Color segmentation SYSTEM CONFIGURATION
2. SYSTEM CONFIGURATION
Figure 1 shows the configuration of the system to implement user’s comamnd. An input scene is segmented and labeled by using image processing process to find the features of objects. From image segmentation and labeling result, the robot gets a features of object. It will use it to confirm tho the user when it finds some ambiguos condition. The system generate an effective and efficient dialog by processing this result according to general rule and dynamic database that obtained from specific characteristic of each features and some stored previous outputs. Figure 1. Configuration System2.1. Color segmentation
We use robust approach of features space method for color image segmentation [6]. This method reduces the number of color in the image and divide image to several region. For getting a best result, we merge some nearest color region by using hue method in Hue Saturation and Intensity HIS color space. [7]. The HSI system separates color information of an image from its intensity information. Color information is represented by hue and saturation values, while intensity describes the brightness of an image, is determined by the amount of the light. Hue represents basic colors, and is determined by the dominant a b c Figure 2. Color segmentation result .a Original Image. b. Segmentation results c HIS result after segmentation process wavelength in the spectral distribution of light wavelengths. The formulas for hue, saturation and intensity are : ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ − − + − − + − = B G B R G R B R G R H 2 5 . arccos 1 ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ + + = 3 B G R Int 2 B G R B G R Sat + + − = , , min 3 1 3 Some threshold of hue value is determined or to remove background and merging of nearest color for achieving small number region. In this paper we fix some threshold of hue value that estimated from 0 to 360 . For example, blue is 240 , yellow is 60 , green Feature characteristics Ask and remove method Dynamic Knowledge base Input Output Color segmentation Labeling and shape detection General rule Image processing part Dialog part Comfortable Dialog for Object Detection – Rahmadi Kurnia ISSN 1858-1633 2005 ICTS 117 is 120 , red is 0 magenta is 300 etc. The result of these processes is shown by Figure 2. 2.2.Labeling and shape detection Some values are put to the image to label the similar color in one region for detecting the number of objects. The shape of object is detected by determine its size, area, surrounding by using curve ratio equation. [8]. 2 4 perimeter area S π = We get some curve ratio is specific for each standard shape objects from experiments. For example, the curve ratio of circle is 0.8, square is around 0.79, rectangular is 0.7, triangle and other irregular shape are less than 0.583. FEATURE CHARACTERISTICS AND GENERAL RULE
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» INTRODUCTION ICTS2005 The Proceeding
» Opening Fundamental Operations of Mathematical Morphology
» Morphological filter Filter theorem
» Granulometry and size distribution
» PGPC texture model and estimation of the optimal structuring element: The PGPC
» CONCLUSIONS ICTS2005 The Proceeding
» Non-ergodicity parameters RESULTS AND DISCUSSIONS 1 Partial structure factors and
» SIMULATIONS CONCLUSION ICTS2005 The Proceeding
» IMAGE RECONSTRUCTION SYSTEM DESIGN
» RESULT CONCLUSION ICTS2005 The Proceeding
» MULTI-RESOLUTION HISTOGRAM TECHNIQUE DATA
» VALIDATION STRATEGY RESULTS AND DISCUSSION
» CONCLUSION ICTS2005 The Proceeding
» INTRODUCTION DISTILATION COLUMN AND ARTIFICIAL NEURAL NETWORK
» Using Temperature Correlation Using Flow Rate Correlation
» INTRODUCTION DETECTION OF SINGLE TREE FELLING WITH SOFT
» Supervised Fuzzy c-means Method
» Neural Network classification METHOD 1. Datasets
» Neural Network Classification Results
» Comparison of Classification Results
» DISCUSSIONS ICTS2005 The Proceeding
» CONCLUSION ACKNOWLEDGEMENT ICTS2005 The Proceeding
» Caching Access List BANDWIDTH MANAGEMENT IMPLEMENTATION
» Rate Limiting BANDWIDTH MANAGEMENT IMPLEMENTATION
» BANDWIDTH MANAGEMENT CONCEPTS RESULT
» The Architecture of UML Elements Model Element
» Diagram Element Editing SYSTEM ARCHITECTURE
» Server Application Architecture Undo
» INTRODUCTION IMPLEMENTATION TESTING ICTS2005 The Proceeding
» INTRODUCTION E-PURSE ICTS2005 The Proceeding
» Interfaces Verification Tool POS – Smart Card
» MULTI AGENT SYSTEM MAS A WEIGHTED-TREE SIMILARITY ALGORITHMS
» RESULTS ICTS2005 The Proceeding
» Facial Animation Morphing and Deformation Cross Dissolve
» Feature Morphing Mesh Morphing Text-to-Speech TTS Basic Block
» Text-to-Video Algorithm Text-To-Video Stake And Desain
» Suggestion CONCLUSION AND SUGGESTION 1 Conclusion
» The Concept SHARE-IT SYSTEM ARCHITECTURE
» SHARING SCENARIO CONCLUSION ICTS2005 The Proceeding
» The Bayesian Network Model and Modified Bayesian Optimization
» Designs and Implementation SCHEDULING MODEL AND IMPLEMENTATION
» Comparison Proposed Schedule with Real Schedule
» Face-to-Face Technique Long Distance Technique
» Scenario to motivate. Context_Selection Applikasi.
