scholarly journals A Vision Based Top-View Transformation Model for a Vehicle Parking Assistant

Sensors ◽  
2012 ◽  
Vol 12 (4) ◽  
pp. 4431-4446 ◽  
Author(s):  
Chien-Chuan Lin ◽  
Ming-Shi Wang
Keyword(s):  
Transformation Model ◽  
View Transformation ◽  
Top View

scholarly journals Toward Automatic Generation of Column-Oriented NoSQL Databases in Big Data Context

2019 ◽  
Vol 15 (09) ◽  
pp. 4
Author(s):  
Redouane Esbai ◽  
Fouad Elotmani ◽  
Fatima Zahra Belkadi
Keyword(s):  
Big Data ◽  
Relational Databases ◽  
Automatic Generation ◽  
Transformation Model ◽  
Model Driven ◽  
Transformation Rules ◽  
New Concepts ◽  
Nosql Database ◽  
Meta Object Facility ◽  
View Transformation

<span>The growth of application architectures in all areas (e.g. Astrology, Meteorology, E-commerce, social network, etc.) has resulted in an exponential increase in data volumes, now measured in Petabytes. Managing these volumes of data has become a problem that relational databases are no longer able to handle because of the acidity properties. In response to this scaling up, new concepts have emerged such as NoSQL. In this paper, we show how to design and apply transformation rules to migrate from an SQL relational database to a Big Data solution within NoSQL. For this, we use the Model Driven Architecture (MDA) and the transformation languages like as MOF 2.0 QVT (Meta-Object Facility 2.0 Query-View-Transformation) and Acceleo which define the meta-models for the development of transformation model. The transformation rules defined in this work can generate, from the class diagram, a CQL code for creation column-oriented NoSQL database.</span>

scholarly journals A Precise Lane Detection Algorithm Based on Top View Image Transformation and Least-Square Approaches

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Byambaa Dorj ◽  
Deok Jin Lee
Keyword(s):  
Detection Method ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Least Square ◽  
Parabolic Model ◽  
Image Transformation ◽  
Driver Assistance Systems ◽  
View Transformation ◽  
Lane Keeping ◽  
Top View

The next promising key issue of the automobile development is a self-driving technique. One of the challenges for intelligent self-driving includes a lane-detecting and lane-keeping capability for advanced driver assistance systems. This paper introduces an efficient and lane detection method designed based on top view image transformation that converts an image from a front view to a top view space. After the top view image transformation, a Hough transformation technique is integrated by using a parabolic model of a curved lane in order to estimate a parametric model of the lane in the top view space. The parameters of the parabolic model are estimated by utilizing a least-square approach. The experimental results show that the newly proposed lane detection method with the top view transformation is very effective in estimating a sharp and curved lane leading to a precise self-driving capability.

Gait Recognition Using a View Transformation Model in the Frequency Domain

2006 ◽  
pp. 151-163 ◽  
Author(s):  
Yasushi Makihara ◽  
Ryusuke Sagawa ◽  
Yasuhiro Mukaigawa ◽  
Tomio Echigo ◽  
Yasushi Yagi
Keyword(s):  
Frequency Domain ◽  
Gait Recognition ◽  
Transformation Model ◽  
View Transformation

CROSS-VIEW GAIT RECOGNITION WITH SHORT PROBE SEQUENCES: FROM VIEW TRANSFORMATION MODEL TO VIEW-INDEPENDENT STANCE-INDEPENDENT IDENTITY VECTOR

2013 ◽  
Vol 27 (06) ◽  
pp. 1350017 ◽  
Author(s):  
MAODI HU ◽  
YUNHONG WANG ◽  
ZHAOXIANG ZHANG
Keyword(s):  
Markov Models ◽  
Gait Recognition ◽  
Gait Pattern ◽  
Transformation Model ◽  
Sequence Length ◽  
Projection Model ◽  
Linear Projection ◽  
Camera Location ◽  
Short Probe ◽  
View Transformation

Considering it is difficult to guarantee that at least one continuous complete gait cycle is captured in real applications, we address the multi-view gait recognition problem with short probe sequences. With unified multi-view population hidden markov models (umvpHMMs), the gait pattern is represented as fixed-length multi-view stances. By incorporating the multi-stance dynamics, the well-known view transformation model (VTM) is extended into a multi-linear projection model in a four-order tensor space, so that a view-independent stance-independent identity vector (VSIV) can be extracted. The main advantage is that the proposed VSIV is stable for each subject regardless of the camera location or the sequence length. Experiments show that our algorithm achieves encouraging performance for cross-view gait recognition even with short probe sequences.

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