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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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Intrinsic image Decomposition using Focal Stacks 16

Intrinsic Image Decomposition using Focal Stacks

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

作者： Saini, Saurabh Sakurikar, Parikshit Narayanan, P. J. IIIT Hyderabad Kohli Ctr Intelligent Syst Ctr Visual Informat Technol Hyderabad Telangana India

ISBN: (纸本)9781450347532

In this paper, we presents a novel method (RGBF-IID) for intrinsic image decomposition of a wild scene without any restrictions on the complexity, illumination or scale of the image. We use focal stacks of the scene as input. A focal stack captures a scene at varying focal distances. Since focus depends on distance to the object, this representation has information beyond an RGB image towards an RGBD image with depth. We call our representation an RGBF image to highlight this. We use a robust focus measure and generalized random walk algorithm to compute dense probability maps across the stack. These maps are used to de fine sparse local and global pixel neighbourhoods, adhering to the structure of the underlying 3D scene. We use these neighbourhood correspondences with standard chromaticity assumptions as constraints in an optimization system. We present our results on both indoor and outdoor scenes using manually captured stacks of random objects under natural as well as artificial lighting conditions. We also test our system on a larger dataset of synthetically generated focal stacks from NYUv2 and MPI Sintel datasets and show competitive performance against current state-of-the-art IID methods that use RGBD images. Our method provides a strong evidence for the potential of RGBF modality in place of RGBD in computer vision.

关键词： Intrinsic image decomposition focal stacks generalized random walk RGBF RGBF-IID

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Automated Brain Tractography Segmentation using Curvature Points 16

Automated Brain Tractography Segmentation using Curvature Po...

引用

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

作者： Patel, Vedang Parmar, Anand Bhavsar, Arnav Nigam, Aditya BITS Pilani Hyderabad Dept Comp Sci Hyderabad Andhra Prades India Indian Inst Technol Mandi Sch Comp & Elect Engn Mandi India

ISBN: (纸本)9781450347532

Classification of brain fiber tracts is an important problem in brain tractography analysis. We propose a supervised algorithm which learns features for anatomically meaningful fiber clusters, from labeled DTI white matter data. The classification is performed at two levels: a) Grey vs White matter (macro level) and b) White matter clusters (micro level). Our approach focuses on high curvature points in the fiber tracts, which embodies the unique characteristics of the respective classes. Any test fiber is classified into one of these learned classes by comparing proximity using the learned curvature-point model (for micro level) and with a neural network classifier (at macro level). The proposed algorithm has been validated with brain DTI data for three subjects containing about 2,50,000 fibers per subject, and is shown to yield high classification accuracy (> 93%) at both macro and micro levels.

关键词： DTI Tractography Fiber Grey matter White matter Segmentation

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Reinforced Random Forest 16

Reinforced Random Forest

引用

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

作者： Paul, Angshuman Mukherjee, Dipti Prasad Indian Stat Inst 203 BT Rd Kolkata India

ISBN: (纸本)9781450347532

Reinforcement learning improves classification accuracy. But use of reinforcement learning is relatively unexplored in case of random forest classifier. We propose a reinforced random forest (RRF) classifier that exploits reinforcement learning to improve classification accuracy. Our algorithm is initialized with a forest. Then the entire training data is tested using the initial forest. In order to reinforce learning, we use mis-classified data points to grow certain number of new trees. A subset of the new trees is added to the existing forest using a novel graph-based approach. We show that addition of these trees ensures improvement in classification accuracy. This process is continued iteratively until classification accuracy saturates. The proposed RRF has low computational burden. We achieve at least 3% improvement in F-measure compared to random forest in three breast cancer datasets. Results on benchmark datasets show significant reduction in average classification error.

关键词： Random forest reinforcement learning classification accuracy shortest path

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Data-driven 2D Effects Animation 16

Data-driven 2D Effects Animation

引用

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

作者： Grover, Divyanshu Chaudhuri, Parag Indian Inst Technol Dept Comp Sci & Engn Bombay 400076 Maharashtra India

ISBN: (纸本)9781450347532

Making plausible, high quality visual effects, like water splashes or fire, in traditional 2D animation pipelines require an animator to draw many frames of phenomena that are very difficult to recreate manually. We present a technique that uses a database of video clips of such phenomena to assist the animator. The animator has to only input sample sketched frames of the phenomena at particular time instants. These are matched to frames of the video clips and a plausible sequence of frames is generated from these clips that contain the animator drawn frames as constraints. The colour style of the hand-drawn frames is used to render the generated frames, thus resulting in a 2D animation that follows the style and intent of the 2D animator. Our system can also create multi-layered effects animation, allowing the animator to draw interacting mixed phenomena, like water being poured on fire.

