Yuma Matsuda

Shape Retrieval With Geometrically Characterized Contour Partitions

This paper proposes a new computational method for retrieving shapes under unpredictable conditions such as when occlusion, geometric distortion, and differences in image resolution occur simultaneously. The human visual system retrieves shapes from incomplete information in the real world, and it has inspired a lot of computational methods of retrieving shapes. In order to retrieve shapes, the observed shapes are decided to be alike or unlike remembered shapes in memory after the comparison of these shapes. To compare the observed and remembered shapes, they must first be appropriately represented so that the points on each shape can be mapped and compared. For this reason, the shape retrieval process needs an appropriate shape representation and shape mapping methods. Moreover, the shape representations should be normalized before the mapping process. However, a normalization process for representations under unpredictable conditions has not yet been established. In this paper, we describe a shape retrieval method that enables us to retrieve shapes under unpredictable conditions with a suitable normalization process. Using curvature partition and angle-length profile, our shape retrieval method normalizes the shape representation before it does the mapping. As a result, unlike the previously proposed methods, it can be used under unpredictable conditions such as when occlusion, geometric distortion, and differences in image resolution occur simultaneously.

Visual shape representation with geometrically characterized contour partitions

This paper proposes a biologically plausible matching method to recognize general shapes based on contour curvature information. The human visual system recognizes general shapes flexibly in real-world scenes through the ventral pathway. The pathway is typically modeled using artificial neural networks. These network models, however, do not construct a shape representation that satisfies the following required constraints: (1) The original shape should be represented by a group of partitioned contours in order to retrieve the whole shape (global information) from the partial contours (local information). (2) Coarse and fine structures of the original shapes should be individually represented in order for the visual system to respond to shapes as quickly as possible based on the least number of their features, and to discriminate between shapes based on detailed information. (3) The shape recognition realized with an artificial visual system should be invariant to geometric transformation such as expansion, rotation, or shear. In this paper, we propose a visual shape representation with geometrically characterized contour partitions described on multiple spatial scales.

High-speed estimation of MIMO system capacity using a hierarchical approach and preprocessing digital maps

We propose a high-speed and highly-accurate estimation method for area-dependent MIMO system capacity. The proposed method employs a hierarchical approach, in which the evaluation area is divided into two groups based on the contribution of diffracted components estimated by a simple ray-launching method without considering the effect of diffraction, and then appropriate estimation method is applied for each group. The method also employs a novel digital map preprocessing that combines adjacent structured objects with similar heights and treats them as one object, which enabled to reduce the number of objects used in a ray-launching analysis. It has been confirmed that the proposed method calculated 58 times faster than the conventional full ray-launching method, enabling efficient radio planning of upcoming MIMO-based wireless communication systems.

Individually targeted radio network optimization

Sporadic network quality problems in a cellular network become prevalent after the initial rollout phase. Such problems occur in limited area of the service coverage and they are often revealed only by on-site measurement or customer feedback. Therefore, their quantitative analysis is difficult. They must therefore be solved individually under time and cost restrictions. We propose a simulation-based framework for solving these problems efficiently. A prototype system based on this framework was developed that supports fast simulation and three-dimensional visualization of radio quality and coordinated analysis of radio quality data and operational statistics. Using our system, we devised and implemented countermeasures (neighbor cell list optimization and antenna tilt tuning) for sporadic network quality problems in part of a commercial 3G cellular network in Hong Kong. The countermeasures mitigated these problems and improved the related key network indicators by as much as 60%.