scholarly journals New approach to dehaze single nighttime image

2021 ◽  
Vol 39 (3) ◽  
pp. 604-610
Author(s):  
Biao Duan ◽  
Jing Li ◽  
Huaimin Chen ◽  
Yi Ru ◽  
Ze Zhang
Keyword(s):  
Present Method ◽  
State Of The Art ◽  
Separation Method ◽  
Superior Performance ◽  
Single Image ◽  
New Approach ◽  
Shift Problem ◽  
Current State ◽  
Color Shift ◽  
Nighttime Image

This paper focus on the dehazing of a single image captured at nighttime. The current state-of-the-art nighttime dehazing approaches usually suffer from the color shift problem due to the fact that the assumptions enforced underdaytime cannot get applied to the nighttime image directly. The classical dehazing methods try to estimate the transmission mapand accurate light to dehaze a single image. The present basic idea is to firstly separate the light layer from the hazy image and thetransmission map can be computed afterwards. A new layer separation method is proposed to solve the non-globalatmospheric light problem. The present method on some real datasets to show its superior performance is validated.

scholarly journals Unsupervised Action Proposals Using Support Vector Classifiers for Online Video Processing

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2953
Author(s):  
Marcos Baptista Ríos ◽  
Roberto Javier López-Sastre ◽  
Francisco Javier Acevedo-Rodríguez ◽  
Pilar Martín-Martín ◽  
Saturnino Maldonado-Bascón
Keyword(s):  
Video Processing ◽  
State Of The Art ◽  
Learning To Rank ◽  
Support Vector ◽  
New Approach ◽  
Support Vector Classifier ◽  
Current State ◽  
Video Frames ◽  
Unsupervised Approach ◽  
Video Sensor

In this work, we introduce an intelligent video sensor for the problem of Action Proposals (AP). AP consists of localizing temporal segments in untrimmed videos that are likely to contain actions. Solving this problem can accelerate several video action understanding tasks, such as detection, retrieval, or indexing. All previous AP approaches are supervised and offline, i.e., they need both the temporal annotations of the datasets during training and access to the whole video to effectively cast the proposals. We propose here a new approach which, unlike the rest of the state-of-the-art models, is unsupervised. This implies that we do not allow it to see any labeled data during learning nor to work with any pre-trained feature on the used dataset. Moreover, our approach also operates in an online manner, which can be beneficial for many real-world applications where the video has to be processed as soon as it arrives at the sensor, e.g., robotics or video monitoring. The core of our method is based on a Support Vector Classifier (SVC) module which produces candidate segments for AP by distinguishing between sets of contiguous video frames. We further propose a mechanism to refine and filter those candidate segments. This filter optimizes a learning-to-rank formulation over the dynamics of the segments. An extensive experimental evaluation is conducted on Thumos’14 and ActivityNet datasets, and, to the best of our knowledge, this work supposes the first unsupervised approach on these main AP benchmarks. Finally, we also provide a thorough comparison to the current state-of-the-art supervised AP approaches. We achieve 41% and 59% of the performance of the best-supervised model on ActivityNet and Thumos’14, respectively, confirming our unsupervised solution as a correct option to tackle the AP problem. The code to reproduce all our results will be publicly released upon acceptance of the paper.

scholarly journals Path-based extensions of local link prediction methods for complex networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Furqan Aziz ◽  
Haji Gul ◽  
Irfan Uddin ◽  
Georgios V. Gkoutos
Keyword(s):  
Complex System ◽  
Link Prediction ◽  
Network Science ◽  
State Of The Art ◽  
Superior Performance ◽  
Prediction Methods ◽  
Local Similarity ◽  
Fundamental Interest ◽  
Current State ◽  
Similarity Indices

AbstractLink prediction in a complex network is a problem of fundamental interest in network science and has attracted increasing attention in recent years. It aims to predict missing (or future) links between two entities in a complex system that are not already connected. Among existing methods, local similarity indices are most popular that take into account the information of common neighbours to estimate the likelihood of existence of a connection between two nodes. In this paper, we propose global and quasi-local extensions of some commonly used local similarity indices. We have performed extensive numerical simulations on publicly available datasets from diverse domains demonstrating that the proposed extensions not only give superior performance, when compared to their respective local indices, but also outperform some of the current, state-of-the-art, local and global link-prediction methods.

scholarly journals Use of a Novel Grammatical Inference Approach in Classification of Amyloidogenic Hexapeptides

