Blind image quality assessment with improved natural scene statistics model

No-reference (NR)/blind image quality assessment (IQA) metrics play an important role in the area of image processing. Natural scene statistics (NSS) model assumes that natural images possess certain regular statistical properties and is widely used in NR IQA metrics. Most existing NSS-based NR algorithms are achieved by measuring the variation of image statistics, which are characterized by the fitting parameters of NSS model, across different distortions. However, distortions not only change the image statistics, but also disturb the statistical regularity held by natural images. As a result, the distribution of distorted images can not well follow the NSS model. There exists fitting error between the real distribution of the distorted image and the fitted one under certain NSS model. In this paper, the statistical distributions of the distorted images are discussed in detail. We suggest to take the fitting errors into account as well as the fitting parameters for feature extraction, and propose a novel NR IQA algorithm. Experimental results on several image databases demonstrate that the proposed metric performs highly consistent with human visual perception.     

Deep Scalogram Representations for Acoustic Scene Classification

Spectrogram representations of acoustic scenes have achieved competitive performance for acoustic scene classification. Yet, the spectrogram alone does not take into account a substantial amount of time-frequency information. In this study, we present an approach for exploring the benefits of deep scalogram representations, extracted in segments from an audio stream. The approach presented firstly transforms the segmented acoustic scenes into bump and morse scalograms, as well as spectrograms; secondly, the spectrograms or scalograms are sent into pre-trained convolutional neural networks; thirdly, the features extracted from a subsequent fully connected layer are fed into (bidirectional) gated recurrent neural networks, which are followed by a single highway layer and a softmax layer; finally, predictions from these three systems are fused by a margin sampling value strategy. We then evaluate the proposed approach using the acoustic scene classification data set of 2017 IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE). On the evaluation set, an accuracy of 64.0% from bidirectional gated recurrent neural networks is obtained when fusing the spectrogram and the bump scalogram, which is an improvement on the 61.0% baseline result provided by the DCASE 2017 organisers. This result shows that extracted bump scalograms are capable of improving the classification accuracy, when fusing with a spectrogram-based system.      

Category-specific scene categorization

Scene categorization with category-specific visual-word construction and image representation is proposed in this study. The proposed scene categorization has effective discriminative ability and class extendibility. The reasons are listed as follows. First, since the visual-word construction and image representation are category-specific, the corresponding learning model for classification has substantial discriminating power. Second, since the visual-word construction and image representation are category-specific, image features related to the original classes need not be recreated when new classes are added, which minimizes reconstruction overhead. Experimental results confirm that the accuracy of the proposed method is superior to existing methods with single-type features both in single-scale and in multi-scale versions.     

Interactive direct illumination in dynamic scenes using graphics APIS

Abstract

We present a progressive refinement system for rendering direct illumination at interactive rates for dynamic scenes using available graphics APIs. We achieve the progression by subdividing object surfaces using a quad‐tree approach. Integrating the visibility and illumination, we can render scenes with soft shadows, taking into account the BRDFs of the scene objects. Our system allows a user to specify the desired frame rates, and the system will achieve such rates with the available time budget. Our system can also render dynamic scenes where the system restores the initial meshes and calculates illumination, producing finer but accurate soft shadows during iterative refinements.     

DiSeg 1.0: The first system for Spanish discourse segmentation

Nowadays discourse parsing is a very prominent research topic. However, there is not a discourse parser for Spanish texts. The first stage in order to develop this tool is discourse segmentation. In this work, we present DiSeg, the first discourse segmenter for Spanish, which uses the framework of Rhetorical Structure Theory and is based on lexical and syntactic rules. We describe the system and we evaluate its performance against a gold standard corpus, divided in a medical and a terminological subcorpus. We obtain promising results, which means that discourse segmentation is possible using shallow parsing.     

浅谈如何做好电力业扩报装工作

随着我国社会经济和科学技术的迅速发展，电力业扩报装工作取得了明显的进步。但是，在业扩报装工作中依然存在许多问题，例如业扩报装的工作时间长、收费的公开度低、工作环节多等，这些问题不仅成为电力用户关注的焦点，对电力企业的售电量也产生了重要的影响。     

TSPN: Term-Based Semantic Peer-to-Peer Networks

In this paper,we propose Term-based Semantic Peerto-Peer Networks (TSPN) to achieve semantic search.For each peer,TSPN builds a full text index of its documents.Through the analysis of resources,TSPN o...     

场景于展览空间的意义

以展示空间为母体,对空间中的场景进行了多方面的分析,以引入场景对展示空间的意义着手,分别论述了场景的基本概念、展览空间中场景的意义和价值、展览空间中场景的研究范围,进而揭示了现阶段大量展览空间中的场景缺失。     

一种基于遗传算法的快速景象匹配算法

常规的归一化灰度互相关匹配算法是搜寻区域上的所有点,效率较低,在实时图存在旋转时,会影响匹配的精度。为此,在对典型的基于灰度的景象匹配算法进行分析的基础上,结合遗传算法和分层搜索策略设计了一种快速匹配算法。算法在匹配过程中考虑了存在旋转变化实时图像的处理,从而省去了图像对准的预处理,节省了时间。遗传算法和分层搜索策略的...