2,714 research outputs found
Parsing Occluded People by Flexible Compositions
This paper presents an approach to parsing humans when there is significant
occlusion. We model humans using a graphical model which has a tree structure
building on recent work [32, 6] and exploit the connectivity prior that, even
in presence of occlusion, the visible nodes form a connected subtree of the
graphical model. We call each connected subtree a flexible composition of
object parts. This involves a novel method for learning occlusion cues. During
inference we need to search over a mixture of different flexible models. By
exploiting part sharing, we show that this inference can be done extremely
efficiently requiring only twice as many computations as searching for the
entire object (i.e., not modeling occlusion). We evaluate our model on the
standard benchmarked "We Are Family" Stickmen dataset and obtain significant
performance improvements over the best alternative algorithms.Comment: CVPR 15 Camera Read
The effect of transparency on recognition of overlapping objects
Are overlapping objects easier to recognize when the objects are transparent or opaque? It is important to know whether the transparency of X-ray images of luggage contributes to the difficulty in searching those images for targets. Transparency provides extra information about objects that would normally be occluded but creates potentially ambiguous depth relations at the region of overlap. Two experiments investigated the threshold durations at which adult participants could accurately name pairs of overlapping objects that were opaque or transparent. In Experiment 1, the transparent displays included monocular cues to relative depth. Recognition of the back object was possible at shorter durations for transparent displays than for opaque displays. In Experiment 2, the transparent displays had no monocular depth cues. There was no difference in the duration at which the back object was recognized across transparent and opaque displays. The results of the two experiments suggest that transparent displays, even though less familiar than opaque displays, do not make object recognition more difficult, and possibly show a benefit. These findings call into question the importance of edge junctions in object recognitio
Occlusion Coherence: Detecting and Localizing Occluded Faces
The presence of occluders significantly impacts object recognition accuracy.
However, occlusion is typically treated as an unstructured source of noise and
explicit models for occluders have lagged behind those for object appearance
and shape. In this paper we describe a hierarchical deformable part model for
face detection and landmark localization that explicitly models part occlusion.
The proposed model structure makes it possible to augment positive training
data with large numbers of synthetically occluded instances. This allows us to
easily incorporate the statistics of occlusion patterns in a discriminatively
trained model. We test the model on several benchmarks for landmark
localization and detection including challenging new data sets featuring
significant occlusion. We find that the addition of an explicit occlusion model
yields a detection system that outperforms existing approaches for occluded
instances while maintaining competitive accuracy in detection and landmark
localization for unoccluded instances
SeGAN: Segmenting and Generating the Invisible
Objects often occlude each other in scenes; Inferring their appearance beyond
their visible parts plays an important role in scene understanding, depth
estimation, object interaction and manipulation. In this paper, we study the
challenging problem of completing the appearance of occluded objects. Doing so
requires knowing which pixels to paint (segmenting the invisible parts of
objects) and what color to paint them (generating the invisible parts). Our
proposed novel solution, SeGAN, jointly optimizes for both segmentation and
generation of the invisible parts of objects. Our experimental results show
that: (a) SeGAN can learn to generate the appearance of the occluded parts of
objects; (b) SeGAN outperforms state-of-the-art segmentation baselines for the
invisible parts of objects; (c) trained on synthetic photo realistic images,
SeGAN can reliably segment natural images; (d) by reasoning about occluder
occludee relations, our method can infer depth layering.Comment: Accepted to CVPR18 as spotligh
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