An improved fast segmentation algorithm for text and graphics data

The application of video and image segmentation is carried out from the aspects of improving the accuracy of segmentation and reducing the calculation time, but the segmentation result is affected by the initial curve position, so this paper proposes a new method. As an important part of the Internet, pictures are usually used to help visitors understand. The image contains a lot of deep-level video information, which is an important basis for video content retrieval and data analysis. In this paper, combining the texture and edge features of the image in the process of text location, a multi-scale Gabor filter bank is proposed to transform the original image, and a priori knowledge of the text region is used to process the non-text object in the transform result. In the part of extracting text from pictures, and improved TF-IDF algorithm, BC-TF-IDF algorithm, is proposed to extract text from pictures. To ensure the integrity of the extracted image, the Sobel algorithm is used to process the image in the edge extraction step. Finally, the above method is applied to the Weibo network, and a system of collecting and recognizing the character content of the Weibo image is set up, which completes the function of collecting and gradually recognizing the Weibo image, and verifies the proposed localization method.

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Group and Shuffle Convolutional Neural Networks with Pyramid Pooling Module for Automated Pterygium Segmentation

Diagnostics ◽

10.3390/diagnostics11061104 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1104

Author(s):

Siti Raihanah Abdani ◽

Mohd Asyraf Zulkifley ◽

Nuraisyah Hani Zulkifley

Keyword(s):

Screening Tool ◽

Jaccard Index ◽

Dense Network ◽

Multi Scale ◽

Spatial Pyramid Pooling ◽

Severity Levels ◽

Set Up ◽

Channel Variation ◽

Mobile Phone Camera ◽

Sunlight Radiation

Pterygium is an eye condition that is prevalent among workers that are frequently exposed to sunlight radiation. However, most of them are not aware of this condition, which motivates many volunteers to set up health awareness booths to give them free health screening. As a result, a screening tool that can be operated on various platforms is needed to support the automated pterygium assessment. One of the crucial functions of this assessment is to extract the infected regions, which directly correlates with the severity levels. Hence, Group-PPM-Net is proposed by integrating a spatial pyramid pooling module (PPM) and group convolution to the deep learning segmentation network. The system uses a standard mobile phone camera input, which is then fed to a modified encoder-decoder convolutional neural network, inspired by a Fully Convolutional Dense Network that consists of a total of 11 dense blocks. A PPM is integrated into the network because of its multi-scale capability, which is useful for multi-scale tissue extraction. The shape of the tissues remains relatively constant, but the size will differ according to the severity levels. Moreover, group and shuffle convolution modules are also integrated at the decoder side of Group-PPM-Net by placing them at the starting layer of each dense block. The addition of these modules allows better correlation among the filters in each group, while the shuffle process increases channel variation that the filters can learn from. The results show that the proposed method obtains mean accuracy, mean intersection over union, Hausdorff distance, and Jaccard index performances of 0.9330, 0.8640, 11.5474, and 0.7966, respectively.

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A Tikhonov-Type Regularization Method for Identifying the Unknown Source in the Modified Helmholtz Equation

Mathematical Problems in Engineering ◽

10.1155/2012/878109 ◽

2012 ◽

Vol 2012 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Jinghuai Gao ◽

Dehua Wang ◽

Jigen Peng

Keyword(s):

Helmholtz Equation ◽

Regularization Method ◽

Regularization Parameter ◽

A Priori ◽

Inverse Source Problem ◽

A Posteriori ◽

Modified Helmholtz Equation ◽

Numerical Tests ◽

Theoretical Frame ◽

Set Up

An inverse source problem in the modified Helmholtz equation is considered. We give a Tikhonov-type regularization method and set up a theoretical frame to analyze the convergence of such method. A priori and a posteriori choice rules to find the regularization parameter are given. Numerical tests are presented to illustrate the effectiveness and stability of our proposed method.

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Assessing biases, relaxing moralism: On ground-truthing practices in machine learning design and application

Big Data & Society ◽

10.1177/20539517211013569 ◽

2021 ◽

Vol 8 (1) ◽

pp. 205395172110135

Author(s):

Florian Jaton

Keyword(s):

Machine Learning ◽

William James ◽

A Priori ◽

Learning Algorithms ◽

Three Dimensional ◽

Ground Truth ◽

Machine Learning Algorithms ◽

Ground Truthing ◽

Set Up ◽

The Moment

This theoretical paper considers the morality of machine learning algorithms and systems in the light of the biases that ground their correctness. It begins by presenting biases not as a priori negative entities but as contingent external referents—often gathered in benchmarked repositories called ground-truth datasets—that define what needs to be learned and allow for performance measures. I then argue that ground-truth datasets and their concomitant practices—that fundamentally involve establishing biases to enable learning procedures—can be described by their respective morality, here defined as the more or less accounted experience of hesitation when faced with what pragmatist philosopher William James called “genuine options”—that is, choices to be made in the heat of the moment that engage different possible futures. I then stress three constitutive dimensions of this pragmatist morality, as far as ground-truthing practices are concerned: (I) the definition of the problem to be solved (problematization), (II) the identification of the data to be collected and set up (databasing), and (III) the qualification of the targets to be learned (labeling). I finally suggest that this three-dimensional conceptual space can be used to map machine learning algorithmic projects in terms of the morality of their respective and constitutive ground-truthing practices. Such techno-moral graphs may, in turn, serve as equipment for greater governance of machine learning algorithms and systems.

