IEEE Circuits and Systems Magazine - Q2 2018 - 94

Data Dimensionality (Number of Objects)

Conventional Sensor Border
Preprocessing
Sensor Border
Regular Flow
High Computational
Demand

Less Regular Flow
Lower Demand

* Image Capture
* Spatial Filtering
* Edge/Motion Detection

* Decision Making
* Algorithm Control
* Conditional Jumps
* Image Segmentation
* Object Labeling
* Feature Extraction

Irregular Flow
Moderate Demand

Abstraction Level (Data Structure Complexity)
(a)
~28 K bytes
Amount of
Data
~4 K bytes

~4 K bytes
<100 bytes

Processing
Results

<10 bytes

Area, Perimeter
Number of
Holes, Major
Axis, Extent...

Yes/No

Blob Analysis
and Features
Extraction (ROI)

Classifier

Local Threshold
HDR
Acquisition
Processing
Chain

Morphological
Processing

Th

High
Bandwidth
LP-Filter

(b)
Figure 3. (a) Processing hierarchy, from left-top to bottom-right, in vision; (b) Illustrative example of vision processing chain [19].

This figure highlights the steps to go from sensor raw
data to vision outcomes. The vertical axis represents data
dimensions while the horizontal one represents the abstraction level of the data. The processing chain follows
94

IEEE cIrcuIts and systEms magazInE

the diagonal arrow in Fig. 3(a). Top-left corresponds to
input data captured by the sensor and bottom-right corresponds to output data which support system actions.
The first stage of the vision processing chain is usually
sEcOnd quartEr 2018



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