Markov random field modeling in computer vision djvu

They are not feed-forward and back-propagation in the context of deep neural networks. We also thank Aditya Khosla who participated in a discussion that is partly related to this work. Skip to main content Skip to sections. This service is more advanced with JavaScript available.

Advertisement Hide. European Conference on Computer Vision. Conference paper First Online: 17 September Download conference paper PDF. As repeatedly shown by previous work [ 5 ], the success of image modeling, to a large extent, hinges on whether the model can successfully capture the spatial relations among pixels. Open image in new window. The primary goal of this work is to develop a generative model for images that can express complex local relationships among pixels while being tractable for inference and learning.

Particularly, we use the 4-connected neighborhood of a 2D grid in this work. Joint Distribution. Below, we discuss the factors that we choose for the proposed model. Considering the stochastic nature of natural images, we formalize this generative process as a Gaussian mixture model GMM. The rationale behind is that pixel values are on a low-dimensional space, where a GMM with a small number of components can usually provide a good approximation to an empirical distribution.

Inference of Hidden States. Connections to RNNs. We observe that Eq. Particularly, given the RNN computations in the form of Eq. Variational Learning Principle. Estimation of probabilistic models based on the maximum likelihood principle is often intractable when the model contains hidden variables.

Given such a decomposition, we can derive an iterative computational procedure, where each cycle couples a forward pass that applies Eq. The study of this problem originated from graphics [ 13 , 14 ].

The key to successful texture reproduction, as we learned from previous work, is to effectively capture the local patterns and variations. Texture synthesis results. We work on two texture datasets, Brodatz [ 49 ] for grayscale images, and VisTex [ 50 ] for color images. From the results shown in Fig. We also compare with the 2D-RNN [ 34 ].

As we can see, the results obtained using 2D-RNN, which synthesizes based only on the left and upper regions, exhibit undesirable effects and often evolve into blacks in the bottom-right parts. Texture synthesis by varying the patch size and the number of mixtures. Two fundamental parameters control the behaviors of our texture model. The training patch size decides the farthest spatial relationships that could be learned from data.

The number of gaussian mixtures control the dynamics of the texture landscape. We analyze our model by changing the two parameters. As shown in Fig.

For non-parametric approaches, bigger patch size would dramatically bring up the computation cost. While for our model, the inference time holds the same regardless of the patch size that the model is trained on.

Moreover, our parametric model is able to scale to large dataset without bringing additional computations. Table 1. Table 2. PSNR dB on various dataset with upscale factor 3. As shown in Tables 1 and 2 , our approach outperforms the CNN-based baseline [ 52 ] and compares favorably with the state-of-the-art methods dedicated to this task [ 53 , 54 ].

One possible explanation for the success is that our model not only learns the mapping, but also learns the image statistics for high resolution images. The training procedure which unrolls the RNN into thousands of steps that share parameters also reduces the risk of overfitting.

The results also demonstrate the particular strength of our model in handling large upscaling factors and difficult images.

Figure 8 shows several examples visually. Image super resolution results from Set 5 with upscaling factor 3. Image synthesis results. Portilla, J. IEEE Trans. Image Process. Freeman, W. Bertalmio, M. McMillan, L. ACM Google Scholar. Huang, J. Turk, M. Wright, J. Hinton, G.

Neural Comput. Werbos, P. Kingma, D. Goodfellow, I. Denton, E. Efros, A. Wei, L. Hertzmann, A. Hays, J. ACM Trans. TOG 26 3 Article no. Lalonde, J. Cross, G. Pattern Anal. Boykov, Y. He, X. Geman, S. Ising, E. Rue, H. Zhu, S. Roth, S. Donahue, J.

Mnih, V. Gregor, K. Graves, A. The latter relates to how data is observed and is problem domain dependent. The former depends on how various prior constraints are expressed. This paper presents a unified approach for MRF modeling in low and high level computer vision. The unification is made possible due to a recent advance in MRF modeling for high level object recognition. Such unification provides a systematic approach for vision modeling based on sound mathematical principles.

Skip to main content Skip to sections. This service is more advanced with JavaScript available. Advertisement Hide. European Conference on Computer Vision. Markov random field models in computer vision. Authors Authors and affiliations S. Conference paper First Online: 16 June Download to read the full conference paper text.

B , —, Such unification provides a systematic approach for vision modeling based on sound mathematical principles. Documents: Advanced Search Include Citations. Authors: Advanced Search Include Citations. Citations: - 18 self.