24,698 research outputs found
Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks
Bilateral filters have wide spread use due to their edge-preserving
properties. The common use case is to manually choose a parametric filter type,
usually a Gaussian filter. In this paper, we will generalize the
parametrization and in particular derive a gradient descent algorithm so the
filter parameters can be learned from data. This derivation allows to learn
high dimensional linear filters that operate in sparsely populated feature
spaces. We build on the permutohedral lattice construction for efficient
filtering. The ability to learn more general forms of high-dimensional filters
can be used in several diverse applications. First, we demonstrate the use in
applications where single filter applications are desired for runtime reasons.
Further, we show how this algorithm can be used to learn the pairwise
potentials in densely connected conditional random fields and apply these to
different image segmentation tasks. Finally, we introduce layers of bilateral
filters in CNNs and propose bilateral neural networks for the use of
high-dimensional sparse data. This view provides new ways to encode model
structure into network architectures. A diverse set of experiments empirically
validates the usage of general forms of filters
Kajian motivasi ekstrinsik di antara Pelajar Lepasan Sijil dan Diploma Politeknik Jabatan Kejuruteraan Awam KUiTTHO
Kajian ini dijalankan untuk menyelidiki pengaruh dorongan keluarga, cara pengajaran pensyarah, pengaruh rakan sebaya dan kemudahan infrastruktur terhadap motivasi ekstrinsik bagi pelajar tahun tiga dan tahun empat lepasan sijil dan diploma politeknik Jabatan Kejuruteraan Awain Kolej Universiti Teknologi Tun Hussein Onn. Sampel kajian ini beijumlah 87 orang bagi pelajar lepasan sijil politeknik dan 38 orang bagi lepasan diploma politeknik. Data kajian telah diperolehi melalui borang soal selidik dan telah dianalisis menggunakan perisian SPSS (Statical Package For Sciences). Hasil kajian telah dipersembahkan dalam bentuk jadual dan histohgrapi. Analisis kajian mendapati bahawa kedua-dua kumpulan setuju bahawa faktor-faktor di atas memberi kesan kepada motivasi ekstrinsik mereka. Dengan kata lain faktpr-faktor tersebut penting dalam membentuk pelajar mencapai kecemerlangan akademik
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