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Recent advances in signal processing for the detection of Steady-State Visual Evoked Potentials (SSVEPs) have moved away from traditionally calibrationless methods, such as canonical correlation analysis, and towards algorithms that require substantial training data. In general, this has improved detection rates, but SSVEP-based brain-computer interfaces (BCIs) now suffer from the requirement of costly...
Canonical correlation analysis (CCA) has been successfully used for extracting frequency components of steady-state visual evoked potential (SSVEP) in electroencephalography (EEG). Recently, a few efforts on CCA-based SSVEP methods have been made to demonstrate the benefits for brain computer interface (BCI). Most of these methods are limited to linear CCA. In this paper consider a deep extension...
In this paper we propose a novel method for finding the fovea in colored images of the eye fundus. We use an image correlation coefficient to find the fovea, which is calculated from a set of template images of the fovea. The results, using the DIARETDB1 database, indicate that our method detects the fovea region with an accuracy of 82,02%.
Prediction of gender and other demographic attributes of individuals from handwriting samples offers an interesting basic, as well as applied research problem. The correlation between gender and the visual appearance of handwriting has been validated by a number of studies and the present study is based on the same idea. We exploit the textural measurements as the discriminating attribute between...
Accurate seismic data visualization is of paramount importance for seismic interpretation such that reservoir quality can be predicted precisely and oil or gas resources can be evaluated truthfully. For micro geological structure in seismic data, due to the variable geometric attribute, smaller size and significant role, seismic data visualization presents an intriguing problem. Existing approaches...
An Application-Programming Interface or API provides a set of program functions that can be used to build new applications. In this paper, we study how to use the expectation-confirmation theory (ECT) to identify API usability problems, and what obstacles a novice developer faces when learning a new API and its accompanying development tools. We conduct a study over the impact of using a visual editor...
Two measures were used to assess to what extent infant's head position was affected by the movements of the apparatus: cross-correlations and fast Fourier transforms (FFTs). Cross-correlations can indicate to what degree the head movements follow the sinusoidal movements of the apparatus and whether the infants' head movements display a systematic reactive or predictive pattern in relation to the...
Video copy detection is still an open problem as current approaches are not able to carry out the detection with enough efficacy and efficiency. These are desirable features in modern video-based applications requiring real-time processing in large scale video databases and without compromising detection performance, especially when facing non-simulated video attacks. These characteristics are also...
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...
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