SlideShare a Scribd company logo
1 of 2
Practical –13
Aim: write the program for face detection in matlab.
Theory:
Syntax for Using the Object Detector
 bbox = detector(I)
 bbox = detector(I,roi)
Description of the Syntax
 bbox = detector(I) returns bbox, an M-by-4 matrix, that defines ‘M’ bounding boxes that contain
the detected objects.
 bbox = detector(I,roi) is used for detecting objects within the rectangular region of interest,
specified by roi.
Input Arguments
 I — Input image: It is specified as true color or grayscale (RGB).
 model — Classification model: It is specified as a character vector and describes the object type
to be detected.
 XMLFILE — Custom classification model: Specified as an XML file, it can be created using
OpenCV training functionality or the trainCascadeObjectDetector function.
 roi — Rectangular region of interest: A four-element vector [x y width height] is used to specify
this input argument.
Step=1
%to detect face
FaceDetector = vision.CascadeObjectDetector():
Step=2
%read input image
Cam = webcam(1);
Step=3
%return
Bounding Box value based on number of object
Step=4
%Figure

More Related Content

Similar to Practical13.docx

EN3085 Assessed Coursework 1 1. Create a class Complex .docx
EN3085 Assessed Coursework 1  1. Create a class Complex .docxEN3085 Assessed Coursework 1  1. Create a class Complex .docx
EN3085 Assessed Coursework 1 1. Create a class Complex .docx
gidmanmary
 
Color Based Object Tracking with OpenCV A Survey
Color Based Object Tracking with OpenCV A SurveyColor Based Object Tracking with OpenCV A Survey
Color Based Object Tracking with OpenCV A Survey
YogeshIJTSRD
 
Can you please separate the code into imagespy sharpenpy.pdf
Can you please separate the code into imagespy sharpenpy.pdfCan you please separate the code into imagespy sharpenpy.pdf
Can you please separate the code into imagespy sharpenpy.pdf
agmbro1
 
asmt7~$sc_210_-_assignment_7_fall_15.docasmt7cosc_210_-_as.docx
asmt7~$sc_210_-_assignment_7_fall_15.docasmt7cosc_210_-_as.docxasmt7~$sc_210_-_assignment_7_fall_15.docasmt7cosc_210_-_as.docx
asmt7~$sc_210_-_assignment_7_fall_15.docasmt7cosc_210_-_as.docx
fredharris32
 
Encontra presentation
Encontra presentationEncontra presentation
Encontra presentation
Ricardo Dias
 
19BCS1815_PresentationAutomatic Number Plate Recognition(ANPR)P.pptx
19BCS1815_PresentationAutomatic Number Plate Recognition(ANPR)P.pptx19BCS1815_PresentationAutomatic Number Plate Recognition(ANPR)P.pptx
19BCS1815_PresentationAutomatic Number Plate Recognition(ANPR)P.pptx
SamridhGarg
 

Similar to Practical13.docx (20)

Objective C Primer (with ref to C#)
Objective C  Primer (with ref to C#)Objective C  Primer (with ref to C#)
Objective C Primer (with ref to C#)
 
Matlab dip
Matlab dipMatlab dip
Matlab dip
 
EN3085 Assessed Coursework 1 1. Create a class Complex .docx
EN3085 Assessed Coursework 1  1. Create a class Complex .docxEN3085 Assessed Coursework 1  1. Create a class Complex .docx
EN3085 Assessed Coursework 1 1. Create a class Complex .docx
 
Introduction to Image Processing with MATLAB
Introduction to Image Processing with MATLABIntroduction to Image Processing with MATLAB
Introduction to Image Processing with MATLAB
 
Noise tolerant color image segmentation using support vector machine
Noise tolerant color image segmentation using support vector machineNoise tolerant color image segmentation using support vector machine
Noise tolerant color image segmentation using support vector machine
 
Robust Real Time Face Detection
Robust Real Time Face DetectionRobust Real Time Face Detection
Robust Real Time Face Detection
 
Color Based Object Tracking with OpenCV A Survey
Color Based Object Tracking with OpenCV A SurveyColor Based Object Tracking with OpenCV A Survey
Color Based Object Tracking with OpenCV A Survey
 
Python-oop
Python-oopPython-oop
Python-oop
 
Project 4
Project 4Project 4
Project 4
 
Ala Stolpnik's Standard Model talk
Ala Stolpnik's Standard Model talkAla Stolpnik's Standard Model talk
Ala Stolpnik's Standard Model talk
 
