This paper will present an enhanced approach for the reconstruction of spectral reflectance by the combination between two methods, the Pseudo-Inverse (PI) as the base formula, whilst adaptively selecting the training samples as performed in the Adaptive Wiener estimation method proposed by Shen and Xin for the estimation of the spectral reflectance. This enhancement will be referred to as Adaptive Pseudo-Inverse (API) through this research. Training and verification datasets have been prepared from GretagMacbeth ColorChecker CC chart, Kodak Color Chart and a specially designed palette of Japanese organic and inorganic mineral pigments to test and compare the estimation results, using the Pseudo-Inverse and Adaptive Pseudo-Inverse method. The performance of spectral reconstruction methods will be presented in terms of spectral and colorimetric error for the estimation accuracy. The experimental results showed that the proposed method achieved better performance and noticeable decline in spectral estimation error.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
This document proposes a new method called multi-surface fitting for enhancing the resolution of digital images. The method fits multiple surfaces, with one surface fitted for each low-resolution pixel, and then fuses the multi-sampling values from these surfaces using maximum a posteriori estimation. This allows more low-resolution pixel information to be utilized to reconstruct the high-resolution image compared to other interpolation-based methods. The method is shown to effectively preserve image details without requiring assumptions about the image prior, as iterative techniques do. It provides error-free high resolution for test images.
An Intelligent Skin Color Detection Method based on Fuzzy C-Means with Big Da...CrimsonpublishersTTEFT
An Intelligent Skin Color Detection Method based on Fuzzy C-Means with Big Data by Chih Huang Yen in Trends in Textile Engineering & Fashion Technology
A Review Paper on Stereo Vision Based Depth EstimationIJSRD
Stereo vision is a challenging problem and it is a wide research topic in computer vision. It has got a lot of attraction because it is a cost efficient way in place of using costly sensors. Stereo vision has found a great importance in many fields and applications in today’s world. Some of the applications include robotics, 3-D scanning, 3-D reconstruction, driver assistance systems, forensics, 3-D tracking etc. The main challenge of stereo vision is to generate accurate disparity map. Stereo vision algorithms usually perform four steps: first, matching cost computation; second, cost aggregation; third, disparity computation or optimization; and fourth, disparity refinement. Stereo matching problems are also discussed. A large number of algorithms have been developed for stereo vision. But characterization of their performance has achieved less attraction. This paper gives a brief overview of the existing stereo vision algorithms. After evaluating the papers we can say that focus has been on cost aggregation and multi-step refinement process. Segment-based methods have also attracted attention due to their good performance. Also, using improved filter for cost aggregation in stereo matching achieves better results.
Image Inpainting Using Cloning AlgorithmsCSCJournals
In image recovery image inpainting has become essential content and crucial topic in research of a new era. The objective is to restore the image with the surrounding information or modifying an image in a way that looks natural for the viewer. The process involves transporting and diffusing image information. In this paper to inpaint an image cloning concept has been used. Multiscale transformation method is used for cloning process of an image inpainting. Results are compared with conventional methods namely Taylor expansion method, poisson editing, Shepard’s method. Experimental analysis verifies better results and shows that Shepard’s method using multiscale transformation not only restores small scale damages but also large damaged area and useful in duplication of image information in an image.
Inpainting refers to the art of restoring lost parts of image and reconstructing them based on the background information i.e Image inpainting is the process of reconstructing lost or deteriorated parts of images using information from surrounding areas. In fine art museums, inpainting of degraded paintings is traditionally carried out by professional artists and usually very time consuming.The purpose of inpainting is to reconstruct missing regions in a visually plausible manner so that it seems reasonable to the human eye. There have been several approaches proposed for the same.
This paper gives an overview of different Techniques of Image Inpainting.The proposed work includes the overview of PDE based inpainting algorithm and Texture synthesis based inpainting algorithm. This paper presents a brief survey on comparative study of these two techniques used for Image Inpainting.
Image in Painting Techniques: A survey IOSR Journals
This document provides a survey of different image inpainting techniques. It discusses approaches such as texture synthesis based inpainting, PDE (partial differential equation) based inpainting, exemplar based inpainting, hybrid inpainting, and semi-automatic inpainting. Texture synthesis approaches recreate textures within missing regions by sampling from surrounding textures. PDE based methods diffuse image information into missing areas. Exemplar based techniques iteratively copy patches from surrounding regions. Hybrid methods combine approaches. The document analyzes strengths and limitations of each technique.
A Survey on Exemplar-Based Image Inpainting Techniquesijsrd.com
Preceding paper include exemplar-based image inpainting technique give idea how to inpaint destroyed region such as Criminisi algorithm, patch shifting scheme, search region prior method. Criminsi’s and Sarawut’s patch shifting scheme needed more time to inpaint an damaged region but proposed method decrease time complexity by searching only in related region of missing portion of image.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
This document proposes a new method called multi-surface fitting for enhancing the resolution of digital images. The method fits multiple surfaces, with one surface fitted for each low-resolution pixel, and then fuses the multi-sampling values from these surfaces using maximum a posteriori estimation. This allows more low-resolution pixel information to be utilized to reconstruct the high-resolution image compared to other interpolation-based methods. The method is shown to effectively preserve image details without requiring assumptions about the image prior, as iterative techniques do. It provides error-free high resolution for test images.
An Intelligent Skin Color Detection Method based on Fuzzy C-Means with Big Da...CrimsonpublishersTTEFT
An Intelligent Skin Color Detection Method based on Fuzzy C-Means with Big Data by Chih Huang Yen in Trends in Textile Engineering & Fashion Technology
A Review Paper on Stereo Vision Based Depth EstimationIJSRD
Stereo vision is a challenging problem and it is a wide research topic in computer vision. It has got a lot of attraction because it is a cost efficient way in place of using costly sensors. Stereo vision has found a great importance in many fields and applications in today’s world. Some of the applications include robotics, 3-D scanning, 3-D reconstruction, driver assistance systems, forensics, 3-D tracking etc. The main challenge of stereo vision is to generate accurate disparity map. Stereo vision algorithms usually perform four steps: first, matching cost computation; second, cost aggregation; third, disparity computation or optimization; and fourth, disparity refinement. Stereo matching problems are also discussed. A large number of algorithms have been developed for stereo vision. But characterization of their performance has achieved less attraction. This paper gives a brief overview of the existing stereo vision algorithms. After evaluating the papers we can say that focus has been on cost aggregation and multi-step refinement process. Segment-based methods have also attracted attention due to their good performance. Also, using improved filter for cost aggregation in stereo matching achieves better results.
