Object segmentation plays an important role in human visual perception, medical image processing and content based image retrieval. It provides information for recognition and interpretation. This paper uses mathematical morphology for image segmentation. Object segmentation is difficult because one usually does not know a priori what type of object exists in an image, how many different shapes are there and what regions the image has. To carryout discrimination and segmentation several innovative segmentation methods, based on morphology are proposed. The present study proposes segmentation method based on multiscale morphological reconstructions. Various sizes of structuring elements have been used to segment simple and complex shapes. It enhances local boundaries that may lead to improve segmentation accuracy.The method is tested on various datasets and results shows that it can be used for both interactive and automatic segmentation.
Fuzzy Region Merging Using Fuzzy Similarity Measurement on Image Segmentation IJECEIAES
Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively.
Automatic dominant region segmentation for natural imagescsandit
Image Segmentation segments an image into different homogenous regions. An efficient
semantic based image retrieval system divides the image into different regions separated by
color or texture sometimes even both. Features are extracted from the segmented regions and
are annotated automatically. Relevant images are retrieved from the database based on the
keywords of the segmented region In this paper, automatic image segmentation is proposed to
obtained dominant region of the input natural images. Dominant region are segmented and
results are obtained . Results are also recorded in comparison to JSEG algorithm
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Novel Feature Extraction Scheme for Medical X-Ray ImagesIJERA Editor
X-ray images are gray scale images with almost the same textural characteristic. Conventional texture or color
features cannot be used for appropriate categorization in medical x-ray image archives. This paper presents a
novel combination of methods like GLCM, LBP and HOG for extracting distinctive invariant features from Xray
images belonging to IRMA (Image Retrieval in Medical applications) database that can be used to perform
reliable matching between different views of an object or scene. GLCM represents the distributions of the
intensities and the information about relative positions of neighboring pixels of an image. The LBP features are
invariant to image scale and rotation, change in 3D viewpoint, addition of noise, and change in illumination A
HOG feature vector represents local shape of an object, having edge information at plural cells. These features
have been exploited in different algorithms for automatic classification of medical X-ray images. Excellent
experimental results obtained in true problems of rotation invariance, particular rotation angle, demonstrate that
good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary
patterns.
Improving Performance of Texture Based Face Recognition Systems by Segmenting...IDES Editor
Textures play an important role in recognition of
images. This paper investigates the efficiency of performance
of three texture based feature extraction methods for face
recognition. The methods for comparative study are Grey Level
Co_occurence Matrix (GLCM), Local Binary Pattern (LBP)
and Elliptical Local Binary Template (ELBT). Experiments
were conducted on a facial expression database, Japanese
Female Facial Expression (JAFFE). With all facial expressions
LBP with 16 vicinity pixels is found to be a better face
recognition method among the tested methods. Experimental
results show that classification based on segmenting face
region improves recognition accuracy.
COLOUR BASED IMAGE SEGMENTATION USING HYBRID KMEANS WITH WATERSHED SEGMENTATIONIAEME Publication
Image processing, arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. The objective of segmentation is partitioning an image into distinct regions containing each pixels with similar attributes. Image segmentation can be done using thresholding, color space segmentation, k-means clustering.
Segmentation is the low-level operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond, actual objects, or parts of them, within the images. Thus, in a large number of applications in image processing and computer vision, segmentation plays a fundamental role as the first step before applying to images higher-level operations such as recognition, semantic interpretation, and representation. Until very recently, attention has been focused on segmentation of gray-level images since these have been the only kind of visual information that acquisition devices were able to take the computer resources to handle. Nowadays, color image has definitely displaced monochromatic information and computation power is no longer a limitation in processing large volumes of data. In this paper proposed hybrid k-means with watershed segmentation algorithm is used segment the images. Filtering techniques is used as noise filtration method to improve the results and PSNR, MSE performance parameters has been calculated and shows the level of accuracy
Fuzzy Region Merging Using Fuzzy Similarity Measurement on Image Segmentation IJECEIAES
Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively.