» INTRODUCTION ARCHITECTURE. CONCLUSION. ICTS2005 The Proceeding
» SUGGESTION ICTS2005 The Proceeding
» Data Flow Database Structure
» EXPERIMENTAL RESULT ICTS2005 The Proceeding
» Investment Stock Prototyping System Design
» Database Model Stock Valuation
» INTRODUCTION METHODOLOGY ICTS2005 The Proceeding
» Buffer Overrun Cryptography Random Numbers
» Anti-Tampering Error Handling Injection Flaws
» Encapsulate Field Restructuring Arrays
» Generating Secure Random Number Storing Deleting Passwords
» Smart Serialization Message Digest
» Convert Message with Private Key to Public Key
» INTRODUCTION CURRENT STATUS ICTS2005 The Proceeding
» INTRODUCTION PROPOSED SIMULATION MODEL
» PARALLELIZATION STRATEGY ICTS2005 The Proceeding
» EXPERIMENTS AND DISCUSSION CONCLUSION
» INTRODUCTION RESULTS AND DISCUSSION
» EXPERIMENTAL ICTS2005 The Proceeding
» RESULT AND DISCUSSION ICTS2005 The Proceeding
» Color segmentation SYSTEM CONFIGURATION
» FEATURE CHARACTERISTICS AND GENERAL RULE
» EXPERIMENTAL RESULT CONCLUSION ICTS2005 The Proceeding
» INTRODUCTION REVIEW OF LITERATURE
» Social Economics Impact. Restructuring Impact
» Manager Application Mobile Agent Generator MAG Mobile Agents MAs
» SNMP Table Polling SNMP Table Filtering
» BREAST CARCINOMA TUMOR ICTS2005 The Proceeding
» WATERSHED ALGORITHM METHODS ICTS2005 The Proceeding
» RESULT AND DISCUSION ICTS2005 The Proceeding
» FADED INFORMATION FIELD ARCHITECTURE
» ALGORITHMS TO CHOOSE NODES TO CREATE THE FADED
» SYSTEM SIMULATIONS ICTS2005 The Proceeding
» Model and Teory MODEL, TEORY, DESIGN, IMPLEMENTATION AND
» INTRODUCTION ANALYSIS AND RESULT
» INTRODUCTION A SIMPLE MODEL OF THE QUEUING SYSTEM
» SIMULATION RESULTS DISCUSSION ICTS2005 The Proceeding
» CONCLUSION INTRODUCTION ICTS2005 The Proceeding
» Dialog Processing ADDING NONVERBAL BEHAVIOUR
» Emotion Expression Experiment ADDING NONVERBAL BEHAVIOUR
» NATURAL LANGUAGE PROCESSING EMOTION REASONING
» Fuzzy Logic Control FLC System Planning
» Digital To Analog Converter DAC Motor Driver Position Sensor Display Unit
» INTRODUCTION CONCLUSION ICTS2005 The Proceeding
» Variable-Centered Rule Structure VARIABLE-CENTERED INTELLIGENT RULE SYSTEM
» Knowledge Refinement VARIABLE-CENTERED INTELLIGENT RULE SYSTEM
» Knowledge Building VARIABLE-CENTERED INTELLIGENT RULE SYSTEM
» Knowledge Inferencing VARIABLE-CENTERED INTELLIGENT RULE SYSTEM
» INTRODUCTION BASIC CONCEPTS OF FUZZY SETS
» Calculation of the Fitness Degree
» ESTIMATING MULTIPLE NULL VALUES IN RELATIONAL
» Chen’s [6] Result This Improving Method’s Result
» The Fuzzy Set HISTOGRAM THRESHOLDING
» Fuzzy Set Similarity HISTOGRAM THRESHOLDING
» EXPERIMENTAL RESULTS ICTS2005 The Proceeding
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