Bryan S. Morse

Ridges for Image Analysis

Representation of object shape by medial structures has been an important aspect of image analysis. Methods for describing objects in a binary image by medial axes are well understood. Many attempts have been made to construct similar medial structures for objects in gray scale images. In particular, researchers have studied images by analyzing the graphs of the intensity data and identifying ridge and valley structures on those surfaces. In this paper we review many of the definitions for ridges. Computational vision models require that medial structures should remain invariant under certain transformations of the spatial locations and intensities. For each ridge definition we point out which invariances the definition satisfies. We also give extensions of the concepts so that we can located-dimensional ridge structures withinn-dimensional images. A comparison of the ridge structures produced by the different definitions is given both by mathematical examples and by an application to a 2-dimensional MR image of a head.

Supporting Wilderness Search and Rescue using a Camera-Equipped Mini UAV

Wilderness Search and Rescue (WiSAR) entails searching over large regions in often rugged remote areas. Because of the large regions and potentially limited mobility of ground searchers, WiSAR is an ideal application for using small (human-packable) unmanned aerial vehicles (UAVs) to provide aerial imagery of the search region. This paper presents a brief analysis of the WiSAR problem with emphasis on practical aspects of visual-based aerial search. As part of this analysis, we present and analyze a generalized contour search algorithm, and relate this search to existing coverage searches. Extending beyond laboratory analysis, lessons from field trials with search and rescue personnel indicated the immediate need to improve two aspects of UAV-enabled search: How video information is presented to searchers and how UAV technology is integrated into existing WiSAR teams. In response to the first need, three computer vision algorithms for improving video display presentation are compared; results indicate that constructing temporally localized image mosaics is more useful than stabilizing video imagery. In response to the second need, a goal-directed task analysis of the WiSAR domain was conducted and combined with field observations to identify operational paradigms and field tactics for coordinating the UAV operator, the payload operator, the mission manager, and ground searchers.

Adaptive boundary detection using 'live-wire' two-dimensional dynamic programming

An adaptive boundary detection algorithm that uses two-dimensional dynamic programming (DP) is presented. The algorithm is less constrained than previous one-dimensional dynamic programming algorithms and allows the user to interactively determine the mathematically optimal boundary between a user-selected seed point and any other dynamically selected free point in the image. Interactive movement of the free point by the cursor causes the boundary to behave like a live wire as it adapts to the new minimum cost path between the seed point and the currently selected free point. The algorithm can also be adapted or customized to learn boundary-defining features for a particular class of images. Adaptive 2-D DP performs well on a variety of images. It accurately detects the boundaries of low contrast objects, which occur with intravenous injections, as well as those found in noisy, low SNR images.<<ETX>>

Geodesic graph cut for interactive image segmentation

Interactive segmentation is useful for selecting objects of interest in images and continues to be a topic of much study. Methods that grow regions from foreground/background seeds, such as the recent geodesic segmentation approach, avoid the boundary-length bias of graph-cut methods but have their own bias towards minimizing paths to the seeds, resulting in increased sensitivity to seed placement. The lack of edge modeling in geodesic or similar approaches limits their ability to precisely localize object boundaries, something at which graph-cut methods generally excel. This paper presents a method for combining geodesic-distance information with edge information in a graphcut optimization framework, leveraging the complementary strengths of each. Rather than a fixed combination we use the distinctiveness of the foreground/background color models to predict the effectiveness of the geodesic distance term and adjust the weighting accordingly. We also introduce a spatially varying weighting that decreases the potential for shortcutting in object interiors while transferring greater control to the edge term for better localization near object boundaries. Results show our method is less prone to shortcutting than typical graph cut methods while being less sensitive to seed placement and better at edge localization than geodesic methods. This leads to increased segmentation accuracy and reduced effort on the part of the user.

