"Herout, Adam" . . "GB - Spojen\u00E9 kr\u00E1lovstv\u00ED Velk\u00E9 Brit\u00E1nie a Severn\u00EDho Irska" . "12"^^ . "RIV/00216305:26230/12:PU95988!RIV13-MSM-26230___" . . "P(LC06008), Z(MSM0021630528)" . "EnMS: Early non-Maxima Suppression"@en . "Non-Maxima Suppression, Object Detection, WaldBoost, Sequential Probability Ratio Test"@en . "[683772D01B41]" . "Hradi\u0161, Michal" . "Zem\u010D\u00EDk, Pavel" . . . "EnMS: Early non-Maxima Suppression"@en . "Detection of objects in images using statistical classifiers is a well studied and documented technique.\u00A0 Different applications of such detectors often require selection of the image position with the highest response of the detector -- they perform non-maxima suppression.\u00A0 This article introduces the concept of Early non-Maxima Suppression, which aims to reduce necessary computations by making the non-Maxima Suppression decision early based on incomplete information provided by a partially evaluated classifier. We show that the error of one such speculative decision with respect to a decision made based on response of the complete classifier can be estimated by collecting statistics on unlabeled data.\u00A0 The article then considers a sequential strategy of multiple early non-Maxima suppression tests which follows the structure of soft-cascade detectors commonly used for object detection. We also show that an optimal (fastest for requested error rate) suppression strategy can be created b"@en . . . "2" . . . "134455" . "EnMS: Early non-Maxima Suppression" . . "PATTERN ANALYSIS AND APPLICATIONS" . "26230" . . . . "2012" . "3"^^ . . . "3"^^ . "RIV/00216305:26230/12:PU95988" . "EnMS: Early non-Maxima Suppression" . . . . . . "Detection of objects in images using statistical classifiers is a well studied and documented technique.\u00A0 Different applications of such detectors often require selection of the image position with the highest response of the detector -- they perform non-maxima suppression.\u00A0 This article introduces the concept of Early non-Maxima Suppression, which aims to reduce necessary computations by making the non-Maxima Suppression decision early based on incomplete information provided by a partially evaluated classifier. We show that the error of one such speculative decision with respect to a decision made based on response of the complete classifier can be estimated by collecting statistics on unlabeled data.\u00A0 The article then considers a sequential strategy of multiple early non-Maxima suppression tests which follows the structure of soft-cascade detectors commonly used for object detection. We also show that an optimal (fastest for requested error rate) suppression strategy can be created b" . "1433-7541" . . .