机械英文论文(pdf).rar
机械英文论文(pdf),----------------------- page 1----------------------- 2 foveated vision sensorand image processing ╟a review 12 mohammed yeasin , rajeev sharma 1. department of...
该文档为压缩文件,包含的文件列表如下:
内容介绍
原文档由会员 amo 发布
----------------------- Page 1-----------------------
2 Foveated Vision Sensor
and Image Processing – A Review
1 2
Mohammed Yeasin , Rajeev Sharma
1. Department of Electrical and Computer Engineering, University of
Memphis, TN 38152-3180
Email: myeasin@memphis.edu
2. Department of Computer Science and Engineering The Pennsyl-
vania State University, University Park, PA-16802
Abstract. The term foveated vision refers to sensor architectures based on smooth
variation of resolution across the visual field, like that of the human visual system.
The foveated vision, however, is usually treated concurrently with the eye motor
system, where fovea focuses on regions of interest (ROI). Such visual sensors
expected to have wide range of machine vision applications in situations where the
constraint of performance, size, weight, data reduction and cost must be jointly
optimized. Arguably, foveated sensors along with a purposefully planned
acquisition strategy can considerably reduce the complexity of processing and
help in designing superior vision algorithms to extract meaningful information
from visual data. Hence, understanding foveated vision sensors is critical for
designing a better machine vision algorithm and understanding biological vision
system.
This chapter will review the state-of-the-art of the retino-cortical (foveated)
mapping models and sensor implementations based on these models. Despite
some notable advantages foveated sensors have not been widely used due to the
lack of elegant image processing tools. Traditional image processing algorithms
are inadequate when applied directly to a space-variant image representation. A
careful design of low level image processing operators (both the spatial and
frequency domain) can offer a meaningful solution to the above mentioned
problems. The utility of such approach was explefied through the computation of
optical flow on log-mapped images.
Key words Foveated vision, Retino-cortical mapping, Optical flow, Stereo
disparity, Conformal mapping, and Chirp transform.
....
2 Foveated Vision Sensor
and Image Processing – A Review
1 2
Mohammed Yeasin , Rajeev Sharma
1. Department of Electrical and Computer Engineering, University of
Memphis, TN 38152-3180
Email: myeasin@memphis.edu
2. Department of Computer Science and Engineering The Pennsyl-
vania State University, University Park, PA-16802
Abstract. The term foveated vision refers to sensor architectures based on smooth
variation of resolution across the visual field, like that of the human visual system.
The foveated vision, however, is usually treated concurrently with the eye motor
system, where fovea focuses on regions of interest (ROI). Such visual sensors
expected to have wide range of machine vision applications in situations where the
constraint of performance, size, weight, data reduction and cost must be jointly
optimized. Arguably, foveated sensors along with a purposefully planned
acquisition strategy can considerably reduce the complexity of processing and
help in designing superior vision algorithms to extract meaningful information
from visual data. Hence, understanding foveated vision sensors is critical for
designing a better machine vision algorithm and understanding biological vision
system.
This chapter will review the state-of-the-art of the retino-cortical (foveated)
mapping models and sensor implementations based on these models. Despite
some notable advantages foveated sensors have not been widely used due to the
lack of elegant image processing tools. Traditional image processing algorithms
are inadequate when applied directly to a space-variant image representation. A
careful design of low level image processing operators (both the spatial and
frequency domain) can offer a meaningful solution to the above mentioned
problems. The utility of such approach was explefied through the computation of
optical flow on log-mapped images.
Key words Foveated vision, Retino-cortical mapping, Optical flow, Stereo
disparity, Conformal mapping, and Chirp transform.
....
