presents an automatic, subject adaptive framework for accurate face detection in the long infrared spectrum on our database for oral cancer detection consisting of malignant, precancerous and normal subjects of varied age group. Previous works on oral cancer detection using Digital Infrared Thermal Imaging(DITI) reveals that patients and normal subjects differ significantly in their facial thermal distribution. Therefore, it is a challenging task to formulate a completely adaptive framework to veraciously localize face from such a subject specific modality. Our model consists of first extracting the most probable facial regions by minimum error thresholding followed by ingenious adaptive methods to leverage the horizontal and vertical projections of the segmented thermal image. Additionally, the model incorporates our domain knowledge of exploiting temperature difference between strategic locations of the face. To our best knowledge, this is the pioneering work on detecting faces in thermal facial images comprising both patients and normal subjects. Previous works on face detection have not specifically targeted automated medical diagnosis;face bounding box returned by those algorithms are thus loose and not apt for further medical automation. Our algorithm significantly outperforms contemporary face detection algorithms in terms of commonly used metrics for evaluating face detection accuracy. Since our method has been tested on challenging dataset consisting of both patients and normal subjects of diverse age groups, it can be seamlessly adapted in any DITI guided facial healthcare or biometric applications.

关键词： Digital Infrared Thermal Imaging Facial Biometric Facial Healthcare Oral Cancer Thermal Face Detection

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Bishnupur Heritage image Dataset (BHID): A resource for various computer vision applications 16

Bishnupur Heritage Image Dataset (BHID): A resource for vari...

引用

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

作者： Ghorai, Mrinmoy Purkait, Pulak Santra, Sanchayan Samanta, Soumitra Chanda, Bhabatosh Indian Stat Inst Kolkata India Adelaide Univ Adelaide SA Australia

ISBN: (纸本)9781450347532

Bishnupur is an attractive tourist place in West Bengal, India and is known for its terracotta temples. The place is one of the prospective candidates to be included in the list of UNESCO World Heritage sites. We intend to preserve this heritage site digitally and also to present some virtual interaction for the tourist and researchers. In this paper, we present an image dataset of different temples (namely, Jor Bangla, Kalachand, Madan Mohan, Radha Madhav, Rasmancha, Shyamrai and Nandalal) in Bishnupur for evaluating different types of computer vision and image processing algorithms (like 3D reconstruction, image inpainting, texture classification and content specific image retrieval). The dataset is captured using four different cameras with different parameter settings. Some datasets are extracted and earmarked for certain applications such as texture classification, image inpainting and content specific image retrieval. Example results of baseline methods are also shown for these applications. Thus we evaluate the usefulness of this dataset. To the best of our knowledge, probably this is the first attempt of combined dataset for evaluating various types of problems for a heritage site in India.

关键词： Heritage data SFM image inpainting temple texture

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An image Analysis Approach for Transcription of Music Played on Keyboard-like Instruments 16

An Image Analysis Approach for Transcription of Music Played...

引用

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

作者： Deb, Souvik Sinha Rajwade, Ajit Indian Inst Technol Bombay Maharashtra India

ISBN: (纸本)9781450347532

Music transcription refers to the process of analyzing a piece of music to generate a sequence of constituent notes and their duration. Transcription of music from audio signals is fraught with problems due to auditory interference such as ambient noise, multiple instruments playing simultaneously, accompanying vocals or polyphonic sounds. For several instruments, there exists added information for music transcription which can be derived from a video sequence of the instrument as it is being played. This paper proposes a method to utilize this visual information for the case of keyboard-like instruments to generate a transcript automatically, by analyzing the video frames. We present encouraging results under varying lighting conditions on different song sequences played out on a keyboard.

关键词： Music transcription computer vision Applications

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image Hallucination at Different Times of Day Using Locally Affine Model and kNN Template Matching From Time-Lapse images 16

Image Hallucination at Different Times of Day Using Locally ...

引用

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

作者： Patel, Nikunj Kataria, Tushar Indian Inst Technol Bombay Elect Engn Bombay Maharashtra India

ISBN: (纸本)9781450347532

image Hallucination has many applications in areas such as image processing, computational photography and image fusion. In this paper, we present an image Hallucination technique based on the template (patch) matching from the database of time lapse images and learned locally affine model. Template based techniques suffer from blocky artifacts. So, we propose two approaches for imposing consistency criteria across neighbouring patches in the form of regularization. We validate our Color transfer technique by hallucinating a variety of natural images at different times the day. We compare the proposed approach with other state of the art techniques of example image based color transfer and show that the images obtained using our approach look more plausible and natural.

关键词： image fusion

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image Defencing via Signal Demixing 16

Image Defencing via Signal Demixing

引用

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

作者： Kumar, Veepin Mukherjee, Jayanta Das Mandal, Shyamal Kumar Indian Inst Technol Ctr Educ Technol Kharagpur 721302 W Bengal India Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)9781450347532

We present a novel algorithm to remove near regular, fence or wire like foreground patterns from an image. The fence detection or fence removal algorithms, developed so far, have poor performance in detecting the fence. We use signal demixing to utilize the sparsity and regularity property of fences to detect them. Results demonstrate the effectiveness of our technique as compared to other state of the art techniques.

