5,753 research outputs found
A Fuzzy Approach to Text Segmentation in Web Images Based on Human Colour Perception
This chapter describes a new approach for the segmentation of text in images on Web pages. In the same spirit as the authors’ previous work on this subject, this approach attempts to model the ability of humans to differentiate between colours. In this case, pixels of similar colour are first grouped using a colour distance defined in a perceptually uniform colour space (as opposed to the commonly used RGB). The resulting colour connected components are then grouped to form larger (character-like) regions with the aid of a propinquity measure, which is the output of a fuzzy inference system. This measure expresses the likelihood for merging two components based on two features. The first feature is the colour distance between the components, in the L*a*b* colour space. The second feature expresses the topological relationship of two components. The results of the method indicate a better performance than previous methods devised by the authors and possibly better (a direct comparison is not really possible due to the differences in application domain characteristics between this and previous methods) performance to other existing methods
Cognitive visual tracking and camera control
Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision
Real-time Video Quality Assessment for Analog Television Based on Adaptive Fuzzy Membership Function Tuning
Real-time VQA (Video Quality Assessment) is an important part in the effort to build tracking antenna system especially for analog TV. In this case, VQA must work in real-time to assess the video clarity level. VQA assessment results are valuable information for the decision-making process. Thus, the antenna can rotate automatically looking for the ideal direction without user’s control. In addition, the video clarity level on the TV screen can reach optimum according to the user's wishes. The biggest challenge to VQA is, VQA must be able to assess the video clarity level according to the user’s visual perception. Therefore, in this study, the MOS-VQS (Mean Opinion Score-Video Quality Subjective) was used as a visual perception approach. In addition, Adaptive FIS (Fuzzy Inference System) with membership function tuning was implemented for decision making. This was conducted as an effort to build a reliable real-time VQA. The test results show that real-time VQA that has been built has a good performance. This is shown from the average accuracy percentage of the lowest assessment reached 77.2% and the highest reached 88.2%
Video Quality Prediction for Video over Wireless Access Networks (UMTS and WLAN)
Transmission of video content over wireless access networks (in particular, Wireless Local
Area Networks (WLAN) and Third Generation Universal Mobile Telecommunication System (3G UMTS)) is growing exponentially and gaining popularity, and is predicted to expose new revenue streams for mobile network operators. However, the success of these video applications over wireless access networks very much depend on meeting the user’s Quality of Service (QoS) requirements. Thus, it is highly desirable to be able to predict and, if appropriate, to control video quality to meet user’s QoS requirements. Video quality is
affected by distortions caused by the encoder and the wireless access network. The impact of these distortions is content dependent, but this feature has not been widely used in existing
video quality prediction models.
The main aim of the project is the development of novel and efficient models for video
quality prediction in a non-intrusive way for low bitrate and resolution videos and to
demonstrate their application in QoS-driven adaptation schemes for mobile video streaming
applications. This led to five main contributions of the thesis as follows:(1) A thorough understanding of the relationships between video quality, wireless access network (UMTS and WLAN) parameters (e.g. packet/block loss, mean burst length
and link bandwidth), encoder parameters (e.g. sender bitrate, frame rate) and content type is provided. An understanding of the relationships and interactions between them
and their impact on video quality is important as it provides a basis for the development of non-intrusive video quality prediction models.(2) A new content classification method was proposed based on statistical tools as content
type was found to be the most important parameter.
(3) Efficient regression-based and artificial neural network-based learning models were
developed for video quality prediction over WLAN and UMTS access networks. The
models are light weight (can be implemented in real time monitoring), provide a measure for user perceived quality, without time consuming subjective tests. The models have potential applications in several other areas, including QoS control and
optimization in network planning and content provisioning for network/service
providers.(4) The applications of the proposed regression-based models were investigated in (i)
optimization of content provisioning and network resource utilization and (ii) A new
fuzzy sender bitrate adaptation scheme was presented at the sender side over WLAN and UMTS access networks.
(5) Finally, Internet-based subjective tests that captured distortions caused by the encoder
and the wireless access network for different types of contents were designed. The
database of subjective results has been made available to research community as there is a lack of subjective video quality assessment databases.Partially sponsored by EU FP7 ADAMANTIUM Project (EU Contract 214751
Adversarial Inpainting of Medical Image Modalities
Numerous factors could lead to partial deteriorations of medical images. For
example, metallic implants will lead to localized perturbations in MRI scans.
