Texture classification using logical operators

Texture analysis and classification with linear regression model based on wavelet transform

The wavelet transform as an important multiresolution analysis tool has already been commonly applied to texture analysis and classification. Nevertheless, it ignores the structural information while capturing the spectral information of the texture image at different scales. In this paper, we propose a texture analysis and classification approach with the linear regression model based on the wavelet transform. This method is motivated by the observation that there exists a distinctive correlation between the sample images, belonging to the same kind of texture, at different frequency regions obtained by 2-D wavelet packet transform. Experimentally, it was observed that this correlation varies from texture to texture. The linear regression model is employed to analyze this correlation and extract texture features that characterize the samples. Therefore, our method considers not only the frequency regions but also the correlation between these regions. In contrast, the pyramid-structured wavelet transform (PSWT) and the tree-structured wavelet transform (TSWT) do not consider the correlation between different frequency regions. Experiments show that our method significantly improves the texture classification rate in comparison with the multiresolution methods, including PSWT, TSWT, the Gabor transform, and some recently proposed methods derived from these.

The motivational looking glass: How significant others implicitly affect goal appraisals

Three studies manipulate the accessibility of significant-other representations to explore how these representations may automatically influence how goals are construed and experienced. Study 1 finds that the perceived attainment expectations of a significant other automatically affect participants' own task-goal expectations and their subsequent task performance and persistence. Study 2 finds that the general perceived value that a significant other places in attaining a task goal automatically affects participants' own attainment value appraisals, their task persistence and performance, and the magnitude of their reaction to success and failure feedback. Finally, Study 3 demonstrates that the regulatory focus prescribed by a significant other may automatically affect participants' own regulatory focus with regards to a task goal, with consequences for their cheerfulness-dejection and relaxation-agitation responses to success and failure feedback. The implications for our understanding of social influence and self-regulation are discussed.