Interacting with displays through mobile device cameras using scale-invariant features matching

2011 ◽  
Author(s):  
Cesare Celozzi ◽  
Fabrizio Lamberti ◽  
Gianluca Paravati ◽  
Andrea Sanna
Keyword(s):  
Mobile Device ◽  
Scale Invariant ◽  
Invariant Features

scholarly journals Spectrum of power laws for curved hand movements

2015 ◽  
Vol 112 (29) ◽  
pp. E3950-E3958 ◽  
Author(s):  
Dongsung Huh ◽  
Terrence J. Sejnowski
Keyword(s):  
Optimal Control ◽  
Motor Planning ◽  
Power Laws ◽  
Angular Frequency ◽  
Control Model ◽  
Hand Movements ◽  
Local Curvature ◽  
Scale Invariant ◽  
The Past ◽  
Invariant Features

In a planar free-hand drawing of an ellipse, the speed of movement is proportional to the −1/3 power of the local curvature, which is widely thought to hold for general curved shapes. We investigated this phenomenon for general curved hand movements by analyzing an optimal control model that maximizes a smoothness cost and exhibits the −1/3 power for ellipses. For the analysis, we introduced a new representation for curved movements based on a moving reference frame and a dimensionless angle coordinate that revealed scale-invariant features of curved movements. The analysis confirmed the power law for drawing ellipses but also predicted a spectrum of power laws with exponents ranging between 0 and −2/3 for simple movements that can be characterized by a single angular frequency. Moreover, it predicted mixtures of power laws for more complex, multifrequency movements that were confirmed with human drawing experiments. The speed profiles of arbitrary doodling movements that exhibit broadband curvature profiles were accurately predicted as well. These findings have implications for motor planning and predict that movements only depend on one radian of angle coordinate in the past and only need to be planned one radian ahead.

scholarly journals Image Detection System Using Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 5356-5362
Author(s):  
Mohini Gawande
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Estimation Method ◽  
Scale Invariant ◽  
Matrix Estimation ◽  
Speeded Up Robust Features ◽  
Invariant Features ◽  
Marketing Analysis ◽  
New Strategies

The increasing popularity of Social Networks makes change the way people interact. These interactions produce a huge amount of data and it opens the door to new strategies and marketing analysis. According to Instagram and Tumblr, an average of 80 and 59 million photos respectively are published every day, and those pictures contain several implicit or explicit brand logos. Image recognition is one of the most important fields of image processing and computer vision. The CNNs are a very effective class of neural networks that is highly effective at the task of image classifying, object detection and other computer vision problems.in recent years, several scale- invariant features have been proposed in literature, this paper analyzes the usage of Speeded Up Robust Features (SURF) as local descriptors, and as we will see, they are not only scale-invariant features, but they also offer the advantage of being computed very efficiently. Furthermore, a fundamental matrix estimation method based on the RANSAC is applied.

scholarly journals Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images

2008 ◽  
Vol 2008 (1) ◽  
Author(s):  
Javier A. Montoya-Zegarra ◽  
João Paulo Papa ◽  
Neucimar J. Leite ◽  
Ricardo da Silva Torres ◽  
Alexandre Falcão
Keyword(s):  
Rotation Invariant ◽  
Scale Invariant ◽  
Invariant Features

Kernel based visual tracking with scale invariant features

2008 ◽  
Vol 6 (3) ◽  
pp. 168-171 ◽  
Author(s):  
韩日升 Risheng Han ◽  
敬忠良 Zhongliang Jing ◽  
李元祥 Yuanxiang Li
Keyword(s):  
Visual Tracking ◽  
Scale Invariant ◽  
Invariant Features

scholarly journals Object Detection Using Support Vector Machine and Convolutional Neural Network - A Survey

2018 ◽  
Vol 7 (2.24) ◽  
pp. 428
Author(s):  
Rishi Khosla ◽  
Yashovardhan Singh ◽  
T Balachander
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Mobile Device ◽  
Confidence Score ◽  
Mobile User ◽  
Mobile Technologies ◽  
Support Vector ◽  
Scale Invariant

Mobile Technologies have been in trend for quite some time and with the advances in machine learning, they have become more powerful. Computer Vision, Computational Analysis and Computer Graphics have changed over the course of time. In this Project, our aim is to figure out the domains in which Machine Learning can be applied to enhance the capabilities of a Mobile Device which would lead to a better and sustainable mobile user experience.  The models we would use are a convolutional neural network (CNN), support vector machine (SVM) and scale-invariant feature transform (SIFT). This project uses the real-time image from a mobile device and does the classification and detection with the help of Tensor Flow and provides the result with a confidence score. 

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