Object Detection by Matching
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Object Detection by Matching
Longin Jan Latecki
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Contour-based object detection
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Database shapes: …..
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Easy for Human Eyes
• Everybody can find the swan in these images.
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Recall humans can draw a swan
• We always draw edges.
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Computer can also capture edges
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Humans can detect shapes given only edges
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Problem: separating noise
• Too much noise, and the computer can’t tell which edges belong to object of interest.
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Object recognition process:
Source:2D image of a 3D object
Matching to database shapes
Contour Segmentation
Contour Extraction
Object Segmentation
Contour Cleaning, e.g., Evolution
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Object detection as matching database shapes to image edge segments
Database shapes: …..
Contour groupingEdge detection Edge linking
?matching
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Main challenges
2 . Part of the true contour of the target object may be wrongly connected to part of a background contour resulting in a single edge fragment
1 . The contour of the desired object is typically fragmented over several pieces.
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How to find the true contours of the target shape in the edge image?
Problem formulation
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Key idea:Given a minimal required coverage of the model contour,we want to select non overlapping model fragments that maximizethe configuration similarity to the corresponding image fragments.
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All relevant edge fragments are mapped to their corresponding model fragments.
Key idea:1. Build an association graph.2. Find maximum weight
subgraph
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Construction of Affinity Matrix
Each vertex of the graph corresponds to a partial match
The affinity between node i and node j is based on their shape similarity.
jv
iv
( , )A i j
The weighted affinity graph is denoted as G = (V, A).
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Construction of Affinity Matrix
High affinity:
Low affinity:
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Problem with Affinity Matrix
Wrong matches may also have high affinity:
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We use model and image location constraints to sparsify the Affinity Matrix
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Maximal Cliques in a Weighted GraphA maximal clique is a subset of V with maximal average affinity between all pairs of its vertices.
In this example, the maximal clique has 4 nodes selected from over 500 nodes. Therefore, most clustering based approach may not succeed.
[ M. Pavan and M. Pelillo. PAMI 2007]
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In order to solve this combinational problem, we relax it to
A vertex is selected as belonging to a MWS iff v V 0vx
Each MWS corresponds to a local maximum of:
Each local solution is not a final solution but a detection hypothesis.
Indicator = selected maximal clique of vertices of V.
Computing Maximum Weight Subgraphs
{0,1}NX
1: 0 and 1NX R X X
( ) Tf X X AX
Tianyang Ma and Longin Jan Latecki. From Partial Shape Matching through Local Deformation to Robust Global Shape Similarity for Object Detection.CVPR 2011.
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Object detection examples