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This paper presents an empirical evaluation of a number of recently developed Automatic Target Recognition algorithms for Forward-Looking Infrared (FLIR) imagery using a large database of real FLIR images. The algorithms evaluated are based on convolutional neural networks (CNN), principal...
This paper presents a performance metric for the document structure extraction algorithms by finding the correspondences between detected entities and ground truth. We describe a method for determining an algorithm's optimal tuning parameters. We evaluate a group of document layout analysis...
Four different texture classification methods (wavelet-based, co-occurrence matrices-based, 1D-histograms-based, and 1D Boolean model-based) are systematically compared and evaluated with respect to their performance in identifying textures from small and irregular samples. Two sets of 135...
Evaluating the performance of optical flow algorithms has been difficult because of the lack of ground truth data sets for complex scenes. We present a new method for generating motion fields from real sequences containing polyhedral objects and present a test suite for benchmarking optical flow...
We demonstrate a method for evaluating edge detector performance based on receiver operating characteristic (ROC) curves. Edge detector output is matched against ground truth to count true positive and false positive edge pixels. A detector's parameter settings are trained to give a best ROC...
Object recognition can be accomplished using a generate and test algorithm: generate a hypothesis to explain a given scene and then test that hypothesis to determine its quality. The role of the test component is to accept or reject a hypothesis based on how well the objects in that hypothesis...
This paper provides performance prediction analysis techniques for a linear point feature tracking algorithm based on different motion models. We provide closed-form expressions for evaluating the probability of correct data association of a tracker (analyzed with different motion models), when...
This paper presents an empirical evaluation methodology for edge detectors. Edge detector performance is measured using a particular edge-based object recognition algorithm as a “higher-level” task. A detector's performance is ranked according to the object recognition performance that it...
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