Image Inpainting Using Cloning AlgorithmsCSCJournals
In image recovery image inpainting has become essential content and crucial topic in research of a new era. The objective is to restore the image with the surrounding information or modifying an image in a way that looks natural for the viewer. The process involves transporting and diffusing image information. In this paper to inpaint an image cloning concept has been used. Multiscale transformation method is used for cloning process of an image inpainting. Results are compared with conventional methods namely Taylor expansion method, poisson editing, Shepard’s method. Experimental analysis verifies better results and shows that Shepard’s method using multiscale transformation not only restores small scale damages but also large damaged area and useful in duplication of image information in an image.
Inpainting refers to the art of restoring lost parts of image and reconstructing them based on the background information i.e Image inpainting is the process of reconstructing lost or deteriorated parts of images using information from surrounding areas. In fine art museums, inpainting of degraded paintings is traditionally carried out by professional artists and usually very time consuming.The purpose of inpainting is to reconstruct missing regions in a visually plausible manner so that it seems reasonable to the human eye. There have been several approaches proposed for the same.
This paper gives an overview of different Techniques of Image Inpainting.The proposed work includes the overview of PDE based inpainting algorithm and Texture synthesis based inpainting algorithm. This paper presents a brief survey on comparative study of these two techniques used for Image Inpainting.
Image in Painting Techniques: A survey IOSR Journals
This document provides a survey of different image inpainting techniques. It discusses approaches such as texture synthesis based inpainting, PDE (partial differential equation) based inpainting, exemplar based inpainting, hybrid inpainting, and semi-automatic inpainting. Texture synthesis approaches recreate textures within missing regions by sampling from surrounding textures. PDE based methods diffuse image information into missing areas. Exemplar based techniques iteratively copy patches from surrounding regions. Hybrid methods combine approaches. The document analyzes strengths and limitations of each technique.
A Survey on Exemplar-Based Image Inpainting Techniquesijsrd.com
Preceding paper include exemplar-based image inpainting technique give idea how to inpaint destroyed region such as Criminisi algorithm, patch shifting scheme, search region prior method. Criminsi’s and Sarawut’s patch shifting scheme needed more time to inpaint an damaged region but proposed method decrease time complexity by searching only in related region of missing portion of image.
Region filling and object removal by exemplar based image inpaintingWoonghee Lee
To get rid of (an) object(s) at a picture or to restore a picture from scratches or holes, Criminisi at el. suggested an algorithm which is combied "texture synthesis" and "inpainting". I made the slide to present at a class to introduce this algorithm. I refered a slide http://bit.ly/1Ng7DNt. I wish this slide may help you to understand the algorithm. Thank you.
Hierarchical Approach for Total Variation Digital Image InpaintingIJCSEA Journal
The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consum ing process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.
An Experiment with Sparse Field and Localized Region Based Active Contour Int...CSCJournals
This paper discusses various experiments conducted on different types of Level Sets interactive segmentation techniques using Matlab software, on select images. The objective is to assess the effectiveness on specific natural images, which have complex image composition in terms of intensity, colour mix, indistinct object boundary, low contrast, etc. Besides visual assessment, measures such as Jaccard Index, Dice Coefficient and Hausdorrf Distance have been computed to assess the accuracy of these techniques, between segmented and ground truth images. This paper particularly discusses Sparse Field Matrix and Localized Region Based Active Contours, both based on Level Sets. These techniques were not found to be effective where object boundary is not very distinct and/or has low contrast with background. Also, the techniques were ineffective on such images where foreground object stretches up to the image boundary.
Histogram Gabor Phase Pattern and Adaptive Binning Technique in Feature Selec...CSCJournals
This document summarizes a research paper that proposes a new method for face recognition using Histogram Gabor Phase Pattern (HGPP) and adaptive binning. The method extracts features from faces using Gabor wavelets and encodes the phase information. It then applies adaptive binning to reduce the dimensionality of the feature space. Spatial histograms of the binned features are used to generate HGPP representations for matching faces. The paper presents the detailed methodology, provides experimental results on FERET databases, and compares performance to existing methods.
A comparison of SIFT, PCA-SIFT and SURFCSCJournals
This document summarizes and compares three feature detection methods: SIFT, PCA-SIFT, and SURF. It finds that SIFT is the slowest but most stable, detecting features accurately across various transformations. SURF is the fastest but less stable than SIFT. PCA-SIFT shows advantages in rotation and illumination changes, detecting features more accurately than SURF in some cases when viewpoint changes are large. The document evaluates the methods using repeatability, matching numbers, and processing time under different image scales, rotations, blurs, illuminations, and affine transformations.
A Novel Approach To Detection and Evaluation of Resampled Tampered ImagesCSCJournals
Most digital forgeries use an interpolation function, affecting the underlying statistical distribution of the image pixel values, that when detected, can be used as evidence of tampering. This paper provides a comparison of interpolation techniques, similar to Lehmann [1], using analyses of the Fourier transform of the image signal, and a quantitative assessment of the interpolation quality after applying selected interpolation functions, alongside an appraisal of computational performance using runtime measurements. A novel algorithm is proposed for detecting locally tampered regions, taking the averaged discrete Fourier transform of the zero-crossing of the second difference of the resampled signal (ADZ). The algorithm was contrasted using precision, recall and specificity metrics against those found in the literature, with comparable results. The interpolation comparison results were similar to that of [1]. The results of the detection algorithm showed that it performed well for determining authentic images, and better than previously proposed algorithms for determining tampered regions.
Mr image compression based on selection of mother wavelet and lifting based w...ijma
Magnetic Resonance (MR) image is a medical image technique required enormous data to be stored and
transmitted for high quality diagnostic application. Various algorithms have been proposed to improve the
performance of the compression scheme. In this paper we extended the commonly used algorithms to image
compression and compared its performance. For an image compression technique, we have linked different
wavelet techniques using traditional mother wavelets and lifting based Cohen-Daubechies-Feauveau
wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7) wavelet transform with Set Partition in
Hierarchical Trees (SPIHT) algorithm. A novel image quality index with highlighting shape of histogram
of the image targeted is introduced to assess image compression quality. The index will be used in place of
existing traditional Universal Image Quality Index (UIQI) “in one go”. It offers extra information about
the distortion between an original image and a compressed image in comparisons with UIQI. The proposed
index is designed based on modelling image compression as combinations of four major factors: loss of
correlation, luminance distortion, contrast distortion and shape distortion. This index is easy to calculate
and applicable in various image processing applications. One of our contributions is to demonstrate the
choice of mother wavelet is very important for achieving superior wavelet compression performances based
on proposed image quality indexes. Experimental results show that the proposed image quality index plays
a significantly role in the quality evaluation of image compression on the open sources “BrainWeb:
Simulated Brain Database (SBD) ”.