关键词： non-photorealistic animation non-photorealistic rendering sketch-based special effects

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Blind image Quality Assessment using Subspace Alignment 16

Blind Image Quality Assessment using Subspace Alignment

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

作者： Kiran, Indra Guha, Tanaya Pandey, Gaurav Indian Inst Technol Kanpur Uttar Pradesh India

ISBN: (纸本)9781450347532

This paper addresses the problem of estimating the quality of an image as it would be perceived by a human. A well accepted approach to assess perceptual quality of an image is to quantify its loss of structural information. We propose a blind image quality assessment method that aims at quantifying structural information loss in a given (possibly distorted) image by comparing its structures with those extracted from a database of clean images. We first construct a subspace from the clean natural images using (i) principal component analysis (PCA), and (ii) overcomplete dictionary learning with sparsity constraint. While PCA provides mathematical convenience, an overcomplete dictionary is known to capture the perceptually important structures resembling the simple cells in the primary visual cortex. The subspace learned from the clean images is called the source subspace. Similarly, a subspace, called the target subspace, is learned from the distorted image. In order to quantify the structural information loss, we use a subspace alignment technique which transforms the target subspace into the source by optimizing over a transformation matrix. This transformation matrix is subsequently used to measure the global and local (patch-based) quality score of the distorted image. The quality scores obtained by the proposed method are shown to correlate well with the subjective scores obtained from human annotators. Our method achieves competitive results when evaluated on three bench-mark databases.

关键词： Blind image quality assessment dictionary learning subspace alignment

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First Quantization Matrix Estimation for Double Compressed JPEG images Utilizing Novel DCT Histogram Selection Strategy 16

First Quantization Matrix Estimation for Double Compressed J...

引用

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

作者： Dalmia, Nandita Okade, Manish Natl Inst Technol Rourkela Dept Elect & Commun Engn Rourkela 769008 India

ISBN: (纸本)9781450347532

The Double JPEG problem in image forensics has been gaining importance since it involves two compression cycles and there is a possibility of tampering having taken place after the first cycle thereby calling for accurate methods to detect and localize the introduced tamper. First quantization matrix estimation which basically retrieves the missing quantization table of the first cycle is one of the ways of image authentication for Double JPEG images. This paper presents a robust method for first quantization matrix estimation in case of double compressed JPEG images by improving the selection strategy which chooses the quantization estimate from the filtered DCT histograms. The selection strategy is made robust by increasing the available statistics utilizing the DCT coefficients from the double compressed image under investigation coupled with performing relative comparison between the obtained histograms followed by a novel priority assignment and selection step, which accurately estimates the first quantization value. Experimental testing and comparative analysis with two state-of-art methods show the robustness of the proposed method for accurate first quantization estimation. The proposed method finds its application in image forensics as well as in steganalysis.

关键词： image forensics double JPEG compression tampering histogram analysis prioritizedlists split noise

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Event Recognition in Broadcast Soccer Videos 16

Event Recognition in Broadcast Soccer Videos

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

作者： Saraogi, Himangi Sharma, Rahul Anand Kumar, Vijay IIIT Hyderabad Ctr Visual Informat Technol Hyderabad Telangana India

ISBN: (纸本)9781450347532

Automatic recognition of important events in soccer broadcast videos plays a vital role in many applications including video summarization, indexing, content-based search, and in performance analysis of players and teams. This paper proposes an approach for soccer event recognition using deep convolutional features combined with domain-specific cues. For deep representation, we use the recently proposed trajectory based deep convolutional descriptor (TDD) [1] which samples and pools the discriminatively trained convolutional features around the improved trajectories. We further improve the performance by incorporating domain specific knowledge based on camera view type and its position. The camera position and view type captures the statistics of occurrence of events in different play-field regions and zoom-level respectively. We conduct extensive experiments on 6 hour long soccer matches and show the effectiveness of deep video representation for soccer and the improvements obtained using domain-specific cues.

