- Read more about SF-CNN: A Fast Compression Artifacts Removal via Spatial-to-Frequency Convolutional Neural Networks
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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
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- Read more about AN ADAPTIVE FITTING APPROACH FOR THE VISUAL DETECTION AND COUNTING OF SMALL CIRCULAR OBJECT IN MANUFACTURING APPLICATIONS
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- Read more about Virtual Reality Video Quality Assessment Based on 3D Convolutional Neural Networks
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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.
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- Read more about TOWARDS REAL-TIME CRACK DETECTION USING A DEEP NEURAL NETWORK WITH A BAYESIAN FUSION ALGORITHM
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- Read more about Estimating Physical Activity Intensity and Energy Expenditure using Computer Vision on Videos
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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.
ICIP2019.pptx
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- Read more about ManGAN: Assisting Colorization of Manga Characters Concept Art using Conditional GAN
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Colorization is a challenging task that has recently been tackled by deep learning. Line art colorization is particularly difficult because there is no grayscale value to indicate the color intensities as there is in black-and-white photograph images. When designing a character, concept artists often need to try different color schemes, however, colorization is a time-consuming task. In this article, we propose a semi-automatic framework for colorizing manga concept arts by letting concept artists try different color schemes and obtain colorized results in fashion time.
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- Read more about Fast 6dof Pose Estimation with Synthetic Textureless Cad Model for Mobile Applications
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Performance of 6DoF pose estimation techniques from RGB/RGB-D images has improved significantly with sophisticated deep learning frameworks. These frameworks require large-scale training data based on real/synthetic RGB/RGB-D information. Difficulty of obtaining adequate training data has limited the scope of these frameworks for ubiquitous application areas. Also, fast pose estimation at inference time often requires high-end GPU(s) that restricts the scope for its application in mobile hardware.
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- Read more about Two-Dimensional Tomography from Noisy Projection Tilt Series Taken at Unknown View Angles with Non-Uniform Distribution
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We consider a problem that recovers a 2-D object and the underlying view angle distribution from its noisy projection tilt series taken at unknown view angles. Traditional approaches rely on the estimation of the view angles of the projections, which do not scale well with the sample size and are sensitive to noise. We introduce a new approach using the moment features to simultaneously recover the underlying object and the distribution of view angles.
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- Read more about ITERATIVE DATASET FILTERING FOR WEAKLY SUPERVISED SEGMENTATION OF DEPTH IMAGES
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In this paper, we propose an approach for segmentation of challenging depth images. We first use a semi-automatic segmentation algorithm that only takes a user-defined rectangular area as an input. The quality of the segmentation is very heterogeneous at this stage, and unsufficient to efficiently train a neural network. We thus introduce a learning process that takes this imperfect nature of data into account, by iteratively filtering the dataset to only keep the best segmented images. We show this method improves the neural network’s performance by a significant amount.
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