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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...
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...
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...
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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