Underwater Image Segmentation Using Human Psycho Visual Phenomenon

2018 ◽  
pp. 130-146 ◽  
Author(s):  
Soumyadip Dhar ◽  
Hiranmoy Roy
Keyword(s):  
Image Segmentation ◽  
Wavelet Packet ◽  
Data Set ◽  
Standard Data ◽  
Underwater Image ◽  
Large Numbers ◽  
Visual Technique ◽  
Visual Phenomenon ◽  
Novel Method ◽  
Adaptive Basis Selection

In this chapter, a novel method is proposed for underwater image segmentation based on human psycho visual phenomenon (HVS). In the proposed method the texture property of an image is captured by decomposing it into frequency sub-bands using M-band wavelet packet transform. The sub-bands represent the image in different scales and orientations. The large numbers of sub-bands are pruned by an adaptive basis selection. The proper sub-bands for segmentation are selected depending on the HVS. The HVS imitates the original visual technique of a human being and it is used to divide each sub-band in Weber, De-Vries Rose, and saturation regions. A wavelet packet sub-band is selected for segmentation depending on those three regions. The performance of the proposed method is found to be superior to that of the state-of-the-art methods for underwater image segmentation on standard data set.

scholarly journals A Multiple Comprehensive Analysis of scATAC-seq Based on Auto-Encoder and Matrix Decomposition

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1467
Author(s):  
Yuyao Huang ◽  
Yizhou Li ◽  
Yuan Liu ◽  
Runyu Jing ◽  
Menglong Li
Keyword(s):  
Neural Network ◽  
Matrix Factorization ◽  
Network Architecture ◽  
Matrix Decomposition ◽  
Open Chromatin ◽  
Data Set ◽  
Standard Data ◽  
Latent Features ◽  
Novel Method ◽  
Factorization Methods

Single-cell ATAC-seq (scATAC-seq), as the updating of ATAC-seq, provides a novel method for probing open chromatin sites. Currently, research of scATAC-seq is faced with the problem of high dimensionality and the inherent sparsity of the generated data. Recently, several works proposed the use of an autoencoder–decoder, a symmetry neural network architecture, and non-negative matrix factorization methods to characterize the high-dimensional data. To evaluate the performance of multiple methods, in this work, we performed a multiple comparison for characterizing scATAC-seq based on four kinds of auto-encoders known as a symmetry neural network, and two kinds of matrix factorization methods. Different sizes of latent features were used to generate the UMAP plots and for further K-means clustering. Using a gold-standard data set, we practically explored the performance among the methods and the number of latent features in a comprehensive way. Finally, we briefly discuss the underlying difficulties and future directions for scATAC-seq characterizing. As a result, the method designed for handling the sparsity outperforms other tools in the generated dataset.

scholarly journals A Fall Posture Classification and Recognition Method Based on Wavelet Packet Transform and Support Vector Machine

2021 ◽  
Vol 11 (11) ◽  
pp. 5030
Author(s):  
Qingyun Zhang ◽  
Jin Tao ◽  
Qinglin Sun ◽  
Xianyi Zeng ◽  
Matthias Dehmer ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wavelet Packet ◽  
Health And Safety ◽  
The Elderly ◽  
Wavelet Packet Transform ◽  
Support Vector ◽  
Data Set ◽  
Multiple Features ◽  
Sample Data ◽  
Novel Method

An accidental fall seriously threatens the health and safety of the elderly. The injuries caused by a fall have a lot to do with the different postures during the fall. Therefore, recognizing the posture of falling is essential for the rescue and care of the elderly. In this paper, a novel method was proposed to improve the classification and recognition accuracy of fall postures. Firstly, the wavelet packet transform was used to extract multiple features from sample data. Secondly, random forest was used to evaluate the importance of the extracted features and obtain effective features through screening. Finally, the support vector machine classifier based on the linear kernel function was used to realize the falling posture recognition. The experiment results on “Simulated Falls and Daily Living Activities Data Set” show that the proposed method can distinguish different types of fall postures and achieve 99% classification accuracy.

