Abstract
In recent years, convolutional neural network gave the state-of-the-art performance on various image recognition benchmarks. Although CNN requires a large number of training images including various locations and sizes of a target, we cannot prepare a lot of supervised intracellular images. In addition, the properties of intracellular images are different from standard images used in computer vision researches. Overlap between particles often occurred in dense regions. In overlapping area, there are ambiguous edges at the peripheral region of particles. This induces the detection error by the conventional method. However, all edges of overlapping particles are not ambiguous. We should use the obvious peripheral edges. Thus, we try to predict the center of a particle from the peripheral regions by CNN, and the prediction results are voted. Since the particle center is predicted from peripheral views, we can prepare many training samples from one particle. High accuracy is obtained in comparison with the conventional binary detector using CNN as a binary classifier.
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References
Abramoff, M.D., et al.: Image Processing with ImageJ. Biophotonics Int. 11(7), 36–42 (2004)
Kumagai, S., et al.: Counting and radius estimation of lipid droplet in intracellular images. In: Proceedings IEEE International Conference on Systems, Man, and Cybernetics, pp. 67–71 (2012)
Gall, J., Lempitsky, V.: Class-specific hough forests for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1022–1029 (2009)
Leibe, B., et al.: Robust object detection with interleaved categorization and segmentation. Int. J. Comput. Vis. 77(1–3), 259–289 (2008)
Krizhevsky, A., et al.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105 (2012)
Lin, M., et al.: Network in network. In: Proceedings of International Conference on Learning Representations (2014)
He, K., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Proceedings of European Conference on Computer Vision, pp. 346–361 (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Xiao, T., et al.: The application of two-level attention models in deep convolutional neural network for fine-grained image classification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 842–850 (2015)
Zhang, N., et al.: PANDA: Pose Aligned Networks for Deep Attribute Modeling. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1637–1644 (2014)
Ahmed, E., et al.: An improved deep learning architecture for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3908–3916 (2015)
Girshick, R., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Li, H., et al.: A convolutional neural network cascade for face detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5325–5334 (2015)
Bertasius, G., et al.: DeepEdge: a multi-scale bifurcated deep network for top-down contour detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4380–4389 (2015)
Liu, S., et al.: Matching-CNN meets KNN: quasi-parametric human parsing. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1419–1427 (2015)
Badrinarayanan, V., et al.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. In: Proceedings of International Conference on Computer Vision (2015)
Karpathy, A., et al.: Large-scale video classification with convolutional neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725–1732 (2014)
Acknowledgements
This work is partially supported by MEXT/JSPS Grant Number 16H01435 “Resonance Bio” and SCAT research grant.
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Nishida, K., Hotta, K. (2016). Particle Detection in Crowd Regions Using Cumulative Score of CNN. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_55
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DOI: https://doi.org/10.1007/978-3-319-50832-0_55
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