scholarly journals A Machine Learning Approach to Horizon Line Detection Using Local Features

2013 ◽  
pp. 181-193 ◽  
Author(s):  
Touqeer Ahmad ◽  
George Bebis ◽  
Emma E. Regentova ◽  
Ara Nefian
Keyword(s):  
Machine Learning ◽  
Local Features ◽  
Line Detection ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Horizon Line ◽  
Horizon Line Detection

Coupling Dynamic Programming with Machine Learning for Horizon Line Detection

2015 ◽  
Vol 24 (04) ◽  
pp. 1540018 ◽  
Author(s):  
Touqeer Ahmad ◽  
George Bebis ◽  
Emma Regentova ◽  
Ara Nefian ◽  
Terry Fong
Keyword(s):  
Machine Learning ◽  
Dynamic Programming ◽  
Texture Features ◽  
Superior Performance ◽  
Support Vector ◽  
Line Detection ◽  
Svm Classifier ◽  
Wide Range ◽  
Horizon Line ◽  
Horizon Line Detection

In this paper, we consider the problem of segmenting an image into sky and non-sky regions, typically referred to as horizon line detection or skyline extraction. Specifically, we present a new approach to horizon line detection by coupling machine learning with dynamic programming. Given an image, the Canny edge detector is applied first and keeping only those edges which survive over a wide range of thresholds. We refer to the surviving edges as Maximally Stable Extremal Edges (MSEEs). The number of edges is further reduced by classifying MSEEs into horizon and non-horizon edges using a Support Vector Machine (SVM) classifier. Dynamic programming is then applied on the horizon classified edges to extract the horizon line. Various local texture features and their combinations have been investigated in training the horizon edge classifier including SIFT, LBP, HOG, SIFT-LBP, SIFT-HOG, LBP-HOG and SIFT-LBP-HOG. We have also investigated various nodal costs in the context of dynamic programming including binary edge scores, normalized edge classification scores, gradient magnitude and their combinations. The proposed approach has been evaluated and compared with a competitive approach on two challenging data sets, illustrating superior performance.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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