scholarly journals An Optimized Approach for Image Segmentation on Mobile Devices

2021 ◽  
Vol 12 (2) ◽  
pp. 3018-3024
Author(s):  
Hemantkumar R. Turkar, Et. al.
Keyword(s):  
Global Optimization ◽  
Image Segmentation ◽  
Clustering Algorithms ◽  
Smart Devices ◽  
Qualitative And Quantitative Analysis ◽  
Data Set ◽  
Qualitative And Quantitative ◽  
Standard Data ◽  
Quad Tree ◽  
Strong Ability

Now a day’s image segmentation is widely used in many multimedia applications. We have introduced the optimized approach for image segmentation based on clustering for use on smart devices. The proposed optimized approach is based on the combination of partitioning of images using quad-tree and Ant Colony Optimization. This approach utilizes the strong ability of ACO i.e global optimization. The proposed optimized algorithm is evaluated on images of standard data set and its performance is compared with existing clustering algorithms. The qualitative and quantitative analysis has been performed to measure the efficacy of the optimized approach over conventional existing algorithms. This procedure obtains better quality results than existing clustering algorithms.

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

Interactions between the land snails Theba pisana and Cernuella virgata in the laboratory

2020 ◽  
Author(s):  
Geoff H Baker
Keyword(s):  
Laboratory Experiments ◽  
Distribution Patterns ◽  
Land Snails ◽  
Snail Species ◽  
Agricultural Pests ◽  
Large Numbers ◽  
Small Scales ◽  
Novel Method ◽  
Theba Pisana ◽  
Inhibitory Components

ABSTRACT Two Mediterranean snails, Theba pisana and Cernuella virgata, are agricultural pests in southern Australia. The two species are rarely found together in large numbers in the field, at small scales (<1 m2). In laboratory experiments, the presence of T. pisana reduced the survival of C. virgata, but only when food (carrot + lettuce) was provided. When C. virgata was exposed to only the mucus trails and faeces of T. pisana, produced while feeding on lettuce, both the survival and activity of C. virgata were reduced. When carrot was substituted for lettuce, there was less effect. In addition, when C. virgata was exposed to T. pisana’s faeces only, derived from access to a mix of lettuce and carrot, there was no effect on C. virgata’s survival. The observed reductions in the survival of C. virgata were stronger in autumn (the breeding season for both snail species) compared with spring. Inhibitory components within the mucus trails of T. pisana may (1) help explain the observed distribution patterns of the two species at small scales in the field and (2) provide a novel method for control of pest populations of C. virgata, in some situations.

scholarly journals The Study of Multiple Classes Boosting Classification Method Based on Local Similarity

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 37
Author(s):  
Shixun Wang ◽  
Qiang Chen
Keyword(s):  
Image Retrieval ◽  
Loss Function ◽  
Single Mode ◽  
Local Similarity ◽  
Text And Image ◽  
Data Set ◽  
Standard Data ◽  
Weak Learner ◽  
Great Progress ◽  
Data Points

Boosting of the ensemble learning model has made great progress, but most of the methods are Boosting the single mode. For this reason, based on the simple multiclass enhancement framework that uses local similarity as a weak learner, it is extended to multimodal multiclass enhancement Boosting. First, based on the local similarity as a weak learner, the loss function is used to find the basic loss, and the logarithmic data points are binarized. Then, we find the optimal local similarity and find the corresponding loss. Compared with the basic loss, the smaller one is the best so far. Second, the local similarity of the two points is calculated, and then the loss is calculated by the local similarity of the two points. Finally, the text and image are retrieved from each other, and the correct rate of text and image retrieval is obtained, respectively. The experimental results show that the multimodal multi-class enhancement framework with local similarity as the weak learner is evaluated on the standard data set and compared with other most advanced methods, showing the experience proficiency of this method.

