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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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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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Low Complexity Encoder for Feedback-Channel-Free Distributed Video Coding using Deep Convolutional Neural Networks at the Decoder 16

Low Complexity Encoder for Feedback-Channel-Free Distributed...

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

作者： Bhagath, P. Raj Mukherjee, Jayanta Mukopadhayay, Sudipta Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur W Bengal India Indian Inst Technol Dept Comp Sci & Engn Kharagpur W Bengal India

ISBN: (纸本)9781450347532

We propose a very low complexity encoder for feedback channel-free distributed video coding (DVC) applications using deep convolutional neural network (CNN) at the decoder side. Deep CNN on super resolution uses low resolution (LR) images with 25% pixels information of high resolution (HR) image to super resolve it by the factor 2. Instead we train the network with 50% of noisy Wyner-Ziv (WZ) pixels to get full original WZ frame. So at the decoder, deep CNN reconstructs the original WZ image from 50% noisy WZ pixels. These noisy samples are obtained from the iterative algorithm called DLRTex. At the encoder side we compute local rank transform (LRT) of WZ frames for alternate pixels instead of all to reduce bit rate and complexity. These local rank transformed values are merged and their rank positions in the WZ frame are entropy coded using MQ-coder. In addition, average intensity values of each block of WZ frame are also transmitted to assist motion estimation. At the decoder, side information (SI) is generated by implementing motion estimation and compensation in LRT domain. The DLRTex algorithm is executed on SI using LRT to get the 50% noisy WZ pixels which are used in reconstructing full WZ frame. We compare our results with pixel domain DVC approaches and show that the coding efficiency of our codec is better than pixel domain distributed video coders based on low-density parity check and accumulate (LDPCA) or turbo codes. We also derive the complexity of our encoder interms of number of operations and prove that its complexity is very less compared to the LDPCA based methods.

关键词： Deep convolutional neural network (CNN) Local rank transform (LRT) delta-LRT with extended neighborhood (DLRTex)

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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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DeepFly: Towards Complete Autonomous Navigation of MAVs with Monocular Camera 16

DeepFly: Towards Complete Autonomous Navigation of MAVs with...

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

作者： Shah, Utsav Khawad, Rishabh Krishna, K. Madhava IIIT Hyderabad KCIS Robot Res Ctr Hyderabad Andhra Pradesh India

ISBN: (纸本)9781450347532

Recently, the interest in Micro Aerial Vehicles (MAVs) and their autonomous flights has increased tremendously and significant advances have been made. The monocular camera has turned out to be most popular sensing modality for MAVs as it is light-weight, does not consume more power, and encodes rich information about the environment around. In this paper, we present DeepFly, our framework for autonomous navigation of a quadcopter equipped with monocular camera. The navigable space detection and waypoint selection are fundamental components of autonomous navigation system. They have broader meaning than just detecting and avoiding immediate obstacles. Finding the navigable space emphasizes equally on avoiding obstacles and detecting ideal regions to move next to. The ideal region can be defined by two properties: 1) All the points in the region have approximately same high depth value and 2) The area covered by the points of the region in the disparity map is considerably large. The waypoints selected from these navigable spaces assure collision-free path which is safer than path obtained from other waypoint selection methods which do not consider neighboring information. In our approach, we obtain a dense disparity map by performing a translation maneuver. This disparity map is input to a deep neural network which predicts bounding boxes for multiple navigable regions. Our deep convolutional neural network with shortcut connections regresses variable number of outputs without any complex architectural add on. Our autonomous navigation approach has been successfully tested in both indoors and outdoors environment and in range of lighting conditions.

关键词： Micro Aerial Vehicles Autonomous Navigation Deep Learning

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Fast and High-Quality Bilateral Filtering Using Gauss-Chebyshev Approximation 11

Fast and High-Quality Bilateral Filtering Using Gauss-Chebys...

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11th International conference on Signal processing and Communications (SPCOM)

作者： Ghosh, Sanjay Chaudhury, Kunal N. Indian Inst Sci Dept Elect Engn Bangalore Karnataka India

ISBN: (纸本)9781509017461

The bilateral filter is an edge-preserving smoother that has diverse applications in image processing, computer vision, computer graphics, and computational photography. The filter uses a spatial kernel along with a range kernel to perform edge-preserving smoothing. In this paper, we consider the Gaussian bilateral filter where both the kernels are Gaussian. A direct implementation of the Gaussian bilateral filter requires O (sigma(2)(s)) operations per pixel, where sigma(s) is the standard deviation of the spatial Gaussian. In fact, it is well-known that the direct implementation is slow in practice. We present an approximation of the Gaussian bilateral filter, whereby we can cut down the number of operations to O (1) per pixel for any arbitrary sigma(s), and yet achieve very high-quality filtering that is almost indistinguishable from the output of the original filter. We demonstrate that the proposed approximation is few orders faster in practice compared to the direct implementation. We also demonstrate that the approximation is competitive with existing fast algorithms in terms of speed and accuracy.