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Iris Recognition Method Based on Feature Discrimination and Category Correlation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.1610 ◽

2014 ◽

Vol 602-605 ◽

pp. 1610-1613

Author(s):

Ming Hai Yao ◽

Na Wang ◽

Jin Song Li

Keyword(s):

Iris Recognition ◽

Predictive Accuracy ◽

Gabor Filter ◽

Recognition Algorithm ◽

Recognition Method ◽

Texture Characteristic ◽

Multi Scale ◽

Laplacian Score ◽

Circular Gabor Filter ◽

Academy Of Sciences

With the increasing number of internet user, the authentication technology is more and more important. Iris recognition as an important method for identification, which has been attention by researchers. In order to improve the predictive accuracy of iris recognition algorithm, the iris recognition method is proposed based feature discrimination and category correlation. The feature discrimination and category correlation are calculated by laplacian score and mutual information. The formula about feature discrimination and category correlation are built. Aiming at texture characteristic of iris image, the multi-scale circular Gabor filter is used to feature extraction. The computational efficiency of algorithm is improved. In order to verify the validity of the algorithm, the CASIA iris database of Chinese Academy of Sciences is used to do the experiment. The experimental results show that our method has high predictive accuracy.

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Semantic Segmentation Network for Surface Defect Detection of Automobile Wheel Hub Fusing High-Resolution Feature and Multi-Scale Feature

Applied Sciences ◽

10.3390/app112210508 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10508

Author(s):

Chaowei Tang ◽

Xinxin Feng ◽

Haotian Wen ◽

Xu Zhou ◽

Yanqing Shao ◽

...

Keyword(s):

High Resolution ◽

Defect Detection ◽

Automobile Industry ◽

Surface Defect ◽

Semantic Segmentation ◽

The Body ◽

Multi Scale ◽

Surface Defect Detection ◽

Edge Features ◽

Automobile Wheel

Surface defect detection of an automobile wheel hub is important to the automobile industry because these defects directly affect the safety and appearance of automobiles. At present, surface defect detection networks based on convolutional neural network use many pooling layers when extracting features, reducing the spatial resolution of features and preventing the accurate detection of the boundary of defects. On the basis of DeepLab v3+, we propose a semantic segmentation network for the surface defect detection of an automobile wheel hub. To solve the gridding effect of atrous convolution, the high-resolution network (HRNet) is used as the backbone network to extract high-resolution features, and the multi-scale features extracted by the Atrous Spatial Pyramid Pooling (ASPP) of DeepLab v3+ are superimposed. On the basis of the optical flow, we decouple the body and edge features of the defects to accurately detect the boundary of defects. Furthermore, in the upsampling process, a decoder can accurately obtain detection results by fusing the body, edge, and multi-scale features. We use supervised training to optimize these features. Experimental results on four defect datasets (i.e., wheels, magnetic tiles, fabrics, and welds) show that the proposed network has better F1 score, average precision, and intersection over union than SegNet, Unet, and DeepLab v3+, proving that the proposed network is effective for different defect detection scenarios.

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Integrating Multiple and Fine Scale Predictors of Habitat Selection and Landscape Resistance for Amphibian Distribution Modelling at Large Scale

10.21203/rs.3.rs-181948/v1 ◽

2021 ◽

Author(s):

Florence Matutini ◽

Jacques Baudry ◽

Marie-Josée Fortin ◽

Guillaume Pain ◽

Joséphine Pithon

Keyword(s):

Large Scale ◽

A Priori ◽

Species Level ◽

Fine Scale ◽

Landscape Resistance ◽

Amphibian Species ◽

Distribution Modelling ◽

Multi Scale ◽

Functional Grain ◽

Level Analysis

Abstract Context – Species distribution modelling is a common tool in conservation biology but two main criticisms remain: (1) the use of simplistic variables that do not account for species movements and/or connectivity and (2) poor consideration of multi-scale processes driving species distributions. Objectives – We aimed to determine if including multi-scale and fine-scale movement processes in SDM predictors would improve accuracy of SDM for low-mobility amphibian species over species-level analysis.Methods – We tested and compared different SDMs for nine amphibian species with four different sets of predictors: (1) simple distance-based predictors; (2) single-scale compositional predictors; (3) multi-scale compositional predictors with a priori selection of scale based on knowledge of species mobility and scale-of-effect (4) multi-scale compositional predictors calculated using a friction-based functional grain to account for resource accessibility with landscape resistance to movement.Results - Using friction-based functional grain predictors produced slight to moderate improvements of SDM performance at large scale. The multi-scale approach, with a priori scale selection led to ambiguous results depending on the species studied, in particular for generalist species.Conclusion - We underline the potential of using a friction-based functional grain to improve SDM predictions for species-level analysis.

