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Local Dominant Binary Patterns for Recognition of Multi-view Facial Expressions 16

Local Dominant Binary Patterns for Recognition of Multi-view...

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

作者： Santra, Bikash Mukherjee, Dipti Prasad Indian Stat Inst 203 BT Rd Kolkata 700108 India

ISBN: (纸本)9781450347532

In this paper, a novel framework is proposed for automatic recognition of facial expressions. However, the face images for the proposed problem are captured at multiple view angle (i.e., multi-view facial expressions). The proposed scheme introduces a local dominant binary pattern (LDBP). Unlike uniform LBP based features, the LDBP uses fewer feature dimension without affecting the recognition performances. The LDBP is computed by improvising LBP with dominant orientations of neighborhood pixels. The eigen-value analysis of structure tensor representation of expressive face images determines the dominant directions of gray value changes in local neighbors of pixels. We use SVM for view-specific classification of multi-view facial expressions. The proposed model is experimented with the benchmark datasets of both near-frontal (CK+ and JAFEE) and multiview (KDEF, SFEW and LFPW) face images. The datasets include faces from posed as well as spontaneous expressions. The proposed scheme outperforms state-of-the-arts by approximately 1% for the near-frontal facial expressions and by at least 3% for multi-view facial expressions on an average.

关键词： Facial expression recognition Multi-view facial expression Local Binary Patterns (LBP) Local Dominant Binary Patterns (LDBP) structure tensor

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Spatio-temporal Weighted Histogram based Mean Shift for Illumination Robust Target Tracking 16

Spatio-temporal Weighted Histogram based Mean Shift for Illu...

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

作者： Deopujari, Kalyani Velmurugan, Rajbabu Tiwari, Kanchan MES Coll Engn Pune Maharashtra India Indian Inst Technol Bombay Maharashtra India

ISBN: (纸本)9781450347532

This paper proposes a simple method to handle illumination variation in a video. The proposed method is based on generative mean shift tracker, which uses energy compaction property of discrete Cosine transform (DCT) to handle illumination variation within and across frames. The proposed method uses spatial and temporal DCT coefficient based approach to assign weights to target and candidate histograms in mean shift. The proposed weighing factor takes care of changes in illumination within a frame i.e., illumination change of the target with respect to background and also across the frames i.e., varying illumination between the consecutive time instances. The algorithm was tested using VOT2015 challenge dataset and also on sequences from OTB and CAVIAR datasets. The proposed method was also tested rigorously for illumination attribute. The qualitative and quantitative evaluation process of the proposed method was twofold. First, the tracker was compared with existing DCT coefficient based method and showed improved results. Secondly, the proposed algorithm was compared with other state of the art trackers. The results show that the proposed algorithm outperformed some state-of-the-art trackers while with others it showed comparable performance.

关键词： Mean Shift Discrete Cosine Transform DC Coefficient Illumination Variation

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

作者： Kumar, Sunil Bhuyan, M. K. Chakraborty, Biplab Ketan Indian Inst Technol Guwahati Gauhati 781039 India

ISBN: (纸本)9781450347532

Recently several multi-view learning-based methods have been proposed, and they are found to be more efficient in many real world applications. However, existing multi-view learning-based methods are not suitable for finding discriminative directions if the data is multi-modal. In such cases, Locality Preserving Projection (LPP) and/or Local Fisher Discriminant Analysis (LFDA) are found to be more appropriate to capture discriminative directions. Furthermore, existing methods show that imposing uncorrelated constraint onto the common space improves classification accuracy of the system. Hence inspired from the above findings, we propose an Uncorrelated Multi-view Discriminant Locality Preserving Projection (UMvDLPP)-based approach. The proposed method searches a common uncorrelated discriminative space for multiple observable spaces. Moreover, the proposed method can also handle the multi-modal characteristic, which is inherently embedded in multi-view facial expression recognition (FER) data. Hence, the proposed method is effectively more efficient for multi-view FER problem. Experimental results show that the proposed method outperforms state-of-the-art multi-view learning-based methods.