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Wojciech Wieczorek ◽  
Olgierd Unold
Keyword(s):  
Support Vector Machine ◽  
Correlation Coefficient ◽  
State Of The Art ◽  
Superior Performance ◽  
Support Vector ◽  
Grammatical Inference ◽  
Regular Expressions ◽  
Inference Algorithm ◽  
Current State

The present paper is a novel contribution to the field of bioinformatics by using grammatical inference in the analysis of data. We developed an algorithm for generating star-free regular expressions which turned out to be good recommendation tools, as they are characterized by a relatively high correlation coefficient between the observed and predicted binary classifications. The experiments have been performed for three datasets of amyloidogenic hexapeptides, and our results are compared with those obtained using the graph approaches, the current state-of-the-art methods in heuristic automata induction, and the support vector machine. The results showed the superior performance of the new grammatical inference algorithm on fixed-length amyloid datasets.

Learning on the Move in the Web 2.0

2013 ◽  
pp. 1693-1714
Author(s):  
Carlos Baladrón ◽  
Javier M. Aguiar ◽  
Lorena Calavia ◽  
Belén Carro ◽  
Antonio Sánchez-Esguevillas
Keyword(s):  
Web 2.0 ◽  
Mobile Devices ◽  
State Of The Art ◽  
New Approach ◽  
Collaborative Tools ◽  
Research And Innovation ◽  
Current State ◽  
Pros And Cons ◽  
Approach To Teaching ◽  
The Web

This work aims at presenting the current state of the art of the m-learning trend, an innovative new approach to teaching focused on taking advantage of mobile devices for learning anytime, anywhere and anyhow, usually employing collaborative tools. However, this new trend is still young, and research and innovation results are still fragmented. This work aims at providing an overview of the state of the art through the analysis of the most interesting initiatives published and reported, studying the different approaches followed, their pros and cons, and their results. And after that, this chapter provides a discussion of where we stand nowadays regarding m-learning, what has been achieved so far, which are the open challenges and where we are heading.

scholarly journals An Appearance-and-Structure Fusion Network for Object Viewpoint Estimation

2018 ◽  
Author(s):  
Yueying Kao ◽  
Weiming Li ◽  
Zairan Wang ◽  
Dongqing Zou ◽  
Ran He ◽  
...  
Keyword(s):  
State Of The Art ◽  
Machine Intelligence ◽  
Intelligence Community ◽  
Challenging Problem ◽  
Single Image ◽  
Structure Information ◽  
Current State ◽  
Art Methods ◽  
Viewpoint Estimation ◽  
Visual Ambiguity

Automatic object viewpoint estimation from a single image is an important but challenging problem in machine intelligence community. Although impressive performance has been achieved, current state-of-the-art methods still have difficulty to deal with the visual ambiguity and structure ambiguity in real world images. To tackle these problems, a novel Appearance-and-Structure Fusion network, which we call it ASFnet that estimates viewpoint by fusing both appearance and structure information, is proposed in this paper. The structure information is encoded by precise semantic keypoints and can help address the visual ambiguity. Meanwhile, distinguishable appearance features contribute to overcoming the structure ambiguity. Our ASFnet integrates an appearance path and a structure path to an end-to-end network and allows deep features effectively share supervision from both the two complementary aspects. A convolutional layer is learned to fuse the two path results adaptively. To balance the influence from the two supervision sources, a piecewise loss weight strategy is employed during training. Experimentally, our proposed network outperforms state-of-the-art methods on a public PASCAL 3D+ dataset, which verifies the effectiveness of our method and further corroborates the above proposition.

scholarly journals The Role of Kinetics as Key Determinant in Toxicity of Pyrrolizidine Alkaloids and Their N-Oxides

Planta Medica ◽  
2021 ◽  
Author(s):  
Frances Widjaja ◽  
Yasser Alhejji ◽  
Ivonne M. C. M. Rietjens
Keyword(s):  
Animal Studies ◽  
Pyrrolizidine Alkaloids ◽  
Liver Toxicity ◽  
State Of The Art ◽  
New Approach ◽  
Current State ◽  
Plant Constituents ◽  
Relative Differences

AbstractPyrrolizidine alkaloids (PAs) are a large group of plant constituents of which especially the 1,2- unsaturated PAs raise a concern because of their liver toxicity and potential genotoxic carcinogenicity. This toxicity of PAs depends on their kinetics. Differences in absorption, distribution, metabolism, and excretion (ADME) characteristics of PAs may substantially alter the relative toxicity of PAs. As a result, kinetics will also affect relative potency (REP) values. The present review summarizes the current state-of-the art on PA kinetics and resulting consequences for toxicity and illustrates how physiologically-based kinetic (PBK) modelling can be applied to take kinetics into account when defining the relative differences in toxicity between PAs in the in vivo situation. We conclude that toxicokinetics play an important role in the overall toxicity of pyrrolizidine alkaloids. and that kinetics should therefore be considered when defining REP values for combined risk assessment. New approach methodologies (NAMs) can be of use to quantify these kinetic differences between PAs and their N-oxides, thus contributing to the 3Rs (Replacement, Reduction and Refinement) in animal studies.

