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In daily life, we can see images of real-life objects on posters, television, or virtually any type of smooth physical surfaces. We seldom confuse these images with the objects per se mainly with the help of the contextual information from the surrounding environment and nearby objects. Without this contextual information, distinguishing an object from an(More)
Image recapture detection (IRD) is to distinguish real-scene images from the recaptured ones. Being able to detect recaptured images, a single image based counter-measure for rebroadcast attack on a face authentication system becomes feasible. Being able to detect recaptured images, general object recognition can differentiate the objects on a poster from(More)
According to a popular pattern recognition method, this paper proposes two methods to generate a secret from individual's biometric information, such as fingerprint feature points. One method is to recover a secret based on Chinese Remainder approach. Another is to fake some chaff points based on the general biometric feature points, and then recover the(More)
Medical image retrieval is still mainly a research domain with a large variety of applications and techniques. With the ImageCLEF 2004 benchmark, an evaluation framework has been created that includes a database, query topics and ground truth data. Eleven systems (with a total of more than 50 runs) compared their performance in various configurations. The(More)
This paper proposes a content-based medical image retrieval (CBMIR) framework using dynamically optimized features from multiple regions of medical images. These regional features, including structural and statistical properties of color, texture and geometry, are extracted from multiple dominant regions segmented by applying Gaussian mixture modeling (GMM)(More)
We propose a novel framework for content-based image retrieval with multiple parallel retrieval engines (MultiPRE) to achieve higher retrieval performance. Visual features, including both low-level features, such as color, texture and region features, and middle-level structure features, such as blob representation of objects are used to capture geometrical(More)
In this paper, a multi-class classification system is developed for medical images. We have mainly explored ways to use different image features, and compared two classifiers: principle component analysis (PCA) and supporting vector machines (SVM) with RBF (radial basis functions) kernels. Experimental results showed that SVM with a combination of the(More)
In this paper we report our work on the fully automatic medical image retrieval task in ImageCLEFmed 2005. First, we manually identify visually similar sample images by visual perception for each query topic. These help us understand the variations of the query topic and form templates for similarity measure. To achieve higher performance, two similarity(More)