Package: alicevision Description-md5: 24f74f2adb49a14d9ddb0dead18f6afd Description-en: Photogrammetric Computer Vision Framework AliceVision aims to provide strong software basis with state-of-the-art computer vision algorithms that can be tested, analyzed and reused. The project is a result of collaboration between academia and industry to provide cutting-edge algorithms with the robustness and the quality required for production usage. . This package provides the executable tools Package: libalicevision-data Description-md5: ddf3277bd12a96a8873eb5b919423593 Description-en: Photogrammetric Computer Vision Framework AliceVision aims to provide strong software basis with state-of-the-art computer vision algorithms that can be tested, analyzed and reused. The project is a result of collaboration between academia and industry to provide cutting-edge algorithms with the robustness and the quality required for production usage. . This package provides the data files Package: libalicevision-dev Description-md5: 7c903fbdb1d3a9427fa77e32ec45b298 Description-en: Photogrammetric Computer Vision Framework AliceVision aims to provide strong software basis with state-of-the-art computer vision algorithms that can be tested, analyzed and reused. The project is a result of collaboration between academia and industry to provide cutting-edge algorithms with the robustness and the quality required for production usage. . This package provides the development files library Package: libalicevision3 Description-md5: bf536eaaaeccf9e2157dcbeb259107be Description-en: Photogrammetric Computer Vision Framework AliceVision aims to provide strong software basis with state-of-the-art computer vision algorithms that can be tested, analyzed and reused. The project is a result of collaboration between academia and industry to provide cutting-edge algorithms with the robustness and the quality required for production usage. . This package provides the shared library Package: sift Description-md5: 7788bf12148938f8dc8e4675657ce605 Description-en: predicts if a substitution in a protein has a phenotypic effect SIFT is a sequence homology-based tool that sorts intolerant from tolerant amino acid substitutions and predicts whether an amino acid substitution in a protein will have a phenotypic effect. SIFT is based on the premise that protein evolution is correlated with protein function. Positions important for function should be conserved in an alignment of the protein family, whereas unimportant positions should appear diverse in an alignment.