Automatic dominant region segmentation for natural imagescsandit
Image Segmentation segments an image into different homogenous regions. An efficient
semantic based image retrieval system divides the image into different regions separated by
color or texture sometimes even both. Features are extracted from the segmented regions and
are annotated automatically. Relevant images are retrieved from the database based on the
keywords of the segmented region In this paper, automatic image segmentation is proposed to
obtained dominant region of the input natural images. Dominant region are segmented and
results are obtained . Results are also recorded in comparison to JSEG algorithm
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Novel Feature Extraction Scheme for Medical X-Ray ImagesIJERA Editor
X-ray images are gray scale images with almost the same textural characteristic. Conventional texture or color
features cannot be used for appropriate categorization in medical x-ray image archives. This paper presents a
novel combination of methods like GLCM, LBP and HOG for extracting distinctive invariant features from Xray
images belonging to IRMA (Image Retrieval in Medical applications) database that can be used to perform
reliable matching between different views of an object or scene. GLCM represents the distributions of the
intensities and the information about relative positions of neighboring pixels of an image. The LBP features are
invariant to image scale and rotation, change in 3D viewpoint, addition of noise, and change in illumination A
HOG feature vector represents local shape of an object, having edge information at plural cells. These features
have been exploited in different algorithms for automatic classification of medical X-ray images. Excellent
experimental results obtained in true problems of rotation invariance, particular rotation angle, demonstrate that
good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary
patterns.
Improving Performance of Texture Based Face Recognition Systems by Segmenting...IDES Editor
Textures play an important role in recognition of
images. This paper investigates the efficiency of performance
of three texture based feature extraction methods for face
recognition. The methods for comparative study are Grey Level
Co_occurence Matrix (GLCM), Local Binary Pattern (LBP)
and Elliptical Local Binary Template (ELBT). Experiments
were conducted on a facial expression database, Japanese
Female Facial Expression (JAFFE). With all facial expressions
LBP with 16 vicinity pixels is found to be a better face
recognition method among the tested methods. Experimental
results show that classification based on segmenting face
region improves recognition accuracy.
COLOUR BASED IMAGE SEGMENTATION USING HYBRID KMEANS WITH WATERSHED SEGMENTATIONIAEME Publication
Image processing, arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. The objective of segmentation is partitioning an image into distinct regions containing each pixels with similar attributes. Image segmentation can be done using thresholding, color space segmentation, k-means clustering.
Segmentation is the low-level operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond, actual objects, or parts of them, within the images. Thus, in a large number of applications in image processing and computer vision, segmentation plays a fundamental role as the first step before applying to images higher-level operations such as recognition, semantic interpretation, and representation. Until very recently, attention has been focused on segmentation of gray-level images since these have been the only kind of visual information that acquisition devices were able to take the computer resources to handle. Nowadays, color image has definitely displaced monochromatic information and computation power is no longer a limitation in processing large volumes of data. In this paper proposed hybrid k-means with watershed segmentation algorithm is used segment the images. Filtering techniques is used as noise filtration method to improve the results and PSNR, MSE performance parameters has been calculated and shows the level of accuracy
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
Object-Oriented Approach of Information Extraction from High Resolution Satel...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Content Based Image Retrieval using Color Boosted Salient Points and Shape fe...CSCJournals
Salient points are locations in an image where there is a significant variation with respect to a chosen image feature. Since the set of salient points in an image capture important local characteristics of that image, they can form the basis of a good image representation for content-based image retrieval (CBIR). Salient features are generally determined from the local differential structure of images. They focus on the shape saliency of the local neighborhood. Most of these detectors are luminance based which have the disadvantage that the distinctiveness of the local color information is completely ignored in determining salient image features. To fully exploit the possibilities of salient point detection in color images, color distinctiveness should be taken into account in addition to shape distinctiveness. This paper presents a method for salient points determination based on color saliency. The color and texture information around these points of interest serve as the local descriptors of the image. In addition, the shape information is captured in terms of edge images computed using Gradient Vector Flow fields. Invariant moments are then used to record the shape features. The combination of the local color, texture and the global shape features provides a robust feature set for image retrieval. The experimental results demonstrate the efficacy of the method.
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.
CONTRAST ENHANCEMENT TECHNIQUES USING HISTOGRAM EQUALIZATION METHODS ON COLOR...IJCSEA Journal
Histogram equalization (HE) is a simple and widely used image contrast enhancement technique. The basic disadvantage of HE is it changes the brightness of the image. In order to overcome this drawback, various HE methods have been proposed. These methods preserves the brightness on the output image but, does not have a natural look. In order to overcome this problem the, present paper uses Multi-HE methods, which decompose the image into several sub images, and classical HE method is applied to each sub image. The algorithm is applied on various images and has been analysed using both objective and subjective assessment.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
FACE RECOGNITION USING DIFFERENT LOCAL FEATURES WITH DIFFERENT DISTANCE TECHN...IJCSEIT Journal
A face recognition system using different local features with different distance measures is proposed in this
paper. Proposed method is fast and gives accurate detection. Feature vector is based on Eigen values,
Eigen vectors, and diagonal vectors of sub images. Images are partitioned into sub images to detect local
features. Sub partitions are rearranged into vertically and horizontally matrices. Eigen values, Eigenvector
and diagonal vectors are computed for these matrices. Global feature vector is generated for face
recognition. Experiments are performed on benchmark face YALE database. Results indicate that the
proposed method gives better recognition performance in terms of average recognized rate and retrieval
time compared to the existing methods.
