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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...
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;...
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...
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...
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...
Correlation filters have been extensively studied to address online visual object tracking task, while achieving favourable performance against the-state-of-the-art methods in various benchmark datasets. Nevertheless, undesired conditions, i.e. partial occlusions or abrupt deformations of the object appearance, severely degrade the performance of correlation filter based tracking methods. To this...
Robust scale and rotation estimation is an important and challenging problem in visual object tracking. There have been proposed many sophisticated trackers to track the location of a target accurately, but most of them do not take much attention to the scale and rotation estimation. Inspired by the success of the correlation filters in visual tracking, we proposed a novel scale-and-rotation correlation...
Correlation filters have recently made significant improvements in visual object tracking on both efficiency and accuracy. In this paper, we propose a sparse correlation filter, which combines the effectiveness of sparse representation and the computational efficiency of correlation filters. The sparse representation is achieved through solving an ℓ0 regularized least squares problem. The obtained...
This paper proposes a method that blindly predicts preference order between inpainted images, aiming at selecting the best one from a plurality of results. Image inpainting, which removes unwanted regions and restores them, has attracted recent attention. However, it is known that the inpainting result varies largely with the method used for inpainting and the parameters set. Thus, in a typical use...
The recent decade has witnessed remarkable developments of SIFT-based approaches for image retrieval. However, such approaches are inherently insufficient in handling the semantic gap and large viewpoint changes, leading to inferior performance. To address these limitations, this paper extends SIFT-based match kernels by integrating the match functions for SIFT and CNN features. Specifically, a thresholded...
Image forensics using sensor photo-response nonuniformity (PRNU) provides a powerful method for associating an image with the camera that captured the image. To preserve privacy despite the availability of this powerful tool, we present a new framework for image anonymization. We formulate anonymization as a feasibility problem subject to multiple constraints that seek to ensure non-detectability...
In this paper, we address the problem of heavy occlusion where the negative samples contaminate the translation model. In this setting, we decompose the task of tracking into translation and scale estimations of objects. We use hierarchical convolutional features to estimate target position and update translation model, and we use HOG features for the scale filter. In addition, we evaluate the translation's...
Estimation of salient regions in an input video is an active area of research due to its wide applications. In this paper, we propose a novel algorithm to estimate the eye gaze movement in a video using motion, color and structural cues with minimum outliers. The algorithm is generalized to capture salient information for the videos taken under different camera motions. The entire algorithm is parallelizable...
A number of image quality assessment (IQA) metrics have been designed in recent years for natural images, leading to a desire to develop IQA approaches for screen content image which is composed of textual as well as pictorial regions and exhibits different visual characteristics from the natural image. In this work, a no reference IQA metric based on convolutional neural network (CNN) is proposed...
Visual texture fidelity evaluation is important but still unsolved problem. Evaluation of how well various texture models conform with human visual perception of their original measured pattern is required not only for assessing the visual dissimilarities between a model output and the original measured texture, but also for optimal settings of model parameters, for fair comparison of distinct models,...
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