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检索条件"任意字段=10th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2016"

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Video Stabilization by Procrustes Analysis of Trajectories 16

Video Stabilization by Procrustes Analysis of Trajectories

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

作者： Jacob, Geethu Miriam Das, Sukhendu Indian Inst Technol Madras Tamil Nadu India

ISBN: (纸本)9781450347532

Video Stabilization algorithms are often necessary at the pre-processing stage for many applications in video analytics. the major challenges in video stabilization are the presence of jittery motion paths of a camera, large foreground moving objects with arbitrary motion and occlusions. In this paper, a simple, yet powerful video stabilization algorithm is proposed, by eliminating the trajectories with higher dynamism appearing due to jitter. A block-wise stabilization of the camera motion is performed, by analyzing the trajectories in Kendall's shape space. A 3-stage iterative process is proposed for each block of frames. At the first stage of the iterative process, the trajectories with relatively higher dynamism (estimated using optical flow) are eliminated. At the second stage, a Procrustes alignment is performed on the remaining trajectories and Frechet mean of the aligned trajectories is estimated. Finally, the Frechet mean is stabilized and a transformation of the stabilized Frechet mean to the original space (of the trajectories) yields the stabilized trajectories. A global optimization function has been designed for stabilization, thus minimizing wobbles and distortions in the frames. As the motion paths of the higher and lower dynamic regions become more distinct after stabilization, this iterative process helps in the identification of the stabilized background trajectories (with lower dynamism), which are used to warp the frames for rendering the stabilized frames. Experiments are done with varying levels of jitter introduced on stable videos, apart from a few benchmarked natural jittery videos. In cases, where synthetic jitter is fused on stable videos, an error norm comparing the groundtruth scores (scores of the stable videos) to the scores of the stabilized videos, is used for comparative study of performance. the results show the superiority of our proposed method over other state-of-the-art methods. [graphics] .

关键词： Kendall's shape space Procrustes Analysis Frechet Mean Video Stabilization Content Preserving Warps

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Recognizing Facial Expressions Using Novel Motion Based Features 16

Recognizing Facial Expressions Using Novel Motion Based Feat...

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

作者： Mukherjee, Snehasis Vamshi, Bandla Reddy, K. V. Sai Vineeth Kumar Krishna, Repala Vamshi Harish, S. V. S. IIIT Chittoor Sricity India

ISBN: (纸本)9781450347532

this paper introduces two novel motion based features for recognizing human facial expressions. the proposed motion features are applied for recognizing facial expressions from a video sequence. the proposed bag-of-words based scheme represents each frame of a video sequence as a vector depicting local motion patterns during a facial expression. the local motion patterns are captured by an efficient derivation from optical flow. Motion features are clustered and stored as words in a dictionary. We further generate a reduced dictionary by ranking the words based on some ambiguity measure. We prune out the ambiguous words and continue with key words in the reduced dictionary. the ambiguity measure is given by applying a graph-based technique, where each word is represented as a node in the graph. Ambiguity measures are obtained by modelling the frequency of occurrence of the word during the expression. We form expression descriptors for each expression from the reduced dictionary, by applying an efficient kernel. the training of the expression descriptors are made following an adaptive learning technique. We tested the proposed approach with standard dataset. the proposed approach shows better accuracy compared to the state-of-the-art.

关键词： Facial expression Optical flow GWOF WOF Adaptive learning

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Deep fusion of visual signatures for client-server facial analysis 16

Deep fusion of visual signatures for client-server facial an...

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

作者： Bhattarai, Binod Sharma, Gaurav Jurie, Frederic Normandie Univ UNICAEN ENSICAEN CNRSGREYC Caen France IIT Kanpur Comp Sci & Engn Kanpur Uttar Pradesh India

ISBN: (纸本)9781450347532

Facial analysis is a key technology for enabling humanmachine interaction. In this context, we present a client-server framework, where a client transmits the signature of a face to be analyzed to the server, and, in return, the server sends back various information describing the face e.g. is the person male or female, is she/he bald, does he have a mustache, etc. We assume that a client can compute one (or a combination) of visual features;from very simple and efficient features, like Local Binary Patterns, to more complex and computationally heavy, like Fisher Vectors and CNN based, depending on the computing resources available. the challenge addressed in this paper is to design a common universal representation such that a single merged signature is transmitted to the server, whatever be the type and number of features computed by the client, ensuring nonetheless an optimal performance. Our solution is based on learning of a common optimal subspace for aligning the different face features and merging them into a universal signature. We have validated the proposed method on the challenging CelebA dataset, on which our method outperforms existing state-of-art methods when rich representation is available at test time, while giving competitive performance when only simple signatures (like LBP) are available at test time due to resource constraints on the client.

