&lt;title&gt;Feature-based target classification in laser radar&lt;/title&gt;

Stevens, Mark R.; Snorrason, Magnus; Ruda, Harald; Amphay, Sengvieng A.

doi:10.1117/12.477046

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…The toolkit can be used on any project were consistent scoring is required, and budget costs prohibit the development of similar tools. We have used it for truthing, registering and scoring a multisensor ATR algorithm and data collection [16] and have found it to increase the truthing operator efficiency significantly while reducing errors. …”

Section: Discussionmentioning

confidence: 99%

“…But in some cases highly accurate truthing is needed to fully measure successful target identification. For example, many model-based ATR algorithms [16][17][18][19] identify the targets in LADAR based on a match between 3D range features and 3D target model features. To ensure the correct target is identified, the pose of the object is obtained.…”

Section: Truthingmentioning

confidence: 99%

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A scoring, truthing, and registration toolkit for ATR evaluation

et al. 2003

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Every year, large volumes of imagery are collected for the sole purpose of evaluating Automatic Target Recognition (ATR) algorithms. However, this data cannot be used without adequate truthing information for each image. Truthing information typically consists of the types and locations of the targets present in the imagery. Specifying this information for a large number of images is tedious, time consuming, and error prone. In this paper, we present a complete truthing system we call the Scoring, Truthing, And Registration Toolkit (START). The first component is registration, which involves aligning heterogeneous and homogenous sensor images of the same scene to a common reference frame. Once that reference frame has been determined, the second component, truthing, is used to specify target identity, position, orientation, and other scene characteristics. The final component, scoring, is used to assess the performance of a given algorithm as compared to the specified truth. The scoring module allows statistical comparisons to assess algorithm sensitivity to specific operating conditions (e.g., sensitive to object occlusion).

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Section: Discussionmentioning

confidence: 99%

Section: Truthingmentioning

confidence: 99%

A scoring, truthing, and registration toolkit for ATR evaluation

et al. 2003

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“…Our match function (which in this context is viewed as the final verification step) could be cued by the E3D ATR pipeline formed by combining Raytheon's work on target detection in LADAR [12], Sarnoff's work on generating target type and pose hypothesis [13], and AlphaTech's work on iterative target pose and articulation refinement [14]. Our previous work on LADAR detection and hypothesis matching could also be used [15][16][17][18][19][20][21] to used produce an end-to-end system for performance evaluation.…”

Section: Figure 1 Typical Model-based Atr Pipelinementioning

confidence: 99%

<title>The role of situational awareness in automatic target recognition</title>

et al. 2004

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Model-based Automatic Target Recognition (ATR) algorithms are adept at recognizing targets in high fidelity 3D LADAR imagery. Most current approaches involve a matching component where a hypothesized target and target pose are iteratively aligned to pre-segmented range data. Once the model-to-data alignment has converged, a match score is generated indicating the quality of match. This score is then used to rank one model hypothesis over another. The main drawback of this approach is twofold. First, to ensure the correct target is recognized, a large number of model hypotheses must be considered. Even with a highly accurate indexing algorithm, the number of target types and variants that need to be explored is prohibitive for real-time operation. Second, the iterative matching step must consider a variety of target poses to ensure that the correct alignment is recovered. Inaccurate alignments produce erroneous match scores and thus errors when ranking one target hypothesis over another. To compensate for such drawbacks, we explore the use of situational awareness information already available to an image analyst. Examples of such information include knowledge of the surrounding terrain (to assess potential occlusion levels) and targets of interest (to account for target variants).

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“…Stevens's [6] target classification work utilizes an interesting approach that incorporates a combination of image transformations that significantly expands the amount of applicable analysis tools. This article is mainly focused on feature selection rather than on the transformation method or the ATR performance.…”

Section: Related Work In Automatic Target Recognitionmentioning

confidence: 99%

Three-dimensional transformation for automatic target recognition using lidar data

Nieves

Reynolds

2010

Laser Radar Technology and Applications XV

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The three-dimensional (3-D) nature and the unorganized structure of topographic LIDAR data pose several challenges for target recognition tasks. In the past, several approaches have applied two-dimensional transformations such as spinimages or Digital Elevation Maps (DEMs) as an intermediate step for analyzing the 3-D data with two-dimensional (2-D) methods. However, these techniques are computationally intensive and often sacrifice some of the overall geometrical relationship of the target points.In this paper, we present a simple and efficient 3-D spatial transformation that preserves the geometrical attributes of the LIDAR data in all its dimensions. This transformation permits the utilization of well established statistical and shapebased descriptors for the implementation of an automatic target recognition algorithm. We evaluate our transformation and analysis technique on a set of simulated LIDAR point clouds of ground vehicles with varied obstructions and noise levels. Classification results demonstrate that our approach is efficient, tolerant to scale, rotation, and robust to noise and other degradations.

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<title>Feature-based target classification in laser radar</title>

Cited by 5 publications

References 12 publications

A scoring, truthing, and registration toolkit for ATR evaluation

A scoring, truthing, and registration toolkit for ATR evaluation

<title>The role of situational awareness in automatic target recognition</title>

Three-dimensional transformation for automatic target recognition using lidar data

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