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Measuring the amount of dependency among multiple variables is an important task in pattern recognition. In the last few years, many new dependency measures have been developed for the exploration of functional relationships. In this paper, we develop a dependency measure between variables based on an extreme-value theoretic treatment of intrinsic dimensionality. Our measure identifies variables with...
Correlation tracker has made a huge success in visual object tracking. However, it is mainly because that the tracker cannot catch the occurrence of appearance changes, tracking based on correlation filters often drifts due to the unexpected appearance changes caused by occlusion, deformation and background clutter. In this paper, we propose a new method to detect the case when the tracker encountered...
Adverse effects, such as voice change and fatigue, are prevalent in cancer treatment duration. These adverse effects have been significant burden for patients physically and emotionally. Predicting multiple adverse effects becomes important for patients and oncologists. In this paper, we formulate the prediction of multiple adverse effects in cancer treatment as a longitudinal multiple-output regression...
This paper introduces a novel method for extracting sets of feature from 3D objects characterising a robust steganalyzer. Specifically, the proposed steganalyzer should mitigate the Cover Source Mismatch (CSM) paradigm. A steganalyzer is considered as a classifier aiming to identify separately cover and stego objects. A steganalyzer behaves as a classifier by considering a set of features extracted...
Myoelectric pattern recognition (MPR) can be used for intuitive control of virtual and robotic effectors in clinical applications such as prosthetic limbs and the treatment of phantom limb pain. The conventional approach is to feed classifiers with descriptive electromyographic (EMG) features that represent the aimed movements. The complexity and consequently classification accuracy of MPR is highly...
We present an approach to automatically generating verbal commentaries for tennis games. We introduce a novel application that requires a combination of techniques from computer vision, natural language processing and machine learning. A video sequence is first analysed using state-of-the-art computer vision methods to track the ball, fit the detected edges to the court model, track the players, and...
Automatic image annotation has been an important research topic in facilitating large scale image management and retrieval. Existing methods focus on learning image-tag correlation or correlation between tags to improve annotation accuracy. However, most of these methods evaluate their performance using top-k retrieval performance, where k is fixed. Although such setting gives convenience for comparing...
In this paper, we present a novel face spoofing detection method based on 3D lighting environment analysis of an image pair collected before and after the lighting environment change. Our idea is inspired from the unimpressive fact that the illumination distributions of the internal spoof face stays stable under the protection of the photo and screen plane, while that of a exposed genuine face changes...
Change detection, in multi-temporal satellite imagery, seeks to discover relevant changes and to discard irrelevant ones. This task is usually achieved by modeling accurate decision criteria that capture the user's intention while being resilient to many irrelevant changes including acquisition conditions. Among existing change detection solutions, correlation-based models - such as canonical correlation...
Template matching is a classical and essential step in many pattern recognition, object detection or video tracking systems. This paper aims at integrating and evaluating different template matching methods in the context of pattern spotting in historical document images — i.e. the search for occurrences of a given visual pattern in document images. Given a query image, our pattern spotting system...
GAT (Global Affine Transformation) and GPT (Global Projection Transformation) correlation matchings were successively proposed by Wakahara and Yamashita which use affine transformation (AT) and 2D projection transformation (PT), respectively, to maximize the normalized cross-correlation value between a template and a GAT/GPT-superimposed input image. In theory, to maximize the degree of matching via...
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