Line Segment Based Approach to Pattern Detection in Mammographic Images

2014 ◽  
pp. 37-48
Author(s):  
Jagoda Lazarek ◽  
Piotr S. Szczepaniak
Keyword(s):  
Line Segment ◽  
Pattern Detection ◽  
Mammographic Images

Less is more: Simple stimuli provoke more variable search strategies.

2020 ◽  
Author(s):  
Anna Nowakowska ◽  
Alasdair D F Clarke ◽  
Jessica Christie ◽  
Josephine Reuther ◽  
Amelia R. Hunt
Keyword(s):  
Line Segment ◽  
Search Strategies ◽  
Complex Objects ◽  
Search Behaviour ◽  
Line Segments ◽  
Surface Level ◽  
Efficient Search ◽  
Less Is More ◽  
Dramatic Shift

We measured the efficiency of 30 participants as they searched through simple line segment stimuli and through a set of complex icons. We observed a dramatic shift from highly variable, and mostly inefficient, strategies with the line segments, to uniformly efficient search behaviour with the icons. These results demonstrate that changing what may initially appear to be irrelevant, surface-level details of the task can lead to large changes in measured behaviour, and that visual primitives are not always representative of more complex objects.

A Novel Airport Detection Method via Line Segment Classification and Texture Classification

2015 ◽  
Vol 12 (12) ◽  
pp. 2408-2412 ◽  
Author(s):  
Gefu Tang ◽  
Zhifeng Xiao ◽  
Qing Liu ◽  
Hua Liu
Keyword(s):  
Line Segment ◽  
Detection Method ◽  
Texture Classification ◽  
Airport Detection

Identifying longitudinal-growth patterns from infancy to childhood: a study comparing multiple clustering techniques

2021 ◽  
Author(s):  
Paraskevi Massara ◽  
Charles D G Keown-Stoneman ◽  
Lauren Erdman ◽  
Eric O Ohuma ◽  
Celine Bourdon ◽  
...  
Keyword(s):  
Latent Class ◽  
Growth Patterns ◽  
Pattern Detection ◽  
Distance Measures ◽  
Longitudinal Growth ◽  
Important Health ◽  
Modelling Techniques ◽  
Different Populations ◽  
Growth Features ◽  
Time Invariant

Abstract Background Most studies on children evaluate longitudinal growth as an important health indicator. Different methods have been used to detect growth patterns across childhood, but with no comparison between them to evaluate result consistency. We explored the variation in growth patterns as detected by different clustering and latent class modelling techniques. Moreover, we investigated how the characteristics/features (e.g. slope, tempo, velocity) of longitudinal growth influence pattern detection. Methods We studied 1134 children from The Applied Research Group for Kids cohort with longitudinal-growth measurements [height, weight, body mass index (BMI)] available from birth until 12 years of age. Growth patterns were identified by latent class mixed models (LCMM) and time-series clustering (TSC) using various algorithms and distance measures. Time-invariant features were extracted from all growth measures. A random forest classifier was used to predict the identified growth patterns for each growth measure using the extracted features. Results Overall, 72 TSC configurations were tested. For BMI, we identified three growth patterns by both TSC and LCMM. The clustering agreement was 58% between LCMM and TS clusters, whereas it varied between 30.8% and 93.3% within the TSC configurations. The extracted features (n = 67) predicted the identified patterns for each growth measure with accuracy of 82%–89%. Specific feature categories were identified as the most important predictors for patterns of all tested growth measures. Conclusion Growth-pattern detection is affected by the method employed. This can impact on comparisons across different populations or associations between growth patterns and health outcomes. Growth features can be reliably used as predictors of growth patterns.

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