The document reviews approaches to image interpolation and super-resolution. It discusses several interpolation methods including polynomial-based, edge-directed, and soft-decision approaches. Edge-directed methods aim to preserve edge sharpness during upsampling by estimating edge orientations or fusing multiple orientations. New edge-directed interpolation uses a Wiener filter to estimate missing pixel values. Soft-decision adaptive interpolation and robust soft-decision interpolation further improve results by modeling image signals within local windows and incorporating outlier weighting. The document provides formulations and comparisons of these methods.
Interpolation Technique using Non Linear Partial Differential Equation with E...CSCJournals
This document presents a new image zooming algorithm that combines edge directed bicubic interpolation and a non-linear partial differential equation (PDE) method. The algorithm first uses edge directed bicubic interpolation to enlarge the image and fill empty pixels, producing a high resolution image. This noisy image is then input to a fourth-order PDE model for noise removal. Simulation results on test images show the proposed method achieves higher peak signal-to-noise ratios and structural similarity indices than other interpolation methods like bilinear and locally adaptive zooming. The method reduces artifacts and blurring near edges in zoomed images.
Usage of Shape From Focus Method For 3D Shape Recovery And Identification of ...CSCJournals
Shape from focus is a method of 3D shape and depth estimation of an object from a sequence of pictures with changing focus settings. In this paper we propose a novel method of shape recovery, which was originally created for shape and position identification of glass pipette in medical hybrid robot. In proposed algorithm, Sum of Modified Laplacian is used as a focus operator. Each step of the algorithm is tested in order to pick the operators with the best results. Reconstruction allows not only to determine shape but also precisely define position of the object. The results of proposed method, performed on real objects, have shown the efficiency of this scheme.
An Iterative Solution for Random Valued Impulse Noise Reductionidescitation
In this paper, an iterative solution for high density random valued impulse noise
reduction of gray scale images is proposed. The algorithm, which works in an iterative
fashion, is designed by considering the different parameters that influence the effect of noise
reduction. Each iteration significantly increases the performance of the proposed algorithm.
Restored Mean Absolute Error (RMAE) is used to measure and compare the performance
of the algorithm. The algorithm is compared with several non-linear algorithms reported in
the literature. Experimental results show that the proposed algorithm produces better
results compared to the existing algorithms.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Performance Evaluation of Image Edge Detection Techniques CSCJournals
The success of an image recognition procedure is related to the quality of the edges marked. The
aim of this research is to investigate and evaluate edge detection techniques when applied to
noisy images at different scales. Sobel, Prewitt, and Canny edge detection algorithms are
evaluated using artificially generated images and comparison criteria: edge quality (EQ) and map
quality (MQ). The results demonstrated that the use of these criteria can be utilized as an aid for
further analysis and arbitration to find the best edge detector for a given image.
In this project we have implemented a tool to inpaint selected regions from an image. Inpainting refers to the art of restoring lost parts of image and reconstructing them based on the background information. The tool provides a user interface wherein the user can open an image for inpainting, select the parts
of the image that he wants to reconstruct. The tool would then automatically inpaint the selected area according to the background information. The image can
then be saved. The inpainting in based on the exemplar based approach. The basic aim of this approach is to find examples (i.e. patches) from the image and
replace the lost data with it. Applications of this technique include the restoration of old photographs and damaged film; removal of superimposed text like
dates, subtitles etc.; and the removal of entire objects from the image like microphones or wires in special effects.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
There exists a plethora of algorithms to perform image segmentation and there are several issues related to
execution time of these algorithms. Image Segmentation is nothing but label relabeling problem under
probability framework. To estimate the label configuration, an iterative optimization scheme is
implemented to alternately carry out the maximum a posteriori (MAP) estimation and the maximum
likelihood (ML) estimations. In this paper this technique is modified in such a way so that it performs
segmentation within stipulated time period. The extensive experiments shows that the results obtained are
comparable with existing algorithms. This algorithm performs faster execution than the existing algorithm
to give automatic segmentation without any human intervention. Its result match image edges very closer to
human perception.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This document reviews different techniques for digital image inpainting. It discusses diffusion-based, exemplar-based, bilateral filter, and fast digital inpainting algorithms. Diffusion-based techniques propagate image information into missing regions but can cause blurring. Exemplar-based methods copy texture patches from surrounding regions to fill holes and avoid blurring. Bilateral filtering uses both spatial and color similarity to inpaint while preserving edges. The document analyzes several papers comparing these methods and their applications like object removal, scratch/damage repair, and text removal.
Abstract: Many applications such as robot navigation, defense, medical and remote sensing performvarious processing tasks, which can be performed more easily when all objects in different images of the same scene are combined into a single fused image. In this paper, we propose a fast and effective method for image fusion. The proposed method derives the intensity based variations that is large and small scale, from the source images. In this approach, guided filtering is employed for this extraction. Gaussian and Laplacian pyramidal approach is then used to fuse the different layers obtained. Experimental results demonstrate that the proposed method can obtain better performance for fusion of
all sets of images. The results clearly indicate the feasibility of the proposed approach.
MR Image Compression Based on Selection of Mother Wavelet and Lifting Based W...ijma
Magnetic Resonance (MR) image is a medical image technique required enormous data to be stored and
transmitted for high quality diagnostic application. Various algorithms have been proposed to improve the
performance of the compression scheme. In this paper we extended the commonly used algorithms to image
compression and compared its performance. For an image compression technique, we have linked different
wavelet techniques using traditional mother wavelets and lifting based Cohen-Daubechies-Feauveau
wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7) wavelet transform with Set Partition in
Hierarchical Trees (SPIHT) algorithm. A novel image quality index with highlighting shape of histogram
of the image targeted is introduced to assess image compression quality. The index will be used in place of
existing traditional Universal Image Quality Index (UIQI) “in one go”. It offers extra information about
the distortion between an original image and a compressed image in comparisons with UIQI. The proposed
index is designed based on modelling image compression as combinations of four major factors: loss of
correlation, luminance distortion, contrast distortion and shape distortion. This index is easy to calculate
and applicable in various image processing applications. One of our contributions is to demonstrate the
choice of mother wavelet is very important for achieving superior wavelet compression performances based
on proposed image quality indexes. Experimental results show that the proposed image quality index plays
a significantly role in the quality evaluation of image compression on the open sources “BrainWeb:
Simulated Brain Database (SBD) ”.
