Motion Object Segmentation Using Regions Classification and Energy Model

<div>A linear free energy model is presented that predicts the second order rate constant for the abiotic reduction of nitroaromatic compounds (NACs). For this situation previously presented models use the one electron reduction potential of the NAC reaction. If such value is not available, it has been has been proposed that it could be computed directly or estimated from the electron affinity (EA). The model proposed herein uses the Gibbs free energy of the hydrogen atom transfer (HAT) as the parameter in the linear free energy model. Both models employ quantum chemical computations for the required thermodynamic parameters. The available and proposed models are compared using second order rate constants obtained from five investigations reported in the literature in which a variety of NACs were exposed to a variety of reductants. A comprehensive analysis utilizing all the NACs and reductants demonstrate that the computed hydrogen atom transfer model and the experimental one electron reduction potential model have similar root mean square errors and residual error probability distributions. In contrast, the model using the computed electron affinity has a more variable residual error distribution with a significant number of outliers. The results suggest that a linear free energy model utilizing computed hydrogen transfer reaction free energy produces a more reliable prediction of the NAC abiotic reduction second order rate constant than previously available methods. The advantages of the proposed hydrogen atom transfer model and its mechanistic implications are discussed as well.</div>

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IMPROVED OBJECT SEGMENTATION USING MULTI SCALE SALIENCY APPROACH

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i4.161167 ◽

2018 ◽

Vol 6 (4) ◽

pp. 161-167

Author(s):

S. Thilagamani ◽

◽

V. Manochitra

Keyword(s):

Object Segmentation ◽

Multi Scale

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Development Of A Modeling Framework For Refined Residential Occupancy Schedules In An Urban Energy Model

Proceedings of Building Simulation 2019: 16th Conference of IBPSA ◽

10.26868/25222708.2019.210553 ◽

2020 ◽

Author(s):

Diba Malekpour Koupaei ◽

Farzad Hashemi ◽

Vinciane Tabard-Fortecoëf ◽

Ulrike Passe

Keyword(s):

Energy Model ◽

Modeling Framework ◽

Urban Energy

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Segmentasi Citra Mri Menggunakan Deteksi Tepi Untuk Identifikasi Kanker Payudara

Matrik Jurnal Manajemen Teknik Informatika dan Rekayasa Komputer ◽

10.30812/matrik.v15i2.38 ◽

2017 ◽

Vol 15 (2) ◽

pp. 17 ◽

Cited By ~ 1

Author(s):

Ervina Varijki ◽

Bambang Krismono Triwijoyo

Keyword(s):

Breast Cancer ◽

Edge Detection ◽

Object Segmentation ◽

Computation Time ◽

Input Image ◽

Boundary Line ◽

Cancer Breast ◽

Average Computation Time ◽

Breast Cancer Mri ◽

Image Format

One type of cancer that is capable identified using MRI technology is breast cancer. Breast cancer is still the leading cause of death world. therefore early detection of this disease is needed. In identifying breast cancer, a doctor or radiologist analyzing the results of magnetic resonance image that is stored in the format of the Digital Imaging Communication In Medicine (DICOM). It takes skill and experience sufficient for diagnosis is appropriate, andaccurate, so it is necessary to create a digital image processing applications by utilizing the process of object segmentation and edge detection to assist the physician or radiologist in identifying breast cancer. MRI image segmentation using edge detection to identification of breast cancer using a method stages gryascale change the image format, then the binary image thresholding and edge detection process using the latest Robert operator. Of the20 tested the input image to produce images with the appearance of the boundary line of each region or object that is visible and there are no edges are cut off, with the average computation time less than one minute.

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