scholarly journals A Multilevel Single Stage Network for Face Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Kanghua Hui ◽  
Jin Wang ◽  
Huaiqing He ◽  
W. H. Ip
Keyword(s):  
Weight Loss ◽  
Object Detection ◽  
Face Detection ◽  
Loss Function ◽  
Single Stage ◽  
Time Performance ◽  
Novel Method ◽  
Feature Information ◽  
Different Levels ◽  
Made In

Recently, tremendous strides have been made in generic object detection when used to detect faces, and there are still some remaining challenges. In this paper, a novel method is proposed named multilevel single stage network for face detection (MSNFD). Three breakthroughs are made in this research. Firstly, multilevel network is introduced into face detection to improve the efficiency of anchoring faces. Secondly, enhanced feature module is adopted to allow more feature information to be collected. Finally, two-stage weight loss function is employed to balance network of different levels. Experimental results on the WIDER FACE and FDDB datasets confirm that MSNFD has competitive accuracy to the mainstream methods, while keeping real-time performance.

Automatic accounting of Baikal diatomic algae: approaches and prospects

2019 ◽  
pp. 295-299
Author(s):  
Кonstantin А. Elshin ◽  
Еlena I. Molchanova ◽  
Мarina V. Usoltseva ◽  
Yelena V. Likhoshway
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
Classification Accuracy ◽  
Diatom Species ◽  
Bounding Box ◽  
Synedra Acus ◽  
And Training

Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.

Effect of Catfish Bone Paste on the Properties of Steamed Bread

2014 ◽  
Vol 881-883 ◽  
pp. 757-760
Author(s):  
Xiao Qing Ren ◽  
Li Zhen Ma ◽  
Xin Yi He
Keyword(s):  
Weight Loss ◽  
Specific Volume ◽  
Cell Structure ◽  
The Other ◽  
Gas Cell ◽  
A Value ◽  
Crumb Structure ◽  
Steamed Bread ◽  
Different Levels ◽  
Structure Properties

The objective of this study was to examine the effect of different levels of catfish bone paste to flour on the physicochemical, textural and crumb structure properties of steamed bread. Six different levels (0, 1, 3, 5, 7,10 %) of catfish bone paste to flour were used in the formulation of the steamed bread. The results showed that the weight loss and TTA of steamed bread decreased with an increase in the levels of the catfish bone paste. On the other hand, the pH increased with an increase in the levels of the catfish bone paste. The specific volume, hardness, chewiness and gas cell structure in the crumb of steamed bread with catfish bone paste at 5% supplementation level were better. Thus, a value of 5% catfish bone paste was considered a better level for incorporation into the steamed bread.

A Densely Connected Network Based on U-Net for Medical Image Segmentation

2021 ◽  
Vol 17 (3) ◽  
pp. 1-14
Author(s):  
Zhenzhen Yang ◽  
Pengfei Xu ◽  
Yongpeng Yang ◽  
Bing-Kun Bao
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Loss Function ◽  
Network Architecture ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Cross Entropy ◽  
Loss Functions ◽  
Feature Maps ◽  
Different Levels

The U-Net has become the most popular structure in medical image segmentation in recent years. Although its performance for medical image segmentation is outstanding, a large number of experiments demonstrate that the classical U-Net network architecture seems to be insufficient when the size of segmentation targets changes and the imbalance happens between target and background in different forms of segmentation. To improve the U-Net network architecture, we develop a new architecture named densely connected U-Net (DenseUNet) network in this article. The proposed DenseUNet network adopts a dense block to improve the feature extraction capability and employs a multi-feature fuse block fusing feature maps of different levels to increase the accuracy of feature extraction. In addition, in view of the advantages of the cross entropy and the dice loss functions, a new loss function for the DenseUNet network is proposed to deal with the imbalance between target and background. Finally, we test the proposed DenseUNet network and compared it with the multi-resolutional U-Net (MultiResUNet) and the classic U-Net networks on three different datasets. The experimental results show that the DenseUNet network has significantly performances compared with the MultiResUNet and the classic U-Net networks.

scholarly journals A Novel Method of Object Detection from a Moving Camera Based on Image Matching and Frame Coupling

PLoS ONE ◽  
2014 ◽  
Vol 9 (10) ◽  
pp. e109809 ◽  
Author(s):  
Yong Chen ◽  
Rong hua Zhang ◽  
Lei Shang
Keyword(s):  
Object Detection ◽  
Image Matching ◽  
Moving Camera ◽  
Novel Method

scholarly journals Entropy-Based Method for Temporal Downscaling of Precipitation to Improve Sediment Delivery Ratio Assessment