A Quantitative Comparative Study of Analytical and Iterative Reconstruction T...CSCJournals
A special image restoration problem is the reconstruction of image from projections – a problem of immense importance in medical imaging, computed tomography and non-destructive testing of objects. This is a problem where a two – dimensional (or higher) object is reconstructed from several one –dimensional projections [1]. The reconstruction techniques are broadly classified into three categories, analytical, iterative, and statistical [2]. The comparative study among these is of great importance in the field of medical imaging. This paper aims at comparative study by analyzing quantitatively the quality of image reconstructed by analytical and iterative techniques. Projections (parallel beam type) for the reconstruction are calculated analytically by defining Shepp logan phantom head model with coverage angle ranging from 0 to ±180o with rotational increment of 2o to 10o. For iterative reconstruction coverage angle of ±90o, iteration up to 10 is used. The original image is grayscale image of size 128 X 128. The Image quality of the reconstructed image is measured by six quality measurement parameters. In this paper as analytical technique; simple back projection and filtered back projection are implemented, while as iterative; algebraic reconstruction technique is implemented. Experiment result reveals that quality of reconstructed image increase as coverage angle, and number of views increases. The processing time is one major deciding component for reconstruction. Keywords: Reconstruction algorithm, Simple-Back projection algorithm (SBP), Filter-Back projection algorithm (FBP), Algebraic Reconstruction Technique algorithm (ART), Image quality, coverage angle, Computed tomography (CT).
Novel algorithm for color image demosaikcing using laplacian maskeSAT Journals
Abstract Images in any digital camera is formed with the help of a monochrome sensor, which can be either a charge-coupled device(CCD) or complementary metal oxide semi-conductor(CMOS). Interpolation is the base for any demsoaicking process. The input for interpolation is the output of the Bayer Color Filter Array which is a mosaic like lattice structure. Bayer Color Filter Array samples the channel information of R,G and B values separately assigning only one channel component per pixel. To generate a complete color image, three channel values are required. In order to find those missing samples we use interpolation. It is a technique of estimating the missing values from the discrete observed samples scattered over the space. Thus Demosaicking or De-bayering is an algorithm of finding missing values from the mosaic patterned output of the Bayer CFA. Interpolation algorithm results in few artifacts such as zippering effect in the edges. This paper introduces an algorithm for demosaicking which outperforms the existing demosaciking algorithms. The main aim of this algorithm is to accurately estimate the Green component. The standard mechanism to compare the performance is PSNR(Peak Signal to Noise Ratio) and the image dataset for comparison was Kodak image dataset. The algorithm was implemented using Matlab2009B version. Keywords: Demosaicking, Interpolation, Bayer CFA, Laplacian Mask, Correlation.
Region filling and object removal by exemplar based image inpaintingWoonghee Lee
To get rid of (an) object(s) at a picture or to restore a picture from scratches or holes, Criminisi at el. suggested an algorithm which is combied "texture synthesis" and "inpainting". I made the slide to present at a class to introduce this algorithm. I refered a slide http://bit.ly/1Ng7DNt. I wish this slide may help you to understand the algorithm. Thank you.
Hierarchical Approach for Total Variation Digital Image InpaintingIJCSEA Journal
The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consum ing process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.
An Experiment with Sparse Field and Localized Region Based Active Contour Int...CSCJournals
This paper discusses various experiments conducted on different types of Level Sets interactive segmentation techniques using Matlab software, on select images. The objective is to assess the effectiveness on specific natural images, which have complex image composition in terms of intensity, colour mix, indistinct object boundary, low contrast, etc. Besides visual assessment, measures such as Jaccard Index, Dice Coefficient and Hausdorrf Distance have been computed to assess the accuracy of these techniques, between segmented and ground truth images. This paper particularly discusses Sparse Field Matrix and Localized Region Based Active Contours, both based on Level Sets. These techniques were not found to be effective where object boundary is not very distinct and/or has low contrast with background. Also, the techniques were ineffective on such images where foreground object stretches up to the image boundary.
Histogram Gabor Phase Pattern and Adaptive Binning Technique in Feature Selec...CSCJournals
This document summarizes a research paper that proposes a new method for face recognition using Histogram Gabor Phase Pattern (HGPP) and adaptive binning. The method extracts features from faces using Gabor wavelets and encodes the phase information. It then applies adaptive binning to reduce the dimensionality of the feature space. Spatial histograms of the binned features are used to generate HGPP representations for matching faces. The paper presents the detailed methodology, provides experimental results on FERET databases, and compares performance to existing methods.
A comparison of SIFT, PCA-SIFT and SURFCSCJournals
This document summarizes and compares three feature detection methods: SIFT, PCA-SIFT, and SURF. It finds that SIFT is the slowest but most stable, detecting features accurately across various transformations. SURF is the fastest but less stable than SIFT. PCA-SIFT shows advantages in rotation and illumination changes, detecting features more accurately than SURF in some cases when viewpoint changes are large. The document evaluates the methods using repeatability, matching numbers, and processing time under different image scales, rotations, blurs, illuminations, and affine transformations.
A Novel Approach To Detection and Evaluation of Resampled Tampered ImagesCSCJournals
Most digital forgeries use an interpolation function, affecting the underlying statistical distribution of the image pixel values, that when detected, can be used as evidence of tampering. This paper provides a comparison of interpolation techniques, similar to Lehmann [1], using analyses of the Fourier transform of the image signal, and a quantitative assessment of the interpolation quality after applying selected interpolation functions, alongside an appraisal of computational performance using runtime measurements. A novel algorithm is proposed for detecting locally tampered regions, taking the averaged discrete Fourier transform of the zero-crossing of the second difference of the resampled signal (ADZ). The algorithm was contrasted using precision, recall and specificity metrics against those found in the literature, with comparable results. The interpolation comparison results were similar to that of [1]. The results of the detection algorithm showed that it performed well for determining authentic images, and better than previously proposed algorithms for determining tampered regions.
Mr image compression based on selection of mother wavelet and lifting based w...ijma
Magnetic Resonance (MR) image is a medical image technique required enormous data to be stored and
transmitted for high quality diagnostic application. Various algorithms have been proposed to improve the
performance of the compression scheme. In this paper we extended the commonly used algorithms to image
compression and compared its performance. For an image compression technique, we have linked different
wavelet techniques using traditional mother wavelets and lifting based Cohen-Daubechies-Feauveau
wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7) wavelet transform with Set Partition in
Hierarchical Trees (SPIHT) algorithm. A novel image quality index with highlighting shape of histogram
of the image targeted is introduced to assess image compression quality. The index will be used in place of
existing traditional Universal Image Quality Index (UIQI) “in one go”. It offers extra information about
the distortion between an original image and a compressed image in comparisons with UIQI. The proposed
index is designed based on modelling image compression as combinations of four major factors: loss of
correlation, luminance distortion, contrast distortion and shape distortion. This index is easy to calculate
and applicable in various image processing applications. One of our contributions is to demonstrate the
choice of mother wavelet is very important for achieving superior wavelet compression performances based
on proposed image quality indexes. Experimental results show that the proposed image quality index plays
a significantly role in the quality evaluation of image compression on the open sources “BrainWeb:
Simulated Brain Database (SBD) ”.
