Dept. of Electr. and Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA.
IEEE Trans Image Process. 1997;6(1):114-25. doi: 10.1109/83.552101.
Detection involves locating all candidate regions of interest (objects) in a scene independent of the object class with object distortions and contrast differences, etc., present. It is one of the most formidable problems in automatic target recognition, since it involves analysis of every local scene region. We consider new detection algorithms and the fusion of their outputs to reduce the probability of false alarm P(FA) while maintaining high probability of detection P(D). Emphasis is given to detecting obscured targets in infrared imagery.
检测涉及在存在目标变形和对比度差异等的情况下，独立于目标类别定位场景中的所有候选感兴趣区域（目标）。这是自动目标识别中最具挑战性的问题之一，因为它涉及对每个局部场景区域的分析。我们考虑新的检测算法及其输出的融合，以降低虚警概率 P(FA)，同时保持高检测概率 P(D)。重点是在红外图像中检测被遮挡的目标。
