BIVSEE  A Biologically Inspired Vision System for Enclosed Environments

Biological vision components like visual attention (VA) algorithms aim to mimic the mechanism of the human vision system. Often VA algorithms are complex and require high computational and memory requirements to be realized. In biologically-inspired vision and embedded systems, the computational capacity and memory resources are of a primary concern. This paper presents a discussion for implementing VA algorithms in embedded vision systems in a resource constrained environment. The authors survey various types of VA algorithms and identify potential techniques which can be implemented in embedded vision systems. Then, they propose a low complexity and low memory VA model based on a well-established mainstream VA model. The proposed model addresses critical factors in terms of algorithm complexity, memory requirements, computational speed, and salience prediction performance to ensure the reliability of the VA in a resource constrained environment. Finally a custom softcore microprocessor-based hardware implementation on a Field-Programmable Gate Array (FPGA) is used to verify the implementation feasibility of the presented model.

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A SURVEY OF BIOLOGICALLY INSPIRED IMAGE PROCESSING FOR OBJECTS RECOGNITION

International Journal of Image and Graphics ◽

10.1142/s021946780900354x ◽

2009 ◽

Vol 09 (04) ◽

pp. 495-510 ◽

Cited By ~ 1

Author(s):

WEIREN SHI ◽

ZUOJIN LI ◽

XIN SHI ◽

ZHI ZHONG

Keyword(s):

Image Processing ◽

Computer Vision ◽

Vision System ◽

Human Vision ◽

Image Perception ◽

Biologically Inspired ◽

Recognition Mechanism ◽

Environmental Objects ◽

Future Work ◽

Animal Vision

The human vision system is a very sophisticated image processing and objects recognition mechanism. However, it is a challenge to simulate the human or animal vision system to automate visual function in machines, because it is difficult to account for the view-invariant perception of universals such as environmental objects or processes and the explicit perception of featural parts and wholes in visual scenes. In this paper, we first present an introduction to the importance of biologically inspired computer vision and review general and key vision functions from neuroscience perspective. And most significantly, we give an important summarization to and discussion on the specific applications of biologically inspired modeling, including biologically inspired image pre-processing, image perception, and objects recognition. In the end, we give some important and challenging topics of computer vision for future work.

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Biologically Inspired Sensors Can Augment Sonar, Vision System in Submarines

Noise & Vibration Worldwide ◽

10.1260/095745607780797577 ◽

2007 ◽

Vol 38 (3) ◽

pp. 25-26

Keyword(s):

Vision System ◽

Biologically Inspired

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Grasping a waving object for a humanoid robot using a biologically-inspired active vision system

The 12th IEEE International Workshop on Robot and Human Interactive Communication, 2003. Proceedings. ROMAN 2003. ◽

10.1109/roman.2003.1251815 ◽

2004 ◽

Cited By ~ 2

Author(s):

Jian Peng ◽

A. Peters ◽

Xinyu Ao ◽

A. Srikaew

Keyword(s):

Humanoid Robot ◽

Vision System ◽

Active Vision ◽

Biologically Inspired

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Biologically-Inspired Models for Attentive Robot Vision

Innovative Research in Attention Modeling and Computer Vision Applications - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-8723-3.ch003 ◽

2016 ◽

pp. 69-98 ◽

Cited By ~ 2

Author(s):

Amirhossein Jamalian ◽

Fred H. Hamker

Keyword(s):

Visual Attention ◽

Information Flow ◽

Vision Research ◽

Vision System ◽

Robot Vision ◽

Artificial Vision ◽

Future Research ◽

Visual Data ◽

Biologically Inspired ◽

Speed Up

A rich stream of visual data enters the cameras of a typical artificial vision system (e.g., a robot) and considering the fact that processing this volume of data in real-rime is almost impossible, a clever mechanism is required to reduce the amount of trivial visual data. Visual Attention might be the solution. The idea is to control the information flow and thus to improve vision by focusing the resources merely on some special aspects instead of the whole visual scene. However, does attention only speed-up processing or can the understanding of human visual attention provide additional guidance for robot vision research? In this chapter, first, some basic concepts of the primate visual system and visual attention are introduced. Afterward, a new taxonomy of biologically-inspired models of attention, particularly those that are used in robotics applications (e.g., in object detection and recognition) is given and finally, future research trends in modelling of visual attention and its applications are highlighted.

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