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With the emergence of deep-learning algorithms, the accuracy of computer-aided supporting systems advanced., However, their adoption in the field of medicine has been limited, partially due to the challenges of generating reliable and timely results. In this research, we focused on classifying four common cutaneous diseases based on dermoscopic images using deep learning algorithms.
The fast growth of surveillance video big data presents great challenges to the video coding technology. Most existing video coding techniques target for visual quality optimization, while the ultimate utility of surveillance videos mainly lies in intelligent analyses, e.g., pedestrian detection and vehicle tracking. In view of this, we aim at proposing an efficient, standard-compatible and simultaneously...
Multi-view-plus-depth (MVD) representation has gained significant attention recently as a means to encode 3D scenes, allowing for intermediate views to be synthesized on-the-fly at the display site through depth-image-based-rendering (DIBR). Automatic quality assessment of MVD images/videos is critical for the optimal design of MVD image/video coding and transmission schemes. Most existing image quality...
Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied...
Object detection aims to identify instances of semantic objects of a certain class in images or videos. The success of state-of-the-art approaches is attributed to the significant progress of object proposal and convolutional neural networks (CNNs). Most promising detectors involve multi-task learning with an optimization objective of softmax loss and regression loss. The first is for multi-class...
Sparse representation is efficient to approximately recover signals by a linear composition of a few bases from an over-complete dictionary. However, in the scenario of data compression, its efficiency and popularity are hindered due to the extra overhead for encoding the sparse coefficients. Therefore, how to establish an accurate rate model in sparse coding and dictionary learning becomes meaningful,...
As the RFID based Internet of Things (loT) gets worldwide attention, to prepare for the rapidly increasing applications in daily life, various security protocols are proposed. But, these protocols, most of which are limited by the tag processing capacity and dangerous exposure during transmission, could only be applied in certain fields. Previously, Chen and Deng's mutual authentication and privacy...
With emerging demand for large-scale video analysis, the Motion Picture Experts Group (MPEG) initiated the Compact Descriptor for Video Analysis (CDVA) standardization in 2014. In this work, we develop novel deep-learning features and incorporate them into the well-established CDVA evaluation framework to study its effectiveness in video analysis. In particular, we propose a Nested Invariance Pooling...
Block partition structure has been recognized as a crucial module in video coding scheme. Recently, a quadtree plus binary tree (QTBT) block partition structure has been proposed in the Joint Video Exploration Team (JVET) development. Compared to the quadtree structure in HEVC, QTBT can achieve better coding performance with hugely increased encoding complexity. Here, we propose an effective QTBT...
Face spoofing detection nowadays has attracted attentions regarding the biometrics authentication issue. Inspired by the observation that face spoofing detection is highly relevant with the inherent image quality which also strongly depends on the properties of the capturing devices and conditions, in this paper, we tackle the spoofing detection problem based on a two-stage learning approach. Firstly,...
In this paper, a new mode decision scheme is proposed for depth map coding in 3D-AVS. The novelty of the paper mainly contains the following two points. Firstly, an improved distortion estimation model of synthesized views is proposed. Secondly, for the mode decision of depth map coding, the distortion is represented to be the weighted sum of depth distortion and estimated distortion of the synthesized...
Sparse representation has been observed to be highly efficient in dealing with rich, varied and directional information in natural scenes. Based on the statistical analysis of primitives in sparse coding, the entropy of primitive (EoP) was proposed for measuring visual information of images, and its changing tendency has been shown to be highly relevant with the human visual system (HVS). But the...
In this contribution, a novel image quality enhancement algorithm based on convolutional network is proposed for low bit rate image compression. Specifically, a downsample procedure is performed to generate lower resolution image for low bit rate compression. While the decoder side, upsample is to be performed firstly to the original resolution. Image quality is further enhanced by the proposed convolutional...
In the latest Joint Video Exploration Team (JVET) development, a quadtree plus binary tree (QTBT) block partition structure is proposed for enhanced video coding. Compared to the quadtree block partition in HEVC, QTBT can achieve much better compression performance at the expense of largely increased encoding computational complexity. In this paper, a fast QTBT block partition algorithm based on the...
High dynamic range (HDR) imaging techniques have been widely advocated that could shape next generation of digital photography. However, the popularity of HDR contents is hindered by the lack of displaying devices for rendering HDR images which could be very expensive. To tackle this, extensive tone-mapping operators (TMOs) have been proposed in order for transforming HDR images to viewable low dynamic...
With the fast advances in video acquisition, computational imaging, and display technologies, there has been a growing interest in high dynamic range (HDR) videos. Tone mapping operators (TMOs) that convert HDR content to low dynamic range (LDR) ones provide a practically useful solution for the visualization of HDR videos on standard LDR displays, where the user experience highly depends on the performance...
Recently, an increasing number of tone-mapping operators (TMOs) have been proposed in order to display high dynamic nge (HDR) images on low dynamic range (LDR) devices. Developing perceptually consistent image quality assessment (QA) measures for TMO is highly desired because traditional LDR based IQA methods cannot support the cross dynamic range quality comparison. In this paper, a novel objective...
Visual feature descriptors have been successfully deployed in a wide range of applications, e.g. visual retrieval and analysis. To transmit these descriptors over bandwidth-limited networks, a high effciency feature coding technique is highly desired to maximize compression capability and achieve compact feature representations. In this paper, a hybrid visual feature descriptor compression framework...
In this paper, we propose a novel image set compression approach based on sparse coding with an ordered dictionary learned from perceptually informative signals. For a group of similar images, one representative image is first selected and transformed into wavelet domain, and then its AC components are utilized as samples to train an over-complete dictionary. In order to improve compression efficiency,...
In-loop filters have been widely utilized in latest video coding standards to improve the video coding efficiency by reducing compression artifacts. However, existing in-loop filters only utilize image local correlations, leading to limited performance improvement. In this paper, we explore a novel adaptive in-loop filter by means of the nonlocal similar content to improve the quality of reconstructed...
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