Applications of intelligent image processing

该项目涉及研究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识别方面的应用。除了人脸识别之外，机器学习技术在计算机视觉应用中的应用相对较新。我们建议研究一种基于大型图像数据库的高效分类和检索方法，并将其应用于皮肤科图像，为皮肤科医生和肿瘤科医生提供一种涉及虹膜识别的生物识别问题的诊断工具。我们还研究了移动计算中涉及基于生物特征的识别和验证的问题。我们提出了一种将图像分类为不同语义类别并使用概率相似性度量的图像检索方法。随着信息技术的日益普及和医学领域的数字成像革命，诊所经常生成大量的数字皮肤科图像，用于图像引导诊断。提出了一种基于内容的智能皮肤医学数字图像检索方法。它可以作为一种有效的训练工具，用于医学生、住院医师和研究人员根据疾病相关插图的视觉属性高效地搜索大量疾病相关插图。作为机器学习在生物识别中的应用，我们提出了一种基于生物识别技术的虹膜识别系统，该系统使用支持向量机进行人物识别。该项目部分是跨学科的，将提供与工业界和医生的互动。该项目将培养3名研究生和1名博士后。

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.