Similarity-invariant signatures for partially occluded planar shapes

Industrial vision systems should be capable of recognising noisy objects, partially occluded objects and randomly located and/or oriented objects. This paper considers the problem of recognition of partially occluded planar shapes using contour segment-based features. None of the techniques suggested in the literature for solving the above problem guarantee reliable results for problem instances which require memory in excess of what is available. In this paper, a heuristic search-based recognition algorithm is presented, which guarantees reliable recognition results even when memory is limited. This algorithm identifies an object, the maximum portion of whose contour is visible in a conglomerate of objects. For increasing efficiency of the method, a two-stage recognition scheme has been designed. In the first phase, a relevant subset of the known model shapes is chosen and in the second stage, matching between the unknown shape and elements of the relevant subset is attempted using the above approach. The technique is general in the sense that it can be used with any kind of contour features. To evaluate the efficiency of the method, experimentation was carried out using polygonal approximations of the object contours. Results are cited for establishing the effectiveness of the approach.

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On differential invariants of planar curves and recognizing partially occluded planar shapes

Annals of Mathematics and Artificial Intelligence ◽

10.1007/bf01530829 ◽

1995 ◽

Vol 13 (3-4) ◽

pp. 227-250 ◽

Cited By ~ 12

Author(s):

Alfred M. Bruckstein ◽

Arun N. Netravali

Keyword(s):

Differential Invariants ◽

Planar Curves ◽

Planar Shapes ◽

Partially Occluded

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Recognition of partially occluded 3D objects

IEE Proceedings E Computers and Digital Techniques ◽

10.1049/ip-e.1989.0018 ◽

1989 ◽

Vol 136 (2) ◽

pp. 124

Author(s):

Ming-Hong Chan ◽

Hung-Tat Tsui

Keyword(s):

3D Objects ◽

Partially Occluded

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Recognition of partially occluded two-dimensional objects

IEE Proceedings E Computers and Digital Techniques ◽

10.1049/ip-e.1987.0002 ◽

1987 ◽

Vol 134 (1) ◽

pp. 9 ◽

Cited By ~ 2

Author(s):

H.T. Tsui ◽

M.H. Chan

Keyword(s):

Two Dimensional ◽

Partially Occluded

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The COVID-19 pandemic masks the way people perceive faces

Scientific Reports ◽

10.1038/s41598-020-78986-9 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Erez Freud ◽

Andreja Stajduhar ◽

R. Shayna Rosenbaum ◽

Galia Avidan ◽

Tzvi Ganel

Keyword(s):

Social Interactions ◽

Face Perception ◽

Face Processing ◽

Qualitative Change ◽

Daily Activities ◽

Human Face ◽

Human Face Recognition ◽

Processing Abilities ◽

Partially Occluded ◽

The Way

AbstractThe unprecedented efforts to minimize the effects of the COVID-19 pandemic introduce a new arena for human face recognition in which faces are partially occluded with masks. Here, we tested the extent to which face masks change the way faces are perceived. To this end, we evaluated face processing abilities for masked and unmasked faces in a large online sample of adult observers (n = 496) using an adapted version of the Cambridge Face Memory Test, a validated measure of face perception abilities in humans. As expected, a substantial decrease in performance was found for masked faces. Importantly, the inclusion of masks also led to a qualitative change in the way masked faces are perceived. In particular, holistic processing, the hallmark of face perception, was disrupted for faces with masks, as suggested by a reduced inversion effect. Similar changes were found whether masks were included during the study or the test phases of the experiment. Together, we provide novel evidence for quantitative and qualitative alterations in the processing of masked faces that could have significant effects on daily activities and social interactions.

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A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

Applied Sciences ◽

10.3390/app11136016 ◽

2021 ◽

Vol 11 (13) ◽

pp. 6016

Author(s):

Jinsoo Kim ◽

Jeongho Cho

Keyword(s):

Object Detection ◽

Autonomous Vehicles ◽

Autonomous Driving ◽

Detection Algorithm ◽

Detection Accuracy ◽

Cloud Data ◽

Detection Techniques ◽

Bounding Boxes ◽

Partially Occluded ◽

Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

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