浏览全部资源
扫码关注微信
Received：26 June 2017，
Revised：2017-10-11，
Published：16 February 2018
移动端阅览
目的
2
为了提高轮廓检测的综合性能，特别是增强弱轮廓边缘的提取能力，在结合视觉机制的基础上提出了本文方法。
方法
2
模拟视觉信息在视通路中的传递和处理过程，首先根据神经节细胞的中心周边拮抗机制，实现初级轮廓信息的快速提取；接着利用高斯函数与高斯差函数之间的差异性来模拟外膝体非经典感受野的调制作用，实现纹理背景的抑制；然后构建了一种V1区多朝向简单细胞感受野模型，提出了一种基于负值效应的DOG（difference of Gaussians）响应改进评价模式；最后考虑V1区复杂细胞在表征视觉高级特征的能力，给出了一种基于并行处理的视通路视觉响应融合模型，实现目标轮廓的检测与增强。
结果
2
为了验证本文方法对自然场景图像的轮廓检测具备有效性，本文选取RuG轮廓检测数据库中的40幅自然场景图进行轮廓检测实验，并与二维高斯导函数模型（DG）、组合感受野模型（CORF）和空间稀疏约束纹理抑制模型（SSC）等3种典型的自然图像轮廓检测方法进行了分析比较。结果表明，本文方法检测提取到的主体轮廓更加完整，具有较高的图像纯净度，整体上反映了本文所提轮廓检测方法所具备的生物智能性。本文方法的平均
P
指标为0.45，相较于对比方法具有更好的轮廓检测性能。
结论
2
本文方法具有较好的自然轮廓检测提取能力，尤其对于图像包含部分弱轮廓边缘的检测。本文构建的新模型将有助于对视通路中各层级功能和内在机制的理解，也将为基于视觉机制的图像分析和理解提供一种新的思路。
Objective
2
Visual information is the main source of human perception of outside information. The visual system of the human brain
as the most important means of obtaining information from the outside world
has a near-perfect information processing capability
which is far superior to existing computer vision systems in all aspects. The model description of the visual information processing mechanism can provide a novel way of solving engineering application problems
such as image analysis and understanding. Therefore
the study of the visual information processing mechanism has become an important direction in brain and cognitive science research. The complexity of the visual system lies in the complexity of information transmission between neurons; multiple information paths exist
and high cortical information demonstrates feedback regulation. Visual computing is an important means of studying visual information processing mechanisms and promoting the development of computer vision-related applications. Researchers can study the coding and processing of visual information from different ranges
such as microcosmic to macroscopic and molecular to behavioral
with the continuous improvement of the technical means of visual mechanism research. However
only the experimental data
which are organized organically from different levels and angles
can help determine the laws and mechanisms of nature. Contour detection is crucial to understanding the function and application of a high cortex visual perception.
Method
2
This study considers the process of visual information transmission by taking the entire visual paths as the object in studying the visual response characteristics of different mechanisms in the paths and constructing the visual fusion model of multiple visual pathways. The response model of the antagonistic mechanism of ganglion cells in the pathway is improved
the negative value of the primary contour response is preserved
and several features of the non-classical receptive field of the lateral geniculate nucleus are enhanced. We designed and implemented a multi-oriented simple cell receptive field model based on the DOG negative effect and constructed a visual fusion model of the complex cells of the primary visual cortex to suppress texture and enhance contours through the visual information differences among various visual pathways. We simulated the transmission and processing of visual information in the visual pathway. First
we realized the rapid extraction of primary contour information according to the antagonistic mechanism of ganglion cells. Then
the difference between the Gaussian function and the DOG function was constructed to simulate the modulation of the non-classical receptive field of the LGN
which could suppress the background texture. A multi-oriented receptive field model of a simple cell in the V1 region was constructed
and an improved evaluation model that considers the negative effect of DOG was proposed. A visual response fusion method based on parallel processing was provided to enhance target contour given the capability of the complex cells in the V1 region to represent advanced visual features.
Result
2
Visual test and quantitative calculation results show that the method has an enhanced contour detection capability in a natural image with complex background and can detect certain weak contour edge information in the image. The miss rate of the DG method is low
but the error rate is high. The CORF method reduces the error rate but increases the miss rate. The mistake rate of the CORF method is improved compared with the DG method
but the miss rate is increased. The overall performance of the CORF method remains low
although the overall performance is higher in the CORF method than in the DG method. The SSC method strengthens the texture suppression while retaining the main contour and achieving improved detection results. However
the SSC method produces additional burrs at the periphery of the main contour
thereby resulting in insufficiently smooth contour lines. The proposed method has clear background and contours
thereby effectively suppressing the background of the texture and enhancing the contour of the subject. The method achieves a certain balance between the error and miss rates
thus improving the overall performance. In addition
the method can effectively suppress the texture background of the adjacent area of the subject contour
in which the extracted contour lines are smooth
and the burr phenomenon in the SSC method is removed. This study selected 40 natural scene graphs from the RuG contour detection database for contour detection experiments and compared them using three typical methods of natural image contour detection
namely
DG
CORF
and SSC
to verify the effectiveness of the proposed method in the contour detection of natural scene images. Results show that the main contours detected by the method proposed in this paper are complete
and the image purity is high. Overall
results reflect the biological intelligence of the proposed contour detection method. The average P index of the proposed method is 0.45
which indicates a better contour detection performance than the contrast methods.
