Abstract: A fully automated and reliable picking of a diverse range of unseen objects in clutter is a challenging problem. The present disclosure provides an optimum grasp pose selection to pick an object from a bin. Initially, the system receives an input image pertaining to a surface. Further, a plurality of sampled grasp poses are generated in a random configuration. Further, a depth difference value is computed for each of a plurality of pixels corresponding to each of the plurality of sampled grasp poses. Further, a binary map is generated for each of the plurality of sampled grasp poses and a plurality of subregions are obtained. Further, a plurality of feasible grasp poses are selected based on the plurality of subregions and a plurality of conditions. Further, the plurality of feasible grasp poses are refined and an optimum grasp pose is obtained based on a Grasp Quality Score (GQS).

Abstract: Object annotation is images is tedious time consuming task when large volume of data needs to annotated. Existing methods limit to semiautomatic approaches for annotation. The embodiments herein provide a method and system for a deep network based architecture for automatic object annotation. The deep network utilized is a two stage network with first stage as an annotation model comprising a Faster Region-based Fully Convolutional Networks (F-RCNN) and Region-based Fully Convolutional Networks (RFCN) providing for two class classification to generate annotated images from a set of single object test images. Further, the newly annotated test object images are then used to synthetically generate cluttered images and their corresponding annotations, which are used to train the second stage of the deep network comprising the multi-class object detection/classification model designed using the F-RCNN and the RFCN as base networks to automatically annotate input test image in real time.

Abstract: Object annotation is images is tedious time consuming task when large volume of data needs to annotated. Existing methods limit to semiautomatic approaches for annotation. The embodiments herein provide a method and system for a deep network based architecture for automatic object annotation. The deep network utilized is a two stage network with first stage as an annotation model comprising a Faster Region-based Fully Convolutional Networks (F-RCNN) and Region-based Fully Convolutional Networks (RFCN) providing for two class classification to generate annotated images from a set of single object test images. Further, the newly annotated test object images are then used to synthetically generate cluttered images and their corresponding annotations, which are used to train the second stage of the deep network comprising the multi-class object detection/classification model designed using the F-RCNN and the RFCN as base networks to automatically annotate input test image in real time.