scholarly journals A Fast Biologically Inspired Algorithm for Recurrent Motion Estimation

2007 ◽  
Vol 29 (2) ◽  
pp. 246-260 ◽  
Author(s):  
Pierre Bayerl ◽  
Heiko Neumann
Keyword(s):  
Motion Estimation ◽  
Biologically Inspired ◽  
Recurrent Motion

scholarly journals Biologically Inspired Motion Encoding for Robust Global Motion Estimation

2017 ◽  
Vol 26 (3) ◽  
pp. 1521-1535 ◽  
Author(s):  
Evangelos Sariyanidi ◽  
Hatice Gunes ◽  
Andrea Cavallaro
Keyword(s):  
Motion Estimation ◽  
Global Motion ◽  
Biologically Inspired ◽  
Global Motion Estimation

scholarly journals Neuromorphic Configurable Architecture for Robust Motion Estimation

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
Guillermo Botella ◽  
Manuel Rodríguez ◽  
Antonio García ◽  
Eduardo Ros
Keyword(s):  
Visual System ◽  
Motion Estimation ◽  
Optical Flow ◽  
Visual Motion ◽  
Specific Model ◽  
Biologically Inspired ◽  
Visual Motion Perception ◽  
Illumination Changes ◽  
And Performance ◽  
Application Fields

The robustness of the human visual system recovering motion estimation in almost any visual situation is enviable, performing enormous calculation tasks continuously, robustly, efficiently, and effortlessly. There is obviously a great deal we can learn from our own visual system. Currently, there are several optical flow algorithms, although none of them deals efficiently with noise, illumination changes, second-order motion, occlusions, and so on. The main contribution of this work is the efficient implementation of a biologically inspired motion algorithm that borrows nature templates as inspiration in the design of architectures and makes use of a specific model of human visual motion perception: Multichannel Gradient Model (McGM). This novel customizable architecture of a neuromorphic robust optical flow can be constructed with FPGA or ASIC device using properties of the cortical motion pathway, constituting a useful framework for building future complex bioinspired systems running in real time with high computational complexity. This work includes the resource usage and performance data, and the comparison with actual systems. This hardware has many application fields like object recognition, navigation, or tracking in difficult environments due to its bioinspired and robustness properties.

scholarly journals ARTFLOW: A Fast, Biologically Inspired Neural Network That Learns Optic Flow Templates for Self-Motion Estimation

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8217
Author(s):  
Oliver W. Layton
Keyword(s):  
Neural Network ◽  
Motion Estimation ◽  
Optic Flow ◽  
Hebbian Learning ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Hierarchical Architecture ◽  
Biologically Inspired ◽  
Fuzzy Art ◽  
Self Motion

Most algorithms for steering, obstacle avoidance, and moving object detection rely on accurate self-motion estimation, a problem animals solve in real time as they navigate through diverse environments. One biological solution leverages optic flow, the changing pattern of motion experienced on the eye during self-motion. Here I present ARTFLOW, a biologically inspired neural network that learns patterns in optic flow to encode the observer’s self-motion. The network combines the fuzzy ART unsupervised learning algorithm with a hierarchical architecture based on the primate visual system. This design affords fast, local feature learning across parallel modules in each network layer. Simulations show that the network is capable of learning stable patterns from optic flow simulating self-motion through environments of varying complexity with only one epoch of training. ARTFLOW trains substantially faster and yields self-motion estimates that are far more accurate than a comparable network that relies on Hebbian learning. I show how ARTFLOW serves as a generative model to predict the optic flow that corresponds to neural activations distributed across the network.

Efficient Frame Error Concealment Using Bilateral Motion Estimation for Low Bit-Rate Video Transmission

2009 ◽  
Vol E92-B (2) ◽  
pp. 461-472
Author(s):  
DinhTrieu DUONG ◽  
Min-Cheol HWANG ◽  
Byeong-Doo CHOI ◽  
Jun-Hyung KIM ◽  
Sung-Jea KO
Keyword(s):  
Motion Estimation ◽  
Video Transmission ◽  
Error Concealment ◽  
Bit Rate ◽  
Low Bit Rate

Low-Power Motion Estimation Processor with 3D Stacked Memory

2015 ◽  
Vol E98.A (7) ◽  
pp. 1431-1441 ◽  
Author(s):  
Shuping ZHANG ◽  
Jinjia ZHOU ◽  
Dajiang ZHOU ◽  
Shinji KIMURA ◽  
Satoshi GOTO
Keyword(s):  
Motion Estimation ◽  
Low Power ◽  
Stacked Memory
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