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

Visual comfort is a quite important factor in 3D media service. Few research efforts have been carried out in this area especially in case of 3D content retargeting which may introduce more complicated visual distortions. In this paper, we propose a Hybrid Distortion Aggregated Visual Comfort Assessment (HDA-VCA) scheme for stereoscopic retargeted images (SRI), considering aggregation of hybrid distortions including structure distortion, information loss, binocular incongruity and semantic distortion. Specifically, a Local-SSIM feature is proposed to reflect the local structural distortion of SRI, and information loss is represented by Dual Natural Scene Statistics (D-NSS) feature extracted from the binocular summation and difference channels. Regarding binocular incongruity, visual comfort zone, window violation, binocular rivalry, and accommodation-vergence conflict of human visual system (HVS) are evaluated. Finally, the semantic distortion is represented by the correlation distance of paired feature maps extracted from original stereoscopic image and its retargeted image by using trained deep neural network. We validate the effectiveness of HDA-VCA on published Stereoscopic Image Retargeting Database (SIRD) and two stereoscopic image databases IEEE-SA and NBU 3D-VCA. The results demonstrate HDA-VCA's superior performance in handling hybrid distortions compared to state-of-the-art VCA schemes.

Hybrid Distortion Aggregated Visual Comfort Assessment for Stereoscopic Image Retargeting.

Visual attention extraction is the ability to rapidly detect the interesting section of a given scene, which is called attention area or region of interest (ROI) in some literature. However, because of the inherent disadvantages of the current attention model structure, the resulted visual attention area is instable along time - that is, the extracted attention areas are quite different even between neighboring frames which are similar in content. Such an un-smooth trajectory of attention areas will lead to impressive subjective quality degradation in attention area based applications like content based video streaming/compression, and this problem cannot be solved simply by smoothing filters. In this paper, we present a strategy to generate stable attention area across time through methods of temporal emendation.

Refinement of extracted visual attention areas in video sequences

In video quality control and estimation, the joint evaluation of compression artifacts and channel artifacts is a problem. The invention considers the joint perception of both a compression artifact level D m (V) and a channel artifact level D h (V) in a method for calculating overall distortion in a video being affected by compression artifacts and channel artifacts. The method comprises a step of adding a compression artifact level D m (V) and a summand S that is derived from log (D h (V)) multiplied with a factor that decreases with increasing compression artifact level D m , such as D(V) = D m (V) + c ⋅ (K - D m (V)) ⋅ log(D h (V)). The result obtained is a measure for the overall distortion.

Method and device for calculating distortion of a video being affected by compression artifacts and channel artifacts

The present invention generally relates to automatic detection of video artifacts. The present invention relates to automatically detect out the images with visible artifacts due to STB decoding error based on a generated artificial edge map. In particular, the application teaches a method to detect out the images with the visible artifacts and how to identify artificial edges from texture (natural) edges. The present system identifies that artificial edges are most significant in local area, while texture edges are often together with some other texture edges.

Method and apparatus to detect artificial edges in images

A method for encoding a sequence of integers includes identifying a contiguous sub-sequence in the sequence of integers wherein the sub-sequence includes interrelated integers having a same prefix when being variable length encoded and an independent last integer. A code for the contiguous sub-sequence is formed using a code for an indication of the number of interrelated integers in the contiguous subsequence, a code of a prefix indication, and the suffixes of variable length codes of the integers in the contiguous sub-sequence. In doing so, a single prefix is sufficient instead of n individual prefixes.

Zhibo Chen

Papers

Hybrid Distortion Aggregated Visual Comfort Assessment for Stereoscopic Image Retargeting.

Refinement of extracted visual attention areas in video sequences

Method and device for calculating distortion of a video being affected by compression artifacts and channel artifacts

Method and apparatus to detect artificial edges in images

Method for encoding and decoding a sequence of integers