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Visual pattern analysis is a fundamental tool in mining data for
knowledge. Computational representations for patterns and texture
allow us to summarize, store, compare, and label in order to learn
about the physical world. Our ability to capture visual imagery
with cameras and sensors has resulted in vast amounts of raw data,
but using this information effectively in a task-specific manner
requires sophisticated computational representations. We enumerate
specific desirable traits for these representations: (1) intraclass
invariance-to support recognition; (2) illumination and geometric
invariance for robustness to imaging conditions; (3) support for
prediction and synthesis to use the model to infer continuation of
the pattern; (4) support for change detection to detect anomalies
and perturbations; and (5) support for physics-based interpretation
to infer system properties from appearance. In recent years,
computer vision has undergone a metamorphosis with classic
algorithms adapting to new trends in deep learning. This text
provides a tour of algorithm evolution including pattern
recognition, segmentation and synthesis. We consider the general
relevance and prominence of visual pattern analysis and
applications that rely on computational models.
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