On detection of multiple object instances using Hough transforms
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Updated: May 23, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
Published on: December 15, 2023
Olga Barinova1, Victor Lempitsky, Pushmeet Kholi
1Lomonosov Moscow State University, Molodezhnaya str. 111, 119296 Moscow, Russia. obarinova@graphics.cs.msu.ru
Abstract:
Hough transform-based methods for detecting multiple objects use nonmaxima suppression or mode seeking to locate and distinguish peaks in Hough images. Such postprocessing requires the tuning of many parameters and is often fragile, especially when objects are located spatially close to each other. In this paper, we develop a new probabilistic framework for object detection which is related to the Hough transform. It shares the simplicity and wide applicability of the Hough transform but, at the same time, bypasses the problem of multiple peak identification in Hough images and permits detection of multiple objects without invoking nonmaximum suppression heuristics. Our experiments demonstrate that this method results in a significant improvement in detection accuracy both for the classical task of straight line detection and for a more modern category-level (pedestrian) detection problem.
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