Can you please separate the code into imagespy sharpenpy.pdf
Can you please separate the code into imagespy sharpenpy.pdfCan you please separate the code into imagespy sharpenpy.pdf
Can you please separate the code into imagespy sharpenpy.pdf
 
Chapter04.pptx
Chapter04.pptxChapter04.pptx
Chapter04.pptx
 
Objects and Graphics
Objects and GraphicsObjects and Graphics
Objects and Graphics
 
asmt7~$sc_210_-_assignment_7_fall_15.docasmt7cosc_210_-_as.docx
asmt7~$sc_210_-_assignment_7_fall_15.docasmt7cosc_210_-_as.docxasmt7~$sc_210_-_assignment_7_fall_15.docasmt7cosc_210_-_as.docx
asmt7~$sc_210_-_assignment_7_fall_15.docasmt7cosc_210_-_as.docx
 
Encontra presentation
Encontra presentationEncontra presentation
Encontra presentation
 
Xii Compsc Hw
Xii Compsc HwXii Compsc Hw
Xii Compsc Hw
 
Joel Landis Net Portfolio
Joel Landis Net PortfolioJoel Landis Net Portfolio
Joel Landis Net Portfolio
 
FEATURE EXTRACTION USING SURF ALGORITHM FOR OBJECT RECOGNITION
FEATURE EXTRACTION USING SURF ALGORITHM FOR OBJECT RECOGNITIONFEATURE EXTRACTION USING SURF ALGORITHM FOR OBJECT RECOGNITION
FEATURE EXTRACTION USING SURF ALGORITHM FOR OBJECT RECOGNITION
 
19BCS1815_PresentationAutomatic Number Plate Recognition(ANPR)P.pptx
19BCS1815_PresentationAutomatic Number Plate Recognition(ANPR)P.pptx19BCS1815_PresentationAutomatic Number Plate Recognition(ANPR)P.pptx
19BCS1815_PresentationAutomatic Number Plate Recognition(ANPR)P.pptx
 
color detection using open cv
color detection using open cvcolor detection using open cv
color detection using open cv
 

More from Central university of Haryana

MATLAB-Cheat-Sheet-for-Data-Science_LondonSchoolofEconomics (1).pdf
MATLAB-Cheat-Sheet-for-Data-Science_LondonSchoolofEconomics (1).pdfMATLAB-Cheat-Sheet-for-Data-Science_LondonSchoolofEconomics (1).pdf
MATLAB-Cheat-Sheet-for-Data-Science_LondonSchoolofEconomics (1).pdf
Central university of Haryana
 

More from Central university of Haryana (20)

Practical --2..pdf
Practical --2..pdfPractical --2..pdf
Practical --2..pdf
 
Practical --1.pdf
Practical --1.pdfPractical --1.pdf
Practical --1.pdf
 
ML Lab.docx
ML Lab.docxML Lab.docx
ML Lab.docx
 
MATLAB-Cheat-Sheet-for-Data-Science_LondonSchoolofEconomics (1).pdf
MATLAB-Cheat-Sheet-for-Data-Science_LondonSchoolofEconomics (1).pdfMATLAB-Cheat-Sheet-for-Data-Science_LondonSchoolofEconomics (1).pdf
MATLAB-Cheat-Sheet-for-Data-Science_LondonSchoolofEconomics (1).pdf
 
LittleBookOfRuby.pdf
LittleBookOfRuby.pdfLittleBookOfRuby.pdf
LittleBookOfRuby.pdf
 
all matlab_prog.pdf
all              matlab_prog.pdfall              matlab_prog.pdf
all matlab_prog.pdf
 
Practical 111.docx
Practical 111.docxPractical 111.docx
Practical 111.docx
 
Matlab Practical--11.pdf
Matlab Practical--11.pdfMatlab Practical--11.pdf
Matlab Practical--11.pdf
 
Matlab Practical--11.docx
Matlab Practical--11.docxMatlab Practical--11.docx
Matlab Practical--11.docx
 
Matlab Practical--9.docx
Matlab Practical--9.docxMatlab Practical--9.docx
Matlab Practical--9.docx
 
Matlab Practical-- 12.pdf
Matlab Practical-- 12.pdfMatlab Practical-- 12.pdf
Matlab Practical-- 12.pdf
 
