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自我他者化:边地影像的诗学过渡与平等悖论 被引量:1
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作者 王奋飞 《吉首大学学报(社会科学版)》 CSSCI 2021年第6期96-104,共9页
"边地影像"是边疆少数民族民众纪录自身的非虚构创作。在朗西埃"可感性的分配"美学理论视野下,边地影像体现了题材选择的一视同仁和人性的解放,以平等维度的引入开拓了其艺术与政治价值。透过"自我他者化"... "边地影像"是边疆少数民族民众纪录自身的非虚构创作。在朗西埃"可感性的分配"美学理论视野下,边地影像体现了题材选择的一视同仁和人性的解放,以平等维度的引入开拓了其艺术与政治价值。透过"自我他者化"的艺术操作,它改变边地的可感性的分配秩序。"客位视角"到"主位视角"和"参与拍摄"到"独立创作"的过渡所释放的乡野表达,成为评测其诗学力量的关键质素。同时,围绕"差异性"展开的影像再现常常导向"微小差异的自恋",并在言语的过度中营造了某种"圣化构想",进而使其差异政治悖论性地生成了新的可感性不平等。 展开更多
关键词 边地影像 自我他者化 美学过渡 差异政治 平等悖论
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ConGrap -Contour Detection Based on Gradient Map of Images
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作者 Frank Nagl Konrad Kolzer +2 位作者 Paul Grimm Tobias Bindel Stephan Rothe 《Computer Technology and Application》 2011年第8期628-637,共10页
In this paper, the authors present ConGrap, a novel contour detector for finding closed contours with semantic connections. Based on gradient-based edge detection, a Gradient Map is generated to store the orientation ... In this paper, the authors present ConGrap, a novel contour detector for finding closed contours with semantic connections. Based on gradient-based edge detection, a Gradient Map is generated to store the orientation of every edge pixel. Using the edge image and the generated Gradient Map, ConGrap separates the image into semantic parts and objects. Each edge pixel is mapped to a contour by a three-stage hierarchical analysis of neighbored pixels and ensures the closing of contours. A final post-process of ConGrap extracts the contour borderlines and merges them, if they semantically relate to each other. In contrast to common edge and contour detections, ConGrap not only produces an edge image, but also provides additional information (e.g., the borderline pixel coordinates the bounding box, etc.) for every contour. Additionally, the resulting contour image provides closed contours without discontinuities and merged regions with semantic connections. Consequently, the ConGrap contour image can be seen as an enhanced edge image as well as a kind of segmentation and object recognition. 展开更多
关键词 Pattern recognition contour detection edge detection SEGMENTATION gradient map.
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