关键词： Multi-view learning facial expression recognition multi-view discriminant analysis locality preserving projection and uncorrelated discriminant analysis

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Scalable Clustering and Applications 16

Scalable Clustering and Applications

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

作者： Shahid, K., I Chaudhury, Santanu Indian Inst Technol Delhi Dept Elect Engn Delhi India Inst Pilani Cent Elect Engn Res Pilani Rajasthan India

ISBN: (纸本)9781450347532

Laprge scale machine learning is becoming an active research area recently. Most of the existing clustering algorithms cannot handle big data due to its high time and space complexity. Among the clustering algorithms, eigen vector based clustering, such as Spectral clustering, shows very good accuracy, but it has cubic time complexity. There are various methods proposed to reduce the time and space complexity for eigen decomposition such as Nystrom method, Lanczos method etc. Nystrom method has linear time complexity in terms of number of data points, but has cubic time complexity in terms of number of sampling points. To reduce this, various Rank k approximation methods also proposed, but which are less efficient compare to the normalized spectral clustering. In this paper we propose a two step algorithm for spectral clustering to reduce the time complexity to O (nmk + m(2) k'), by combining both Nystrom and Lanczos method, where k is the number of clusters and k' is the rank k approximation of the sampling matrix (k < k' << m << n). It shows very good results, with various data sets, image segmentation problems and churn prediction of a telecommunication data set, even with very low sampling (for 10 Million x 10 Million matrix, sampled only 100 columns) with lesser time, which confirms the validity of the algorithm.

关键词： Large scale machine learning Segmentation and grouping

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Robust Registration of Mouse Brain Slices with Severe Histological Artifacts 16

Robust Registration of Mouse Brain Slices with Severe Histol...

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

作者： Agarwal, Nitin Xu, Xiangmin Gopi, M. Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA Univ Calif Irvine Dept Anat Neurobiol Irvine CA USA

ISBN: (纸本)9781450347532

Brain mapping research is facilitated by first aligning digital images of mouse brain slices to standardized atlas framework such as the Allen Reference Atlas (ARA). However, conventional processing of these brain slices introduces many histological artifacts such as tears and missing regions in the tissue, which make the automatic alignment process extremely challenging. We present an end-to-end fully automatic registration pipeline for alignment of digital images of mouse brain slices that may have histological artifacts, to a standardized atlas space. We use a geometric approach where we first align the bounding box of convex hulls of brain slice contours and atlas template contours, which are extracted using a variant of Canny edge detector. We then detect the artifacts using Constrained Delaunay Triangulation (CDT) and remove them from the contours before performing global alignment of points using iterative closest point (ICP). This is followed by a final non-linear registration by solving the Laplace's equation with Dirichlet boundary conditions. We tested our algorithm on 200 mouse brain slice images including slices acquired from conventional processing techniques having major histological artifacts, and from serial two-photon tomography (STPT) with no major artifacts. We show significant improvement over other registration techniques, both qualitatively and quantitatively, in all slices especially on slices with significant histological artifacts.

关键词： Non-Linear Registration image Artifacts Histology Mouse Brain Allen Reference Atlas Point Correspondences

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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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On Improved CS-SS image Watermark Detection Over Radio Mobile Channel 16

On Improved CS-SS Image Watermark Detection Over Radio Mobil...