关键词： Local binary pattern

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Reduction of Variance of Observations on Pelvic Structures in CBCT images using Novel Mean-Shift and Mutual Information based image Registration 16

Reduction of Variance of Observations on Pelvic Structures i...

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

作者： Malladi, Sai Phani Kumar Veduruparthi, Bijju Kranthi Mukherjee, Jayanta Das, Partha Pratim Chakrabarti, Saswat Mallick, Indranil Indian Inst Technol Kharagpur Adv Technol Dev Ctr Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India Indian Inst Technol Kharagpur GS Sanyal Sch Telecommun Kharagpur W Bengal India Tata Med Ctr Dept Radiat Oncol Kolkata India

ISBN: (纸本)9781450347532

In this paper, Cone-Beam Computed Tomography(CBCT) image data of colorectal cancer patients are considered for registering standard reference locations of bony structures in pelvic region. A solution is provided in this paper to automatically compute and resolve irregularities involved in locating bony structures in the pelvic region. A new algorithm is proposed to automatically locate the lowest 3D coordinates of the Pubic Symphysis (pb) and the Coccyx on a daily basis. the irregularities involved are reduced to minimum by registering CBCT images. the conventional three dimensional mutual information (MI) based registration and a novel mean shift based mutual information techniques are compared. the variations in the position of pelvic region are also compared for unregistered and registered CBCT images. the proposed algorithm, tested on CBCT image data of 25 patients, each taken over a span of 27 days consecutively, provide promising results. the variations in the locations of coccyx, pb, and the distance between them were found to be reduced due to registration of 3D CBCT images.

关键词： Cone-Beam Computed Tomography 3D Registration Mutual Information Pelvic structure

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Autoregressive Hidden Markov Model with Missing Data for Modelling Functional MR Imaging Data 16

Autoregressive Hidden Markov Model with Missing Data for Mod...

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

作者： Dang, Shilpa Chaudhury, Santanu Lall, Brejesh Roy, Prasun Kumar Indian Inst Technol Delhi New Delhi India Cent Elect Engn Res Inst Pilani Rajasthan India Natl Brain Res Ctr Gurgaon Haryana India

ISBN: (纸本)9781450347532

Functional Magnetic Resonance Imaging (fMRI) has opened ways to look inside active human brain. However, fMRI signal is an indirect indicator of underlying neuronal activity and has low-temporal resolution due to acquisition process. this paper proposes autoregressive hidden Markov model with missing data (AR-HMM-md) framework which aims at addressing aforementioned issues while allowing accurate capturing of fMRI time series characteristics. the proposed work models unobserved neuronal activity over time as sequence of discrete hidden states, and shows how exact inference can be obtained with missing fMRI data under the "Missing not at Random" (MNAR) mechanism. this mechanism requires explicit modelling of the missing data along with the observed data. the performance is evaluated by observing convergence characteristic of log-likelihoods and classification capability of the proposed model over existing models for two fMRI datasets. the classification is performed between real fMRI time series from a task-based experiment and randomly-generated time series. Another classification experiment is performed between children and elder subjects using fMRI time series from resting-state data. the proposed model captured the fMRI characteristics efficiently and thus converged to better posterior probability resulting into higher classification accuracy over existing models for both the datasets.

关键词： Activation detection autoregressive hidden Markov model connectivity detection fMRI missing data

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Hierarchical Structured Learning for Indoor Autonomous Navigation of Quadcopter 16

Hierarchical Structured Learning for Indoor Autonomous Navig...

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

作者： Duggal, Vishakh Bipin, Kumar Shah, Utsav Krishna, K. Madhava IIIT KCIS RRC Hyderabad Andhra Prades India

ISBN: (纸本)9781450347532

Autonomous navigation of generic monocular quadcopter in the indoor environment requires sophisticated approaches for perception, planning and control. this paper presents a system which enables a miniature quadcopter with a frontal monocular camera to autonomously navigate and explore the unknown indoor environment. Initially, the system estimates dense depth map of the environment from a single video frame using our proposed novel supervised Hierarchical Structured Learning (fin) technique, which yields both high accuracy levels and better generalization. the proposed HSL approach discretizes the overall depth range into multiple sets. It structures these sets hierarchically and recursively through partitioning the set of classes into two subsets with subsets representing apportioned depth range of the parent set, forming a binary tree. the binary classification method is applied to each internal node of binary tree separately using Support Vector Machine (svM). Whereas, the depth estimation of each pixel of the image starts from the root node in top-down approach, classifying repetitively till it reaches any of the leaf node representing its estimated depth. the generated depth map is provided as an input to Convolutional Neural Network (CNN), which generates flight planning commands. Finally, trajectory planning and control module employs a convex programming technique to generate collision-free minimum time trajectory which follows these flight planning commands and produces appropriate control inputs for the quadcopter. the results convey unequivocally the advantages of depth perception by HSL, while repeatable flights of successful nature in typical indoor corridors confirm the efficacy of the pipeline.