Matlab practical ---9.pdf
Matlab practical ---9.pdfMatlab practical ---9.pdf
Matlab practical ---9.pdf
 
Matlab practical ---7.pdf
Matlab practical ---7.pdfMatlab practical ---7.pdf
Matlab practical ---7.pdf
 
Matlab practical ---6.pdf
Matlab practical ---6.pdfMatlab practical ---6.pdf
Matlab practical ---6.pdf
 
Matlab practical ---5.pdf
Matlab practical ---5.pdfMatlab practical ---5.pdf
Matlab practical ---5.pdf
 
Matlab practical ---4.pdf
Matlab practical ---4.pdfMatlab practical ---4.pdf
Matlab practical ---4.pdf
 
Matlab practical ---3.pdf
Matlab practical ---3.pdfMatlab practical ---3.pdf
Matlab practical ---3.pdf
 
Matlab practical ---2.pdf
Matlab practical ---2.pdfMatlab practical ---2.pdf
Matlab practical ---2.pdf
 
Matlab practical ---1.pdf
Matlab practical ---1.pdfMatlab practical ---1.pdf
Matlab practical ---1.pdf
 
Matlab practical --8.pdf
Matlab practical --8.pdfMatlab practical --8.pdf
Matlab practical --8.pdf
 

Recently uploaded

scipt v1.pptxcxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx...
scipt v1.pptxcxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx...scipt v1.pptxcxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx...
scipt v1.pptxcxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx...
HenryBriggs2
 
INTERRUPT CONTROLLER 8259 MICROPROCESSOR
INTERRUPT CONTROLLER 8259 MICROPROCESSORINTERRUPT CONTROLLER 8259 MICROPROCESSOR
INTERRUPT CONTROLLER 8259 MICROPROCESSOR
TanishkaHira1
 
Cybercrimes in the Darknet and Their Detections: A Comprehensive Analysis and...
Cybercrimes in the Darknet and Their Detections: A Comprehensive Analysis and...Cybercrimes in the Darknet and Their Detections: A Comprehensive Analysis and...
Cybercrimes in the Darknet and Their Detections: A Comprehensive Analysis and...
dannyijwest
 
Hospital management system project report.pdf
Hospital management system project report.pdfHospital management system project report.pdf
Hospital management system project report.pdf
Kamal Acharya
 
Integrated Test Rig For HTFE-25 - Neometrix
Integrated Test Rig For HTFE-25 - NeometrixIntegrated Test Rig For HTFE-25 - Neometrix
Integrated Test Rig For HTFE-25 - Neometrix
Neometrix_Engineering_Pvt_Ltd
 
Digital Communication Essentials: DPCM, DM, and ADM .pptx
Digital Communication Essentials: DPCM, DM, and ADM .pptxDigital Communication Essentials: DPCM, DM, and ADM .pptx
Digital Communication Essentials: DPCM, DM, and ADM .pptx
pritamlangde
 

Recently uploaded (20)

Fundamentals of Internet of Things (IoT) Part-2
Fundamentals of Internet of Things (IoT) Part-2Fundamentals of Internet of Things (IoT) Part-2
Fundamentals of Internet of Things (IoT) Part-2
 
Introduction to Geographic Information Systems
Introduction to Geographic Information SystemsIntroduction to Geographic Information Systems
Introduction to Geographic Information Systems
 
Augmented Reality (AR) with Augin Software.pptx
Augmented Reality (AR) with Augin Software.pptxAugmented Reality (AR) with Augin Software.pptx
Augmented Reality (AR) with Augin Software.pptx
 
Introduction to Serverless with AWS Lambda
Introduction to Serverless with AWS LambdaIntroduction to Serverless with AWS Lambda
Introduction to Serverless with AWS Lambda
 
Path loss model, OKUMURA Model, Hata Model
Path loss model, OKUMURA Model, Hata ModelPath loss model, OKUMURA Model, Hata Model
Path loss model, OKUMURA Model, Hata Model
 
litvinenko_Henry_Intrusion_Hong-Kong_2024.pdf
litvinenko_Henry_Intrusion_Hong-Kong_2024.pdflitvinenko_Henry_Intrusion_Hong-Kong_2024.pdf
litvinenko_Henry_Intrusion_Hong-Kong_2024.pdf
 
Post office management system project ..pdf
Post office management system project ..pdfPost office management system project ..pdf
Post office management system project ..pdf
 
scipt v1.pptxcxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx...
scipt v1.pptxcxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx...scipt v1.pptxcxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx...
scipt v1.pptxcxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx...
 
