畳み込みニューラルネットワークと時空間サポートベクター回帰に基づくギターの運指の自動評価システム

早大学位記番号:新7965早稲田大

Mathematical morphology for tensor data induced by the Loewner orderingin higher dimensions

Positive semidefinite matrix fields are becoming increasingly important in digital imaging. One reason for this tendency consists of the introduction of diffusion tensor magnetic resonance imaging (DTMRI). In order to perform shape analysis, enhancement or segmentation of such tensor fields, appropriate image processing tools must be developed. This paper extends fundamental morphological operations to the matrix-valued setting. We start by presenting novel definitions for the maximum and minimum of a set of matrices since these notions lie at the heart of the morphological operations. In contrast to naive approaches like the component-wise maximum or minimum of the matrix channels, our approach is based on the Loewner ordering for symmetric matrices. The notions of maximum and minimum deduced from this partial ordering satisfy desirable properties such as rotation invariance, preservation of positive semidefiniteness, and continuous dependence on the input data. We introduce erosion, dilation, opening, closing, top hats, morphological derivatives, shock filters, and mid-range filters for positive semidefinite matrix-valued images. These morphological operations incorporate information simultaneously from all matrix channels rather than treating them independently. Experiments on DT-MRI images with ball- and rod-shaped structuring elements illustrate the properties and performance of our morphological operators for matrix-valued data

Discrimintive Image Warping with Attribute Flow

We address the problem of finding deformation between two images for the purpose of recognizing objects. The challenge is that discriminative features are often transformation-variant (e.g. histogram of oriented gradients, texture), while transformation-invariant features (e.g. intensity, color) are often not discriminative. We introduce the concept of attribute flow which explicitly models how image attributes vary with its deformation. We develop a non-parametric method to approximate this using histogram matching, which can be solved efficiently using linear programming. Our method produces dense correspondence between images, and utilizes discriminative, transformation-variant features for simultaneous detection and alignment. Experiments on ETHZ shape categories dataset show that we can accurately recognize highly deformable objects with few training examples

Towards an Interactive Humanoid Companion with Visual Tracking Modalities

The idea of robots acting as human companions is not a particularly new or original one. Since the notion of “robot ” was created, the idea of robots replacing humans in dangerous, dirty and dull activities has been inseparably tied with the fantasy of human-like robots being friends and existing side by side with humans. In 1989, Engelberger (Engelberger

The research for shape-based visual recognition of object categories

摘要 视觉目标类识别旨在识别图像中特定的某类目标，基于形状的目标类识别是目前计算机视觉研究的热点之一。真实图像中物体姿态的多样性以及环境的复杂性，给目标的形状提取和识别带来巨大挑战。本文借鉴生物视觉机制的研究成果，对基于形状的目标类识别算法进行研究。主要研究内容如下： 1. 研究与形状认知相关的视觉机制，分析形状知觉整体性的生理基础及其生理模型。以形状知觉整体性为基础，建立基于形状的目标类识别系统框架。框架既重视整体性在自下而上的特征加工中的作用，也重视整体约束在自上而下的识别中的作用。 2. 受生物视觉上的整合野模型启发，本文提出了一个三阶段轮廓检测算法。阶段1利用结构自适应滤波器平滑...Categorical object detection addresses determining the number of instances of a particular object category in an image, and localizing those instances in space and scale. The shape-based visual recognition of object categories is one of hot topics in computer vision. The diversity of poses of targets and complexity of the environment in real images bring huge challenges to shape extraction and obj...学位：工学博士院系专业：信息科学与技术学院自动化系_控制理论与控制工程学号：2322006015337