Feature detection may include the information about the image to comprehend what the object is. Feature Extraction may include object segmentation using features like grey-level edges, texture edges, and shape attributes, depending on the application’s requirements. It transforms arbitrary input data, such as images, into a set of features. This helps in revealing unique parts of an image as a compressed feature vector. Parallel Processing is the ability to process colour, form, and motion of an object simultaneously for feature detection. Whereas, for feature extraction using different parallel processing techniques and methods can accelerate computational problems. So, the effectiveness of a multi-microprocessor system where the tasks can be distributed among different processors will work in parallel.

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Feature Detection &
Extraction Using Parallel
Programming
04.09.2017
─
Arysha Channa
CT-056
BCIT
NEDUET

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Table of Contents
Overview............................................................................................................3
Feature Detection ...........................................................................................3
Feature Extraction ........................................................................................3
Parallel Processing....................................................................................3
Goals..................................................................................................................4
Specifications.....................................................................................................4
Introduction.....................................................................................................4
Feature Detection (Classification).................................................................4
Feature Extraction (GP-GPU)....................................................................4
Parallel processing using OPENMP, SSE and CUDA............................................5
Configuration Using OPENMP.......................................................................5
Configuration Using SSE...........................................................................5
Configuration Using CUDA...................................................................6
Reference...........................................................................................................7

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Overview
It has been estimated that the process of recognizing objects and
performing differentfeature detectionand extraction tasks requires
processorspeedto be 1 to 100 billion operations per second.
An image consists of differentfeatures. Features are basically, the
distinctive properties of an image or any input that help in differentiating the
objects from one another into differentcategories.
Feature detectionis the ability to break down an image into its components
i.e. its features, such as color, form, and motion. Moreover, if the detection
of various features occurs at once, or in parallel, it is known as parallel
processing.Thus, detectionof various features of an image simultaneously
is referred to as “Feature DetectionUsing Parallel Processing”.
Feature detectionmay include the information about the image to
comprehend what the objectis.
The major features are: Color, Form, and Motion
 Color detectionis recognized through cones in eye (red, green, and
blue)
 Form detectionuses parvo pathway [1] (responsible forfiguring out
the shape/outline of the object) by establishing the boundaries of the
object,the shape of the features.
 Motion Detectionutilizes magno pathway in the brain, the movement
of the object.
Parvo Pathway
- Vital for spatialresolution; efficientat identifying outboundaries of objects
- Good spatialresolution,but very poor temporalresolution
- Also,helps us to see in color
Magno Pathway
- Set of specialized cells that permitus to encrypt motion
- Very great temporal resolution;low spatialresolution
- Does not encode color;only motion

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Feature Extraction [2] may include objectsegmentationusing features like
gray-level edges,texture edges,and shape attributes, depending on the
application’s requirements. It transforms arbitrary input data, such as
images, into a set of features. This helps in revealing unique parts of an
image as a compressedfeature vector.
Parallel Processing is the ability to processcolor,form, and motion of an
objectsimultaneously for feature detection.
Whereas,for feature extraction using different parallel processing
techniques and methods can accelerate computational problems.So,the
effectivenessof a multi-microprocessor [2] system where the tasks can be
distributed among differentprocessors will work in parallel.
Goals
1. To utilize parallel processing forfeature detectionand extraction
method to be executed at real-time with high-speed processing.
2. Identifyand extract distinctive features simultaneously.
3. Feature detectionand extraction are important componentof various
computervision applications.
Like, Image representation, image classification,and retrieval object
recognition and matching, 3Dscene reconstruction, motion tracking,
texture classification, robotlocalization and biometrics systems.
Specifications
I. INTRODUCTION
Feature detectionhas becomean influential tool in various fields of artificial
intelligence and is being applied immenselyin various other applications.
Thus, detecting image feature is an essential step for such applications.
One of the basic necessityfor feature detectionis to establish saline
feature correspondenceamong a collectionof differentimages sharing
similar features. This can be achieved by using classificationtask, where
feature details of test image are coordinated with all the trained image