This will affect further post-processing tasks such as attenuation correction
in PET/MRI or radiation therapy planning. In this work, we propose the
inpainting of medical images via Generative Adversarial Networks (GANs). The
proposed framework incorporates two patch-based discriminator networks with
additional style and perceptual losses for the inpainting of missing
information in realistically detailed and contextually consistent manner. The
proposed framework outperformed other natural image inpainting techniques both
qualitatively and quantitatively on two different medical modalities.Comment: To be submitted to ICASSP 201
Highly efficient low-level feature extraction for video representation and retrieval.
PhDWitnessing the omnipresence of digital video media, the research community has
raised the question of its meaningful use and management. Stored in immense
multimedia databases, digital videos need to be retrieved and structured in an
intelligent way, relying on the content and the rich semantics involved. Current
Content Based Video Indexing and Retrieval systems face the problem of the semantic
gap between the simplicity of the available visual features and the richness of user
semantics.
This work focuses on the issues of efficiency and scalability in video indexing and
retrieval to facilitate a video representation model capable of semantic annotation. A
highly efficient algorithm for temporal analysis and key-frame extraction is developed.
It is based on the prediction information extracted directly from the compressed domain
features and the robust scalable analysis in the temporal domain. Furthermore,
a hierarchical quantisation of the colour features in the descriptor space is presented.
Derived from the extracted set of low-level features, a video representation model that
enables semantic annotation and contextual genre classification is designed.
Results demonstrate the efficiency and robustness of the temporal analysis algorithm
that runs in real time maintaining the high precision and recall of the detection task.
Adaptive key-frame extraction and summarisation achieve a good overview of the
visual content, while the colour quantisation algorithm efficiently creates hierarchical
set of descriptors. Finally, the video representation model, supported by the genre
classification algorithm, achieves excellent results in an automatic annotation system by
linking the video clips with a limited lexicon of related keywords
Evaluation of Wirelessly Transmitted Video Quality Using a Modular Fuzzy Logic System
Video transmission over wireless computer networks is increasingly popular as new
applications emerge and wireless networks become more widespread and reliable. An ability to
quantify the quality of a video transmitted using a wireless computer network is important for
determining network performance and its improvement. The process requires analysing the
images making up the video from the point of view of noise and associated distortion as well as
traffic parameters represented by packet delay, jitter and loss. In this study a modular fuzzy logic
based system was developed to quantify the quality of video transmission over a wireless
computer network. Peak signal to noise ratio, structural similarity index and image difference were
used to represent the user's quality of experience (QoE) while packet delay, jitter and percentage
packet loss ratio were used to represent traffic related quality of service (QoS). An overall measure
of the video quality was obtained by combining QoE and QoS values. Systematic sampling was
used to reduce the number of images processed and a novel scheme was devised whereby the
images were partitioned to more sensitively localize distortions. To further validate the developed
system, a subjective test involving 25 participants graded the quality of the received video. The
image partitioning significantly improved the video quality evaluation. The subjective test results
correlated with the developed fuzzy logic approach. The video quality assessment developed in
this study was compared against a method that uses spatial efficient entropic differencing and
consistent results were observed. The study indicated that the developed fuzzy logic approaches
could accurately determine the quality of a wirelessly transmitted video
Network Selection Problems - QoE vs QoS Who is the Winner?
In network selection problem (NSP), there are now two schools of thought. There are those who think using QoE (Quality of Experience) is the best yardstick to measure the suitability of a Candidate Network (CN) to handover to. On the other hand, Quality of Service (QoS) is also advocated as the solution for network selection problems. In this article, a comprehensive framework that supports effective and efficient network selection is presented. The framework  attempts to provide a holistic solution to network selection problem that is achieved by combining both of the QoS and QoE measures.  Using this hybrid solution the best qualities in both methods are combined to overcome issues of the network selection problem According to ITU-R (International Telecommunications Union – Radio Standardization Sector), a 4G network is defined as having peak data rates of 100Mb/s for mobile nodes with speed up to 250 km/hr and 1Gb/s for mobile nodes moving at pedestrian speed. Based on this definition, it is safe to say that mobile nodes that can go from pedestrian speed to speed of up to 250 km/hr will be the norm in future. This indicates that the MN’s mobility will be highly dynamic. In particular, this article addresses the issue of network selection for high speed Mobile Nodes (MN) in 4G networks. The framework presented in this article also discusses how the QoS value collected from CNs can be fine-tuned to better reflect an MN’s current mobility scenario
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