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—The state-of-the-art performance for object detection has been significantly improved over the past two years. Besides the introduction of powerful deep neural networks such as GoogleNet [1] and VGG [2], novel object detection frameworks such as R-CNN [3] and its successors, Fast R-CNN [4] and Faster R-CNN [5], play an essential role in improving the(More)
In this paper, we propose multi-stage and deformable deep convolutional neural networks for object detection. This new deep learning object detection diagram has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint(More)
  • Ruohui Wang
  • 2010
This paper is written to exemplify the basic design idea and show the design procedure of a parameter drawing. Use of AutoCAD ActiveX Automation to access AutoCAD objects and ADO to build up data communication between AutoCAD and Access the parametric drawing software program based on AutoCAD VBA is poised for realization. In the course of exploration of(More)
An intrinsic Fabry-Perot interferometeric sensor based on a microfiber has been demonstrated. The micro-size suspended core is created by chemical etching a photonics crystal fiber, of which the cladding has a micrometer-spaced, hexagonal array of air holes. The sensing head is fabricated by chemical etching a short section of photonics crystal fiber(More)
Segmentation of gray level image is not easy when the edge of the object is not clear enough. The Convexity of Illumination Distribution (CID) feature can be utilized to recognize objects from the background in this situation. In this paper, we propose three models to prove the convexity of the illumination distribution. Therefore, we are able to find(More)
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