Jen-Mei Chang

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Automated detection of chemical plumes presents a segmentation challenge. The segmentation problem for gas plumes is difficult due to the diffusive nature of the cloud. The advantage of considering hyperspectral images in the gas plume detection problem over the conventional RGB imagery is the presence of non-visual data, allowing for a richer(More)
The theory of illumination subspaces is well developed and has been tested extensively on the Yale Face Database B (YDB) and CMU-PIE (PIE) data sets. This paper shows that if face recognition under varying illumination is cast as a problem of matching sets of images to sets of images, then the minimal principal angle between subspaces is sufficient to(More)
Recent work has established that digital images of a human face, collected under various illumination conditions, contain discriminatory information that can be used in classification. In this paper we demonstrate that sufficient discriminatory information persists at ultralow resolution to enable a computer to recognize specific human faces in settings(More)
Illumination spaces capture how the appearances of human faces vary under changing illumination. This work models illumination spaces as points on a Grassmann manifold and uses distance measures on this manifold to show that every person in the CMU-PIE and Yale data sets has a unique and identifying illumination space. This suggests that variations under(More)
We consider the challenge of detection of chemical plumes in hyperspectral image data. Segmentation of gas is difficult due to the diffusive nature of the cloud. The use of hyperspectral imagery provides non-visual data for this problem, allowing for the utilization of a richer array of sensing information. We consider several videos of different gases(More)
We present a face recognition method using multiple images where pose and illumination are uncontrolled. The set-toset framework can be utilized whenever multiple images are available for both gallery and probe subjects. We can then transform the set-to-set classification problem as a geometric one by realizing the linear span of the images in a given(More)
Recently, placental pathology evidence has contributed to current understanding of causes of low birth weight and pre-term birth, each linked to an increased risk of later neuro-developmental disorders. Among various factors that cause such disorders, the vessel network on the placenta has been hypothesized to offer the most clues in bridging that(More)
We present a feature-invariant classification model that recognizes images under various analytic and nonanalytic transformations in the category of face recognition where human faces to be recognized are seen under varying lighting conditions and viewpoints. Our method exploits the idea of tangent approximation to differentiable manifolds, which motivates(More)
Empirical studies have shown that the collection of handwritten digits when acquired under a uniform condition forms a differentiable manifold which can be well approximated with linear structures. That is, each point on the manifold is associated with a geometry that parameterizes linear structures. Because of this, the problem of comparing a pair of(More)
We propose a novel method to detect and correct drift in non-raster scanning probe microscopy. In conventional raster scanning drift is usually corrected by subtracting a fitted polynomial from each scan line, but sample tilt or large topographic features can result in severe artifacts. Our method uses self-intersecting scan paths to distinguish drift from(More)