Image Segmentation Using Pairwise Correlation ClusteringIJERA Editor
A pairwise hypergraph based image segmentation framework is formulated in a supervised manner for various images. The image segmentation is to infer the edge label over the pairwise hypergraph by maximizing the normalized cuts. Correlation clustering which is a graph partitioning algorithm, was shown to be effective in a number of applications such as identification, clustering of documents and image segmentation.The partitioning result is derived from a algorithm to partition a pairwise graph into disjoint groups of coherent nodes. In the pairwise correlation clustering, the pairwise graph which is used in the correlation clustering is generalized to a superpixel graph where a node corresponds to a superpixel and a link between adjacent superpixels corresponds to an edge. This pairwise correlation clustering also considers the feature vector which extracts several visual cues from a superpixel, including brightness, color, texture, and shape. Significant progress in clustering has been achieved by algorithms that are based on pairwise affinities between the datasets. The experimental results are shown by calculating the typical cut and inference in an undirected graphical model and datasets.
Comparative Study and Analysis of Image Inpainting TechniquesIOSR Journals
Abstract: Image inpainting is a technique to fill missing region or reconstruct damage area from an image.It
removes an undesirable object from an image in visually plausible way.For filling the part of image, it use
information from the neighboring area. In this dissertation work, we present a Examplar based method for
filling in the missing information in an image, which takes structure synthesis and texture sysnthesis together.
In exemplar based approach it used local information from an image to patch propagation.We have also
implement Nonlocal Mean approach for exemplar based image inpainting.In Nonlocal mean approach it find
multiple samples of best exemplar patches for patch propagation and weight their contribution according to
their similarity to the neighborhood under evaluation. We have further extended this algorithm by considering
collaborative filtering method to synthesize and propagate with multiple samples of best exemplar patches. We
have to preformed experiment on many images and found that our algorithm successfully inpaint the target
region.We have tested the accuracy of our algorithm by finding parameter like PSNR and compared PSNR
value for all three different approaches.
Keywords: Texture Synthesis, Structure Synthesis, Patch Propagation ,imageinpainting ,nonlocal approach,
collabrative filtering.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
Image segmentation is aimed at cutting out, a ROI (Region of Interest) from an image. For
medical images, segmentation is done for: studying the anatomical structure, identifying ROI ie tumor
or any other abnormalities, identifying the increase in tissue volume in a region, treatment planning.
Currently there are many different algorithms available for image segmentation. This paper lists and
compares some of them. Each has their own advantages and limitations.
A novel predicate for active region merging in automatic image segmentationeSAT Publishing House
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.
Text-Image Separation in Document Images using Boundary/Perimeter DetectionIDES Editor
Document analysis plays an important role in office
automation, especially in intelligent signal processing. The
proposed system consists of two modules: block segmentation
and block identification. In this approach, first a document is
segmented into several non-overlapping blocks by utilizing a
novel recursive segmentation technique, and then extracts
the features embedded in each segmented block are extracted.
Two kinds of features, connected components and image
boundary/perimeter features are extracted. Document with
text inside image pose limitations in earlier reported literature.
This is taken care of by applying additional pass of the Run
Length Smearing on the extracted image that contains text.
Proposed scheme is independent of type and language of the
document.
At the end of this lecture, you should be able to;
describe the importance of morphological features in an image.
describe the operation of erosion, dilation, open and close operations.
identify the practical advantage of the morphological operations.
apply morphological operations for problem solving.