The document reviews approaches to image interpolation and super-resolution. It discusses several interpolation methods including polynomial-based, edge-directed, and soft-decision approaches. Edge-directed methods aim to preserve edge sharpness during upsampling by estimating edge orientations or fusing multiple orientations. New edge-directed interpolation uses a Wiener filter to estimate missing pixel values. Soft-decision adaptive interpolation and robust soft-decision interpolation further improve results by modeling image signals within local windows and incorporating outlier weighting. The document provides formulations and comparisons of these methods.
Interpolation Technique using Non Linear Partial Differential Equation with E...CSCJournals
This document presents a new image zooming algorithm that combines edge directed bicubic interpolation and a non-linear partial differential equation (PDE) method. The algorithm first uses edge directed bicubic interpolation to enlarge the image and fill empty pixels, producing a high resolution image. This noisy image is then input to a fourth-order PDE model for noise removal. Simulation results on test images show the proposed method achieves higher peak signal-to-noise ratios and structural similarity indices than other interpolation methods like bilinear and locally adaptive zooming. The method reduces artifacts and blurring near edges in zoomed images.
Usage of Shape From Focus Method For 3D Shape Recovery And Identification of ...CSCJournals
Shape from focus is a method of 3D shape and depth estimation of an object from a sequence of pictures with changing focus settings. In this paper we propose a novel method of shape recovery, which was originally created for shape and position identification of glass pipette in medical hybrid robot. In proposed algorithm, Sum of Modified Laplacian is used as a focus operator. Each step of the algorithm is tested in order to pick the operators with the best results. Reconstruction allows not only to determine shape but also precisely define position of the object. The results of proposed method, performed on real objects, have shown the efficiency of this scheme.
An Iterative Solution for Random Valued Impulse Noise Reductionidescitation
In this paper, an iterative solution for high density random valued impulse noise
reduction of gray scale images is proposed. The algorithm, which works in an iterative
fashion, is designed by considering the different parameters that influence the effect of noise
reduction. Each iteration significantly increases the performance of the proposed algorithm.
Restored Mean Absolute Error (RMAE) is used to measure and compare the performance
of the algorithm. The algorithm is compared with several non-linear algorithms reported in
the literature. Experimental results show that the proposed algorithm produces better
results compared to the existing algorithms.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Performance Evaluation of Image Edge Detection Techniques CSCJournals
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quality (MQ). The results demonstrated that the use of these criteria can be utilized as an aid for
further analysis and arbitration to find the best edge detector for a given image.
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probability framework. To estimate the label configuration, an iterative optimization scheme is
implemented to alternately carry out the maximum a posteriori (MAP) estimation and the maximum
likelihood (ML) estimations. In this paper this technique is modified in such a way so that it performs
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comparable with existing algorithms. This algorithm performs faster execution than the existing algorithm
to give automatic segmentation without any human intervention. Its result match image edges very closer to
human perception.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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MR Image Compression Based on Selection of Mother Wavelet and Lifting Based W...ijma
Magnetic Resonance (MR) image is a medical image technique required enormous data to be stored and
transmitted for high quality diagnostic application. Various algorithms have been proposed to improve the
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compression and compared its performance. For an image compression technique, we have linked different
wavelet techniques using traditional mother wavelets and lifting based Cohen-Daubechies-Feauveau
wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7) wavelet transform with Set Partition in
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index is designed based on modelling image compression as combinations of four major factors: loss of
correlation, luminance distortion, contrast distortion and shape distortion. This index is easy to calculate
and applicable in various image processing applications. One of our contributions is to demonstrate the
choice of mother wavelet is very important for achieving superior wavelet compression performances based
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A Quantitative Comparative Study of Analytical and Iterative Reconstruction T...CSCJournals
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SENSITIVITY ANALYSIS IN A LIDARCAMERA CALIBRATIONcscpconf
In this paper, variability analysis was performed on the model calibration methodology between
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are used to digitize urban environments. A practical and complete methodology is presented to
predict the error propagation inside the LiDAR-camera calibration. We perform a sensitivity
analysis in a local and global way. The local approach analyses the output variance with
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parameters are varied simultaneously and sensitivity indexes are calculated on the total
variation range of the input parameters. We quantify the uncertainty behaviour in the intrinsic
camera parameters and the relationship between the noisy data of both sensors and their
calibration. We calculated the sensitivity indexes by two techniques, Sobol and FAST (Fourier
amplitude sensitivity test). Statistics of the sensitivity analysis are displayed for each sensor, the
sensitivity ratio in laser-camera calibration data
Sensitivity analysis in a lidar camera calibrationcsandit
In this paper, variability analysis was performed o
n the model calibration methodology between
a multi-camera system and a LiDAR laser sensor (Lig
ht Detection and Ranging). Both sensors
are used to digitize urban environments. A practica
l and complete methodology is presented to
predict the error propagation inside the LiDAR-came
ra calibration. We perform a sensitivity
analysis in a local and global way. The local appro
ach analyses the output variance with
respect to the input, only one parameter is varied
at once. In the global sensitivity approach, all
parameters are varied simultaneously and sensitivit
y indexes are calculated on the total
variation range of the input parameters. We quantif
y the uncertainty behaviour in the intrinsic
camera parameters and the relationship between the
noisy data of both sensors and their
calibration. We calculated the sensitivity indexes
by two techniques, Sobol and FAST (Fourier
amplitude sensitivity test). Statistics of the sens
itivity analysis are displayed for each sensor, the
sensitivity ratio in laser-camera calibration data
Face Image Restoration based on sample patterns using MLP Neural NetworkIOSR Journals
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BEHAVIOR STUDY OF ENTROPY IN A DIGITAL IMAGE THROUGH AN ITERATIVE ALGORITHM O...ijscmcj
Image segmentation is a critical step in computer vision tasks constituting an essential issue for pattern
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through an iterative algorithm of mean shift filtering. The order of a digital image in gray levels is defined.
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iterations of our algorithm, with the maximum entropy that could be achieved under the same order. The
use of equivalence classes it induced, which allow us to interpret entropy as a hyper-surface in real m-
dimensional space. The difference of the maximum entropy of order n and the entropy of the image is used
to group the the iterations, in order to caractrizes the performance of the algorithm
This presentation was made on a thesis paper for my M.Sc academic curriculum. Color Guided Thermal image Super Resolution Technic is declared here.This paper is collected from IEEE.Publish in 2016.