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Vibration Data Processing for Bedload Monitoring in Underwater Environments

Remote Sensing ◽

10.3390/rs12172797 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2797

Author(s):

Gabriel Vasile

Keyword(s):

Data Processing ◽

Total Energy ◽

Steel Plate ◽

A Priori ◽

A Priori Knowledge ◽

Vibration Spectra ◽

Vibration Data ◽

Accurate Localization ◽

Set Up ◽

Processing Framework

This paper proposes a novel data processing framework dedicated to bedload monitoring in underwater environments. After calibration, by integration the of total energy in the nominal bandwidth, the proposed experimental set-up is able to accurately measure the mass of individual sediments hitting the steel plate. This requires a priori knowledge of the vibration transients in order to match a predefined dictionary. Based on unsupervised hierarchical agglomeration of complex vibration spectra, the proposed algorithms allow accurate localization of the transients corresponding to the shocks created by sediment impacts on a steel plate.

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Lactate-Based Model Predictive Control Strategy of Cell Growth for Cell Therapy Applications

Bioengineering ◽

10.3390/bioengineering7030078 ◽

2020 ◽

Vol 7 (3) ◽

pp. 78

Author(s):

Kathleen Van Beylen ◽

Ali Youssef ◽

Alberto Peña Fernández ◽

Toon Lambrechts ◽

Ioannis Papantoniou ◽

...

Keyword(s):

Cell Growth ◽

Model Predictive Control ◽

Predictive Control ◽

Feeding Strategy ◽

A Priori ◽

Process Models ◽

Feeding Strategies ◽

Cell Culture Process ◽

Reference Trajectory ◽

Set Up

Implementing a personalised feeding strategy for each individual batch of a bioprocess could significantly reduce the unnecessary costs of overfeeding the cells. This paper uses lactate measurements during the cell culture process as an indication of cell growth to adapt the feeding strategy accordingly. For this purpose, a model predictive control is used to follow this a priori determined reference trajectory of cumulative lactate. Human progenitor cells from three different donors, which were cultivated in 12-well plates for five days using six different feeding strategies, are used as references. Each experimental set-up is performed in triplicate and for each run an individualised model-based predictive control (MPC) controller is developed. All process models exhibit an accuracy of 99.80% ± 0.02%, and all simulations to reproduce each experimental run, using the data as a reference trajectory, reached their target with a 98.64% ± 0.10% accuracy on average. This work represents a promising framework to control the cell growth through adapting the feeding strategy based on lactate measurements.

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Exploring the Role of Complexity, Content and Individual Differences in Aesthetic Reactions to Semi-Abstract Art Photographs

Art & Perception ◽

10.1163/22134913-20191139 ◽

2020 ◽

Vol 8 (1) ◽

pp. 89-119

Author(s):

Nathalie Vissers ◽

Pieter Moors ◽

Dominique Genin ◽

Johan Wagemans

Keyword(s):

Large Scale ◽

A Priori ◽

Data Driven ◽

Future Research ◽

Response Patterns ◽

Online Study ◽

Exploratory Data ◽

Science Collaboration ◽

Set Up ◽

Different Response

Artistic photography is an interesting, but often overlooked, medium within the field of empirical aesthetics. Grounded in an art–science collaboration with art photographer Dominique Genin, this project focused on the relationship between the complexity of a photograph and its aesthetic appeal (beauty, pleasantness, interest). An artistic series of 24 semi-abstract photographs that play with multiple layers, recognisability vs unrecognizability and complexity was specifically created and selected for the project. A large-scale online study with a broad range of individuals (n = 453, varying in age, gender and art expertise) was set up. Exploratory data-driven analyses revealed two clusters of individuals, who responded differently to the photographs. Despite the semi-abstract nature of the photographs, differences seemed to be driven more consistently by the ‘content’ of the photograph than by its complexity levels. No consistent differences were found between clusters in age, gender or art expertise. Together, these results highlight the importance of exploratory, data-driven work in empirical aesthetics to complement and nuance findings from hypotheses-driven studies, as they allow to go further than a priori assumptions, to explore underlying clusters of participants with different response patterns, and to point towards new venues for future research. Data and code for the analyses reported in this article can be found at https://osf.io/2fws6/.

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