Learning on the Move in the Web 2.0

2013 ◽  
pp. 437-458
Author(s):  
Carlos Baladrón ◽  
Javier M. Aguiar ◽  
Lorena Calavia ◽  
Belén Carro ◽  
Antonio Sánchez-Esguevillas
Keyword(s):  
Web 2.0 ◽  
Mobile Devices ◽  
State Of The Art ◽  
New Approach ◽  
Collaborative Tools ◽  
Research And Innovation ◽  
Current State ◽  
Pros And Cons ◽  
Approach To Teaching ◽  
The Web

This work aims at presenting the current state of the art of the m-learning trend, an innovative new approach to teaching focused on taking advantage of mobile devices for learning anytime, anywhere and anyhow, usually employing collaborative tools. However, this new trend is still young, and research and innovation results are still fragmented. This work aims at providing an overview of the state of the art through the analysis of the most interesting initiatives published and reported, studying the different approaches followed, their pros and cons, and their results. And after that, this chapter provides a discussion of where we stand nowadays regarding m-learning, what has been achieved so far, which are the open challenges and where we are heading.

scholarly journals Improved Color Attenuation Prior for Single-Image Haze Removal

2019 ◽  
Vol 9 (19) ◽  
pp. 4011 ◽  
Author(s):  
Dat Ngo ◽  
Gi-Dong Lee ◽  
Bongsoon Kang
Keyword(s):  
Background Noise ◽  
State Of The Art ◽  
Median Filter ◽  
Depth Map ◽  
Superior Performance ◽  
Single Image ◽  
Vast Number ◽  
Haze Removal ◽  
Quantitative Metrics ◽  
Successful Resolution

This paper proposes a single image haze removal algorithm that shows a marked improvement on the color attenuation prior-based method. Through a vast number of experiments on a wide variety of images, it is discovered that there are problems in the color attenuation prior, such as color distortion and background noise, which arise due to the fact that the priors do not hold true in all circumstances. Successful resolution of these problems using the proposed algorithm shows its superior performance to other state-of-the-art methods in terms of both subjective visual quality and quantitative metrics, on both synthetic and natural hazy image datasets. The proposed algorithm also is computationally friendly, due to the use of an efficient quad-decomposition algorithm for atmospheric light estimation and a simple modified hybrid median filter for depth map refinement.

scholarly journals Data-Oriented Language Implementation of the Lattice–Boltzmann Method for Dense and Sparse Geometries

2021 ◽  
Vol 11 (20) ◽  
pp. 9495
Author(s):  
Tadeusz Tomczak
Keyword(s):  
Lattice Boltzmann ◽  
High Performance ◽  
State Of The Art ◽  
Source Code ◽  
Memory Access ◽  
Memory Bandwidth ◽  
New Approach ◽  
Current State ◽  
Access Patterns ◽  
Boltzmann Method

The performance of lattice–Boltzmann solver implementations usually depends mainly on memory access patterns. Achieving high performance requires then complex code which handles careful data placement and ordering of memory transactions. In this work, we analyse the performance of an implementation based on a new approach called the data-oriented language, which allows the combination of complex memory access patterns with simple source code. As a use case, we present and provide the source code of a solver for D2Q9 lattice and show its performance on GTX Titan Xp GPU for dense and sparse geometries up to 40962 nodes. The obtained results are promising, around 1000 lines of code allowed us to achieve performance in the range of 0.6 to 0.7 of maximum theoretical memory bandwidth (over 2.5 and 5.0 GLUPS for double and single precision, respectively) for meshes of sizes above 10242 nodes, which is close to the current state-of-the-art. However, we also observed relatively high and sometimes difficult to predict overheads, especially for sparse data structures. The additional issue was also a rather long compilation, which extended the time of short simulations, and a lack of access to low-level optimisation mechanisms.

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