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
Object-Oriented Approach of Information Extraction from High Resolution Satel...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Content Based Image Retrieval using Color Boosted Salient Points and Shape fe...CSCJournals
Salient points are locations in an image where there is a significant variation with respect to a chosen image feature. Since the set of salient points in an image capture important local characteristics of that image, they can form the basis of a good image representation for content-based image retrieval (CBIR). Salient features are generally determined from the local differential structure of images. They focus on the shape saliency of the local neighborhood. Most of these detectors are luminance based which have the disadvantage that the distinctiveness of the local color information is completely ignored in determining salient image features. To fully exploit the possibilities of salient point detection in color images, color distinctiveness should be taken into account in addition to shape distinctiveness. This paper presents a method for salient points determination based on color saliency. The color and texture information around these points of interest serve as the local descriptors of the image. In addition, the shape information is captured in terms of edge images computed using Gradient Vector Flow fields. Invariant moments are then used to record the shape features. The combination of the local color, texture and the global shape features provides a robust feature set for image retrieval. The experimental results demonstrate the efficacy of the method.
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.
CONTRAST ENHANCEMENT TECHNIQUES USING HISTOGRAM EQUALIZATION METHODS ON COLOR...IJCSEA Journal
Histogram equalization (HE) is a simple and widely used image contrast enhancement technique. The basic disadvantage of HE is it changes the brightness of the image. In order to overcome this drawback, various HE methods have been proposed. These methods preserves the brightness on the output image but, does not have a natural look. In order to overcome this problem the, present paper uses Multi-HE methods, which decompose the image into several sub images, and classical HE method is applied to each sub image. The algorithm is applied on various images and has been analysed using both objective and subjective assessment.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
FACE RECOGNITION USING DIFFERENT LOCAL FEATURES WITH DIFFERENT DISTANCE TECHN...IJCSEIT Journal
A face recognition system using different local features with different distance measures is proposed in this
paper. Proposed method is fast and gives accurate detection. Feature vector is based on Eigen values,
Eigen vectors, and diagonal vectors of sub images. Images are partitioned into sub images to detect local
features. Sub partitions are rearranged into vertically and horizontally matrices. Eigen values, Eigenvector
and diagonal vectors are computed for these matrices. Global feature vector is generated for face
recognition. Experiments are performed on benchmark face YALE database. Results indicate that the
proposed method gives better recognition performance in terms of average recognized rate and retrieval
time compared to the existing methods.
Image Segmentation Using Pairwise Correlation ClusteringIJERA Editor
A pairwise hypergraph based image segmentation framework is formulated in a supervised manner for various images. The image segmentation is to infer the edge label over the pairwise hypergraph by maximizing the normalized cuts. Correlation clustering which is a graph partitioning algorithm, was shown to be effective in a number of applications such as identification, clustering of documents and image segmentation.The partitioning result is derived from a algorithm to partition a pairwise graph into disjoint groups of coherent nodes. In the pairwise correlation clustering, the pairwise graph which is used in the correlation clustering is generalized to a superpixel graph where a node corresponds to a superpixel and a link between adjacent superpixels corresponds to an edge. This pairwise correlation clustering also considers the feature vector which extracts several visual cues from a superpixel, including brightness, color, texture, and shape. Significant progress in clustering has been achieved by algorithms that are based on pairwise affinities between the datasets. The experimental results are shown by calculating the typical cut and inference in an undirected graphical model and datasets.
Comparative Study and Analysis of Image Inpainting TechniquesIOSR Journals
Abstract: Image inpainting is a technique to fill missing region or reconstruct damage area from an image.It
removes an undesirable object from an image in visually plausible way.For filling the part of image, it use
information from the neighboring area. In this dissertation work, we present a Examplar based method for
filling in the missing information in an image, which takes structure synthesis and texture sysnthesis together.
In exemplar based approach it used local information from an image to patch propagation.We have also
implement Nonlocal Mean approach for exemplar based image inpainting.In Nonlocal mean approach it find
multiple samples of best exemplar patches for patch propagation and weight their contribution according to
their similarity to the neighborhood under evaluation. We have further extended this algorithm by considering
collaborative filtering method to synthesize and propagate with multiple samples of best exemplar patches. We
have to preformed experiment on many images and found that our algorithm successfully inpaint the target
region.We have tested the accuracy of our algorithm by finding parameter like PSNR and compared PSNR
value for all three different approaches.
Keywords: Texture Synthesis, Structure Synthesis, Patch Propagation ,imageinpainting ,nonlocal approach,
collabrative filtering.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
Image segmentation is aimed at cutting out, a ROI (Region of Interest) from an image. For
medical images, segmentation is done for: studying the anatomical structure, identifying ROI ie tumor
or any other abnormalities, identifying the increase in tissue volume in a region, treatment planning.
Currently there are many different algorithms available for image segmentation. This paper lists and
compares some of them. Each has their own advantages and limitations.
A novel predicate for active region merging in automatic image segmentationeSAT Publishing House
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.
