69 research outputs found
Stereoscopic Omnidirectional Image Quality Assessment Based on Predictive Coding Theory
Objective quality assessment of stereoscopic omnidirectional images is a
challenging problem since it is influenced by multiple aspects such as
projection deformation, field of view (FoV) range, binocular vision, visual
comfort, etc. Existing studies show that classic 2D or 3D image quality
assessment (IQA) metrics are not able to perform well for stereoscopic
omnidirectional images. However, very few research works have focused on
evaluating the perceptual visual quality of omnidirectional images, especially
for stereoscopic omnidirectional images. In this paper, based on the predictive
coding theory of the human vision system (HVS), we propose a stereoscopic
omnidirectional image quality evaluator (SOIQE) to cope with the
characteristics of 3D 360-degree images. Two modules are involved in SOIQE:
predictive coding theory based binocular rivalry module and multi-view fusion
module. In the binocular rivalry module, we introduce predictive coding theory
to simulate the competition between high-level patterns and calculate the
similarity and rivalry dominance to obtain the quality scores of viewport
images. Moreover, we develop the multi-view fusion module to aggregate the
quality scores of viewport images with the help of both content weight and
location weight. The proposed SOIQE is a parametric model without necessary of
regression learning, which ensures its interpretability and generalization
performance. Experimental results on our published stereoscopic omnidirectional
image quality assessment database (SOLID) demonstrate that our proposed SOIQE
method outperforms state-of-the-art metrics. Furthermore, we also verify the
effectiveness of each proposed module on both public stereoscopic image
datasets and panoramic image datasets
Music Composition from the Brain Signal: Representing the Mental State by Music
This paper proposes a method to translate human EEG into music, so as to represent mental state by music. The arousal levels of the brain mental state and music emotion are implicitly used as the bridge between the mind world and the music. The arousal level of the brain is based on the EEG features extracted mainly by wavelet analysis, and the music arousal level is related to the musical parameters such as pitch, tempo, rhythm, and tonality. While composing, some music principles (harmonics and structure) were taken into consideration. With EEGs during various sleep stages as an example, the music generated from them had different patterns of pitch, rhythm, and tonality. 35 volunteers listened to the music pieces, and significant difference in music arousal levels was found. It implied that different mental states may be identified by the corresponding music, and so the music from EEG may be a potential tool for EEG monitoring, biofeedback therapy, and so forth
In-painting Radiography Images for Unsupervised Anomaly Detection
We propose space-aware memory queues for in-painting and detecting anomalies
from radiography images (abbreviated as SQUID). Radiography imaging protocols
focus on particular body regions, therefore producing images of great
similarity and yielding recurrent anatomical structures across patients. To
exploit this structured information, our SQUID consists of a new Memory Queue
and a novel in-painting block in the feature space. We show that SQUID can
taxonomize the ingrained anatomical structures into recurrent patterns; and in
the inference, SQUID can identify anomalies (unseen/modified patterns) in the
image. SQUID surpasses the state of the art in unsupervised anomaly detection
by over 5 points on two chest X-ray benchmark datasets. Additionally, we have
created a new dataset (DigitAnatomy), which synthesizes the spatial correlation
and consistent shape in chest anatomy. We hope DigitAnatomy can prompt the
development, evaluation, and interpretability of anomaly detection methods,
particularly for radiography imaging.Comment: Main paper with appendi
Subjective quality assessment of stereoscopic omnidirectional image
Stereoscopic omnidirectional images are eye-catching because they can provide realistic and immersive experience. Due to the extra depth perception provided by stereoscopic omnidirectional images, it is desirable and urgent to evaluate the overall quality of experience (QoE) of these images, including image quality, depth perception, and so on. However, most existing studies are based on 2D omnidirectional images and only image quality is taken into account. In this paper, we establish the very first Stereoscopic OmnidirectionaL Image quality assessment Database (SOLID). Three subjective evaluating factors are considered in our database, namely image quality, depth perception, and overall QoE. Additionally, the relationship among these three factors is investigated. Finally, several well-known image quality assessment (IQA) metrics are tested on our SOLID database. Experimental results demonstrate that the objective overall QoE assessment is more challenging compared to IQA in terms of stereoscopic omnidirectional images. We believe that our database and findings will provide useful insights in the development of the QoE assessment for stereoscopic omnidirectional images
Robust aperiodic-disturbance rejection in an uncertain modified repetitive-control system
