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Signal & Image Processing

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    Blind Deconvolution with Model Discrepancies

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 13 Months ago

    Blind deconvolution is a strongly ill-posed problem comprising of simultaneous blur and image estimation. Recent advances in prior modeling and/or inference methodology led to methods that started to perform reasonably well in real cases

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    Blind Facial Image Quality Enhancement using Non-Rigid Semantic Patches

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 13 Months ago

    We propose a new way to solve a very general blind inverse problem of multiple simultaneous degradations, such as blur, resolution reduction, noise, and contrast changes, without explicitly estimating the degradation.

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    Rank Pooling for Action Recognition

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 1 Months ago

    We propose a function-based temporal pooling method that captures the latent structure of the video sequence data - e.g., how frame-level features evolve over time in a video. We show how the parameters of a function that has been fit to the video data can serve as a robust new video representation

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    Learning Category-Specific Deformable 3D Models for Object Reconstruction

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 12 Months ago

    We address the problem of fully automatic object localization and reconstruction from a single image. This is both a very challenging and very important problem which has, until recently, received limited attention due to difficulties in segmenting objects and predicting their poses. Here we leverage recent advances in learning convolutional networks for object detection and segmentation and introduce a complementary network for the task of camera viewpoint prediction

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    Probabilistic Tensor Canonical Polyadic Decomposition With Orthogonal Factors

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 12 Months ago

    Tensor canonical polyadic decomposition (CPD), which recovers the latent factor matrices from multidimensional data, is an important tool in signal processing. In many applications, some of the factor matrices are known to have orthogonality structure, and this information can be exploited to improve the accuracy of latent factors recovery. However, existing methods for CPD with orthogonal factors all require the knowledge of tensor rank, which is difficult to acquire, and have no mechanism to handle outliers in measurements. To overcome these disadvantages, in this paper, a novel tensor CPD algorithm based on the probabilistic inference framework is devised.

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    Face Verification via Learned Representation on Feature-Rich Video Frames

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 12 Months ago

    Abundance and availability of video capture devices, such as mobile phones and surveillance cameras, have instigated research in video face recognition, which is highly pertinent in law enforcement applications. While the current approaches have reported high accuracies at equal error rates, performance at lower false accept rates requires significant improvement. In this paper, we propose

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