Adaptive Sampling for Feature Detection, Tracking, and Recognition on Mobile Platforms

2011 ◽  
Vol 21 (10) ◽  
pp. 1467-1475 ◽  
Author(s):  
Mosalam Ebrahimi ◽  
Walterio W. Mayol-Cuevas
Keyword(s):  
Adaptive Sampling ◽  
Feature Detection ◽  
Mobile Platforms ◽  
Tracking And Recognition

A Comparison of Feature Extraction Algorithms Based on Sonar Sensor Data

2013 ◽  
Author(s):  
Hesham Ismail ◽  
Balakumar Balachandran
Keyword(s):  
Feature Extraction ◽  
Feature Detection ◽  
Industrial Applications ◽  
Sensor Data ◽  
Mobile Platforms ◽  
Fusion Algorithm ◽  
Unknown Environment ◽  
Mobile Vehicle ◽  
Localization And Mapping ◽  
Ongoing Work

Simultaneous localization and mapping (SLAM) is a technique used to determine the location of a mobile vehicle in an unknown environment, while constructing a map of the unknown environment at the same time. Mobile platforms, which make use of SLAM algorithms, have industrial applications in autonomous maintenance, such as the inspection of flaws and defects in oil pipelines and storage tanks. An important component of SLAM is feature extraction, which is the process of detecting and extracting significant features such as corners, edges, and walls in an environment. Here, the use of sonars as sensors mounted on a mobile platform is examined, and a comparison of different algorithms currently in use is made and presented. This comparison is performed through a combination of experimental and numerical studies. The triangulation-based fusion algorithm is examined for point feature detection, and the standard Hough Transform and the triangulation Hough fusion (THF) are used for line detection. Comparisons are discussed and presented along with ongoing work.

Ergodic Exploration for Adaptive Sampling of Water Columns Using Gliding Robotic Fish

2018 ◽  
Author(s):  
Osama Ennasr ◽  
Giorgos Mamakoukas ◽  
Todd Murphey ◽  
Xiaobo Tan
Keyword(s):  
Trajectory Optimization ◽  
Adaptive Sampling ◽  
Three Dimensional ◽  
Sampling Strategy ◽  
Robotic Fish ◽  
Mobile Platforms ◽  
Uniform Sampling ◽  
Expected Information ◽  
Water Columns ◽  
Novel Strategy

In recent years, gliding robotic fish have emerged as promising mobile platforms for underwater sensing and monitoring due to their notable energy efficiency and maneuverability. For sensing of aquatic environments, it is important to use efficient sampling strategies that incorporate previously observed data in deciding where to sample next so that the gained information is maximized. In this paper, we present an adaptive sampling strategy for mapping a scalar field in an underwater environment using a gliding robotic fish. An ergodic exploration framework is employed to compute optimal exploration trajectories. To effectively deal with the challenging complexity of finding optimum three-dimensional trajectories that are feasible for the gliding robotic fish, we propose a novel strategy that combines a unicycle model-based 2D trajectory optimization with spiral-enabled water column sampling. Gaussian process (GP) regression is used to infer the field values at unsampled locations, and to update a map of expected information density (EID) in the environment. The outputs of GP regression are then fed back to the ergodic exploration engine for trajectory optimization. We validate the proposed approach with simulation results and compare its performance with a uniform sampling grid.

Using Popular Mobile Devices in Voice Therapy

2013 ◽  
Vol 23 (3) ◽  
pp. 82-87 ◽  
Author(s):  
Eva van Leer
Keyword(s):  
Exercise Behavior ◽  
Voice Therapy ◽  
Pure Oxygen ◽  
Mobile Platforms ◽  
Self Monitoring ◽  
Web Based ◽  
Motivational Messages ◽  
Related Information ◽  
Health Related ◽  
Tailored Approach

Mobile tools are increasingly available to help individuals monitor their progress toward health behavior goals. Commonly known commercial products for health and fitness self-monitoring include wearable devices such as the Fitbit© and Nike + Pedometer© that work independently or in conjunction with mobile platforms (e.g., smartphones, media players) as well as web-based interfaces. These tools track and graph exercise behavior, provide motivational messages, offer health-related information, and allow users to share their accomplishments via social media. Approximately 2 million software programs or “apps” have been designed for mobile platforms (Pure Oxygen Mobile, 2013), many of which are health-related. The development of mobile health devices and applications is advancing so quickly that the Food and Drug Administration issued a Guidance statement with the purpose of defining mobile medical applications and describing a tailored approach to their regulation.

Feature Detection Requires Spatial Attention

2007 ◽  
Author(s):  
Jan Theeuwes ◽  
Erik van der Burg ◽  
Artem V. Belopolsky
Keyword(s):  
Spatial Attention ◽  
Feature Detection

Designing Apps Interoperable and Functional on Multiple Mobile Platforms using Google Environment

2018 ◽  
Vol 2018 (6) ◽  
pp. 115-1-115-11
Author(s):  
Devasena Inupakutika ◽  
Chetan Basutkar ◽  
Sahak Kaghyan ◽  
David Akopian ◽  
Patricia Chalela ◽  
...  
Keyword(s):  
Mobile Platforms

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

Recognition of Birds in Blurred and Illumination Images by Local Features

2017 ◽  
Vol 7 (7) ◽  
pp. 243
Author(s):  
Suresha .M ◽  
. Sandeep
Keyword(s):  
Computer Vision ◽  
Time Complexity ◽  
Feature Detection ◽  
Feature Matching ◽  
Fast Algorithms ◽  
Local Features ◽  
Experimental Demonstration ◽  
Harris Corner ◽  
Blurred Image ◽  
Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

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