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Despite being an essential prerequisite at the basis of many applications ranging from surveillance to computational photography, the problem of initial background estimation seems to be marginally investigated. In this paper, we present a reliable CNN-based solution to estimate the initial background (BG) of a scene, given not necessarily a whole sequence but just a small set of frames containing...
In this paper, we develop a new transitive aligned Weisfeiler-Lehman subtree kernel. This kernel not only overcomes the shortcoming of ignoring correspondence information between isomorphic substructures that arises in existing R-convolution kernels, but also guarantees the transitivity between the correspondence information that is not available for existing matching kernels. Our kernel outperforms...
We propose a novel framework which integrates human hand detection and pose estimation into one single pipeline. Unlike most of previous works which only focus on the pose estimation part subject to some strong assumptions or relying on a weak detector to detect human hands, we employ a deep learning architecture to complete both aforementioned tasks. By letting three different neural networks share...
The ubiquitous hand gesture plays an important role in the natural human machine interaction (HMI). Recently, the consumer color and depth cameras have been used to estimate hand shapes and postures for the mid-air HMI. Under the observation that 3D hand contours possess much information of hand postures, we estimate 3D hand contours from infrared images with a limited computation complexity for the...
The simple yet subtle structures of faces make it difficult to capture the fine differences between different facial regions in the depth map, especially for consumer devices like Kinect. To address this issue, we present a novel method to super-solve and recover the facial depth map nicely. The key idea of our approach is to exploit the learning-based method to obtain the reliable face priors from...
Understanding where people attention focuses is a challenging and extremely valuable task that can be solved using computer vision technologies. In this paper we address this problem on surveillance-like scenarios, where head and body imagery are usually low resolution. We propose a method to profile the attention of people moving in a known space. We exploit coarse gaze estimation and a novel model...
This paper addresses the problem of segmenting objects for natural images by leveraging multiple segmentation methods. Existing image segmentation algorithms mostly partition the image into some coherent segments instead of extracting the object entirely. We observe that basic elements (e.g., superpixels) in the common segment produced by many methods are highly-correlated - they generally belong...
A new efficient measure for predicting estimation accuracy is proposed and successfully applied to multistream-based unsupervised adaptation of ASR systems to address data uncertainty when the ground-truth is unknown. The proposed measure is an extension of the M-measure, which predicts confidence in the output of a probability estimator by measuring the divergences of probability estimates spaced...
Video summarization is useful to find a concise representation of the original video, nevertheless its evaluation is somewhat challenging. This paper proposes a simple and efficient method for precisely evaluating the video summaries produced by the existing techniques. This method includes two steps. The first step is to establish a set of matched frames between automatic summary (AT) and the ground...
We propose a novel supervised initialization scheme for cascaded face alignment by searching nearest neighbors based on global image descriptors. Unlike existing schemes which resort to additional large training data sets for learning features, our method does not require additional training steps; thus making our method low computational. Moreover, we found that it is sufficient to use a simple low-dimensional...
Based on minimum reconstruction error criterion and the intrinsic sparse property of natural data, sparse representation (SR) has shown promising performance on various image recognition tasks. However, in the field of person re-identification (re-id), the state-of-the-art is still dominated by other methods such as metric learning or CNN. It is because samples in one view may not be representative...
A number of critical factors arises when a complex 3D scene is to be reconstructed by means of a large sequence of different views. Some of them are related to the ability to recover the correct identity and the accurate projection of each observed feature. Other sources of error are tied to the reliability of the orientation estimate for each view. With this paper we propose a method that tries to...
Warping-based image stitching methods often suffer from perspective variations among multiple images and lead to shape and perspective distortions in stitching results. Moreover, they also quickly lose their efficiency in low-textured images, due to the lack of reliable point correspondences. To solve these problems, this paper presents a locally warping-based image stitching by imposing line constraints...
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