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Convolutional neural network (CNN) based trackers have achieved significant performances in tracking recently. Most existing CNN-based trackers regard tracking as a classification or similarity searching problem. The two methods have their respective superiorities and limitations because of different supervised objectives. In this paper, we propose a multi-task CNN for visual tracking, not only fully...
A lighting system and method has been developed which has shown in testing to allow quality images to be obtained that are free from two particular problems, specular reflections on the subject, and light intensity variation. These problems both diminish the ability to compare objects for attributes such as colour variation, edges, contours, and many other features. The system developed eliminates...
We present OCSB, a novel online Bayesian framework for imbalance multi-class data streams. To the best of our knowledge, OCSB is the first online method applying both cost-sensitive learning and sampling technique in a single classifier to deal with class imbalance learning. Specifically, an artificial cost matrix is designed and adapted in a sequential manner to not only boost the accuracy of minority...
The popularly used subjective estimator- mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain expertise, and many other factors that may actively influence on actual assessment. We therefore, devise a no- reference subjective quality assessment metric by exploiting the nature of human eye browsing on videos. The participants' eye-tracker recorded gaze-data indicate...
With the development of displaying techniques, free viewpoint video (FVV) system shows its potential to provide immersive perceptual feeling by changing viewpoints. To provide this luxury, a large number of high quality views have to be synthesised from limited number of viewpoints. However, in this process, a portion of the background is occluded by the foreground object in the generated synthesised...
Cancerous masses detection in dense background is a particularly challenging task for even experienced radiologists due to their similarity of intensity with the overlapped normal dense tissues, obscured boundaries and low contrast between mass and surrounding regions. This paper proposes a novel approach for the identification of cancerous regions located in a dense part of a breast. Careful analysis...
Spectral methods refer to the problem of finding eigenvectors of an affinity matrix. Despite promising performance on revealing manifold structure, they are limited in its applicability to large-scale problems due to the high computational cost of eigendecomposition. Nyström method, as a classic method, seeks an approximate solution by first solving a smaller eigenproblem defined on a subset of landmarks,...
Recent research in tomographic reconstruction is motivated by the need to efficiently recover detailed anatomy from limited measurements. One of the ways to compensate for the increasingly sparse sets of measurements is to exploit the information from \emph{templates}, i.e., prior data available in the form of already reconstructed, structurally similar images. Towards this, previous work has exploited...
This paper suggests a new method for image registration, based on a new similarity measure, the standard deviation normalized summed squared difference. Such a similarity measure is explicitly defined on the effective overlap between two images, and the image registration is fulfilled by searching for a global minimum peak of this measure over the entire parameter space. Conceptually the suggested...
Deep learning based hyperspectral image (HSI) classification have recently shown promising performance. However, complex network architecture, tedious training process and effective utilization of spatial/contextual information in deep network limits the application and performance of deep learning. In this paper, for an effective spectral-spatial feature extraction , an improved deep network, spatial...
In this paper, we present a method for stereo super-resolution which employs a deep network. The network is trained using the residual image so as to obtain a high resolution image from two, low resolution views. Our network is comprised by two deep sub-nets which share, at their output, a single convolutional layer. This last layer in the network delivers an estimate of the residual image which is...
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