Conclusion
2
In this paper
we improved the classical receptive field responses of ganglion cells in the visual pathway. We studied the enhancement effect of the LGN cells in the visual pathway by considering the visual pathway the main body of the non-classical receptive field regulation mechanism. We focused on the negative effects of the DOG produced by the antagonistic mechanism of ganglion cells and designed a multi-oriented simple cell receptive field model. We introduced a parallel mechanism in multiple visual pathways for visual information processing
and the visual pathway was divided into the main and vice paths. We proposed a method that uses the visual information difference of the different visual pathways for suppressing the texture and enhancing the contours. The proposed method has enhanced natural contour detection and extraction capabilities
especially in detecting several weak contour edges in images. The new model constructed in this study will help in elucidating the function and internal mechanisms of the visual pathway and provide a new approach for image analysis and understanding based on visual mechanism.
Martin D R, Fowlkes C C, Malik J. Learning to detect natural image boundaries using local brightness, color, and texture cues[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(5):530-549.[DOI:10.1109/TPAMI.2004.1273918]
Papari G, Petkov N. Edge and line oriented contour detection:state of the art[J]. Image and Vision Computing, 2011, 29(2-3):79-103.[DOI:10.1016/j.imavis.2010.08.009]
Tang Q L, Sang N, Liu H H. Contrast-dependent surround suppression models for contour detection[J]. Pattern Recognition, 2016, 60:51-61.[DOI:10.1016/j.patcog.2016.05.009]
Azzopardi G, Rodríguez-Sánchez A, Piater J, et al. A push-pull CORF model of a simple cell with antiphase inhibition improves SNR and contour detection[J]. PLoS One, 2014, 9(7):#e98424.[DOI:10.1371/journal.pone.0098424]
Mignotte M. A biologically inspired framework for contour detection[J]. Pattern Analysis and Applications, 2017, 20(2):365-381.[DOI:10.1007/s10044-015-0494-y]
Yang K F, Li C Y, Li Y J. Multifeature-based surround inhibition improves contour detection in natural images[J]. IEEE Transactions on Image Processing, 2014, 23(12):5020-5032.[DOI:10.1109/TIP.2014.2361210]
Arbelaez P, Maire M, Fowlkes C, et al. Contour detection and hierarchical image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(5):898-916.[DOI:10.1109/TPAMI.2010.161]
Grigorescu C, Petkov N, Westenberg M A. Contour detection based on nonclassical receptive field inhibition[J]. IEEE Transactions on Image Processing, 2003, 12(7):729-739.[DOI:10.1109/TIP.2003.814250]
Sang N, Tang Q L, Zhang T X. Contour detection based on inhibition of primary visual cortex[J]. Journal of Infrared and Millimeter Waves, 2007, 26(1):47-51, 60.
桑农, 唐奇伶, 张天序.基于初级视皮层抑制的轮廓检测方法[J].红外与毫米波学报, 2007, 26(1):47-51, 60. [DOI:10.3321/j.issn:1001-9014.2007.01.011]
Du X F, Li C H, Li J. Contour detection based on compound receptive field[J]. Journal of Electronics&Information Technology, 2009, 31(7):1630-1634.[DOI:10.1109/CISP.2012.6469689]
Yang K F, Gao S B, Guo C F, et al. Boundary detection using double-opponency and spatial sparseness constraint[J]. IEEE Transactions on Image Processing, 2015, 24(8):2565-2578.[DOI:10.1109/TIP.2015.2425538]
Lansky P, Sacerdote L, Zucca C. The Gamma renewal process as an output of the diffusion leaky integrate-and-fire neuronal model[J]. Biological Cybernetics, 2016, 110(2-3):193-200.[DOI:10.1007/s00422-016-0690-x]
Michael G, Richard B, George R. Cognitive Neuroscience the Biology of the Mind[M]. 5th ed. Beijing:China Light Industry Press, 2010:27-35.
Li G, Zhu R, Chai H L. A contour detector with improved corner detection[J]. Multimedia Tools and Applications, 2017, 76(4):5965-5984.[DOI:10.1007/s11042-015-2809-9]
Pont-Tuset J, Arbeláez P, Barron J T, et al. Multiscale combinatorial grouping for image segmentation and object proposal generation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(1):128-140.[DOI:10.1109/TPAMI.2016.2537320]
Yang Y, Tong S, Huang S, et al. Log-Gabor energy based multimodal medical image fusion in NSCT domain[J]. Computational and Mathematical Methods in Medicine, 2014, 2014:#835481.[DOI:10.1155/2014/835481]
Venkataramani S, Taylor W R. Orientation selectivity in rabbit retinal ganglion cells is mediated by presynaptic inhibition[J]. The Journal of Neuroscience, 2010, 30(46):15664-15676.[DOI:10.1523/JNEUROSCI.2081-10.2010]
Yang K F. Non classical receptive field model based on multiple visual features and its application[D]. Chengdu: University of Electronic Science and Technology of China, 2012.
杨开富. 基于多视觉特征的非经典感受野模型及应用研究[D]. 成都: 电子科技大学, 2012.
Azzopardi G, Petkov N. A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model[J]. Biological Cybernetics, 2012, 106(3):177-189.[DOI:10.1007/s00422-012-0486-6]
相关文章
相关作者
相关机构
京公网安备11010802024621
