Detecting Partially Occluded Objects with an Implicit Shape Model Random Field

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Learning Non-maximum Suppression (2017, background): “Hough voting establishes correspondences between detections and the image evidence supporting them, which can avoid overusing image content for several detections [15, 1, 14, 34].”
Finely-grained annotated datasets for image-based plant phenotyping (2016, background): “Due to size di ﬀ erences, shape and appearance similarities, and heavy occlusions, leaf detection is a complex task, and can beneﬁt from approaches in computer vision of detecting overlapping objects in medicine, transportation, and surveillance (Arteta et al., 2013; Wohlhart et al., 2013).”
CoDeL: A Human Co-detection and Labeling Framework (2013, methodology): “Representative methods include implicit shape model [26], latent hough transform [16] and deformable part-based model [8] where the latter one provides leading performance as reported in several recent VOC competitions.”
Non-maximum Suppression for Object Detection by Passing Messages Between Windows (2014, background): “For instance, with Hough Forests [20–22], patches vote for the location of the object center.”
A Convnet for Non-maximum Suppression (2015, methodology): “Input grid As preprocessing all detections in an image are mapped into a 2d grid (based on their centre location).”