8th International Conference on Soft Computing, Mathematics and Control (SMC ...
8th International Conference on Soft Computing, Mathematics and Control (SMC ...8th International Conference on Soft Computing, Mathematics and Control (SMC ...
8th International Conference on Soft Computing, Mathematics and Control (SMC ...
 
INTERRUPT CONTROLLER 8259 MICROPROCESSOR
INTERRUPT CONTROLLER 8259 MICROPROCESSORINTERRUPT CONTROLLER 8259 MICROPROCESSOR
INTERRUPT CONTROLLER 8259 MICROPROCESSOR
 
Cybercrimes in the Darknet and Their Detections: A Comprehensive Analysis and...
Cybercrimes in the Darknet and Their Detections: A Comprehensive Analysis and...Cybercrimes in the Darknet and Their Detections: A Comprehensive Analysis and...
Cybercrimes in the Darknet and Their Detections: A Comprehensive Analysis and...
 
Hospital management system project report.pdf
Hospital management system project report.pdfHospital management system project report.pdf
Hospital management system project report.pdf
 
Passive Air Cooling System and Solar Water Heater.ppt
Passive Air Cooling System and Solar Water Heater.pptPassive Air Cooling System and Solar Water Heater.ppt
Passive Air Cooling System and Solar Water Heater.ppt
 
Computer Networks Basics of Network Devices
Computer Networks  Basics of Network DevicesComputer Networks  Basics of Network Devices
Computer Networks Basics of Network Devices
 
Worksharing and 3D Modeling with Revit.pptx
Worksharing and 3D Modeling with Revit.pptxWorksharing and 3D Modeling with Revit.pptx
Worksharing and 3D Modeling with Revit.pptx
 
Integrated Test Rig For HTFE-25 - Neometrix
Integrated Test Rig For HTFE-25 - NeometrixIntegrated Test Rig For HTFE-25 - Neometrix
Integrated Test Rig For HTFE-25 - Neometrix
 
Digital Communication Essentials: DPCM, DM, and ADM .pptx
Digital Communication Essentials: DPCM, DM, and ADM .pptxDigital Communication Essentials: DPCM, DM, and ADM .pptx
Digital Communication Essentials: DPCM, DM, and ADM .pptx
 
Signal Processing and Linear System Analysis
Signal Processing and Linear System AnalysisSignal Processing and Linear System Analysis
Signal Processing and Linear System Analysis
 
Databricks Generative AI Fundamentals .pdf
Databricks Generative AI Fundamentals  .pdfDatabricks Generative AI Fundamentals  .pdf
Databricks Generative AI Fundamentals .pdf
 
HOA1&2 - Module 3 - PREHISTORCI ARCHITECTURE OF KERALA.pptx
HOA1&2 - Module 3 - PREHISTORCI ARCHITECTURE OF KERALA.pptxHOA1&2 - Module 3 - PREHISTORCI ARCHITECTURE OF KERALA.pptx
HOA1&2 - Module 3 - PREHISTORCI ARCHITECTURE OF KERALA.pptx
 

Practical13.docx

  • 1. Practical –13 Aim: write the program for face detection in matlab. Theory: Syntax for Using the Object Detector  bbox = detector(I)  bbox = detector(I,roi) Description of the Syntax  bbox = detector(I) returns bbox, an M-by-4 matrix, that defines ‘M’ bounding boxes that contain the detected objects.  bbox = detector(I,roi) is used for detecting objects within the rectangular region of interest, specified by roi. Input Arguments  I — Input image: It is specified as true color or grayscale (RGB).  model — Classification model: It is specified as a character vector and describes the object type to be detected.  XMLFILE — Custom classification model: Specified as an XML file, it can be created using OpenCV training functionality or the trainCascadeObjectDetector function.  roi — Rectangular region of interest: A four-element vector [x y width height] is used to specify this input argument. Step=1 %to detect face FaceDetector = vision.CascadeObjectDetector(): Step=2 %read input image Cam = webcam(1);
  • 2. Step=3 %return Bounding Box value based on number of object Step=4 %Figure