Many studies of material property estimation and material recognition have been conducted. Previous approaches evaluate the validity or usefulness of hand-designed image features. Thus, we propose a method to directly and naturally acquire image features for material perception using convolutional neural networks. Using a fine-tuned network, we achieved approximately the same recognition accuracy as recent results on the Flickr Material Database. We found that neurons in deep layers are trained to respond to textures or materials.

Much research in augmented reality (AR) technology attempts to match the textures of virtual objects with real world. However, the textures of real objects must also be rendered consistent with virtual information. This paper proposes a method for representing the degradation of real objects in virtual time. Real-world depth information, used to build three-dimensional models of real objects, is captured by a RGB-D camera. The degradation of real objects is then represented by superimposing the degradation texture onto the real object.

This paper proposes an augmented reality marker that can be robustly detected even on a cylindrical surface. The marker enables the surface normal estimation of a cylindrical object to realize the presentation of appropriate virtual information on the object. Conventional markers have difficulty detecting and obtaining accurate surface normal in the presence of occlusion or distortion of the marker in the image. Furthermore, it is difficult to identify a feature on a cylindrical object on which to position a marker. These problems are resolved by relying on the characteristic that a line parallel to the central axis of the cylinder maintains linearity. In addition, surface normal is calculated by estimating the object's shape by using transformation matrices.

It is thought that human being recognizes a complicated figure by combining simple figures. This is "figure alphabet hypothesis" and these simple figures are called "figure alphabet". We considered "the mechanism in which a complicated figure is recognized with the combination of the figure chosen from comparatively simple figure groups", and applies it to a pattern classification. The proposed method assumes the figure alphabet to be the dot pattern (Alphabet Dot Pattern, ADP) of an N × N pixels. Because there are many kinds of ADP, ADP group is optimized by Genetic Algorithm (GA). And, the euclidean distance of an input figure and an ADP group is calculated, and classifies the figure. The proposal technique was previously applied to the classification problem of the binary multifont figure, and the validity was shown. In this research, the result applied to the gradation images. As a result, the classification of the face image and the pedestrian image obtained a high correct answer rate.

Image processing and recognition technologies are becoming increasingly important. Automatic construction methods for image transformation algorithms proposed to date approximate adequate image transformation from original images to their target images using a combination of several known image processing filters by evolutionary computation techniques. In this paper, we introduce the adaptive image processing filters that process according to the features of an input image. The processing of the adaptive filters is decided based on the local features of an input image. We implement them to feed-forward genetic image network (FFGIN) that is one of the automatic construction methods for image transformations. Then we apply our method to the problems of segmentation of organs and tissues in medical images. Experimental results show that our method constructs the effective segmentation algorithms that extract multiple regions respectively.

When we evaluate the search performance of an evolutionary computation (EC) technique, we usually apply it to typical benchmark functions and evaluate its performance in comparison to other techniques. In experiments on limited benchmark functions, it can be difficult to understand the features of each technique. In this paper, the search spaces that emphasize the performance difference of EC techniques are evolved by Cartesian genetic programming. We focus on a real-coded genetic algorithm, which is a type of genetic algorithm that has a real-valued vector as a chromosome. In particular, we generate search spaces using the performance difference of real-coded crossovers. In the experiments, we evolve the search spaces using the combination of three types of real-coded crossovers. As a result of our experiments, the search spaces that exhibit the largest performance difference of two crossovers are generated for all the combinations.

Using well-established techniques of Genetic Programming (GP), we automatically optimize image feature filters over several inputs and within transformation images, improving the Automatic Construction of Tree-Structural Image Transformation (ACTIT) system. Our objective is to also produce optimal solutions in substantially less computation time than require for generating features of ACTIT. We improved the algorithm feature filters in the process through GP, which are expressed by trees in Automatic Construction of Tree-Structural Image Transformation, to reduce computation time. Through our experimentation, we show that our new approach is accurate and requires less computation time by maintaining the feature images in conjunction with the original images.