In wilderness search and rescue, objects not native or typical to a scene may provide clues that indicate the recent presence of the missing person. This paper presents the results of augmenting an aerial wilderness search-and-rescue system with an automated spectral anomaly detector for identifying unusually colored objects. The detector dynamically builds a model of the natural coloring in the scene and identifies outlier pixels, which are then filtered both spatially and temporally to find unusually colored objects. These objects are then highlighted in the search video as suggestions for the user, thus shifting a portion of the user's task from scanning the video to verifying the suggestions. This paper empirically evaluates multiple potential detectors then incorporates the best-performing detector into a suggestion system. User study results demonstrate that even with an imperfect detector users' detection increased significantly. Results further indicate that users' false positive rates did not increase, though performance in a secondary task did decrease. Furthermore, users subjectively reported that the use of detector-based suggestions made the overall task easier. These results suggest that such suggestion-based systems for search can increase overall searcher performance but that additional external tasks should be limited.

Camera-equipped mini-UAVs are popular for many applications, including search and surveillance, but video from them is commonly plagued with distracting jittery motions and disorienting rotations that make it difficult for human viewers to detect objects of interest and infer spatial relationships. For time-critical search situations there are also inherent tradeoffs between detection and search speed. These problems make the use of dynamic mosaics to expand the spatiotemporal properties of the video appealing. However, for many applications it may not be necessary to maintain full mosaics of all of the video but to mosaic and retain only a number of recent (temporally local) frames, still providing a larger field of view and effectively longer temporal view as well as natural stabilization and consistent orientation. This paper presents and evaluates a real-time system for displaying live video to human observers in search situations by using temporally local mosaics while avoiding masking effects from dropped or noisy frames. Its primary contribution is an empirical study of the effectiveness of using such methods for enhancing human detection of objects of interest, which shows that temporally local mosaics increase task performance and are easier for humans to use than non-mosaiced methods, including stabilized video.

The use of aerial video for search and surveillance has been popularized by the increased use of camera-equipped unmanned aerial vehicles. For many search applications, objects may also be missed by observers due to their small size, brief visibility, or the inherent monotony of the scene. This paper presents a novel method for automatically emphasizing unusually colored objects to improve their detectability. We use a hue histogram and a local saliency measure to find unusually colored objects, then boost the saturation of these objects while desaturating more common colors, thus drawing the observer's attention and facilitating video search.

This paper presents a novel method for generating color schemes based on images intended to anchor color designs. This has wide applicability for Web pages, printed materials, or other applications where images are used as a key part of the overall design. Unlike methods that are variants of color quantization and try to pixel-wise approximate the image, this method draws on graphic-design principles by emphasizing hue selection first, weighting effects of color by saturation, and considering the local spatial coherency in order to determine the overall visual impact of a color. Results demonstrate that the method generalizes to a wide range of images, choosing visually important elements of the coloration of the source image.