Zoom-Invariant Vision of Figural Shape

Believing that figural zoom invariance and the cross-figural boundary linking implied by medial loci are important aspects of object shape, we present the mathematics of and algorithms for the extraction of medial loci directly from image intensities. The medial loci called cores are defined as generalized maxima in scale space of a form of medial information that is invariant to translation, rotation, and, in particular, zoom. These loci are very insensitive to image disturbances, in strong contrast to previously available medial loci, as demonstrated in a companion paper. Core-related geometric properties and image object representations are laid out which, together with the aforementioned insensitivities, allow the core to be used effectively for a variety of image analysis objectives.

LIVEcut: Learning-based interactive video segmentation by evaluation of multiple propagated cues

Video sequences contain many cues that may be used to segment objects in them, such as color, gradient, color adjacency, shape, temporal coherence, camera and object motion, and easily-trackable points. This paper introduces LIVEcut, a novel method for interactively selecting objects in video sequences by extracting and leveraging as much of this information as possible. Using a graph-cut optimization framework, LIVEcut propagates the selection forward frame by frame, allowing the user to correct any mistakes along the way if needed. Enhanced methods of extracting many of the features are provided. In order to use the most accurate information from the various potentially-conflicting features, each feature is automatically weighted locally based on its estimated accuracy using the previous implicitly-validated frame. Feature weights are further updated by learning from the user corrections required in the previous frame. The effectiveness of LIVEcut is shown through timing comparisons to other interactive methods, accuracy comparisons to unsupervised methods, and qualitatively through selections on various video sequences.

MuItiscale medial analysis of medical images

Abstract The Multiscale Medial Axis is a means for detecting and representing object shape at multiple scales simultaneously. One of its key characteristics is that the scale used to measure object properties (and hence to represent the object) is proportional to the local width of the object. This allows it to separate fine-scale detail from larger-scale gross shape properties of the object in a manner dictated by the object itself. It works directly from image intensities and does not require a prior segmentation of the image or explicit determination of object boundaries. Fuzzy (non-binary) boundary measures are used to compute fuzzy medial measures, and axis points are identified as ridges in this fuzzy medial space. This paper presents some of the basic concepts of the Multiscale Medial Axis, describes ils computation, and demonstrates some preliminary results of its application to medical images from variety of imaging modalities.

Isophote-based interpolation

Standard methods for image interpolation are based on smoothly fitting the image intensity surface. Previous edge-directed interpolation methods add limited geometric information (edge maps) to build more accurate and visually appealing interpolations at key contours in the image. This paper presents a method for geometry-based interpolation that smoothly fits the isophote (intensity level curve) contours at all points in the image rather than just at selected contours. By using level set methods for curve evolution, no explicit extraction or representation of these contours is required (unlike earlier edge-directed methods). The method uses existing interpolation techniques as an initial approximation and then iteratively reconstructs the isophotes using constrained smoothing. Results show that the technique produces results that are more visually realistic than standard function-fitting methods.

Object shape before boundary shape: Scale-space medial axes

Representing object shape in two or three dimensions has typically involved the description of the object boundary. This paper proposes a means for characterizing object structure and shape that avoids the need to find an explicit boundary. Rather, it operates directly from the imageintensity distribution in the object and its background, using operators that do indeed respond to “boundariness.” It produces a sort of medial-axis description that recognizes that both axis location and object width must be defined according to a tolerance proportional to the object width. This generalized axis is called themultiscale medial axis because it is defined as a curve or set of curves in scale space. It has all of the advantages of the traditional medial axis: representation of protrusions and indentations in the object, decomposition of object-curvature and object-width properties, identification of visually opposite points of the object, incorporation of size constancy and orientation independence, and association of boundary-shape properties with medial locations. It also has significant new advantages: it does not require a predetermination of exactly what locations are included in the object, it provides gross descriptions that are stable against image detail, and it can be used to identify subobjects and regions of boundary detail and to characterize their shape properties.

The multiscale medial axis and its applications in image registration

Abstract The multiscale medial axis (MMA) is a principled means of describing both the spatial and width properties of objects in grey-scale images. We describe its computation and provide an example of its use in an image registration task.