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In this paper, we propose SF-CNN, a fast convolutional neural network structure for JPEG image compression artifacts removal. Recently, Convolutional Neural Network (CNN) based image restoration has shown great performance improvement. However, their heavy computational cost makes it difficult to apply other applications such as high-level vision tasks. Since heavy computation arises from maintaining spatial resolution of an input image, some works make a structure which is composed of spatial downsampling and

Diabetic retinopathy (DR) is a common retinal disease that leads to blindness. For diagnosis purposes, DR image grading aims to provide automatic DR grade classification, which is not addressed in conventional research methods of binary DR image classification. Small objects in the eye images, like lesions and microaneurysms, are essential to DR grading in medical imaging, but they could easily be influenced by other objects.

As a new medium, Virtual Reality (VR) has attracted widespread attentions and research interests. More and more researchers have built their VR image/video database and devise related algorithms. However, the existing methods of VR video quality assessment are not very effective, and one of the most important reasons is that the database is not suitable. To this end, this paper proposes an efficient VR quality assessment method on self-built database.

Spectral methods such as the improved Fourier Mellin Invariant (iFMI) transform have proved to be faster, more robust

Automated curvilinear image segmentation is a crucial step to characterize and quantify the morphology of blood vessels across scale. We propose a dual pipeline RF_OFB+U-NET that fuses U-Net deep learning features with a low level image feature filter bank using the random forests classifier for vessel segmentation. We modify the U-Net CNN architecture to provide a foreground vessel regression likelihood map that is used to segment both arteriole and venule blood vessels in mice dura mater tissues.

Zero-Shot learning (ZSL) recently has drawn a lot of attention due to its ability to transfer knowledge from seen classes to novel unseen classes, which greatly reduces human labor of labelling data for building new classifiers. Much effort on ZSL however has focused on the standard multi-class setting, the more challenging multi-label zero-shot problem has received limited attention. In this paper, we propose a transfer-aware embedding projection approach to tackle multi-label zero-shot learning.

Estimating physical activity (PA) intensity and energy expenditure (EE) is a problem that typically requires the use of wearable sensors such as a heart rate monitor, or accelerometer. We investigate the accuracy of a computer vision system using videos recorded from a pair of wearable video glasses to estimate PA strength and EE automatically using age, gender, speed, and activity cues. Age and gender are obtained using the Deep EXpectation network, while activity is estimated from joint angles and movement speed.