scholarly journals Combination of Spatial and Frequency Domains for Floating Object Detection on Complex Water Surfaces

2019 ◽  
Vol 9 (23) ◽  
pp. 5220
Author(s):  
Sun ◽  
Deng ◽  
Liu ◽  
Deng
Keyword(s):  
Object Detection ◽  
Saliency Detection ◽  
Small Sample ◽  
Low Rank ◽  
Support Vector ◽  
Frequency Domains ◽  
Floating Object ◽  
Texture Detection ◽  
Floating Objects ◽  
Global And Local

In order to address the problems of various interference factors and small sample acquisition in surface floating object detection, an object detection algorithm combining spatial and frequency domains is proposed. Firstly, a rough texture detection is performed in a spatial domain. A Fused Histogram of Oriented Gradient (FHOG) is combined with a Gray Level Co-occurrence Matrix (GLCM) to describe global and local information of floating objects, and sliding windows are classified by Support Vector Machines (SVM) with new texture features. Then, a novel frequency-based saliency detection method used in complex scenes is proposed. It adopts global and local low-rank decompositions to remove redundant regions caused by multiple interferences and retain floating objects. The final detection result is obtained by a strategy of combining bounding boxes from different processing domains. Experimental results show that the overall performance of the proposed method is superior to other popular methods, including traditional image segmentation, saliency detection, hand-crafted texture detection, and Convolutional Neural Network Based (CNN-based) object detection. The proposed method is characterized by small sample training and strong anti-interference ability in complex water scenes like ripple, reflection, and uneven illumination. The average precision of the proposed is 97.2%, with only 0.504 seconds of time consumption.

scholarly journals Intelligent Image Saliency Detection Method Based on Convolution Neural Network Combining Global and Local Information

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Songshang Zou ◽  
Wenshu Chen ◽  
Hao Chen
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Feature Fusion ◽  
Essential Feature ◽  
Saliency Detection ◽  
Experimental Result ◽  
Image Saliency ◽  
Detection Technology ◽  
Global And Local ◽  
Saliency Object Detection

Image saliency object detection can rapidly extract useful information from image scenes and further analyze it. At present, the traditional saliency target detection technology still has the edge of outstanding target that cannot be well preserved. Convolutional neural network (CNN) can extract highly general deep features from the images and effectively express the essential feature information of the images. This paper designs a model which applies CNN in deep saliency object detection tasks. It can efficiently optimize the edges of foreground objects and realize highly efficient image saliency detection through multilayer continuous feature extraction, refinement of layered boundary, and initial saliency feature fusion. The experimental result shows that the proposed method can achieve more robust saliency detection to adjust itself to complex background environment.

scholarly journals Virtual Grounding Point Concept for Detecting Abnormal and Normal Events in Home Care Monitoring Systems

2020 ◽  
Vol 10 (9) ◽  
pp. 3005
Author(s):  
Swe Nwe Nwe Htun ◽  
Thi Thi Zin ◽  
Hiromitsu Hama
Keyword(s):  
Decision Making ◽  
Feature Extraction ◽  
Home Care ◽  
Object Detection ◽  
Local Maximum ◽  
Low Rank ◽  
Support Vector ◽  
Visual Object ◽  
High Detection Rate ◽  
Aspect Ratios

In this paper, an innovative home care video monitoring system for detecting abnormal and normal events is proposed by introducing a virtual grounding point (VGP) concept. To be specific, the proposed system is composed of four main image processing components: (1) visual object detection, (2) feature extraction, (3) abnormal and normal event analysis, and (4) the decision-making process. In the object detection component, background subtraction is first achieved using a specific mixture of Gaussians (MoG) to model the foreground in the form of a low-rank matrix factorization. Then, a theory of graph cut is applied to refine the foreground. In the feature extraction component, the position and posture of the detected person is estimated by using a combination of the virtual grounding point, along with its related centroid, area, and aspect ratios. In analyzing the abnormal and normal events, the moving averages (MA) for the extracted features are calculated. After that, a new curve analysis is computed, specifically using the modified difference (MD). The local maximum (lmax), local minimum (lmin), and half width value (vhw) are determined on the observed curve of the modified difference. In the decision-making component, the support vector machine (SVM) method is applied to detect abnormal and normal events. In addition, a new concept called period detection (PD) is proposed to robustly detect the abnormal events. The experimental results were obtained using the Le2i fall detection dataset to confirm the reliability of the proposed method, and that it achieved a high detection rate.