This document presents a new color image segmentation approach based on overlap wavelet transform (OWT). OWT extracts wavelet features to better separate different patterns in an image. The proposed method also uses morphological operators and 2D histogram clustering for effective segmentation. It is concluded that the proposed OWT method improves segmentation quality, is reliable, fast and computationally less complex than direct histogram clustering. When tested on various color spaces, the proposed segmentation scheme produced better results in RGB color space compared to others. The main advantages are its use of a single parameter and faster speed.
Analysis of collaborative learning methods for image contrast enhancementIAEME Publication
The document describes collaborative learning methods for image contrast enhancement. It begins with background on image enhancement techniques like histogram equalization. It then summarizes an existing collaborative learning method that determines pixel values from multiple randomly sampled windows. The document proposes a modified method that combines collaborative learning with block-based histogram equalization using randomly sized sliding windows. It is evaluated on medical and underwater images and is found to provide better results than the original collaborative learning method. Quality metrics are used to measure enhancement.
This document summarizes research on using particle swarm optimization to reconstruct microwave images of two-dimensional dielectric scatterers. It formulates the inverse scattering problem as an optimization problem to find the dielectric parameter distribution that minimizes the difference between measured and simulated scattered field data. Numerical results show that a particle swarm optimization approach can accurately reconstruct the shape and dielectric properties of a test cylindrical scatterer, with lower background reconstruction error than a genetic algorithm approach. The research demonstrates that particle swarm optimization is a suitable technique for high-dimensional microwave imaging problems.
Parameter Optimisation for Automated Feature Point DetectionDario Panada
Parameter optimization for an automated feature point detection model was explored. Increasing the number of random displacements up to 20 improved performance but additional increases did not. Larger patch sizes consistently improved performance. Increasing the number of decision trees did not affect performance for this single-stage model, unlike previous findings for a two-stage model. Overall, some parameter tuning was found to enhance the model's accuracy but not all parameters significantly impacted results.
Textural Feature Extraction of Natural Objects for Image ClassificationCSCJournals
The field of digital image processing has been growing in scope in the recent years. A digital image is represented as a two-dimensional array of pixels, where each pixel has the intensity and location information. Analysis of digital images involves extraction of meaningful information from them, based on certain requirements. Digital Image Analysis requires the extraction of features, transforms the data in the high-dimensional space to a space of fewer dimensions. Feature vectors are n-dimensional vectors of numerical features used to represent an object. We have used Haralick features to classify various images using different classification algorithms like Support Vector Machines (SVM), Logistic Classifier, Random Forests Multi Layer Perception and Naïve Bayes Classifier. Then we used cross validation to assess how well a classifier works for a generalized data set, as compared to the classifications obtained during training.
Novel Iterative Back Projection ApproachIOSR Journals
This document presents a novel iterative back projection approach for single image super resolution. The approach combines iterative back projection with an infinite symmetrical exponential filter to improve edge preservation. Iterative back projection can minimize reconstruction error through back projecting errors iteratively but suffers from ringing and chessboard effects without edge guidance. The infinite symmetrical exponential filter provides edge smoothing by adding high frequency information. The proposed approach integrates these methods by back projecting the error and high frequency components estimated from the exponential filter. This improves visual quality with fine edge details compared to other interpolation methods. Simulation results on different image types show the approach achieves higher peak signal to noise ratios than existing methods.
This document summarizes a novel iterative back projection approach for single image super resolution. The approach combines iterative back projection with an infinite symmetrical exponential filter to improve edge preservation. Iterative back projection can minimize reconstruction error but suffers from ringing and chessboard effects without edge guidance. The infinite symmetrical exponential filter provides edge-smoothed images with high frequency detail. The proposed approach integrates these methods by back-projecting the error and estimated high frequency components from the exponential filter to improve visual quality while preserving fine edges over multiple iterations.
Face recognition using gaussian mixture model & artificial neural networkeSAT Journals
Abstract
Face recognition is a non-contact and friendly biometric identification technology. It has broad application prospects in the
military, public security and economic security. In this work, we also consider illumination variable database. The images have
taken from far distance and do not consider the close view face of the individual as in most of the face databases, clear face view
has been considered. In this first we located face as region of interest and then LBP and LPQ descriptors are used which is
illuminance invariant in nature. After this GMM has been used to reduce feature set by taking negative log-likelihood from each
LBP and LPQ descripted image histograms. After this ANN consumes stayed used for organization purposes. The investigational
consequencesshow excellent correctness rates in overall testing of input data.
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INFLUENCE OF QUANTITY OF PRINCIPAL COMPONENT IN DISCRIMINATIVE FILTERINGcsandit
Discriminative filtering is a pattern recognition technique which aim maximize the energy of
output signal when a pattern is found. Looking improve the performance of filter response, was
incorporated the principal component analysis in discriminative filters design. In this work, we
investigate the influence of the quantity of principal components in the performance of
discriminative filtering applied to a facial fiducial point detection system. We show that quantity
of principal components directly affects the performance of the system, both in relation of true
and false positives rate.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This document summarizes a research paper on computed tomography (CT) dose reduction and view number optimization. It discusses how CT uses X-rays to create images but that radiation exposure is a concern, especially for pediatric patients. The paper explores how iterative reconstruction techniques and compressed sensing theories have aimed to reduce views and dose while maintaining image quality. It presents the goal of investigating the relationship between image quality, view number, and radiation dose level. Numerical tests were performed to determine the optimal view number for a given dose level that achieves the best reconstruction quality.
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Enhanced Spectral Reflectance Reconstruction Using Pseudo-Inverse Estimation Method
1. Ibrahim El-Rifai, Hend Mahgoub, Mennat-Allah Magdy, Jay Arre Toque & Ari Ide-Ektessabi
International Journal of Image Processing (IJIP), Volume (7) : Issue (3) : 2013 278
Enhanced Spectral Reflectance Reconstruction Using
Pseudo-Inverse Estimation Method
Ibrahim El-Rifai ibrahimeg@yahoo.com
Cairo, 11577, Egypt
Hend Mahgoub eng_hend_fci@yahoo.com
Cairo, Egypt
Mennat-Allah Magdy eng_menna2009@yahoo.com
Cairo, Egypt
Jay Arre Toque jayarre81@gmail.com
Kyoto, Japan
Ari Ide-Ektessabi ektessabi@gmail.com
Kyoto, Japan
Abstract
This paper will present an enhanced approach for the reconstruction of spectral reflectance by
the combination between two methods, the Pseudo-Inverse (PI) as the base formula, whilst
adaptively selecting the training samples as performed in the Adaptive Wiener estimation method
proposed by Shen and Xin for the estimation of the spectral reflectance. This enhancement will
be referred to as Adaptive Pseudo-Inverse (API) through this research.
