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In this paper, we propose an ordinal hyperplane ranking algorithm called OHRank, which estimates human ages via facial images. The design of the algorithm is based on the relative order information among the age labels in a database. Each ordinal hyperplane separates all the facial images into two groups according to the relative order, and a cost-sensitive(More)
Many existing approaches used iterative-reenement techniques for 3D registration of partially-overlapping range images. The major drawback of these approaches is that they require a good initial estimate to guarantee that the correct solution can always be found. In this paper, we propose a new method, the RANSAC-based DARCES (data-aligned(More)
A popular approach for 3D registration of partially-overlapping range images is the ICP (iterative closest point) method and many o f i t s v ariations. The major drawback o f t h i s type of iterative approaches is that they require a good initial estimate to guarantee that the correct solution can always be found. In this paper , we propose a new method,(More)
In this paper, we introduce the concept of Intrinsic Illumination Subspace which is based on the intrinsic images. This intrinsic illumination subspace enables an analytic generation of the illumination images under varying lighting conditions. When objects of the same class are concerned, our method allows a class-based generic intrinsic illumination(More)
This paper introduces an approach for face cognizance throughout age and in addition a dataset containing variations of age in the wild. We use a data-driven system to deal with the go-age face realization challenge, known as cross-age reference coding (CARC). By using leveraging a colossal-scale snapshot dataset freely available on the web as a reference(More)
We propose a method that can detect humans in a single image based on a novel cascaded structure. In our approach, both intensity-based rectangle features and gradient-based 1-D features are employed in the feature pool for weak-learner selection. The Real AdaBoost algorithm is used to select critical features from a combined feature set and learn the(More)
Detecting moving objects by using an adaptive background model is a critical component for many vision-based applications. Most background models were maintained in pixel-based forms, while some approaches began to study block-based representations which are more robust to non-stationary backgrounds. In this paper, we propose a method that combines(More)
With the advances in imaging technologies for robot or machine vision, new imaging devices are being developed for robot navigation or image-based rendering. However, to satisfy some design criterion, such as image resolution or viewing ranges, these devices are not necessarily being designed to follow the perspective rule and, thus, the imaging rays may(More)