FPGA based accelerated orientation calculation in SIFT using luts

Agrawal, Kshitij; Chowdhury, Shubhajit Roy

doi:10.1109/primeasia.2013.6731210

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…(2) Extract the main directions of the corners. Read out the amplitudes and directions of the 3 × 3 region around the corners from RAM, and use θ × π/180 to judge whether the angles are integers [11]. Adding the corresponding amplitudes of the same integral directions to get the gradient histogram.…”

Section: Feature Point Descriptionmentioning

confidence: 99%

Real-Time Panoramic Image Mosaic via Harris Corner Detection on FPGA

Chen

Han

Zhang

et al. 2015

Lecture Notes in Computer Science

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Abstract.To solve the problems such as low matching precision, high algorithm complexity and poor real-time in real-time panoramic image mosaic, this paper makes full use of the significance and parallelism of Harris corners as well as the invariance of feature describing methods in light intensity changes, translation and rotation, and proposes a real-time panoramic image mosaic algorithm which uses Harris corner detection and is logically implemented on FPGA. According to the basic features of classical algorithms and the properties of FPGA, several modules like feature point extraction, description and matching are optimized based on the logical implementation of FPGA. The optimized system realizes the high-precision matching in real time. The new algorithm collects images of 256 × 256 pixels by CCD camera on Spartan-6 hardware platform of Xilinx. After going through the algorithm, the mosaic images will finally be output on HD display in the form of DVI. The results show that the new algorithm based on FPGA has high precision, good real-time and robustness.

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Section: Feature Point Descriptionmentioning

confidence: 99%

Real-Time Panoramic Image Mosaic via Harris Corner Detection on FPGA

Chen

Han

Zhang

et al. 2015

Lecture Notes in Computer Science

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“…They also proposes a general framework to enhance grasping interactions [10]. Further research about individual robotic hand kinematics can be found in [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Nature grasping by a cable-driven under-actuated anthropomorphic robotic hand

Tian¹,

Thalman²,

Thalmann³

et al. 2019

TELKOMNIKA

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Human hand is the best sample for humanoid robotic hand and a nature grasping is the final target that most robotic hands are pursuing. Many prior researches had been done in virtual and real for simulation the human grasping. Unfortunately, there is no perfect solution to duplicate the nature grasping of human. The main difficulty comes from three points. 1. How to 3D modelling and fabricate the real hand. 2. How actuated the robotic hand as real hand. 3. How to grasp objects in different shapes like human hand. To deal with these three problems and further to provide a partial solution for duplicate human grasping, this paper introduces our method to solve these problems from robotic hand design, fabrication, actuation and grasping plan. Our modelling progress takes only around 12 minutes that include 10 minutes of 3D scanning of a real human hand and two minutes for changing the scanned model to an articulated model by running our algorithm. Our grasping plan is based on the sampled trajectory and easy to implement for grasping different objects. Followed these steps, a seven DOF robotic hand is created and tested in the experiments.

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Real time multisensor Laplacian fusion on FPGA

Agrawal

Chowdhury

2015

2015 19th International Symposium on VLSI Design and Test

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FPGA based accelerated orientation calculation in SIFT using luts

Cited by 6 publications

References 6 publications

Real-Time Panoramic Image Mosaic via Harris Corner Detection on FPGA

Real-Time Panoramic Image Mosaic via Harris Corner Detection on FPGA

Nature grasping by a cable-driven under-actuated anthropomorphic robotic hand

Real time multisensor Laplacian fusion on FPGA

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