Text-Image Separation in Document Images using Boundary/Perimeter DetectionIDES Editor
Document analysis plays an important role in office
automation, especially in intelligent signal processing. The
proposed system consists of two modules: block segmentation
and block identification. In this approach, first a document is
segmented into several non-overlapping blocks by utilizing a
novel recursive segmentation technique, and then extracts
the features embedded in each segmented block are extracted.
Two kinds of features, connected components and image
boundary/perimeter features are extracted. Document with
text inside image pose limitations in earlier reported literature.
This is taken care of by applying additional pass of the Run
Length Smearing on the extracted image that contains text.
Proposed scheme is independent of type and language of the
document.
At the end of this lecture, you should be able to;
describe the importance of morphological features in an image.
describe the operation of erosion, dilation, open and close operations.
identify the practical advantage of the morphological operations.
apply morphological operations for problem solving.
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
Image enhancement technique plays vital role in improving the quality of the image. Enhancement
technique basically enhances the foreground information and retains the background and improve the
overall contrast of an image. In some case the background of an image hides the structural information of
an image. This paper proposes an algorithm which enhances the foreground image and the background
part separately and stretch the contrast of an image at inter-object level and intra-object level and then
combines it to an enhanced image. The results are compared with various classical methods using image
quality measures
A Survey of Image Segmentation based on Artificial Intelligence and Evolution...IOSR Journals
Abstract : In image analysis, segmentation is the partitioning of a digital image into multiple regions (sets of
pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in
literature and there are a wide variety of approaches that are used. Different approaches are suited to different
types of images and the quality of output of a particular algorithm is difficult to measure quantitatively due to
the fact that there may be much correct segmentation for a single image. Image segmentation denotes a process
by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous
and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest
domain-independent abstraction of an input image. Image segmentation is an important processing step in many
image, video and computer vision applications. Extensive research has been done in creating many different
approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm
produces more accurate segmentations than another, whether it be for a particular image or set of images, or
more generally, for a whole class of images.
In this paper, The Survey of Image Segmentation using Artificial Intelligence and Evolutionary Approach
methods that have been proposed in the literature. The rest of the paper is organized as follows. 1.
Introduction, 2.Literature review, 3.Noteworthy contributions in the field of proposed work, 4.Proposed
Methodology, 5.Expected outcome of the proposed research work, 6.Conclusion.
Keywords: Image Segmentation, Segmentation Algorithm, Artificial Intelligence, Evolutionary Algorithm,
Neural Network, Fuzzy Set, Clustering.
Semi-Supervised Method of Multiple Object Segmentation with a Region Labeling...sipij
Efficient and efficient multiple object segmentation is an important task in computer vision and object recognition. In this work; we address a method to effectively discover a user’s concept when multiple objects of interest are involved in content based image retrieval. The proposed method incorporate a framework for multiple object retrieval using semi-supervised method of similar region merging and flood fill which models the spatial and appearance relations among image pixels. To improve the effectiveness of similarity based region merging we propose a new similarity based object retrieval. The users only need to roughly indicate the after which steps desired objects contour is obtained during the automatic merging of similar regions. A novel similarity based region merging mechanism is proposed to guide the merging process with the help of mean shift technique and objects detection using region labeling and flood fill. A region R is merged with its adjacent regions Q if Q has highest similarity with Q (using Bhattacharyya descriptor) among all Q’s adjacent regions. The proposed method automatically merges the regions that are initially segmented through mean shift technique, and then effectively extracts the object contour by merging all similar regions. Extensive experiments are performed on 12 object classes (224 images total) show promising results.
MRI Image Segmentation Using Level Set Method and Implement an Medical Diagno...CSEIJJournal
Image segmentation plays a vital role in image processing over the last few years. The goal of image
segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual
surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using
level set method for segmenting the MRI image which investigates a new variational level set algorithm
without re- initialization to segment the MRI image and to implement a competent medical diagnosis
system by using MATLAB. Here we have used the speed function and the signed distance function of the
image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique
and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising
results by detecting the normal or abnormal condition specially the existence of tumers. This system will be
applied to both simulated and real images with promising results.
