Virtual Reality (VR) depends on haptic feedback to create immersive experiences. Traditional passive proxies align physical props with their virtual counterparts but remain limited in scalability and expressiveness, or require bulky actuators to support reconfiguration. We introduce User-reconfigured Haptics, an approach that utilizes implicit user actions to reconfigure haptic interfaces to extend the gamut of VR haptic experiences. Modular 3D-printed cells are assembled into dynamic interfaces that express diverse haptic properties such as softness and weight. By masking physical reconfigurations with visual (re)mapping, user actions unnoticeably change haptic properties, resulting in user-driven, dynamic haptic experiences. User studies show that our design can provide distinguishable haptic experiences and is perceived as realistic and enjoyable in a VR task. We further showcase four applications: a fishing rod that changes weight and flexibility, a dynamic desktop of pressable buttons, a glove with adjustable squeezing, and a crossbow with variable pulling resistance.

Ship detection from SAR images is an important remote sensing application. However, in complex scenes, i.e., the shore or harbor area, traditional ship detection methods cannot disentangle background information from the target ship, and the detection performance drops dramatically. Moreover, the severe coherent speckle noise also challenges ship detection from SAR images. In order to address the aforementioned issues, this paper proposes a SAR image ship detection method based on scene interpretation to improve the performance of ship detection in complex scenes. Firstly, segmentation algorithm based on Mask R-CNN is utilized to interpret the scene into two catalogs, i.e., the sea and the land. Then, ship detection algorithm based on Faster R-CNN is performed on the sea area and the land area respectively. Finally, non-maximum suppression is used to integrate detection results. Experimental results on SAR ship detection dataset illustrate that the proposed method produces high detection accuracy and low false alarm rate in complex scenes.