关键词： Soccer event recognition deep convolutional features playground registeration view-shot estimation

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Iris recognition using partial sum of second order Taylor Series Expansion 16

Iris recognition using partial sum of second order Taylor Se...

引用

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

作者： Shekar, B. H. Bhat, Sharada S. Mangalore Univ Dept Comp Sci Mangalore Karnataka India Govt Arts & Sci Coll Karwar Karnataka India

ISBN: (纸本)9781450347532

Iris is presently one among the most sought after traits in biometric research. Extracting well-suited features from iris has been a favourite topic of the researchers. This paper proposes a novel iris feature extraction technique based on partial sum of second order Taylor series expansion (TSE). The finite sum of TSE computed on an arbitrary small neighbourhood on multiple scales can approximate the function extremely well and hence provides a powerful mechanism to extract the complex natured localised features of iris structure. To compute the higher order derivatives of TSE, we propose kernel structures by extending the Sobel operators. Extensive experiments are conducted with multiple scales on IITD, MMU v-2 and CASIA v-4 distance databases and comparative analysis is performed with the existing algorithms to substantiate the performance of the proposed method.

关键词： Taylor Series Expansion Iris Feature Extraction Iris Recognition Biometrics

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A Distribution-Independent Risk Estimator for image Denoising 16

A Distribution-Independent Risk Estimator for Image Denoisin...

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

作者： Subramanian, Babu Kishore Gupta, Ashutosh Seelamantula, Chandra Sekhar Sattva MedTech Bangalore 560025 Karnataka India Indian Space Res Org Ahmadabad 380015 Gujarat India Indian Inst Sci Bangalore 560012 Karnataka India

ISBN: (纸本)9781450347532

We address the problem of image denoising for an additive white noise model without placing any restrictions on the statistical distribution of noise. We assume knowledge of only the first-and second-order noise statistics. In the recent mean-square error (MSE) minimization approaches for image denoising, one considers a particular noise distribution and derives an expression for the unbiased risk estimate of the MSE. For additive white Gaussian noise, an unbiased estimate of the MSE is Stein's unbiased risk estimate (SURE), which relies on Stein's lemma. We derive an unbiased risk estimate without using Stein's lemma or its counterparts for additive white noise model irrespective of the noise distribution. We refer to the MSE estimate as the generic risk estimate (GenRE). We demonstrate the effectiveness of GenRE using shrinkage in the undecimated Haar wavelet transform domain as the denoising function. The estimated peak-signal-to-noise-ratio (PSNR) using GenRE is typically within 1% of the PSNR obtained when optimizing with the oracle MSE. The performance of the proposed method is on par with SURE for Gaussian noise distribution, and better than SURE-based methods for other noise distributions such as uniform and Laplacian distribution in terms of both PSNR and structural similarity (SSIM).

关键词： Risk estimation undecimated wavelet transform image denoising wavelet shrinkage generic risk estimation (GenRE)

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User guided generation of corroded objects 16

User guided generation of corroded objects

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

作者： Jain, Nisha Ranjan, Rohit Kalra, Prem Kumar, Subodh Indian Inst Technol Delhi New Delhi India

ISBN: (纸本)9781450347532

Rendering of corrosion often requires pain-staking modeling and texturing. On the other hand, there exist techniques for stochastic modeling of corrosion, which can automatically perform simulation and rendering under control of some user-specified parameters. Unfortunately, these parameters are non-intuitive and have a global impact. It is hard to determine the values of these parameters to obtain a desired look. For example, in real life corrosion gets influenced by both internal object-specific geometric factors, like sharp corners and curvatures, and external interventions like scratches, blemishes etc. Further, a graphics designer may want to selectively corrode areas to obtain a particular scene. We present a technique for user guided spread of corrosion. Our framework encapsulates both structural and aesthetic factors. Given the material properties and the surrounding environmental conditions of an object, we employ a physio-chemically based stochastic model to deduce the decay of different points on that object. Our system equips the user with a platform where the imperfections can be provided by either manual or systematic interference on a rendering of the three dimensional object. We demonstrate several user guided characteristic simulations encompassing varied influences including material, object characteristics and environment conditions. Our results are visually validated to understand the impact of imperfections with elapsed time.

关键词： Corrosion Simulation Rendering User guided Curvatures

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