1,045 research outputs found
Complex network classification using partially self-avoiding deterministic walks
Complex networks have attracted increasing interest from various fields of
science. It has been demonstrated that each complex network model presents
specific topological structures which characterize its connectivity and
dynamics. Complex network classification rely on the use of representative
measurements that model topological structures. Although there are a large
number of measurements, most of them are correlated. To overcome this
limitation, this paper presents a new measurement for complex network
classification based on partially self-avoiding walks. We validate the
measurement on a data set composed by 40.000 complex networks of four
well-known models. Our results indicate that the proposed measurement improves
correct classification of networks compared to the traditional ones
Using deterministic tourist walk as a small-world metric on Watts-Strogatz networks
The Watts-Strogatz model (WS) has been demonstrated to effectively describe
real-world networks due to its ability to reproduce the small-world properties
commonly observed in a variety of systems, including social networks, computer
networks, biochemical reactions, and neural networks. As the presence of
small-world properties is a prevalent characteristic in many real-world
networks, the measurement of "small-worldness" has become a crucial metric in
the field of network science, leading to the development of various methods for
its assessment over the past two decades. In contrast, the deterministic
tourist walk (DTW) method has emerged as a prominent technique for texture
analysis and network classification. In this paper, we propose the use of a
modified version of the DTW method to classify networks into three categories:
regular networks, random networks, and small-world networks. Additionally, we
construct a small-world metric, denoted by the coefficient , from the DTW
method. Results indicate that the proposed method demonstrates excellent
performance in the task of network classification, achieving over
accuracy. Furthermore, the results obtained using the coefficient on
real-world networks provide evidence that the proposed method effectively
serves as a satisfactory small-world metric.Comment: 9 pages, 4 figure
Texture analysis and Its applications in biomedical imaging: a survey
Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity
and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications.
This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021;
date of current version January 24, 2022. This work was supported in
part by the Portuguese Foundation for Science and Technology (FCT)
under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by
FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio
Learning object behaviour models
The human visual system is capable of interpreting a remarkable variety of often subtle, learnt, characteristic behaviours. For instance we can determine the gender of a distant walking figure from their gait, interpret a facial expression as that of surprise, or identify suspicious behaviour in the movements of an individual within a car-park. Machine vision systems wishing to exploit such behavioural knowledge have been limited by the inaccuracies inherent in hand-crafted models and the absence of a unified framework for the perception of powerful behaviour models.
The research described in this thesis attempts to address these limitations, using a statistical modelling approach to provide a framework in which detailed behavioural knowledge is acquired from the observation of long image sequences. The core of the behaviour modelling framework is an optimised sample-set representation of the probability density in a behaviour space defined by a novel temporal pattern formation strategy.
This representation of behaviour is both concise and accurate and facilitates the recognition of actions or events and the assessment of behaviour typicality. The inclusion of generative capabilities is achieved via the addition of a learnt stochastic process model, thus facilitating the generation of predictions and realistic sample behaviours. Experimental results demonstrate the acquisition of behaviour models and suggest a variety of possible applications, including automated visual surveillance, object tracking, gesture recognition, and the generation of realistic object behaviours within animations, virtual worlds, and computer generated film sequences.
The utility of the behaviour modelling framework is further extended through the modelling of object interaction. Two separate approaches are presented, and a technique is developed which, using learnt models of joint behaviour together with a stochastic tracking algorithm, can be used to equip a virtual object with the ability to interact in a natural way. Experimental results demonstrate the simulation of a plausible virtual partner during interaction between a user and the machine
An Overview of Deep Semi-Supervised Learning
Deep neural networks demonstrated their ability to provide remarkable
performances on a wide range of supervised learning tasks (e.g., image
classification) when trained on extensive collections of labeled data (e.g.,
ImageNet). However, creating such large datasets requires a considerable amount
of resources, time, and effort. Such resources may not be available in many
practical cases, limiting the adoption and the application of many deep
learning methods. In a search for more data-efficient deep learning methods to
overcome the need for large annotated datasets, there is a rising research
interest in semi-supervised learning and its applications to deep neural
networks to reduce the amount of labeled data required, by either developing
novel methods or adopting existing semi-supervised learning frameworks for a
deep learning setting. In this paper, we provide a comprehensive overview of
deep semi-supervised learning, starting with an introduction to the field,
followed by a summarization of the dominant semi-supervised approaches in deep
learning.Comment: Preprin
空間的なテクスチャ解析によるコンプレックスネットワークに基づくテクスチャ解析の改善
This thesis proposes a new texture analysis model which enhanced from traditional complex network-based model for texture characterization via spatial texture analysis. The conceptual framework of the proposed model is to synergize between pattern recognition and graph theory research areas. The results of experiment show that the proposed model can capture robust textural information under various uncontrolled environments using standard texture databases. Texture analysis has played an important role in the last few decades. There are a growing number of techniques described in the literature, one of new area research is a complex network for texture characterization, which has developed in recent years. Inspired by the human brain system, the relation among structure texture elements on an image can be derived using the complex network model. Compared to the task of texture classification, development of the original complex network model is required in order to improve classification performance in environment variations. To fulfill this requirement, the enhancing complex network by spatial texture analysis (i.e., spatial distribution and spatial relation) has been achieved in this thesis. The proposed approach addresses the above requirement by investigating and modifying the original complex network model by extracting more discriminative information. A new graph connectivity measurement has been devised, including local spatial pattern mapping, which is denoted as a LSPM, to encode and describe local spatial arrangement of pixels. To the best of the author\u27s knowledge, as investigated in this thesis, the encoding spatial information which has been adapted within the original complex network model presented here were first proposed and reported by the author. The essence of this proposed graph connectivity measurement describes the spatial structure of local image texture cause it can effectively capture and detect micro-structures (e.g., edges, lines, spots) information which is critical being used to distinguish various pattern structures and invariant uncontrolled environments. Moreover, the graph-based representation has been investigated for improving the performance of texture classification. Spatial vector property has been comprised of deterministic graph modeling which decomposing the two component of the magnitude and the direction. Then, the proposed hybrid-based complex network comprises the enhancing graph-based representation, and the new graph connectivity measurement has been devised as an enhancing complex network-based model for texture characterization in this thesis. The experiments are evaluated by using four standard texture databases include Brodatz, UIUC, KTH-TIPS, and UMD. The experimental results are presented in terms of classification rate in this thesis to demonstrate that: firstly, the proposed graph connectivity measurement (LSPM) approach achieved on-average 86.25%, 77.25%, 89.38% and 94.06% respectively based on four databases. Secondly, the proposed graph-based spatial property approach achieved on-average 90.92%, 87.92%, 96.56% and 92.65%, respectively; finally, the hybrid-based complex network model achieved on-average 88.92%, 85.46%, 95.14% and 95.52% respectively. Accordingly, this thesis has advanced the original complex network-based model for texture characterization.九州工業大学博士学位論文 学位記番号:生工博甲第329号 学位授与年月日:平成30年9月21日1 Introduction|2 Literature Review|3 Complex Network Model and Spatial Information|4 Graph-based Representation in Texture Analysis|5 Hybrid-based Complex Network Model|6 Conclusions九州工業大学平成30年
Object Tracking
Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application
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