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
Image segmentation plays a vital role in image processing over the last few years. The goal of image segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using level set method for segmenting the MRI image which investigates a new variational level set algorithm without re- initialization to segment the MRI image and to implement a competent medical diagnosis system by using MATLAB. Here we have used the speed function and the signed distance function of the image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising results by detecting the normal or abnormal condition specially the existence of tumers. This system will be applied to both simulated and real images with promising results
Image Segmentation from RGBD Images by 3D Point Cloud Attributes and High-Lev...CSCJournals
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Image restoration based on morphological operationsijcseit
Image processing including noise suppression, feature extraction, edge detection, image segmentation,
shape recognition, texture analysis, image restoration and reconstruction, image compression etc uses
mathematical morphology which is a method of nonlinear filters.
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Image Segmentation segments an image into different homogenous regions. An efficient semantic based image retrieval system divides the image into different regions separated by color or texture sometimes even both. Features are extracted from the segmented regions and are annotated automatically. Relevant images are retrieved from the database based on the
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results are obtained . Results are also recorded in comparison to JSEG algorithm
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
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OBJECT SEGMENTATION USING MULTISCALE MORPHOLOGICAL OPERATIONS
1. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.3,No.4,August 2013
DOI : 10.5121/ijcseit.2013.3402 11
OBJECT SEGMENTATION USING MULTISCALE
MORPHOLOGICAL OPERATIONS
Dr. A. Srikrishna1
, P. Pallavi2
, V. Geetha Madhuri3
, N. Neelima4
1,2,3,4
Department of Information Technology,
RVR & JC College of Engineering,
Chowdavaram, Guntur, Andhra Pradesh, India
atlurisrikrishna@yahoo.com
paladugupallavi3@gmail.com
madhurigeetha33@gmail.com
neelima_nalla@yahoo.co.in
ABSTRACT
Object segmentation plays an important role in human visual perception, medical image processing and
content based image retrieval. It provides information for recognition and interpretation. This paper uses
mathematical morphology for image segmentation. Object segmentation is difficult because one usually
does not know a priori what type of object exists in an image, how many different shapes are there and
what regions the image has. To carryout discrimination and segmentation several innovative segmentation
methods, based on morphology are proposed. The present study proposes segmentation method based on
multiscale morphological reconstructions. Various sizes of structuring elements have been used to segment
simple and complex shapes. It enhances local boundaries that may lead to improve segmentation accuracy.
The method is tested on various datasets and results shows that it can be used for both interactive and
automatic segmentation.
KEYWORDS
Morphology, Structuring Element, Segmentation, Edge Detection, Skeletanization
1. INTRODUCTION
Humans recognize various objects in an image though the objects may vary somewhat in different
viewpoints and on various transformations. Object segmentation is useful task in object
recognition. The object recognition determines an object in a given set of objects in an image or
image sequence. In order to perform object recognition the objects from a give image or image
sequence are to be identified. For this object segmentation that is to distinguish objects from
background is performed.
Object segmentation [3] is the process of partitioning a digital image into multiple segments (sets
of pixels, also known as super pixels). The goal of segmentation is to simplify and/or change the
representation of an image into something that is more meaningful and easier to analyze. Object
segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images.
More precisely, object segmentation is the process of assigning a label to every pixel in an image
such that pixels with the same label share certain visual characteristics.
Some of the applications of object segmentation are content based image retrieval, machine
vision, medical imaging, object detection, recognition tasks, traffic control systems, video
2. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.3,No.4,August 2013
12
surveillance. Object segmentation is the partition of an image into a set of non overlapping
regions whose union is the entire image. The purpose of segmentation is to decompose the image
into part that is meaningful with respect to a particular application. Concerning visual signal
processing, image segmentation is essential for various applications. It describes the process
whereby each pixel in an image is labeled, such that pixels with the same label present coherent
visual characteristics. This allow for a semantic approach to image analysis. One way to perform
image segmentation is to simply utilize the clustering algorithm in the color space domain [1],
i.e., HSV or RGB; segmentation can also be based on the statistics of the color space description
of the image, e.g., color histogram. These methods are carried out in the color space domain
instead of the image pixel domain, whose results depend on the initial cluster setting. Edge-based
segmentation is simple but it requires a further linking procedure to segment an image [2], [3],
[4]. Among color region-based approaches, the region-growing approach [5] provides an initial
set of seeds; regions are then grown by comparing neighboring pixels, which are merged [6] with
the region with the closest mean color. JSEG [7], [8], [9], [10] seeks to divide an image into
spatially continuous disjoint and homogenous regions based on the image. It uses a region
merging approach, but the color information between entire neighboring regions, rather than
individual pixels, is utilized. Experiments show that JSEG provides satisfactory results on most
color images. The watershed technique splits one image into regions based on its gray-level
topology and is performed on the gradient image. Regions are split by watersheds, which are
constructed from adjacent catchment basins. Although it has the advantage of being able to
segment regions with closed contours, it suffers from over segmentation and requires region
merging processing afterward. Multiscale morphological reconstruction [11] is used to eliminate
the over segmentation in the watershed algorithm. Graph based segmentation [12], [13] takes the
global image information and local spatial relationships into consideration to perform image
segmentation. It defines a predicate to measure the boundary evidence between two neighboring
regions to yield a graph-based representation of one image.
