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Motion estimation from video is an increasingly important problem with applications in ego-motion estimation of an unmanned vehicle, segmentation from video, object detection and tracking, and many others. Recent advances in optical flow have made motion estimation possible in many applications with high-resolution imagery. However, in the presence of noise and compression artifacts, these state-of-the-art...
Online outlier detection is fundamental for expediting the processing of data and focusing processing resources on portions of data that may be most informative. This work develops online robust dictionary learning algorithms that are able to identify outliers in the training data. The algorithms are based on lasso updates for computing the vector of expansion coefficients for a new training vector...
Robust dictionary learning algorithms seek to learn a dictionary while being robust to the presence of outliers in the training set. Often, the elements of the training set have an underlying structure due to, for example, their spatial relation or their similarity. When outliers are present as elements of the training set, they often inherit the underlying structure of the training set. This work...
Research into deep learning has demonstrated performance competitive with humans on some visual tasks, however, these systems have been primarily trained through supervised and unsupervised learning algorithms. Alternatively, research is showing that evolution may have a significant role in the development of visual systems. Thus neuroevolution for deep learning is investigated in this paper. In particular,...
This paper presents two novel algorithms for estimating the (local and global) motion in a series of range images based on a polynomial expansion. The use of polynomial expansion has been quite successful in estimating optical flow in 2D imagery, but has not been used extensively in 3D or range imagery. In both methods, each range image is approximated by applying a high-order polynomial expansion...
In this paper we introduce a method that utilizes a high-order polynomial expansion of range imagery for the purposes of image segmentation and classification. The use of polynomial expansion has been quite successful in segmenting and estimating optical flow in 2D imagery, but has not been used extensively in 3D or range imagery. We derive features using the coefficients of the high-order polynomial...
Robust object tracking still remains a difficult problem in computer vision research and surveillance applications. One promising development in this area is the increased availability of surveillance cameras with overlapping views. Intuitively, these overlapping views may lead to more robust object tracking and recognition. However, combining the information from the multiple cameras in a meaningful...
Face recognition research has gained significant interest in recent years which has resulted in the development of many state-of-the-art methods. However, it is not well-known how domain specific these methods are to the problem of face recognition. Could these algorithms be used to classify and identify other objects, such as ships seen from electro-optical satellite imagery? Face recognition research...
This paper presents a novel approach to object tracking by using multiple views to assist with handling occlusion which improves the overall tracking result. The approach is applied to face tracking using a 3D cylinder head model, but any 3D rigid object may be tracked using this approach. All cameras in the system are used to estimate a joint motion model of the face, which is updated at each frame...
Recent research in the area of automatic machine recognition of human faces has shown that there may be an advantage in utilizing face symmetry to improve recognition accuracy. While promising, this work has led to several open questions. What is a good feature description or score of the symmetry of the face? Is there a statistical significance between face symmetry and face recognition? We present...
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