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- Hiroshi Ishikawa
- IEEE Trans. Pattern Anal. Mach. Intell.
- 2003

We introduce a method to solve exactly a first order Markov Random Field optimization problem in more generality than was previously possible. The MRF shall have a prior term that is convex in termsâ€¦ (More)

- Ian H. Jermyn, Hiroshi Ishikawa
- IEEE Trans. Pattern Anal. Mach. Intell.
- 2001

We describe a new form of energy functional for the modelling and identification of regions in images. The energy is defined on the space of boundaries in the image domain, and can incorporate veryâ€¦ (More)

- Satoshi Iizuka, Edgar Simo-Serra, Hiroshi Ishikawa
- ACM Trans. Graph.
- 2017

We present a novel approach for image completion that results in images that are both locally and globally consistent. With a fully-convolutional neural network, we can complete images of arbitraryâ€¦ (More)

- Hiroshi Ishikawa
- IEEE Transactions on Pattern Analysis and Machineâ€¦
- 2011

We introduce a transformation of general higher-order Markov random field with binary labels into a first-order one that has the same minima as the original. Moreover, we formalize a framework forâ€¦ (More)

- Satoshi Iizuka, Edgar Simo-Serra, Hiroshi Ishikawa
- ACM Trans. Graph.
- 2016

We present a novel technique to automatically colorize grayscale images that combines both global priors and local image features. Based on Convolutional Neural Networks, our deep network features aâ€¦ (More)

- Hiroshi Ishikawa, Davi Geiger
- CVPR
- 1998

We propose a methodfor segmenting gray-value images. By segmentation, we mean a map from the set of pixels to a small set of levels such that each connected component of the set of pixels with theâ€¦ (More)

- Hiroshi Ishikawa
- 2009 IEEE Conference on Computer Vision andâ€¦
- 2009

We introduce a new technique that can reduce any higher-order Markov random field with binary labels into a first-order one that has the same minima as the original. Moreover, we combine theâ€¦ (More)

- Hiroshi Ishikawa, Davi Geiger
- ECCV
- 1998

Binocular stereo is the process of obtaining depth information from a pair of left and right views of a scene. We present a new approach to compute the disparity map by solving a global optimizationâ€¦ (More)

- Edgar Simo-Serra, Hiroshi Ishikawa
- 2016 IEEE Conference on Computer Vision andâ€¦
- 2016

We propose a novel approach for learning features from weakly-supervised data by joint ranking and classification. In order to exploit data with weak labels, we jointly train a feature extractionâ€¦ (More)

- Hiroshi Ishikawa
- 2000

One of the challenges of computer vision is that the information we seek to extract from images is not even defined for most images. Because of this, we cannot hope to find a simple process thatâ€¦ (More)