Combining depth and color cues for scale- and viewpoint-invariant object segmentation and recognition using Random Forests

2010 ◽  
Author(s):  
Jorg Stuckler ◽  
Sven Behnke
Keyword(s):  
Random Forests ◽  
Object Segmentation ◽  
Color Cues

IMPROVED OBJECT SEGMENTATION USING MULTI SCALE SALIENCY APPROACH

2018 ◽  
Vol 6 (4) ◽  
pp. 161-167
Author(s):  
S. Thilagamani ◽  
◽  
◽  
V. Manochitra
Keyword(s):  
Object Segmentation ◽  
Multi Scale

Segmentasi Citra Mri Menggunakan Deteksi Tepi Untuk Identifikasi Kanker Payudara

2017 ◽  
Vol 15 (2) ◽  
pp. 17 ◽  
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.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

Video Object Segmentation Research Based on Features Joint Modeling

2014 ◽  
Vol 36 (11) ◽  
pp. 2356-2363
Author(s):  
Zong-Min LI ◽  
Xu-Chao GONG ◽  
Yu-Jie LIU
Keyword(s):  
Object Segmentation ◽  
Joint Modeling ◽  
Video Object Segmentation ◽  
Video Object

Touched human object segmentation based on Mean-shift algorithm

2009 ◽  
Vol 29 (1) ◽  
pp. 51-53 ◽  
Author(s):  
Sen GUO ◽  
Wei LIU ◽  
Jing-hua WANG
Keyword(s):  
Object Segmentation ◽  
Mean Shift ◽  
Mean Shift Algorithm ◽  
Human Object

A Diverse Assimilation of Sequence and Structure Dependent Features for Amyloid Plaque Prediction Using Random Forests

2013 ◽  
Vol 10 (1) ◽  
pp. 38-44
Author(s):  
Smitha Sunil Nair ◽  
N. V. Reddy ◽  
K. Hareesha ◽  
S. Balaji
Keyword(s):  
Random Forests ◽  
Amyloid Plaque
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