A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model

A graph theoretic approach is proposed for object shape representation in a hierarchical compositional architecture called Compositional Hierarchy of Parts (CHOP). In the proposed approach, vocabulary learning is performed using a hybrid generative-descriptive model. First, statistical relationships between parts are learned using a Minimum Conditional Entropy Clustering algorithm. Then, selection of descriptive parts is defined as a frequent subgraph discovery problem, and solved using a Minimum Description Length (MDL) principle. Finally, part compositions are constructed by compressing the internal data representation with discovered substructures. Shape representation and computational complexity properties of the proposed approach and algorithms are examined using six benchmark two-dimensional shape image datasets. Experiments show that CHOP can employ part shareability and indexing mechanisms for fast inference of part compositions using learned shape vocabularies. Additionally, CHOP provides better shape retrieval performance than the state-of-the-art shape retrieval methods.Comment: Paper : 17 pages. 13th European Conference on Computer Vision (ECCV 2014), Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III, pp 566-581. Supplementary material can be downloaded from http://link.springer.com/content/esm/chp:10.1007/978-3-319-10578-9_37/file/MediaObjects/978-3-319-10578-9_37_MOESM1_ESM.pd

Modelling Objects Using Kernel Principal Component Analysis

Object detection is a technologically challenging and practically useful field of computer vision.The success of object detection relies on modelling of an object class. Statistical shape modelling is one of the popular method. Object modelling starts with asset of examples shapes (the training set), and learn from this the pattern of variability of the shape of the class of objects for which the training set can be considered a representative sample. Modelling can considered as the process of modelling the distribution of the training points in shape space. In this paper we present Kernel principal component analysis (KPCA) based active shape models (ASM) for learning the intra –class deformation modes of an object. KPCA is the non-linear dimensionality reduction method. The comparison on performance and space of KPCA and principal component analysis (PCA) are shownKeywords: Object model, KPCA, PCA, ASM.Cite as: Rajkumari Bidyalakshmi Devi, Romesh Laishram, Y.J. Singh, “Modelling Objects Using KernelPrincipal Component Analysis†ADBU J.Engg.Tech.,2(1)(2015) 0021102(5pp

СЕГМЕНТАЦИЯ ОБЪЕКТОВ НА БИОМЕДИЦИНСКИХ ИЗОБРАЖЕНИЯХ С ИСПОЛЬЗОВАНИЕМ БИБЛИОТЕКИ ШАБЛОНОВ

The purpose of this paper is to introduce a robust framework to facilitate simultaneous detection and segmentation of objects with arbitrary size and shape on different kinds of medical images using a library of arbitrary irregular smooth shapes.Рассматривается система компьютеризированной диагностики для обнаружения объектов с произвольными размерами и формой и сегментации их на медицинских изображениях различной мо-дальности с использованием библиотеки шаблонов нерегулярной гладкой формы

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