Training and verification datasets have been prepared from GretagMacbeth ColorChecker CC
chart, Kodak Color Chart and a specially designed palette of Japanese organic and inorganic
mineral pigments to test and compare the estimation results, using the Pseudo-Inverse and
Adaptive Pseudo-Inverse method. The performance of spectral reconstruction methods will be
presented in terms of spectral and colorimetric error for the estimation accuracy. The
experimental results showed that the proposed method achieved better performance and
noticeable decline in spectral estimation error.
Keywords: Adaptive Pseudo-Inverse, Spectral Reflectance Reconstruction, Colorimetry.
1. INTRODUCTION
Some of the main aims of a multispectral system are the efficient extraction of spectral and
colorimetric information, and in this regard several algorithms have been developed for best
estimation of spectral reflectance and the reproduction of color information.
In this research we will focus on the estimation of spectral reflectance using Pseudo-Inverse (PI)
method [1:3]. PI is known to be a traditional and straightforward method while it lack of accuracy
and efficiency. PI estimates spectral reflectance without priori knowledge of acquisition system,
depends on system responses and reflectance of training samples to get best transformation
matrix aiming to minimize the spectral error between the actual reflectance measurement and the
estimated one.
More accurate estimation can be extracted from other methods like Wiener estimation method
[4:8] which depends on spectral responsivity, spectral reflectance and imaging noise. While the
original calculations of Wiener depends on all available training samples, Shen and Xin proposed
a modified approach of the original Wiener called Adaptive Wiener [9] which can estimate
2. Ibrahim El-Rifai, Hend Mahgoub, Mennat-Allah Magdy, Jay Arre Toque & Ari Ide-Ektessabi
International Journal of Image Processing (IJIP), Volume (7) : Issue (3) : 2013 279
spectral reflectance - without priori knowledge about the spectral characteristics of the verification
sample - depending on the idea of adaptively selecting the training samples which had a strong
impact on the color accuracy of the reflectance reconstruction.
This can be achieved by, firstly, selecting the closest responses of training samples to the
verification sample, then calculating the weights of each and finally, recalculating the
transformation matrix according to the selected training samples for the verification sample.
As in Shen and Xin approach we will adopt the adaptive selection of training samples to be
integrated with Pseudo-Inverse method for the estimation of spectral reflectance which will be
examined for estimation accuracy using some datasets.
2. FORMULATION OF MULTISPECTRAL IMAGING
The response of a digital camera can be formulated by this equation [9-10]:
(1)
i = 1, ... m,
Where is the response of the camera in (x,y) coordinate with ith color filters, is the
transmission of the ith filter, is the spectral power distribution of the light, is the
sensitivity of the camera, is the reflectance in (x,y) coordinates, and is the additive
noise for each channel which is ignored for simplicity and m is the number of channels.
, and are the unknown factors where they are merged in which is known as
spectral responsivity.
Using vector-matrix notation, this equation can be written as follows:
(2)
For each pixel in the image, v is the vector of camera response and r is the vector of the
reflectance spectrum.
The estimation of reflectance spectra can be obtained by
(3)
where v is the response of the camera and G is the estimation matrix which aims to reduce the
minimum square error between original r and estimated rest according to the used estimation
method.
Using the traditional Pseudo-Inverse [1-3] to get the estimation matrix GPI
(4)
rtraining and vtraining are known from the measured training samples and Pinv(vtraining) is the
Pseudo-Inverse of the camera response vtraining. By solving Eq(4) and getting GPI (Estimation
Matrix based on Pseudo-Inverse method) we can substitute it in Eq(3) using vverfication as system
response of verification sample to get the spectral reflectance rest of this sample.
From this point in the research we will integrate the original Pseudo-Inverse with the adaptive
approach of Shen and Xin [9]; after getting the estimated spectral reflectance rest of the
verification sample based on all training samples Eq(3), we start adaptively to select the training
samples according to their spectral similarity to the rest which is calculated from the following
equation:
3. Ibrahim El-Rifai, Hend Mahgoub, Mennat-Allah Magdy, Jay Arre Toque & Ari Ide-Ektessabi
International Journal of Image Processing (IJIP), Volume (7) : Issue (3) : 2013 280
(5)
Where dk is the spectral similarity between each training sample and the verification sample, is
a scaling factor ( = 0.5 in this research ), |x| means the absolute values of elements in vector x
and finally rk is the measured spectral reflectance of each training sample. All reflectances are
normalized from [0 - 1] and the mean and max spectral distances between two similar
reflectances shouldn’t be large.
By sorting the training samples in ascending order according to their spectral similarity and
selecting L samples of it where d1 < d2 < … < dL we get the L samples which are the more close
samples to the verification sample.
Recalculating the Eq(4) and getting the new estimation matrix based on the selected L training
sample as follows,
(6)
Then substituting GAPI in Eq(3) to get the new spectral reflectance of verification sample rest.
3. EXPERIMENTAL
In this research, the multispectral imaging system comprises Niji Scanner [14] of Kyoto University
(Advanced Imaging Technology Laboratory) with a monochrome line camera, led light system of
known spectral power distribution and set of 8 band pass and sharp cut filters in the range of
420nm to 700nm. IR cut filter were used throughout the full scanning process which has been
done in a dark room. The GretagMacbeth ColorChecker CC (24 patches), Kodak Color Chart (18
patches) and a specially designed palette of Japanese organic and inorganic mineral pigments
(173 patches) [15] were collected and prepared to be used as the primary training and verification
datasets. The reflectance of color targets were measured by X-Rite spectrophotometer [16] and
re-sampled in the range of 400-700nm at 10nm interval. In this experiment, each target is tested
separately for training and testing and the spectra has been reconstructed for each patch using
different L (number of selected training samples) to see the effect of that on the color and
estimation accuracy. Comparisons have been conducted between the PI and the API methods.
The results of the comparison have been concluded according to the spectral and colorimetric
error, using the mean squared error equation for spectral error between the actual and the
estimated spectral reflectance and the equation of ΔE00 obtained from the formula of
CIEDE2000[11:13] under D65 as standard illuminant for colorimetric error.
FIGURE 1: Color Targets Left and Spectral Transmittance of the Band Pass Filters Right.