关键词： Fast algorithm bilateral filter Gaussian kernel Chebyshev polynomial minimax approximation

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COMA-Boost: Co-Operative Multi Agent AdaBoost 16

COMA-Boost: Co-Operative Multi Agent AdaBoost

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

作者： Lahiri, Avisek Paria, Biswajit Biswas, Prabir Kumar IIT Kharagpur Dept E&ECE Kharagpur W Bengal India IIT Kharagpur Dept CSE Kharagpur W Bengal India

ISBN: (纸本)9781450347532

A Abstract : Multi feature space representation is a common practise in computer vision applications. Traditional features such as HOG, SIFT, SURF etc., individually encapsulates certain discriminative cues for visual classification. On the other hand, each layer of a deep neural network generates multi ordered representations. In this paper we present a novel approach for such multi feature representation learning using Adaptive Boosting (AdaBoost). General practise in AdaBoost [8] is to concatenate components of feature spaces and train base learners to classify examples as correctly/incorrectly classified. We posit that multi feature space learning should be viewed as a derivative of cooperative multi agent learning. To this end, we propose a mathematical framework to leverage performance of base learners over each feature space, gauge a measure of "difficulty" of training space and finally make soft weight updates rather than strict binary weight updates prevalent in regular AdaBoost. This is made possible by periodically sharing of response states by our learner agents in the boosting framework. Theoretically, such soft weight update policy allows in finite combinations of weight updates on training space compared to only two possibilities in AdaBoost. This opens up the opportunity to identify 'more difficult' examples compared to 'less difficult' examples. We test our model on traditional multi feature representation of MNIST handwritten character dataset and 100-Leaves classification challenge. We consistently outperform traditional and variants of multi view boosting in terms of accuracy while margin analysis reveals that proposed method fosters formation of more con fident ensemble of learner agents. As an application of using our model in conjecture with deep neural network, we test our model on the challenging task of retinal blood vessel segmentation from fundus images of DRIVE dataset by using kernel dictionaries from layers of unsupervised trained stac

关键词： AdaBoost multi view learning deep learning

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Unsupervised segmentation of cervical cell images using Gaussian Mixture Model 29

Unsupervised segmentation of cervical cell images using Gaus...

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29th IEEE conference on computer vision and Pattern Recognition (CVPR)

作者： Ragothaman, Srikanth Narasimhan, Sridharakumar Basavaraj, Madivala G. Dewar, Rajan Indian Inst Technol Madras Madras Tamil Nadu India Univ Michigan Dept Pathol Ann Arbor MI 48109 USA

ISBN: (纸本)9781509014378

Cervical cancer is one of the leading causes of cancer death in women. Screening at early stages using the popular Pap smear test has been demonstrated to reduce fatalities significantly. Cost effective, automated screening methods can significantly improve the adoption of these tests worldwide. Automated screening involves image analysis of cervical cells. Gaussian Mixture Models (GMM) are widely used in image processing for segmentation which is a crucial step in image analysis. In our proposed method, GMM is implemented to segment cell regions to identify cellular features such as nucleus, cytoplasm while addressing shortcomings of existing methods. This method is combined with shape based identification of nucleus to increase the accuracy of nucleus segmentation. This enables the algorithm to accurately trace the cells and nucleus contours from the pap smear images that contain cell clusters. The method also accounts for inconsistent staining, if any. The results that are presented shows that our proposed method performs well even in challenging conditions.

关键词： Cells

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ACM International conference Proceeding Series

ACM International Conference Proceeding Series

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10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

ISBN: (纸本)9781450347532

The proceedings contain 95 papers. The topics discussed include: reinforced random forest;prototypes based discriminative appearance model for object tracking;adaptive artistic stylization of images;improving person re-identification systems: a novel score fusion framework for rank-n recognition;an image analysis approach for transcription of music played on keyboard-like instruments;deep automatic license plate recognition system;how much can a Gaussian smoother denoise?;supervised deep segmentation network for brain extraction;robust registration of mouse brain slices with severe histological artifacts;and hierarchical structured learning for indoor autonomous navigation of quadcopter.

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Voxel-based Morphometry and Minimum Redundancy Maximum Relevance Method for Classification of Parkinson's Disease and Controls from T1-Weighted MRI 16

Voxel-based Morphometry and Minimum Redundancy Maximum Relev...

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

作者： Rana, Bharti Juneja, Akanksha Saxena, Mohit Gudwani, Sunita Kumaran, S. Senthil Agrawal, R. K. Behari, Madhuri Jawaharlal Nehru Univ Sch Comp & Syst Sci Delhi India Case Western Reserve Univ Dept Neurol Case Med Ctr Cleveland OH 44106 USA All India Inst Med Sci Dept NMR Delhi India All India Inst Med Sci Dept Neurol Delhi India

ISBN: (纸本)9781450347532

Parkinson's disease (PD) is a neurodegenerative disorder, which needs to be accurately diagnosed in early stage. Voxel-based morphometry (VBM) has been extensively utilized to determine focal changes between PD patients and controls. However, it is not much utilized in differential diagnosis of an individual subject. Thus, in this study, VBM findings in conjunction with minimum redundancy maximum relevance (mRMR) method are utilized to obtain a set of relevant and non-redundant features for computeraided diagnosis (CAD) of PD using T1-weighted MRI. In the proposed method, firstly, statistical features are extracted from the clusters obtained from statistical maps, generated using VBM, of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) independently and their different combinations. Then mRMR, a multivariate feature selection method, is utilized to find a minimal set of relevant and non-redundant features. Finally, support vector machine is utilized to learn a decision model using the selected features. Experiments are performed on newly acquired T1-weighted MRI of 30 PD patients and 30 age & gender matched controls. The performance is evaluated using leave-one out cross-validation scheme in terms of sensitivity, specificity and classification accuracy. The maximum accuracy of 88.33% is achieved for GM+WM and GM+WM+CSF. In addition, the proposed method outperforms the existing methods. It is also observed that the selected clusters belong to regions namely middle and superior frontal gyrus for GM, inferior, middle frontal gyrus and insula for WM and lateral ventricle for CSF. Further, correlation of UPDRS/H&Y staging scale with GM/WM/CSF volume is observed to be not significant. Appreciable classification performance of the proposed method highlights the potential of the proposed method in CAD support system for the clinicians in PD diagnosis.

关键词： Parkinson's disease computer-aided diagnosis Voxel-based morphometry Minimum redundancy maximum relevance Magnetic resonance imaging

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