Performance of Single Board Computers for Vision Processing

2021 ◽  
Author(s):  
Curtis Manore ◽  
Pratheek Manjunath ◽  
Dominic Larkin
Keyword(s):  
Vision Processing

A Multichip Neuromorphic System for Spike-Based Visual Information Processing

2007 ◽  
Vol 19 (9) ◽  
pp. 2281-2300 ◽  
Author(s):  
R. Jacob Vogelstein ◽  
Udayan Mallik ◽  
Eugenio Culurciello ◽  
Gert Cauwenberghs ◽  
Ralph Etienne-Cummings
Keyword(s):  
Visual Information ◽  
Reversal Potential ◽  
Multiple Components ◽  
Vision Processing ◽  
Look Up Table ◽  
Neural Spike ◽  
Vlsi System ◽  
Silicon Neurons ◽  
Neuromorphic System ◽  
Synaptic Parameters

We present a multichip, mixed-signal VLSI system for spike-based vision processing. The system consists of an 80 × 60 pixel neuromorphic retina and a 4800 neuron silicon cortex with 4,194,304 synapses. Its functionality is illustrated with experimental data on multiple components of an attention-based hierarchical model of cortical object recognition, including feature coding, salience detection, and foveation. This model exploits arbitrary and reconfigurable connectivity between cells in the multichip architecture, achieved by asynchronously routing neural spike events within and between chips according to a memory-based look-up table. Synaptic parameters, including conductance and reversal potential, are also stored in memory and are used to dynamically configure synapse circuits within the silicon neurons.

Approaches for M-Health Environment

2021 ◽  
pp. 60-94
Keyword(s):  
Imaging System ◽  
Vision System ◽  
Intensity Variation ◽  
Gaussian Filter ◽  
Illumination Variation ◽  
Vision Processing ◽  
Random Intensity ◽  
Hardware Architectures

It is a well-known fact that when a camera or other imaging system captures an image, often, the vision system for which it is captured cannot implement it directly. There may be several reasons behind this fact such as there can exist random intensity variation in the image. There can also be illumination variation in the image or poor contrast. These drawbacks must be tackled at the primitive stages for optimum vision processing. This chapter will discuss different filtering approaches for this purpose. The chapter begins with the Gaussian filter, followed by a brief review of different often used approaches. Moreover, this chapter will also render different filtering approaches including their hardware architectures.

Research on the Identification of Hand-Painted Thangka and Printed Thangka Based on Computer Vision Processing Method

2021 ◽  
pp. 214-223
Author(s):  
Chunhua Pan ◽  
Decai Zhao ◽  
Jinglong Ren ◽  
Shumin Cui ◽  
Wenjing Li
Keyword(s):  
Computer Vision ◽  
Processing Method ◽  
Vision Processing

Acquirement of Melon Morphologies through High-Precision and Non-Destructive Vision Measurement

2011 ◽  
Vol 383-390 ◽  
pp. 5292-5299
Author(s):  
Fu Xiang Lv ◽  
Yu Bin Miao ◽  
Qiang Zhu
Keyword(s):  
Spline Interpolation ◽  
Measuring System ◽  
Projection Area ◽  
Response Relationship ◽  
Temporal And Spatial Variation ◽  
Edge Information ◽  
Vision Measurement ◽  
Vision Processing ◽  
Temporal And Spatial ◽  
Non Destructive

For the purpose of measuring the micro changes of morphological parameters of melon organ, this paper put forth a new algorithm based on mathematical morphology and spline interpolation to obtain the phenotype information of melon such as area and horizontal and vertical diameter and developed a high-resolution non-destructive and contactless measuring system based on vision processing to get the projection area of melon and its diameter. The algorithm is easy to carry out, and can get more ideal edge information than some traditional algorithms. It supplies theoretical basis for revealing the combined response relationship and temporal and spatial variation character between melon morphologies and key environmental factors.

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