This paper concerns the problem of designing an EID-based robust output-feedback modified repetitive-control system (ROFMRCS) that provides satisfactory aperiodic-disturbance rejection performance for a class of plants with time-varying structured uncertainties. An equivalent-input-disturbance (EID) estimator is added to the ROFMRCS that estimates the influences of all types of disturbances and compensates them. A continuous-discrete two-dimensional model is built to describe the EID-based ROFMRCS that accurately presents the features of repetitive control, thereby enabling the control and learning actions to be preferentially adjusted. A robust stability condition for the closed-loop system is given in terms of a linear matrix inequality. It yields the parameters of the repetitive controller, the output-feedback controller, and the EID-estimator. Finally, a numerical example demonstrates the validity of the method
Access time oracle for planar graphs
The study of urban networks reveals that the accessibility of important city objects for the vehicle traffic and pedestrians is significantly correlated to the popularity, micro-criminality, micro-economic vitality, and social liveability of the city, and is always the chief factor in regulating the growth and expansion of the city. The accessibility between different components of an urban structure are frequently measured along the streets and routes considered as edges of a planar graph, while the traffic ultimate destination points and street junctions are treated as vertices. For estimation of the accessibility of destination vertex j from vertex i through urban networks, in particular, the random walks are used to calculate the expected distance a random walker starting from i makes before j is visited (known as access time). The state-of-the-art of access time computation is costly in large planar graphs since it involves matrix operation over entire graph. The time complexity is O(n^{2.376}) where n is the number of vertices in the planar graph. To enable efficient access time query answering in large planar graphs, this work proposes the first access time oracle which is based on the proposed access time decomposition and reconstruction scheme. The oracle is a hierarchical data structure with deliberate design on the relationships between different hierarchical levels. The storage requirement of the proposed oracle is O(n^{frac{4}{3}}\log \log n) and the access time query response time is O(n^{frac{2}{3}}) . The extensive tests on a number of large real-world road networks (with up to about 2 million vertices) have verified the superiority of the proposed oracle
Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network
In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data
Generalized sketch families for network traffic measurement
Traffic measurement provides critical information for network management, resource allocation, traffic engineering, and attack detection. Most prior art has been geared towards specific application needs with specific performance objectives. To support diverse requirements with efficient and future-proof implementation, this paper takes a new approach to establish common frameworks, each for a family of traffic measurement solutions that share the same implementation structure, providing a high level of generality, for both size and spread measurements and for all flows. The designs support many options of performance-overhead tradeoff with as few as one memory update per packet and as little space as several bits per flow on average. Such a family-based approach will unify implementation by removing redundancy from different measurement tasks and support reconfigurability in a plug-n-play manner. We demonstrate the connection and difference in the design of these traffic measurement families and perform experimental comparisons on hardware/software platforms to find their tradeoff, which provide practical guidance for which solutions to use under given performance goals
Quick identification of near-duplicate video sequences with cut signature
Online video stream data are surging to an unprecedented level. Massive video publishing and sharing impose heavy demands on continuous video near-duplicate detection for many novel video applications. This paper presents an accurate and accelerated system for video near-duplicate detection over continuous video streams. We propose to transform a high-dimensional video stream into a one-dimensional Video Trend Stream (VTS) to monitor the continuous luminance changes of consecutive frames, based on which video similarity is derived. In order to do fast comparison and effective early pruning, a compact auxiliary signature named CutSig is proposed to approximate the video structure. CutSig explores cut distribution feature of the video structure and contributes to filter candidates quickly. To scan along a video stream in a rapid way, shot cuts with local maximum AI (average information value) in a query video are used as reference cuts, and a skipping approach based on reference cut alignment is embedded for efficient acceleration. Extensive experimental results on detecting diverse near-duplicates in real video streams show the effectiveness and efficiency of our method
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