关键词： Object detection fence removal image inpainting signal demixing

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Compensating for Large In-Plane Rotations in Natural images 16

Compensating for Large In-Plane Rotations in Natural Images

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

作者： Boominathan, Lokesh Srinivas, Suraj Babu, R. Venkatesh Indian Inst Sci Video Analyt Lab Bangalore 560012 Karnataka India

ISBN: (纸本)9781450347532

Rotation invariance has been studied in the computer vision community primarily in the context of small in-plane rotations. This is usually achieved by building invariant image features. However, the problem of achieving invariance for large rotation angles remains largely unexplored. In this work, we tackle this problem by directly compensating for large rotations, as opposed to building invariant features. This is inspired by the neuro-scientific concept of mental rotation, which humans use to compare pairs of rotated objects. Our contributions here are three-fold. First, we train a Convolutional Neural Network (CNN) to detect image rotations. We find that generic CNN architectures are not suitable for this purpose. To this end, we introduce a convolutional template layer, which learns representations for canonical 'unrotated' images. Second, we use Bayesian Optimization to quickly sift through a large number of candidate images to find the canonical 'unrotated' image. Third, we use this method to achieve robustness to large angles in an image retrieval scenario. Our method is task-agnostic, and can be used as a pre-processing step in any computer vision system.

关键词： Deep neural networks image retrieval rotation invariance Bayesian optimization

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Fusion-based Skin Detection using image Distribution Model 16

Fusion-based Skin Detection using Image Distribution Model

引用

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

作者： Chakraborty, Biplab Ketan Bhuyan, M. K. Kumar, Sunil Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati Assam India

ISBN: (纸本)9781450347532

Skin colour detection under poor or varying illumination condition is a big challenge for various image processing and human-computer interaction applications. In this paper, a novel skin detection method utilizing image pixel distribution in a given colour space is proposed. The pixel distribution of an image can provide a better localization of the actual skin colour distribution of an image. Hence, a local skin distribution model (LSDM) is derived using the image pixel distribution model and its similarity with the global skin distribution model (GSDM). Finally, a fusion-based skin model is obtained using both the GSDM and the LSDM. Subsequently, a dynamic region growing method is employed to improve the overall detection rate. Experimental results show that proposed skin detection method can significantly improve the detection accuracy in presence of varying illumination conditions.

关键词： Skin detection Skin probability map Mixture of Gaussians Region growing

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Crowd Motion Analysis for Group Detection 16

Crowd Motion Analysis for Group Detection

引用

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

作者： Bhargava, Neha Chaudhuri, Subhasis Indian Inst Technol Bombay Maharashtra India

ISBN: (纸本)9781450347532

Understanding crowd dynamics is an interesting problem in computer vision owing to its various applications. We propose a dynamical system to model the dynamics of collective motion of the crowd. The model learns the spatio-temporal interaction pattern of the crowd from the track data captured over a time period. The model is trained under a least square formulation with spatial and temporal constraints. The spatial constraint allows the model to consider only the neighbors of a particular agent and the temporal constraint enforces temporal smoothness in the model. We also propose an effective group detection algorithm that utilizes the eigenvectors of the interaction matrix of the model. The group detection is cast as a spectral clustering problem. Extensive experimentation demonstrates a superlative performance of our group detection algorithm over state-of-the-art methods.

关键词： crowd dynamics group detection

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Alternate Formulation for Transform Learning 16

Alternate Formulation for Transform Learning

引用

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

作者： Maggu, Jyoti Majumdar, Angshul Indraprastha Inst Informat Technol Okhla Phase 3 New Delhi 110020 India

ISBN: (纸本)9781450347532

Dictionary learning has been used to solve inverse problems in imaging and as an unsupervised feature extraction tool in vision. The main disadvantage of dictionary learning for applications in vision is the relatively long feature extraction time during testing;owing to the requirement of solving an iterative optimization problem (10-minimization). The newly developed analysis framework of transform learning does not suffer from this shortcoming;feature extraction only requires a matrix vector multiplication. This work proposes an alternate formulation for transform learning that improves the accuracy even further. Experiments on benchmark databases show that our proposed transform learning yields results better than dictionary learning, autoencoder (AE) and restricted Boltzmann machine (RBM). The feature extraction time is fast as AE and RBM.

关键词： Dictionary learning dimensionality reduction feature extraction

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Efficient Adaptive Weighted Minimization for Compressed Sensing Magnetic Resonance image Reconstruction 16

Efficient Adaptive Weighted Minimization for Compressed Sens...

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

作者： Datta, Sumit Deka, Bhabesh Tezpur Univ Dept ECE CVIP Lab Tezpur 784028 Assam India

ISBN: (纸本)9781450347532

Compressed sensing magnetic resonance imaging (CSMRI) have demonstrated that it is possible to accelerate MRI scan time by reducing the number of measurements in the k-space without significant loss of anatomical details. The number of k-space measurements is roughly proportional to the sparsity of the MR signal under consideration. Recently, a few works on CSMRI have revealed that the sparsity of the MR signal can be enhanced by suitable weighting of different regularization priors. In this paper, we have proposed an efficient adaptive weighted reconstruction algorithm for the enhancement of sparsity of the MR image. Experimental results show that the proposed algorithm gives better reconstructions with less number of measurements without significant increase of the computational time compared to existing algorithms in this line.

关键词： Magnetic resonance imaging compressed sensing l(1)-norm total variation regularization image reconstruction

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