We present a morphological texture contrast (MTC) operator that allows detection of textural and non-texture regions in images. We show that in contrast to other approaches, the MTC discriminates between texture details and isolated features and does not extend borders of texture regions. A comparison with other methods used for texture detection is provided. Using the ideas underlying the MTC operator, we develop a complementary operator called morphological feature contrast (MFC) that allows extraction of isolated features while not being confused by texture details. We illustrate an application of the MFC operator to extraction of isolated objects such as individual trees or buildings that should be distinguished from forests or urban centers. We also propose an MFC based detector of isolated linear features and compare it with an alternative approach used for detection of edges and lines in cluttered scenes. We furthermore derive an extended version of the MFC that can be directly applied to vector-valued images.
“While crystallized intelligence (gc) is recognized in many contemporary intelligence frameworks, there is no consensus as to the nature and contents of the construct. Originally conceptualized as capturing acquired skills and declarative knowledge in different content domains, more recent definitions and typical indicators focus on verbal ability. We investigated the relationship between verbal ability and declarative knowledge under consideration of individual differences in fluid intelligence in a large-scale assessment study with 6,701 adolescents. Structural equation modeling was used to examine the factorial distinctness of verbal ability and declarative knowledge with three analytical strategies: (i) Estimating correlations between latent variables, (ii) estimating the amount of unique variance in each factor after accounting for differences in the other ability constructs, and (iii) investigating associations with covariates including school achievement, students’ characteristics, and psychological traits. The correlation between latent variables representing verbal ability, measured with items from six language domains, and knowledge in 16 content domains was very high (ρ = .91), but significantly different from unity. About 17% of the variance in the knowledge factor was independent of individual differences in verbal ability and fluid intelligence. Associations with covariates revealed unique correlational patterns for each ability construct. The findings suggest that verbal ability and knowledge are closely related, but empirically distinguishable facets of crystallized intelligence. The discussion focuses on the construct validity of verbal tests for the measurement of gc and the interpretation of the common factor of a broad knowledge assessment as a causal variable.”
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