Signal Processing, Image Processing and Pattern Recognition,
International Conference, SIP 2009, Held as Part of the Future Generation Information Technology Conference, FGIT 2009, Jeju Island, Korea, December 10-12, 2009. Proceedings
1st Edition
Published on 10. May 2010
XII, 330 pages
978-3-642-10546-3 (ISBN)
Description
As future generation information technology (FGIT) becomes specialized and fr- mented, it is easy to lose sight that many topics in FGIT have common threads and, because of this, advances in one discipline may be transmitted to others. Presentation of recent results obtained in different disciplines encourages this interchange for the advancement of FGIT as a whole. Of particular interest are hybrid solutions that c- bine ideas taken from multiple disciplines in order to achieve something more signi- cant than the sum of the individual parts. Through such hybrid philosophy, a new principle can be discovered, which has the propensity to propagate throughout mul- faceted disciplines. FGIT 2009 was the first mega-conference that attempted to follow the above idea of hybridization in FGIT in a form of multiple events related to particular disciplines of IT, conducted by separate scientific committees, but coordinated in order to expose the most important contributions. It included the following international conferences: Advanced Software Engineering and Its Applications (ASEA), Bio-Science and Bio-Technology (BSBT), Control and Automation (CA), Database Theory and Application (DTA), D- aster Recovery and Business Continuity (DRBC; published independently), Future G- eration Communication and Networking (FGCN) that was combined with Advanced Communication and Networking (ACN), Grid and Distributed Computing (GDC), M- timedia, Computer Graphics and Broadcasting (MulGraB), Security Technology (SecTech), Signal Processing, Image Processing and Pattern Recognition (SIP), and- and e-Service, Science and Technology (UNESST).
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Dominik Slezak | Sankar Pal | Byeong-Ho Kang
Signal Processing, Image Processing and Pattern Recognition,
International Conference, SIP 2009, Held as Part of the Future Generation Information Technology Conference, FGIT 2009, Jeju Island, Korea, December 10-12, 2009. Proceedings
Book
11/2009
Springer
€53.49
Shipment within 7-9 days
Content
A Blind Image Wavelet-Based Watermarking Using Interval Arithmetic.- Hand Gesture Spotting Based on 3D Dynamic Features Using Hidden Markov Models.- Objective Quality Evaluation of Laser Markings for Assembly Control.- An Improved Object Detection and Contour Tracking Algorithm Based on Local Curvature.- An Efficient Method for Noisy Cell Image Segmentation Using Generalized ?-Entropy.- An Algorithm for Moving Multi-target Prediction in a Celestial Background.- Automatic Control Signal Generation of a CCD Camera for Object Tracking.- An Efficient Approach for Region-Based Image Classification and Retrieval.- An Improved Design for Digital Audio Effect of Flanging.- Robust Speech Enhancement Using Two-Stage Filtered Minima Controlled Recursive Averaging.- Automatic Colon Cleansing in CTC Image Using Gradient Magnitude and Similarity Measure.- A Scale and Rotation Invariant Interest Points Detector Based on Gabor Filters.- Signature Verification Based on Handwritten Text Recognition.- Effect of Image Linearization on Normalized Compression Distance.- A Comparative Study of Blind Speech Separation Using Subspace Methods and Higher Order Statistics.- Automatic Fatigue Detection of Drivers through Yawning Analysis.- GA-SVM Based Lungs Nodule Detection and Classification.- Link Shifting Based Pyramid Segmentation for Elongated Regions.- A Robust Algorithm for Fruit-Sorting under Variable Illumination.- Selection of Accurate and Robust Classification Model for Binary Classification Problems.- Robust Edge-Enhanced Fragment Based Normalized Correlation Tracking in Cluttered and Occluded Imagery.- Robust and Imperceptible Watermarking of Video Streams for Low Power Devices.- A New Ensemble Scheme for Predicting Human Proteins Subcellular Locations.- Designing Linear Phase FIR Filters with Particle Swarm Optimization and Harmony Search.- Blind Image Steganalysis Based on Local Information and Human Visual System.- An Effective Combination of MPP Contour-Based Features for Off-Line Text-Independent Arabic Writer Identification.- Ringing Artifact Removal in Digital Restored Images Using Multi-Resolution Edge Map.- Upmixing Stereo Audio into 5.1 Channel Audio for Improving Audio Realism.- Multiway Filtering Based on Multilinear Algebra Tools.- LaMOC - A Location Aware Mobile Cooperative System.- Modelling of Camera Phone Capture Channel for JPEG Colour Barcode Images.- Semantic Network Adaptation Based on QoS Pattern Recognition for Multimedia Streams.- Active Shape Model-Based Gait Recognition Using Infrared Images.- About Classification Methods Based on Tensor Modelling for Hyperspectral Images.- Comparative Analysis of Wavelet-Based Scale-Invariant Feature Extraction Using Different Wavelet Bases.- A Novel Video Segmentation Algorithm with Shadow Cancellation and Adaptive Threshold Techniques.- Considerations of Image Compression Scheme Hiding a Part of Coded Data into Own Image Coded Data.- Binarising SIFT-Descriptors to Reduce the Curse of Dimensionalityin Histogram-Based Object Recognition.