2020 ◽  
Author(s):  
Pedro Alencar ◽  
Eva Paton ◽  
José de Araújo
Keyword(s):  
Maximum Entropy ◽  
Sediment Yield ◽  
Absolute Error ◽  
Reanalysis Data ◽  
Delivery Ratio ◽  
Sediment Delivery ◽  
Precipitation Characteristics ◽  
Erosion Modelling ◽  
Novel Method ◽  
Made In

Scarcity of precipitation data is still a problem in erosion modelling, especially when working in remote and data-scare areas. While much effort was made in the past to use remote sensing or reanalysis data, they are still considered to be not completely reliable, notably for sub-daily measures such as duration and intensity. A way forward are statistical analyses, which can help modellers to obtain sub-daily precipitation characteristics by using daily totals. In this paper, we propose a novel method (Maximum Entropy Distribution of Rainfall Intensity and Duration - MEDRID) to assess the duration and intensity of sub-daily rainfalls relevant for the modelling of sediment delivery ratios. We use the generated data to improve the sediment yield assessment in seven catchments with areas varying from 10 to 10 km and a broad timespan of measured data (1 to 81 years). The best probability density function derived from MEDRID to reproduce sub-daily duration is the generalised gamma distribution (NSE = 0.98), whereas for rain intensity it is the uniform (NSE = 0.87). The MEDRID method coupled with the SYPoME model (Sediment Yield using the Principle of Maximum Entropy) represents a significant improvement over empirically-based SDR models, given its average absolute error of 21% and a Nash Sutcliffe Efficiency of 0.96, (rather than 105% and -4.49, respectively).

scholarly journals Molecules and Functions of Cornus officinalis Bark Volatiles

2018 ◽  
pp. 828
Author(s):  
Wanxi Peng
Keyword(s):  
Amino Acids ◽  
Weight Loss ◽  
Human Body ◽  
Essential Elements ◽  
Pharmacological Effects ◽  
Bioactive Components ◽  
Medicinal Material ◽  
Cornus Officinalis ◽  
Analysis Methods ◽  
Made In

Cornus officinalis Sieb. et Zucc is a traditional Chinese valuable medicinal material. Clinically, it is customary to use ripe fruits from which seeds have been removed for medicinal purposes. The pulp contains 16 amino acids and a large number of essential elements for the human body. In recent years, with the expansion of the application of cornus officinalis, its pharmacological and pharmacological effects have been increasingly studied. At present, significant achievements have been made in the study of the bioactive components of cornus officinalis. The research of these achievements has been based on the research of the fruit of cornus officinalis. The study of branches or bark of cornus officinalis is very rare. With the fruit of cornus officinalis getting more and more attention, in order to solve the problem of the shortage of cornus officinalis fruit in the market, in this paper, starting from the study of bark of cornus officinalis, TGA-DTG and PY-GC-MS analysis methods were used to study the weight loss and pyrolysis of cornus officinalis bark, providing a basis for more fully utilizing cornus officinalis resources. With reference.

scholarly journals The value of blood serotonin for effective weight loss in obese women

2015 ◽  
Vol 12 (3) ◽  
pp. 31-35
Author(s):  
Natal'ya Vadimovna Anikina ◽  
Elena Nikolaevna Smirnova
Keyword(s):  
Weight Loss ◽  
Body Weight ◽  
Clinical Laboratory ◽  
Normal Weight ◽  
Obese Patients ◽  
Obese Women ◽  
Endothelin 1 ◽  
Excessive Accumulation ◽  
Made In

Introduction. Obesity is a disorder of energy balance, which leads to excessive accumulation of fat. In recent years, many important discoveries were made in this field, including the discovery of hormones produced by adipose tissue and the identification of many of the central and peripheral pathways of energy balance.Objective. To study the levels of hormones that affect appetite and metabolism in women with obesity baseline and after weight loss while taking sibutramine.Materials and methods. The study included 56 women aged 42,9±9,5 years, with a BMI of 34,6±6,1 kg/m2. All patients underwent clinical, laboratory and instrumental examination. Hormonal study included determination of serotonin, leptin, ghrelin, endothelin-1, adiponectin.Results: In women with obesity we identified hyperleptinemia and increased serotonin levels. The decrease in body weight in patients receiving sibutramine was accompanied by lower levels of serotonin, leptin, ghrelin, endothelin-1, and increase of adiponectin.Conclusions: Obese patients have significantly elevated levels of leptin, serotonin, ghrelin compared to women of normal weight. Sibutramine treatment leads to a decrease in serotonin, leptin, ghrelin and is more effective in women with a BMI less than 36,5 kg/m2.

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