Scene text erasing, which replaces text regions with reasonable content in natural images, has drawn significant attention in the computer vision community in recent years. There are two potential subtasks in scene text erasing: text detection and image inpainting. Both subtasks require considerable data to achieve better performance; however, the lack of a large-scale real-world scene-text removal dataset does not allow existing methods to realize their potential. To compensate for the lack of pairwise real-world data, we made considerable use of synthetic text after additional enhancement and subsequently trained our model only on the dataset generated by the improved synthetic text engine. Our proposed network contains a stroke mask prediction module and background inpainting module that can extract the text stroke as a relatively small hole from the cropped text image to maintain more background content for better inpainting results. This model can partially erase text instances in a scene image with a bounding box or work with an existing scene-text detector for automatic scene text erasing. The experimental results from the qualitative and quantitative evaluation on the SCUT-Syn, ICDAR2013, and SCUT-EnsText datasets demonstrate that our method significantly outperforms existing state-of-the-art methods even when they are trained on real-world data.

We propose a GAN-based image compression method working at extremely low bitrates below 0.1bpp. Most existing learned image compression methods suffer from blur at extremely low bitrates. Although GAN can help to reconstruct sharp images, there are two drawbacks. First, GAN makes training unstable. Second, the reconstructions often contain unpleasing noise or artifacts. To address both of the drawbacks, our method adopts two-stage training and network interpolation. The two-stage training is effective to stabilize the training. Moreover, the network interpolation utilizes the models in both stages and reduces undesirable noise and artifacts, while maintaining important edges. Hence, we can control the trade-off between perceptual quality and fidelity without re-training models. The experimental results show that our model can reconstruct high quality images. Furthermore, our user study confirms that our reconstructions are preferable to state-of-the-art GAN-based image compression model. Our source code is available at https://github.com/iwa-shi/fidelity_controllable_compression.

Blue and chub mackerels are often caught at the same time, and their market prices are different. Thus, humans need to classify them by their hands. Therefore, the demand for automatic sorting machines of these mackerels using image processing technique is increasing. Classification for blue and chub mackerels is a challenging task due to their quite similar appearance. In this paper, we propose a method for classifying blue and chub mackerels by image processing technique. The difference of the two mackerels appears on the whole and the local specific parts. Therefore, we use global and local features that are extracted from the whole and the specific part in fish images, respectively. Experimental results showed that the proposed method is superior to the methods using only global or local feature.

In this paper, we address a problem of data augmentation for character recognition. Particularly, we focus on incorporating variation in glyph into data augmentation of character images, which is a simple approach for data augmentation. Generally, existing methods increase data size by distorting images, whereas the proposed method applies noise injection into glyphs, resulting in data with radical variation in glyph. The proposed method exploits public database of glyphs for kanji and augments glyphs by injecting noise into glyphs. Then, we generate images of kanji automatically by deploying stroke images on the augmented glyphs. We carried out experiments for kanji character recognition using augmented data. The results show the effectiveness of the proposed method.

Various indoor navigation methods have been developed recently, but digitalized data of indoor map is not always available. Therefore, an indoor navigation framework using an image of information board has been proposed. In this method, the process to extract map regions from the image of an information board is necessary to be done by hands beforehand, and the process to estimate passageway regions is important because its information is used in map matching. However, the method of passageway discrimination is very heuristic, which is intended for a specific type of floor maps. Therefore, in this paper, we propose a semi-automatic method to extract map regions from the image of information board with simple user's operation. We use GrabCut method and Snakes method for the extraction method. In GrabCut method, we detect closed regions to prevent the degradation of accuracy when conducting GrabCut to the downsizing image. The proposed method can extract a map region with few deficits in short calculation time. In addition, we propose a machine learning based method to classify passageway regions and other regions from a segment image. We confirmed that the proposed methods are effective and promising by experiments.

Distributed power generation, using renewable energy, has been attracting attention to cope with global environment issues; a microgrid is a promising configuration for distributed power generation. To augment the stability and efficiency of the microgrid, an intelligent control, which considers the restrictions and characteristics of each unit, is indispensable. It can be achieved by constructing an efficient operation schedule for each power plant in the microgrid, depending on energy demand, and predicting passive power generation. The operation scheduling is regarded as a constrained optimization problem, which must have nonlinear characteristics in case of actual systems. Although several methods using metaheuristic optimization have been proposed, it would be trapped into a local minimum in some cases. In this paper, we statistically analyze operation schedules, computed for an actual power network of the demonstrative project. In addition, we conduct an investigation of the relationship between the input parameter space and the solution space, which can be exploited to obtain more appropriate initial solutions leading to better and faster converging solutions.

There exist distributed processing environments composed of many heterogeneous computers. It is required to schedule distributed parallel processes in an appropriate manner. For the scheduling, prediction of execution load of a process is effective to exploit resources of environments. We propose long-term load prediction methods with references of properties of processes and of runtime predictions. Since an appropriate prediction method is different according to the situation, we propose a prediction module selection to select an appropriate prediction method according to a state of changing CPU load using a neural network. We also discuss about the implementation of a long-term CPU load prediction system, which provides information including prediction of load for schedulers, system administrators and users.