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

作者： Bose, Anirban Maity, Santi P. IIEST Dept Informat Technol Sibpur 711103 Howrah India

ISBN: (纸本)9781450347532

Recently compressed sensing or compressive sampling (CS), apart from its intrinsic applications of sub-sample signal reconstruction, is explored a lot in the design of bandwidth preserving-energy efficient wireless networks. At the same time, due to open nature of wireless channel, digital data (media) transmission needs their protection from unauthorized access and digital watermarking has been devised as one form of potential solution over the years. Among the various methods, spread spectrum (SS) watermarking is found to be efficient due to its improved robustness and imperceptibility. SS watermarking on digital images in presence of additive and multiplicative noise is studied a lot. To the best of knowledge, CS-SS watermarking in presence of both multiplicative (fading channel) and additive noise is not explored much in the existing literature. To address this problem, a wireless communication theoretic model is suggested here to develop an improved detection scheme on additive SS image watermark framework. System model considers sub sample (CS) transmission of the watermarked image over both non-fading and fading channel. Then a diversity assisted weighted combining scheme for the improved watermark detection is developed. An optimization problem is formulated where the weight for the individual link is calculated through eigen filter approach to maximize the watermark detection probability for a fixed false alarm rate under the constraint of an embedding power (strength). A large set of simulation results validate the mathematical model of the diversity assisted compressive watermark detector.

关键词： Spread spectrum watermarking compressive transmission

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Fast Frontier Detection in Indoor Environment for Monocular SLAM 16

Fast Frontier Detection in Indoor Environment for Monocular ...

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

作者： Upadhyay, Sarthak Krishna, K. Madhava Kumar, Swagat IIIT Hyderabad KCIS RRC Hyderabad Telangana India TCS Innovat Labs Bombay Maharashtra India

ISBN: (纸本)9781450347532

Frontier detection is a critical component in autonomous exploration, wherein the robot decides the next best location to move in order to continue its mapping process. The existing frontier detection methods require dense reconstruction which is difficult to attain in a poorly textured indoor environment using a monocular camera. In this effort, we present an alternate method of detecting frontiers during the course of robot motion that circumvents the requirement of dense mapping. Based on the observation that frontiers typically occur around areas with sudden change in texture (zero-crossings), we propose a novel linear chain Conditional Random Field(CRF) formulation that is able to detect the presence or absence of frontier regions around such areas. We use cues like spread of 3D points and scene change around these areas as an observation to CRF. We demonstrate that this method gives us more relevant frontiers compared to other monocular camera based methods in the literature. Finally, we present results in an indoor environment, wherein frontiers are reliably detected around walls leading to new corridors, doors leading to new rooms or corridors and tables and other objects that open up to a new space in rooms.

关键词： Mapping

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A Framework to assess Sun Salutation videos 16

A Framework to assess Sun Salutation videos

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

作者： Jain, Hiteshi Harit, Gaurav Indian Inst Technol Jodhpur Dept Comp Sci & Engn Jodhpur 342011 Rajasthan India

ISBN: (纸本)9781450347532

There are many exercises which are repetitive in nature and are required to be done with perfection to derive maximum benefits. Sun Salutation or Surya Namaskar is one of the oldest yoga practice known. It is a sequence of ten actions or 'asanas' where the actions are synchronized with breathing and each action and its transition should be performed with minimal jerks. Essentially, it is important that this yoga practice be performed with Grace and Consistency. In this context, Grace is the ability of a person to perform an exercise with smoothness i.e. without sudden movements or jerks during the posture transition and Consistency measures the repeatability of an exercise in every cycle. We propose an algorithm that assesses how well a person practices Sun Salutation in terms of grace and consistency. Our approach works by training individual HMMs for each asana using STIP features[11] followed by automatic segmentation and labeling of the entire Sun Salutation sequence using a concatenated-HMM. The metric of grace and consistency are then laid down in terms of posture transition times. The assessments made by our system are compared with the assessments of the yoga trainer to derive the accuracy of the system. We introduce a dataset for Sun Salutation videos comprising 30 sequences of perfect Sun Salutation performed by seven experts and used this dataset to train our system. While Sun Salutation can be judged on multiple parameters, we focus mainly on judging Grace and Consistency.

关键词： Sun Salutation HMM STIP grace consistency

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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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