Disconnected Skeleton: Shape at its Absolute Scale

We present a new skeletal representation along with a matching framework to address the deformable shape recognition problem. The disconnectedness arises as a result of excessive regularization that we use to describe a shape at an attainably coarse scale. Our motivation is to rely on the stable properties of the shape instead of inaccurately measured secondary details. The new representation does not suffer from the common instability problems of traditional connected skeletons, and the matching process gives quite successful results on a diverse database of 2D shapes. An important difference of our approach from the conventional use of the skeleton is that we replace the local coordinate frame with a global Euclidean frame supported by additional mechanisms to handle articulations and local boundary deformations. As a result, we can produce descriptions that are sensitive to any combination of changes in scale, position, orientation and articulation, as well as invariant ones.Comment: The work excluding {\S}V and {\S}VI has first appeared in 2005 ICCV: Aslan, C., Tari, S.: An Axis-Based Representation for Recognition. In ICCV(2005) 1339- 1346.; Aslan, C., : Disconnected Skeletons for Shape Recognition. Masters thesis, Department of Computer Engineering, Middle East Technical University, May 200

Joint acoustic-video fingerprinting of vehicles, part II

In this second paper, we first show how to estimate the wheelbase length of a vehicle using line metrology in video. We then address the vehicle fingerprinting problem using vehicle silhouettes and color invariants. We combine the acoustic metrology and classification results discussed in Part I with the video results to improve estimation performance and robustness. The acoustic video fusion is achieved in a Bayesian framework by assuming conditional independence of the observations of each modality. For the metrology density functions, Laplacian approximations are used for computational efficiency. Experimental results are given using field data

Joint Target Tracking and Recognition Using Shape-based Generative Model

Recently a generative model that combines both of identity and view manifolds was proposed for multi-view shape modeling that was originally used for pose estimation and recognition of civilian vehicles from image sequences. In this thesis, we extend this model to both civilian and military vehicles, and examine its effectiveness for real-world automated target tracking and recognition (ATR) applications in both infrared and visible image sequences. A particle filter-based ATR algorithm is introduced where the generative model is used for shape interpolation along both the view and identity manifolds. The ATR algorithm is tested on the newly released SENSIAC (Military Sensing Information Analysis Center) infrared database along with some visible-band image sequences. Overall tracking and recognition performance is evaluated in terms of the accuracy of 3D position/pose estimation and target classification.</School of Electrical & Computer Engineerin

Augmenting shape with appearance in vehicle category recognition

Shape is an important cue for generic object recognition but can be insufficient without other cues such as object appearance. We explore a number of ways in which the geometric aspects of an object can be augmented with its appearance. The main idea is to construct a dense correspondence between the interior regions of two shapes based on a shape-based correspondence so that the intensity and gradient distributions can be compared, e.g., using a mutual information paradigm. Three methods for regional alignment are suggested and compared here, based on: (i) propagation of correspondences from the silhouette to parallel curves in the interior, (ii) intersection of line segments anchored on corresponding points on the contour, and (iii) correspondence of shape skeletons. These methods have been implemented and applied to vehicle category recognition from aerial videos under known viewing and illumination conditions. We have constructed a photo-realistic synthetic video database to explore the performance of these methods under controlled conditions. We have also tested these algorithms on real video collected for this purpose from a balloon. Our findings indicate that (i) augmenting shape with appearance significantly increases recognition rate, and (ii) the region correspondence induced by the shape skeleton yields the highest performance. 1