Applications of intelligent image processing

智能图像处理的应用

• 批准号: 314529-2006
• 负责人: Bhattacharya, Prabir
• 金额: $ 2.04万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2006
• 资助国家: 加拿大
• 起止时间: 2006-01-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=338950
• 关键词: Applications; intelligent; image; processing

The project involves investigations on the applications of pattern recognition and machine learning techniques to problems in image retrieval, image scene analysis and biometrics. The use of machine learning techniques in computer vision applications is relatively new except for applications to face recognition. We propose to investigate an efficient classification and retrieval from large image databases, and also its applications to dermatological images to provide a diagnostic tool to dermatologists and oncologists, problems in biometrics involving iris recognition. We also investigate problems in mobile computing involving biometrics-based identification and verification. We propose an approach to image retrieval by classifying images into different semantic categories and using probabilistic similarity measures. With the growing availability of information technology and digital imaging revolution in the medical domain, large collections of digital dermatological images are regularly being generated by clinics for image-guided diagnosis. We propose an intelligent content-based image retrieval approach of digital medical images in the domain of dermatology. It may be used as an efficient training tool for medical students, residents and researchers to search efficiently large collection of disease-related illustrations by their visual attributes. As an application of machine learning to biometrics, we propose an iris recognition system as a biometrically based technology for person identification using support vector machines. The project is partly interdisciplinary and would provide interactions with the industry and medical doctors. The project would train three  graduate students and a Postdoctoral Fellow.

该项目涉及模式识别和机器学习技术在图像检索，图像场景分析和生物统计学问题上的应用。机器学习技术在计算机视觉应用中的使用相对较新，除了人脸识别应用。我们建议调查一个有效的分类和检索从大型图像数据库，并将其应用到皮肤病图像提供一个诊断工具，皮肤科医生和肿瘤学家，生物识别技术中涉及虹膜识别的问题。我们还研究了移动的计算中的问题，涉及基于生物特征的识别和验证。我们提出了一种方法，图像检索分类到不同的语义类别，并使用概率相似性措施。随着信息技术和数字成像革命在医学领域的日益普及，诊所定期生成大量数字皮肤病图像，用于图像引导诊断。提出了一种基于内容的皮肤科数字医学图像智能检索方法。它可以作为一个有效的培训工具，为医学生，居民和研究人员有效地搜索他们的视觉属性的疾病相关的插图大集合。作为机器学习在生物特征识别中的应用，我们提出了一个虹膜识别系统，作为一个基于生物特征的技术，使用支持向量机进行身份识别。该项目部分是跨学科的，将提供与行业和医生的互动。该项目将培训三名研究生和一名博士后研究员。