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

scholarly journals G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Florent Le Borgne ◽  
Arthur Chatton ◽  
Maxime Léger ◽  
Rémi Lenain ◽  
Yohann Foucher
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Statistical Power ◽  
Small Sample ◽  
Causal Effects ◽  
Small Samples ◽  
Support Vector ◽  
Sample Sizes ◽  
Super Learner ◽  
Small Sample Sizes

AbstractIn clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.

Free-Surface Flow Simulations With Interactively Moving Bodies

2017 ◽  
Author(s):  
Arthur E. P. Veldman ◽  
Henk Seubers ◽  
Peter van der Plas ◽  
Joop Helder
Keyword(s):  
Free Surface ◽  
Free Fall ◽  
Free Surface Flow ◽  
Surface Flow ◽  
Numerical Solution Method ◽  
Flow Simulations ◽  
Floating Object ◽  
Offshore Industry ◽  
Floating Objects ◽  
Moving Bodies

The simulation of free-surface flow around moored or floating objects faces a series of challenges, concerning the flow modelling and the numerical solution method. One of the challenges is the simulation of objects whose dynamics is determined by a two-way interaction with the incoming waves. The ‘traditional’ way of numerically coupling the flow dynamics with the dynamics of a floating object becomes unstable (or requires severe underrelaxation) when the added mass is larger than the mass of the object. To deal with this two-way interaction, a more simultaneous type of numerical coupling is being developed. The paper will focus on this issue. To demonstrate the quasi-simultaneous method, a number of simulation results for engineering applications from the offshore industry will be presented, such as the motion of a moored TLP platform in extreme waves, and a free-fall life boat dropping into wavy water.

Object detection by color histogram-based fuzzy classifier with support vector learning

2009 ◽  
Vol 72 (10-12) ◽  
pp. 2464-2476 ◽  
Author(s):  
Chia-Feng Juang ◽  
Wen-Kai Sun ◽  
Guo-Cyuan Chen
Keyword(s):  
Object Detection ◽  
Color Histogram ◽  
Support Vector ◽  
Fuzzy Classifier

Snowflake Removal for Videos via Global and Local Low-Rank Decomposition

2018 ◽  
Vol 20 (10) ◽  
pp. 2659-2669 ◽  
Author(s):  
Jiandong Tian ◽  
Zhi Han ◽  
Weihong Ren ◽  
Xiai Chen ◽  
Yandong Tang
Keyword(s):  
Low Rank ◽  
Rank Decomposition ◽  
Global And Local ◽  
Low Rank Decomposition

Virtual samples based robust block-diagonal dictionary learning for face recognition

2021 ◽  
Vol 25 (5) ◽  
pp. 1273-1290
Author(s):  
Shuangxi Wang ◽  
Hongwei Ge ◽  
Jinlong Yang ◽  
Shuzhi Su
Keyword(s):  
Face Recognition ◽  
Dictionary Learning ◽  
Recognition Performance ◽  
Small Sample Size ◽  
Small Sample ◽  
Low Rank ◽  
Negative Effects ◽  
Linear Classifier ◽  
Virtual Samples ◽  
Block Diagonal

It is an open question to learn an over-complete dictionary from a limited number of face samples, and the inherent attributes of the samples are underutilized. Besides, the recognition performance may be adversely affected by the noise (and outliers), and the strict binary label based linear classifier is not appropriate for face recognition. To solve above problems, we propose a virtual samples based robust block-diagonal dictionary learning for face recognition. In the proposed model, the original samples and virtual samples are combined to solve the small sample size problem, and both the structure constraint and the low rank constraint are exploited to preserve the intrinsic attributes of the samples. In addition, the fidelity term can effectively reduce negative effects of noise (and outliers), and the ε-dragging is utilized to promote the performance of the linear classifier. Finally, extensive experiments are conducted in comparison with many state-of-the-art methods on benchmark face datasets, and experimental results demonstrate the efficacy of the proposed method.

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