The general-purpose segmentation algorithms [1], [7], [8], [9], [10], [12], [13] represent one
image with disjoint regions of homogeneous color/texture features for higher level applications,
and the object segmentation ones [11], [14] extract image objects with different gray level
variations and noise attacks. The former does not address object segmentation from its design
target. The latter focuses on segmenting the object of different scale but do not perform parameter
adaptation when dealing with images with different background (BG) variation and object
contents. To utilize both region and object-based segmentation capabilities to handle the object
segmentation for large-scale database images in a robust and principled manner an algorithm is
proposed. The proposed algorithm is known as object segmentation using multiscale morphology
(OSMM). Morphological open (close) by reconstruction [14], [15], i.e., OR (CR), with a proper
structure element (SE) on the gray levels, to automatically segment images’ object region is used.
Section 2 discusses the basic terminology of mathematical morphology. Section 3 discusses
segmentation using multiscale morphology. Section 4 deals with results and discussions.
2. BASIC TERMINOLOGY OF MATHEMATICAL MORPHOLOGY
2.1. Mathematical Morphological Operations
2.1.1. Dilation
Dilation is one of the elementary operators of mathematical morphology, that is, it is a building
block for a large class of operators. The key process in the dilation operator is the local
comparison of a shape, called structural element, with the object to be transformed. When the
structural element is positioned at a given point and it touches the object, then this point will
appear in the result of the transformation, otherwise it will not. In dilation the value of the output
3. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.3,No.4,August 2013
13
pixel is the maximum value of all the pixels in the input pixel's neighbourhood. In a binary image,
if any of the pixels is set to the value 1, the output pixel is set to 1. The dilation of a gray level
image I(x, y) by two-dimensional structuring element B is defined as follows
(1)
2.1.2. Erosion
Erosion is one of two fundamental operations (the other being dilation) in morphological image
processing from which all other morphological operations are defined. It was originally defined
for binary images, later being extended to gray scale images, and subsequently to complete
lattices.
The erosion is one of the elementary operators of mathematical morphology, that is, it is one of
the building blocks of a large class of operators. The key mechanism under the erosion operator is
the local comparison of a shape, called structural element, with the object that will be
transformed. If, when positioned at a given point, the structural element is included in the object
then this point will appear in the result of the transformation, otherwise not. The value of the
output pixel is the minimum value of all the pixels in the input pixel's neighbourhood. The
erosion of a gray level image I(x, y) by two dimensional structuring element B is defined as
follows
(2)
2.1.3. Opening
Opening smoothes the contour of an object, breaks narrow isthmuses, and eliminates thin
protrusions. The opening of I by structuring element B is obtained by the erosion of I by B,
followed by dilation of the resulting image by B is denoted by I○B which is given in the form of
equation as follows
(3)
2.1.4. Closing
Closing tends to smooth sections of contours but, as opposed to opening, it generally fuses narrow
breaks and long thin gulfs, eliminates small holes, and fills gaps in the contour. The closing of I
by B is obtained by the dilation of I by B, followed by erosion of the resulting structure by B is
denoted by I●B which is given in the form of equation as follows
(4)
2.2. Multiscale Morphological Operations
Let the structuring element defined in Equations 1, 2, 3, 4 be where denotes the smallest
structuring element size in the discrete domain. The homothetic of a convex structuring
element can be obtained by dilating recursively times with itself given by Equation 5.
(5)
4. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.3,No.4,August 2013
14
By controlling , the multiscale morphological operations decompose one image into a set of
filtered images. These operations are self-calibrated in which the filtered image produced by a
structuring element of a particular scale should strictly contain only the features of that scale.
Multiscale morphological reconstruction operations on gray-level images can be applied in a dual
approach to segment objects. Let , and the morphological open by reconstruction (OR)
can be defined by Equation 6.
(6)
Where
Similarly, let the morphological close by reconstruction (CR) can be defined by
Equation 7.
