Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

IEEE Transactions on Pattern Analysis and Machine Intelligence

High Efficiency Video Coding (HEVC) has higher encoding complexity due to sophisticated coding tree unit (CTU) partition with recursive rate-distortion optimization (RDO) procedures. In this paper, we propose a specified Asymmetric-Kernel CNN (AK-CNN) for fast CTU and PU (prediction unit) partition prediction. Shallow network structures with asymmetric horizontal and vertical convolution kernels are designed to precisely extract the texture features of each block with much lower complexity. We establish our own dataset with complete CTU partition patterns together with their RD-cost for network training. The confidence threshold decision scheme is designed in the PU partition part to achieve the best trade-off between the coding performance and complexity reduction. Experimental results demonstrate that our approach achieves 69.8% intra mode encoding complexity reduction with negligible rate-distortion performance degradation, superior to the existing fast partition algorithms.

Asymmetric-Kernel CNN Based Fast CTU Partition for HEVC Intra Coding

Visual Quality Assessment of 3D/stereoscopic video (3D VQA) is significant for both quality monitoring and optimization of the existing 3D video services. In this paper, we build a 3D video database based on the latest 3D-HEVC video coding standard, to investigate the relationship among video quality, depth quality, and overall quality of experience (QoE) of 3D/stereoscopic video. We also analyze the pivotal factors to the video and depth qualities. Moreover, we develop a No-Reference 3D-HEVC bitstream-level objective video quality assessment model, which utilizes the key features extracted from the 3D video bitstreams to assess the perceived quality of the stereoscopic video. The model is verified to be effective on our database as compared with widely used 2D Full-Reference quality metrics as well as a state-of-the-art 3D FR pixel-level video quality metric.

3D-HEVC visual quality assessment: Database and bitstream model

In this paper, we propose a no-reference stereoscopic video quality assessment (NR-SVQA) method based on an end-to-end dual stream deep neural network (DNN), which incorporates left and right view sub-networks. The end-to-end dual stream network takes image patch pairs from left and right view pivotal frames as inputs and evaluates the perceptual quality of each image patch pair. By combining multiple convolution, max-pooling and fully-connected layers with regression in the framework, distortion related features are learned end-to-end and purely data driven. Then, a spatiotemporal pooling strategy is employed on these image patch pairs to estimate the entire stereoscopic video quality. The proposed network architecture, which we name End-to-end Dual stream deep Neural network (EDN), is trained and tested on the well-known stereoscopic video dataset divided by reference videos. Experimental results demonstrate that our proposed method outperforms state-of-the-art algorithms.

Stereoscopic Video Quality Prediction Based on End-to-End Dual Stream Deep Neural Networks

Learning “motion” online or from video tutorials is usually inefficient since it is difficult to deliver “motion” information in traditional ways and in the ordinary PC platform. This paper presents ImmerTai, a system that can efficiently teach motion, in particular Chinese Taichi motion, in various immersive environments. ImmerTai captures the Taichi expert's motion and delivers to students the captured motion in multi-modal forms in immersive CAVE, HMD as well as ordinary PC environments. The students' motions are captured too for quality assessment and utilized to form a virtual collaborative learning atmosphere. We built up a Taichi motion dataset with 150 fundamental Taichi motions captured from 30 students, on which we evaluated the learning effectiveness and user experience of ImmerTai. The results show that ImmerTai can enhance the learning efficiency by up to 17.4% and the learning quality by up to 32.3%.

Immersive and collaborative Taichi motion learning in various VR environments

For most large 3D engineering models, the instance positions of repeating instances of connected components show significant multiple spatial aggregation. The invention uses several KD-trees, each for one cluster of points which are spatially aggregated. The multiple KD-trees generate a relatively short data stream, and thus improve the total compression ratio. A method for encoding points of a 3D mesh model comprises steps of determining that the mesh model comprises repeating instances of a connected component, and determining for each repeating instance at least one reference point, clustering the reference points of the repeating instances into one or more clusters, and encoding the clustered reference points using KD-tree coding, wherein for each cluster a separate KD-tree is generated.

Zhibo Chen

Papers

Asymmetric-Kernel CNN Based Fast CTU Partition for HEVC Intra Coding

3D-HEVC visual quality assessment: Database and bitstream model

Stereoscopic Video Quality Prediction Based on End-to-End Dual Stream Deep Neural Networks

Immersive and collaborative Taichi motion learning in various VR environments

Method and apparatus for encoding a mesh model, encoded mesh model, and method and apparatus for decoding a mesh model