Scott Sorensen

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With image capturing technology growing ubiquitous in consumer products and scientific studies, there is a corresponding growth in the applications that utilize scene structure for deriving information. This trend has also been reflected in the plethora of recent studies on reconstruction using robust structure from motion, bundle adjustment, and related(More)
Material classification is an important area of research in computer vision. Typical algorithms use color and texture information for classification, but there are problems due to varying lighting conditions and diversity of colors in a single material class. In this work we study the use of long wave infrared (i.e. thermal) imagery for material(More)
Figures play an important role within biomedical publications. A typical and essential first step toward using images is the detection of compound figures and their separation into panels. In Image-CLEF'16 our team has participated in the compound figure detection and separation tasks, where we utilized a method based on connected component analysis (CCA)(More)
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