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features. The trained image features show a greater proportionof
correspondenceis consideredthe best match with the test images.[3, 4]
Another important area of research for image features is feature extraction.
Feature extraction is applied on CPU version, as the algorithms and the
codes are improvised and then executed using parallel processing resulting
in feature extraction in parallel. [4]
Then, using CUDA (based on the SIFT [4] language), a parallel
descriptoris constructed via SSE instructions, and the GPU version is also
implemented by parallel processing.
Using GPU-Parallel descriptorspeed up the process five times faster
than the CPU version. Whereas,CPU version accelerates by four and half
times than the SIFT.
As the feature-based approaches,like SIFT (Scale Invariant Feature
Transform) algorithms shows signs of robust performance;therefore,to
improve performance and make it faster, algorithms like PCA-SIFTand
SURF (Speed Up RobustFeatures) have beenintroduced. [4]
But, all the SIFT methods were hurdled by a higher-dimensional
descriptorwhich is extremely time-consuming and complexto function for
real-time processing.
In counter to all performance issues,parallel systems, [4] like:
OpenMP (an API- ApplicationProgram Interface),SSE (Streaming SIMD
Extension) and CUDA (Compute Unified Device Architecture) programming
are highly in demand for their stable high-speed algorithms to accomplish
fast and real-time feature extraction in objectfor images.
II. Parallel processing using OPENMP, SSE and CUDA
A. Parallelprocessing configurationusing OpenMP
OpenMP is used to make several threads for parallel programming
in a public, shared memoryenvironment. OpenMP comprises of
[4]: 1- compilerdirectives,2- a run-time library & 3- environmental
variables.
The drawback of this approach is that the OpenMP compiler
does not automatically assist analysis of all problems and

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parallelization. Therefore,the pre-analysis of the sequential
program must directly handle the issues of data dependency,
synchronization initiated by parallelization.
B. Parallelprocessing configurationusing SSE
SSE are the extended versions of MMX instruction set, used in
Intel Pentium 4 and Core 2 Duo processorgenerations.SSE can
generate multiple SIMD (Single Instruction and Multiple Data) of
x87-bit floating point instructions via n-bit register(according to the
version used).
The SIMD instructions can be executed in an Intrinsic function, Automatic
Vectorization, or Inline Assembler.[4]
C. Parallelprocessing configurationusing CUDA
Graphics applications face the problems of the bottleneck.
Therefore, GPU was designed to settle this issue of bottlenecks.
GPU is a special-functionprocessing unit [4] with advanced
transistor densities and SIMD parallel hardware architecture. This
software and hardware architecture provide an independent
platform for CUDA by giving a programming environment to it, and
thus, reducing the need for considering graphics pipeline.
CUDA is designed using C language for general computing on
the GPU hardware and software.
GP-GPU (General Purpose Computation on GPUs) exhibits
real-time performance in applications with a large amount of data.

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The figure shows the processof feature extraction using CUDA. For
parallel processing to be performed,each block produces alarge number
of threads to share data with other threads. [4]
References
[1] M. P.Ali Yoonessi,"Functional Assessmentof Magno,Parvoand Konio-CellularPathways;,"J
OphthalmicVisRes, vol.6, no.2, p. 119–126, 2011.
[2] T. N. M. Edward J.Delp,"Parallel processingforcomputervision," Robotvision, vol.336, 1982.
[3] A. A.A. H. A.A. M. Hassaballah,"FeaturesDetection," ImageFeaturesDetection,Description, pp.1-
36, 2016.
[4] Y. J. X. C.W. A. G. Junchul Kim,"Feature Extraction," A FastFeatureExtraction in ObjectRecognition,
p. 2, 2009.
.

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Index
A
applications, 1
B
bottleneck, 1
C
classification, 1
Color, 1
CPU, 1
CUDA, 1
D
data, 1
detection, 1
E
extraction, 1
F
feature, 1
form, 1
functions, 1
G
GPU, 1
graphics, 1
I
Instructions, 1
M
magno, 1
Motion, 1
O
OPENMP, 1
P
Parallel, 1
parvo, 1
pathway, 1
process, 1
processing, 1
processor, 1
R
real-time, 1
S
SIFT, 1
SIMD, 1
SSE, 1
SURF, 1
T
techniques, 1
thread, 1