|Place of Conferral||中国科学院自动化研究所|
|Keyword||目标检测 动态修正 旋转卷积 密集排列 自监督 大尺度目标 图像可视化|
Object detection is a long-standing basic problem in the field of computer vision. Its goal is to determine whether there are object instances of a given category in a given image; if so, return the spatial location and bounding box of each instance. Image visualization refers to the technology of selecting and displaying images of interest from an image data set, which mainly includes two processes of image summarization and layout generation. This technology plays an important role for people to quickly capture important information of visual big data. Image object detection and visualization technology analyzes visual data from both objects and images of interest, which is of great significance for people to access and understand visual big data.
This dissertation focuses on the research of object detection and visualization of images in complex environments. The main problems of current image object detection and visualization technology are: (1) under the memory constraint, the input scale of the object detection methods based on deep learning can only be limited to a small range. When the scale of the image in the application scene is extremely large, the existing methods usually adopt operations such as scaling or cropping, which inevitably reduces the performance and increases the computational complexity of the model; (2) the objects usually are in arbitrary orientations and with large appearance variation in some scenes. Current mainstream methods are difficult to extract accurate object features, and cannot dynamically refine the predictions in accordance with samples, which limits the performance of the models; (3) the existing image collage methods usually use handcrafted or single scene image features, which makes it impossible to fully describe the image. For layout generation, existing methods cannot simultaneously satisfy the constraints of shape retention and content preservation, resulting in unsatisfactory collage results. In response to the above problems, this dissertation combines the latest developments/achievements in machine learning, computer vision, and other fields to propose high-performance image object detection and visualization methods for complex scenes.
The main contributions of this dissertation are summarized as follows:
|潘兴甲. 复杂环境下的图像目标检测与可视化[D]. 中国科学院自动化研究所. 中国科学院大学,2020.|
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