"A Robust Algorithm for Fruit-Sorting under Variable Illumination thresholding. (p. 153-154)
Abstract. Computer vision techniques are essential for defect segmentation in any automatized fruit-sorting system. Conventional sorting methods employ algorithms that are specific to standard illumination conditions and may produce undesirable results if ideal conditions are not maintained. This paper outlines a scheme that employs adaptive filters for pre-processing to negate the effect of varying illumination followed by defect segmentation using a localized adaptive threshold in an apple sorting experimental system based on a reference image. This technique has been compared with other methods and the results indicate an improved sorting performance. This can also be applied to other fruits with curved contours.
Keywords: Computer Vision, On-line fruit sorting, Surface defect, Adaptive thresholding.
1 Introduction
Fruit inspection and grading is an indispensable horticultural procedure. Uniformity in size, shape and colour are few of the many parameters that are vital in determining consumer acceptance. While the task at hand is to develop a machine vision system that identifies defective fruits based on odd shapes and surface defects, and to categorize them depending on consumer acceptability, the objective has to be accomplished with certain constraints [1].
Such a system has to be operable at high speeds suitable for real-time processing yielding a high throughput, must inspect the entire fruit surface, must be adaptable to varying fruit size, shape etc., and be applicable under various physical conditions like brightness, luminance etc. Over the past decade, various techniques have been proposed for defect segmentation and grading. Reference [2] uses flooding algorithm to identify and characterize different types of defects based on perimeter, area etc.
The snake algorithm discussed in [3] can be used to localize the defect and reduce false positives. Reference [4] employs a raw approach based on colour frequency distribution to associate pixels to a specific class while [5] accomplishes the same using ‘Linear discriminant analysis’ Hyper-spectral and multispectral imaging systems have also been proposed for sorting various food commodities as discussed in [6]-[7]. An inherent limitation in most of the existing techniques is their sensitivity to changing illumination conditions.
Any flash of external stimulus can result in bright patches in the captured image which could result in misclassification. Practical considerations dictate that any technique should be immune to occasional changes in external conditions and deliver acceptable performance. This paper incorporates the use of adaptive filters based on the conventional LMS algorithm as a pre-processing step prior to segmenting defects using an adaptive threshold. This paper has been organized as follows. Section 2 explains the components of the practical set-up used to capture images of the fruit to be sorted. Section 3 discusses the proposed methodology for pre-processing and defect segmentation. Results of the experiment have been tabulated and discussed towards the end."
Abstract. Computer vision techniques are essential for defect segmentation in any automatized fruit-sorting system. Conventional sorting methods employ algorithms that are specific to standard illumination conditions and may produce undesirable results if ideal conditions are not maintained. This paper outlines a scheme that employs adaptive filters for pre-processing to negate the effect of varying illumination followed by defect segmentation using a localized adaptive threshold in an apple sorting experimental system based on a reference image. This technique has been compared with other methods and the results indicate an improved sorting performance. This can also be applied to other fruits with curved contours.
Keywords: Computer Vision, On-line fruit sorting, Surface defect, Adaptive thresholding.
1 Introduction
Fruit inspection and grading is an indispensable horticultural procedure. Uniformity in size, shape and colour are few of the many parameters that are vital in determining consumer acceptance. While the task at hand is to develop a machine vision system that identifies defective fruits based on odd shapes and surface defects, and to categorize them depending on consumer acceptability, the objective has to be accomplished with certain constraints [1].
Such a system has to be operable at high speeds suitable for real-time processing yielding a high throughput, must inspect the entire fruit surface, must be adaptable to varying fruit size, shape etc., and be applicable under various physical conditions like brightness, luminance etc. Over the past decade, various techniques have been proposed for defect segmentation and grading. Reference [2] uses flooding algorithm to identify and characterize different types of defects based on perimeter, area etc.
The snake algorithm discussed in [3] can be used to localize the defect and reduce false positives. Reference [4] employs a raw approach based on colour frequency distribution to associate pixels to a specific class while [5] accomplishes the same using ‘Linear discriminant analysis’ Hyper-spectral and multispectral imaging systems have also been proposed for sorting various food commodities as discussed in [6]-[7]. An inherent limitation in most of the existing techniques is their sensitivity to changing illumination conditions.
Any flash of external stimulus can result in bright patches in the captured image which could result in misclassification. Practical considerations dictate that any technique should be immune to occasional changes in external conditions and deliver acceptable performance. This paper incorporates the use of adaptive filters based on the conventional LMS algorithm as a pre-processing step prior to segmenting defects using an adaptive threshold. This paper has been organized as follows. Section 2 explains the components of the practical set-up used to capture images of the fruit to be sorted. Section 3 discusses the proposed methodology for pre-processing and defect segmentation. Results of the experiment have been tabulated and discussed towards the end."
