Personal Robotics: Deep Learning for Grasping Novel Objects
Robot Learning Lab Personal Robotics, Co-Robots, Robotic Perception. Computer Science Department, Cornell University. Learning-based approaches in previous works have been succeesfully used for grasping novel objects, but required manual design of features for image and depth data. We use deep learning, which allow us to learn the basic features used by our algorithm directly from RGB-D data. Our
KLT: Kanade-Lucas-Tomasi Feature Tracker
KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker KLT is an implementation, in the C programming language, of a feature tracker for the computer vision community. The source code is in the public domain, available for both commercial and non-commerical use. The tracker is based on the early work of Lucas and Kanade [1], was developed fully by Tomasi and Kanade [2], and was explain
IPOL Journal · Image Processing On Line
IPOL is a research journal of image processing and image analysis which emphasizes the role of mathematics as a source for algorithm design and the reproducibility of the research. Each article contains a text on an algorithm and its source code, with an online demonstration facility and an archive of experiments. Text and source code are peer-reviewed and the demonstration is controlled. IPOL is
Spatial envelope
Modeling the shape of the scene: a holistic representation of the spatial envelope Aude Oliva, Antonio Torralba International Journal of Computer Vision, Vol. 42(3): 145-175, 2001. PDF Abstract: In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very
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RGB-D Tracking: Depth Scaling Kernelised Correlation Filters DS-KCF
Parthipan Siva( Research - Simple Tracker )
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Code - Sam Hare