关键词： Autonomous Navigation vision Quadcopter

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Improving Person Re-Identification Systems: A Novel Score Fusion Framework for Rank-n Recognition 16

Improving Person Re-Identification Systems: A Novel Score Fu...

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

作者： Barman, Arko Shah, Shishir K. Univ Houston 4800 Calhoun Rd Houston TX 77004 USA

ISBN: (纸本)9781450347532

Person re-identification is an essential technique for video surveillance applications. Most existing algorithms for person re-identification deal with feature extraction, metric learning or a combination of both. Combining successful state-ofthe-art methods using score fusion from the perspective of person re-identification has not yet been widely explored. In this paper, we endeavor to boost the performance of existing systems by combining them using a novel score fusion framework which requires no training or dataset-dependent tuning of parameters. We develop a robust and efficient method called Unsupervised Posterior Probability-based Score Fusion (UPPSF) for combination of raw scores obtained from multiple existing person re-identification algorithms in order to achieve superior recognition rates. We propose two novel generalized linear models for estimating the posterior probabilities of a given probe image matching each of the gallery images. Normalization and combination of these posterior probability values computed from each of the existing algorithms individually, yields a set of unified scores, which is then used for ranking the gallery images. Our score fusion framework is inherently capable of dealing with different ranges and distributions of matching scores emanating from existing algorithms, without requiring any prior knowledge about the algorithms themselves, effectively treating them as "black-box" methods. Experiments on widely-used challenging datasets like VIPeR, CUHK01, CUHK03, EthZ1 and EthZ2 demonstrate the efficiency of UPPSF in combining multiple algorithms at the score level.

关键词： Person Re-identification Score Fusion Posterior Probability Estimation

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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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Supervised Deep Segmentation Network for Brain Extraction 16

Supervised Deep Segmentation Network for Brain Extraction

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

作者： Sikka, Apoorva Mittal, Gaurav Bathula, Deepti R. Krishnan, Narayanan C. IIT Ropar Rupnagar Punjab India

ISBN: (纸本)9781450347532

Recent past has seen an inexorable shift towards the use of deep learning techniques to solve a myriad of problems in the field of medical imaging. In this paper, a novel segmentation method involving a fully-connected deep neural network called Deep Segmentation Network (DSN) is proposed to perform supervised regression for brain extraction from T1-weighted magnetic resonance (MR) images. In contrast to the existing patch-based feature learning techniques, DSN works on full 3D volumes, simplifying pre- and post processing operations, to efficiently provide a voxel-wise binary mask delineating the brain region. the model is evaluated using three publicly available datasets and is observed to either outdo or perform comparably to the state-of-the-art methods. DSN is able to achieve a maximum and minimum Dice Similarity Coefficient (DSC) of 97.57 and 92.82 respectively across all the datasets. Experiments conducted in this paper highlight the ability of the DSN model to automatically learn feature representations;making it a simple yet highly effective approach for brain segmentation. Preliminary experiments also suggest that the proposed model has the potential to segment sub-cortical structures accurately.

关键词： brain extraction segmentation deep learning supervised neural network

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Extended Multi-spectral Face Recognition Across Two Different Age Groups: An Empirical Study 16

Extended Multi-spectral Face Recognition Across Two Differen...

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

作者： Vetrekar, N. T. Raghavendra, R. Gaonkar, A. A. Naik, G. M. Gad, R. S. Goa Univ Taleigao Goa India NTNU Trondheim Norway

ISBN: (纸本)9781450347532

Face recognition has attained a greater importance in biometric authentication due to its non-intrusive property of identifying individuals at varying stand-off distance. Face recognition based on multi-spectral imaging has recently gained prime importance due to its ability to capture spatial and spectral information across the spectrum. Our first contribution in this paper is to use extended multi-spectral face recognition in two different age groups. the second contribution is to show empirically the performance of face recognition for two age groups. thus, in this paper, we developed a multi-spectral imaging sensor to capture facial database for two different age groups (<= 15years and >= 20years) at nine different spectral bands covering 530nm to 1000nm range. We then collected a new facial images corresponding to two different age groups comprises of 168 individuals. Extensive experimental evaluation is performed independently on two different age group databases using four different state-of-the-art face recognition algorithms. We evaluate the verification and identification rate across individual spectral bands and fused spectral band for two age groups. the obtained evaluation results shows higher recognition rate for age groups >= 20years than <= 15years, which indicates the variation in face recognition across the different age groups.

关键词： Face Recognition Facial Age Groups Multi-spectral Imaging Feature Extraction Collaborative Representation

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