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We introduce the first visual dataset of fast foods with a total of 4,545 still images, 606 stereo pairs, 303 360 0 videos for structure from motion, and 27 privacy-preserving videos of eating events of volunteers. This work was motivated by research on fast food recognition for dietary assessment. The data was collected by obtaining three instances of 101(More)
As a safe and feasible alternative to enriching and enhancing traditional surgical training, virtual-reality-based surgical simulators have been investigated for a long time. But it is still a challenge for researchers to accurately depict the behavior of human tissue without losing the flexibility of simulation. In this paper, we propose an improved scheme(More)
We propose a multiclass (MC) classification approach to text categorization (TC). To fully take advantage of both positive and negative training examples, a maximal figure-of-merit (MFoM) learning algorithm is introduced to train high performance MC classifiers. In contrast to conventional binary classification, the proposed MC scheme assigns a uniform(More)
—A fast and robust framework for incrementally detecting text on road signs from video is presented in this paper. This new framework makes two main contributions. 1) The framework applies a divide-and-conquer strategy to decompose the original task into two subtasks, that is, the localization of road signs and the detection of text on the signs. The(More)
To meet the requirement of computer-aided medical operations, apart from the real-time deformation, it is also necessary in the design to simulate the tissue cutting and suturing in a surgery simulation. In this paper, we present a model on topology change and deformation of soft tissue, referred to as the hybrid condensed finite element model, based on the(More)
A novel maximal figure-of-merit (MFoM) learning approach to text categorization is proposed. Different from the conventional techniques, the proposed MFoM method attempts to integrate any performance metric of interest (e.g. accuracy, recall, precision, or F1 measure) into the design of any classifier. The corresponding classifier parameters are learned by(More)
This paper proposes a fast and robust framework for incrementally detecting text on road signs from natural scene video. The new framework makes two main contributions. First, the framework applies a Divide-and-Conquer strategy to decompose the original task into two sub-tasks, that is, localization of road signs and detection of text. The algorithms for(More)
The automatic extraction of blood vessels in non-fluorescein eye fundus images is a tough task in applications such as diabetic retinopathy screening. However, vessel shapes have complex variations, and accurate modeling of retinal vascular structures is challenging. We have therefore developed a new approach to accurately extract blood vessels in(More)