Figure 1 shows the three color targets that have been used; The Kodak Color Chart, the
Japanese pigments palette and the GretagMacbeth ColorChecker CC, in addition to the spectral
transmittance of the 7 band pass filters Fujifilm BPB42, BPB45, BPB50, BPB55, BPB60, SC64,
SC70 in the range from 380 to 980nm.
4. Ibrahim El-Rifai, Hend Mahgoub, Mennat-Allah Magdy, Jay Arre Toque & Ari Ide-Ektessabi
International Journal of Image Processing (IJIP), Volume (7) : Issue (3) : 2013 281
Each dataset has been investigated with different number of selected training samples (L) for
Eq(6) as presented in Figure 2. In case of Kodak and Macbeth targets, which have less number
of patches between 18 and 24, L value = 5 was the most appropriate number with the least
spectral and colorimetric errors but in the Japanese palette case with 173 patches L value =10
was the point with the least errors for both spectral and colorimetric. In general datasets shows
that both errors are changing in an increase monotonic trend with L.
FIGURE 2:.Japanese pigments palette (left) , Kodak chart (middle), Macbeth CC patches (right).
The effect of using different L values on the spectral and colorimetric errors for the three datasets.
The spectral rms and colorimetric errors are shown in the Table1 that includes the mean,
standard deviation and maximum of the tested methods for the three target datasets. Results of
API method are presented according to the most appropriate L value. It is noticed that the results
of the proposed method API is showing noticeable improvement over the PI method after
applying the adaptive selection on the training samples. Except for the Japanese palette,
although the rmse of the API method is less than the PI, the max value of the colorimetric error is
showing higher value than the PI method, this is probably the influence of the duplicates of dark
and shiny patches. Samples from best and worst cases for the spectral and colorimetric errors
are presented in Figure (3a:3c) for each dataset.
A comparison have been presented in Figure 4 showing the colorimetric error between Munsell
[18], BabelColor [17] and Estimated API spectral Data for Macbeth ColorChecker CC which are
less than 1 and within acceptable error range [23].
API PI
Kodak Chart
(18 patches)
RMSE
Mean 3.60592E-16 0.003603183
STD 5.56468E-16 0.001756709
Max 2.35017E-15 0.007424878
ΔE00
under
D65
Mean 1.42027E-14 0.128468081
STD 1.54E-14 0.070764767
Max 6.27972E-14 0.238846758
Macbeth CC
(24 patches)
RMSE
Mean 0.000427617 0.006133283
STD 0.001984025 0.006493265
Max 0.009728257 0.033024449
ΔE00
under
D65
Mean 0.008956815 0.163237335
STD 0.038608483 0.192726688
Max 0.188428881 0.976090041
Japanese
Palette (173
patches)
RMSE
Mean 0.001460306 0.004998975
STD 0.003768597 0.00461258
Max 0.028263637 0.032854126
ΔE00
under
D65
Mean 0.071885883 0.128116604
STD 0.236286633 0.123883428
Max 2.248539644 0.680269435
TABLE 1: Spectral and Colorimetric Errors for PI and API Methods.
5. Ibrahim El-Rifai, Hend Mahgoub, Mennat-Allah Magdy, Jay Arre Toque & Ari Ide-Ektessabi
International Journal of Image Processing (IJIP), Volume (7) : Issue (3) : 2013 282
FIGURE 3a: Kodak Chart, Measured and reconstructed spectral reflectance of the API and PI methods (left)
best case with spectral error = 2.34E-17 and colorimetric error =6.37E-17, (right) worst case with spectral
error = 2.35E-15 and colorimetric error =6.28E-14.
FIGURE 3b: Macbeth Chart, Measured and reconstructed spectral reflectance of the API and PI methods
(left) best case with spectral error = 2.22E-17 and colorimetric error =1.59E-17, (right) worst case with
spectral error = 0.09728 and colorimetric error =0.188429.
FIGURE 3c: Japanese Palette, Measured and reconstructed spectral reflectance of the API and PI methods
(left) best case with spectral error = 1.42E-05 and colorimetric error =0.000713, (right) worst case with
spectral error = 0.028264 and colorimetric error =2.24854.
FIGURE 4:.Comparision between Munsell, BabelColor and Estimated API spectral Data for Macbeth
ColorChecker CC target (24 Patches) using Delta E2000 – colorimetric error.
0
0.2
0.4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
ΔE 2000
ΔE00 (Munsell , API Estimated) ΔE00 (BabelColor , API Estimated)
6. Ibrahim El-Rifai, Hend Mahgoub, Mennat-Allah Magdy, Jay Arre Toque & Ari Ide-Ektessabi
International Journal of Image Processing (IJIP), Volume (7) : Issue (3) : 2013 283
4. DISCUSSION
Since the acquisition of good quality spectral information is one of the main concerns of color
systems, exhaustive research has been conducted for the acquisition, processing and estimation
and even evaluation of spectral information. Spectral estimation methods as API depend on the
accuracy of the spectral data in the reference database which depends on the spectral properties
of the system calibration target [19].
Once the spectral information is acquired either measured by spectrometers, published by
standard color targets manufacturers or estimated by spectral estimation methods as in this
research, due attention has to be paid for the evaluation of the spectra. Delta E2000 [11:13,20]
has been used for the evaluation and comparison of different estimation methods as it is a
quantitative evaluation of color differences in LAB color space which matches with the future aims
of this research in the reconstruction of color [21].
The performance of the proposed method (Adaptive Pseudo-Inverse) was tested by three
different datasets and the results of the spectral estimation have been compared for the original
and proposed methods using spectral and colorimetric errors (Table 1), Results showed
noticeable improvement of the estimation accuracy. Moreover, the resulted spectra have been
compared with other different Spectral data for GretagMacbeth Colorchecker CC according to the
colorimetric error (Figure 4) which showed a potential improvement in the quality of the color
reconstruction.
5. CONCLUSION AND FURTHER WORK
This research introduced integrated approach for the reconstruction of spectral reflectance by the
combination between the original Pseudo-Inverse method and the adaptive selection of samples
as stated in Adaptive Wiener introduced by Xin and Shen. By the adaptive selection of training
samples according to their spectral similarity to the verification sample, a new transformation
matrix has been calculated for the estimation of spectral reflectance which improved the accuracy
of the spectral estimation.
Further work would be developed for the comparison and evaluation of API method with other
spectral estimation methods such as wiener, adaptive wiener…etc. Also, it is worth mentioning
that more investigation about the appropriate L value (number of selected training samples)
should be performed to achieve least spectral and colorimetric errors which consequently lead to
better accuracy to be used in further research in the development of a portable multi-spectral
system for the pigment identification and color reproduction [22].
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