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In this paper, we propose to learn object representations with inference from temporal correlation in videos to achieve effective visual tracking. Unlike traditional methods which perform feature learning either at image level or based on intuitive temporal constraint, we employ the recurrent network with Long Short Term Memory (LSTM) units to directly learn temporally correlated representations of...
We propose a novel method for the demosaicing of event-based images that offers substantial performance improvement of far-distance gesture recognition based on deep Convolutional Neural Network. Unlike the conventional demosaicing technique using the spatial color interpolation of Bayer patterns, our new approach utilizes spatiotemporal correlation between pixel arrays, whereby timestamps of high-resolution...
A large number of images are available on online photo-sharing services along with rich meta-data, including tags, groups, and locations, etc. For associating two domains of different modalities, e.g. images and tags, Canonical Correlation Analysis (CCA) and its extended methods are used widely. We employ a more flexible graph embedding method called Cross-Domain Matching Correlation Analysis (CDMCA),...
In this paper we propose an online multi-task learning algorithm for video concept detection. In particular, we extend the Efficient Lifelong Learning Algorithm (ELLA) in the following ways: a) we solve the objective function of ELLA using quadratic programming instead of solving the Lasso problem, b) we add a new label-based constraint that considers concept correlations, c) we use linear SVMs as...
Subjective test methodologies are morphing to enable researchers to answer questions relevant to rapidly evolving technologies in an efficient and reliable manner. This paper is an exploration of how subjective testing that employs crowdsourcing can be refined to drive stability and reliability in subjective results. We investigate how various design decisions can lead to disparate subjective responses;...
Multispectral demosaicking, which is an extension of color demosaicking, is a challenging problem because each band is significantly undersampled and thus precise reconstruction is needed for the restoration of high-frequency components, such as edges, textures etc. In general, existing algorithms borrow high-frequency information either from different bands via inter-color correlation or from within...
Resolution in medical images is limited by diverse physical, technological and economical considerations. In conventional medical practice, resolution enhancement is usually performed with bicubic or B-spline interpolations, strongly affecting the accuracy of subsequent processing steps such as segmentation or registration. In this paper, we propose a coupled dictionary learning approach for super...
In recent years, correlation filter based trackers outperform better than other trackers. Nevertheless, they only employ one feature and a single kernel, so they are usually not robust in complex scenes. In this paper, we derive a multi-feature and multi-kernel correlation filter based tracker which fully takes advantage of the invariance-discriminative power spectrums of various features and kernels...
Sequential dictionary learning via the K-SVD algorithm has been revealed as a successful alternative to conventional data driven methods such as independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data analysis. fMRI data sets are however structured data matrices with notions of spatio-temporal correlation. This prior information has not been included in the K-SVD...
The accuracy of end-to-end distortion (EED) estimation is crucial to achieving effective error resilient video coding. An established solution, the recursive optimal per-pixel estimate (ROPE), does so by tracking the first and second moments of decoder-reconstructed pixels. An alternative estimation approach, the spectral coefficient-wise optimal recursive estimate (SCORE), tracks instead moments...
Subjective experimental results are widely used as the ground truth in objective Image Quality Assessment (IQA). Specifically, Pairwise Comparison method has superiority over Mean Opinion Scores (MOS), but there is a problem when measuring the consistency between subjective pairwise comparisons and objective quality predictions. In this paper, we first analyze the existing problem of current evaluation...
This paper proposes a novel approach for real-time video summarization on mobile using Dictionary Learning, Global Camera Motion analysis and Colorfulness. A dictionary is represented as a distinct set of events that are described as spatio-temporal features. Uniqueness measure is predicted based on the correlation scores of the dictionary elements whereas the quality measure is estimated using Global...
Most traditional video summarization methods are designed to generate effective summaries for single-view videos, and thus they cannot fully exploit the complicated intra- and inter-view correlations in summarizing multi-view videos. In this paper, we introduce a novel framework for summarizing multi-view videos in a way that takes into consideration both intra- and inter-view correlations in a joint...
We present a method searching for the main symmetric axis in an image based on the SAX representation which converts pixels to symbols and a classical linear time palindrome detecting algorithm. This method generates a curve outlining the axis by dynamic programming and produces a straight axis by RANSAC, a linear fitting method tolerates outliers. The computational complexity is O(mn) on an m×n image,...
Convolutional Neural Networks (CNNs), which have nowadays dominated image analysis tasks, constitute feed-forward methods that model increasingly complex data structures and patterns along the subsequent hidden layers of the network. However, the common practice of using the activation features from the last network layer inevitably leads to a visual recognition bottleneck. This is due to the fact...
Compressive imagers, e.g. the single-pixel camera (SPC), acquire measurements in the form of random projections of the scene instead of pixel intensities. Compressive Sensing (CS) theory allows accurate reconstruction of the image even from a small number of such projections. However, in practice, most reconstruction algorithms perform poorly at low measurement rates and are computationally very expensive...
A personal or enterprise collection of a large set of face images may contain many types of tags used for querying the collection. Often the tags have many irrelevant content that may not reflect the image content in terms of the facial characteristics. In this paper, we propose a data curation method to filter out the irrelevant face images using a face recognition based subgraph identification....
Photo Response Non-Uniformity (PRNU) noise based source attribution is a well known technique to verify the source camera of an anonymous image or video. Researchers have proposed various counter measures to PRNU based source camera attribution. Forced seam-carving is a recently proposed counter forensics measure that was proposed to defeat PRNU based source attribution by disturbing the alignment...
With the availability of high resolution digital technology, there has been increased interest in developing statistical and image processing techniques that can enhance the existing capabilities of analyzing works of art for authenticity. This work explores the merits of using advanced correlation filters in supplementing art experts efforts in identifying forgeries among disputed paintings. We show...
With the increased focus on visual attention (VA) in the last decade, a large number of computational visual saliency methods have been developed. These models are evaluated by using performance evaluation metrics that measure how well a predicted map matches eye-tracking data obtained from human observers. Though there are a number of existing performance evaluation metrics, there is no clear consensus...
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