scholarly journals Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India

2020 ◽  
Vol 30 (1) ◽  
pp. 273-286
Author(s):  
Kalyan Mahata ◽  
Rajib Das ◽  
Subhasish Das ◽  
Anasua Sarkar
Keyword(s):  
Land Use ◽  
Image Segmentation ◽  
Cellular Automata ◽  
Land Cover ◽  
Satellite Imagery ◽  
Current Model ◽  
Discrete Dynamical System ◽  
Land Use Land Cover ◽  
Data Set ◽  
Validity Indices

Abstract Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.

scholarly journals Elemental ratio measurements of organic compounds using aerosol mass spectrometry: characterization, improved calibration, and implications

2015 ◽  
Vol 15 (1) ◽  
pp. 253-272 ◽  
Author(s):  
M. R. Canagaratna ◽  
J. L. Jimenez ◽  
J. H. Kroll ◽  
Q. Chen ◽  
S. H. Kessler ◽  
...  
Keyword(s):  
High Resolution ◽  
Vacuum Ultraviolet ◽  
Laboratory Data ◽  
Detailed Examination ◽  
Relative Difference ◽  
Data Set ◽  
Elemental Ratios ◽  
Standard Data ◽  
Aerosol Mass ◽  
Relative Errors

Abstract. Elemental compositions of organic aerosol (OA) particles provide useful constraints on OA sources, chemical evolution, and effects. The Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) is widely used to measure OA elemental composition. This study evaluates AMS measurements of atomic oxygen-to-carbon (O : C), hydrogen-to-carbon (H : C), and organic mass-to-organic carbon (OM : OC) ratios, and of carbon oxidation state (OS C) for a vastly expanded laboratory data set of multifunctional oxidized OA standards. For the expanded standard data set, the method introduced by Aiken et al. (2008), which uses experimentally measured ion intensities at all ions to determine elemental ratios (referred to here as "Aiken-Explicit"), reproduces known O : C and H : C ratio values within 20% (average absolute value of relative errors) and 12%, respectively. The more commonly used method, which uses empirically estimated H2O+ and CO+ ion intensities to avoid gas phase air interferences at these ions (referred to here as "Aiken-Ambient"), reproduces O : C and H : C of multifunctional oxidized species within 28 and 14% of known values. The values from the latter method are systematically biased low, however, with larger biases observed for alcohols and simple diacids. A detailed examination of the H2O+, CO+, and CO2+ fragments in the high-resolution mass spectra of the standard compounds indicates that the Aiken-Ambient method underestimates the CO+ and especially H2O+ produced from many oxidized species. Combined AMS–vacuum ultraviolet (VUV) ionization measurements indicate that these ions are produced by dehydration and decarboxylation on the AMS vaporizer (usually operated at 600 °C). Thermal decomposition is observed to be efficient at vaporizer temperatures down to 200 °C. These results are used together to develop an "Improved-Ambient" elemental analysis method for AMS spectra measured in air. The Improved-Ambient method uses specific ion fragments as markers to correct for molecular functionality-dependent systematic biases and reproduces known O : C (H : C) ratios of individual oxidized standards within 28% (13%) of the known molecular values. The error in Improved-Ambient O : C (H : C) values is smaller for theoretical standard mixtures of the oxidized organic standards, which are more representative of the complex mix of species present in ambient OA. For ambient OA, the Improved-Ambient method produces O : C (H : C) values that are 27% (11%) larger than previously published Aiken-Ambient values; a corresponding increase of 9% is observed for OM : OC values. These results imply that ambient OA has a higher relative oxygen content than previously estimated. The OS C values calculated for ambient OA by the two methods agree well, however (average relative difference of 0.06 OS C units). This indicates that OS C is a more robust metric of oxidation than O : C, likely since OS C is not affected by hydration or dehydration, either in the atmosphere or during analysis.

Sign in / Sign up

Export Citation Format

Share Document