(7)
where
3. SEGMENTATION USING MULTISCALE MORPHOLOGY
The general-purpose segmentation algorithms such as mean shift segmentation, edge based
segmentation represent one image with disjoint regions of homogeneous color or texture features
for higher level applications, and the object segmentation ones such as watershed segmentation,
graph based segmentation extract image objects with different gray level variations and noise
attacks. The former does not address object segmentation from its design target. The latter
focuses on segmenting the object of different scale but do not perform parameter adaptation when
dealing with images with different background (BG) variation and object contents. In order to
overcome the drawbacks, segmentation using multiscale morphology has been proposed. This
method utilizes both region- and object-based segmentation capabilities to handle the object
segmentation in a robust and principled manner. Figure 1 shows the block diagram of the object
segmentation using multiscale morphological reconstructions.
Figure 1. Object Segmentation using Multiscale Morphology.
5. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.3,No.4,August 2013
15
3.1. Background Variations
Open by reconstruction (OR),close by reconstruction (CR) and top- (bottom-) hat operations are
used to segment object regions and their gray levels , where ⋀ is a binary AND
operation. The reconstruction operations of OR (CR) would not be fully iterated for stable
outcome so that convex (concave) gray-level variation regions can be located. The image
processed by this partial OR (CR) operation is denoted as with to be
distinguished from the fully reconstructed one in that requires iterations. The basic idea is
images with identifiable back ground regions (BGs) usually present homogeneously evolving
gray-level BGs, i.e. , is continuous. When performing gray-level open by
reconstruction (OR) operations on image with suitable structuring element sizes most background
(BG) regions would coincide with the processed image . To precisely locate back ground
regions (BGs) the structuring element size in should be properly selected such that object
boundaries are identifiable. For precisely locating the structuring element, structuring element
size is gradually enlarged and the frame difference of gray levels between I and i.e.
which is given by Equation 8 is calculated ( difference of gray levels for consecutive
structuring elements is calculated).
(8)
The threshold value is calculated by using the Equation 9
(9)
where is the size of the image.
To find a proper structuring element such that it yields a nearly stable gray-level variation, i.e.,
threshold given by Equation 9 should nearly approach zero. The proper has threshold given by
Equation 9 values between 0 and 1, and different images would require different to
achieve the aforementioned stability due to differing object contents.
3.2. Edge Detection
The edge pixels of the input image I are detected by using morphological operations. The edge
pixels denoted by Edge(I) for an image I is given by the Equation 10
(10)
where Dilation(I), Erosion (I) are dilation, erosion operations on input image I as defined in
Equations 1, 2 respectively.
3.3. Skeletanization
The skeleton of an object L(I) can be obtained by edge image and background variations. The
skeleton obtained using skeletanization process is given by Equation 11.
(11)
6. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.3,No.4,August 2013
16
3.4. Segmentation
The skeleton obtained using skeletanization process is given as the input for reconstruction of
object. In the reconstruction the input image is dilated by varying sizes of structuring element B
which satisfies the condition using threshold value given by Equation 9 which is in the range of 0
and 1. Let be the outputs of dilation for varying sizes of structuring elements.
Then the segmented object is given by Equation 12.
(12)
The overall process of segmentation is given in the Algorithm
3.5. Algorithm Object Segmentation using Multiscale Morphology (OSMM)
Step 1: Read the input image.
Step 2: Convert the image into gray scale image.
Step 3: Perform “opening(erosion followed by dilation) or closing (dilation followed by
erosion)” of the input image using varying sizes of structuring elements.
Step 4: The structuring element size is gradually enlarged and the frame difference of gray levels
between I and i.e. which is given by Equation 8 is recorded.
Step 5: Calculate the threshold by using the formula in Equation 9.
Step 6: The structuring elements whose threshold values are between 0 and 1 are stored.
Step 7: Calculate the edge pixels of the input image by using the formula in Equation 10.
Step 8: Perform detaching process given by Equation 11.
Step 9: Perform reconstruction of segmented by using Equation 12.
4. RESULTS AND DISCUSSIONS
We evaluate shape prototypes in the context of object segmentation. These object segmentation
techniques are used as a pre processing step for object recognition. We begin with a set of 200
objects, representing distinct views of collection of 101 objects from Caltech101 database. We are
using this step as a pre processing step for object recognition so the input image consists of only
one object.
To evaluate the performance of the algorithm correctness and completeness criteria are
considered which are defined by Equations 13 and 14 respectively.
Correctness can be defined as the percentage of correctly extracted region (ground truth) by the
segmentation algorithm and can be calculated using Equation 13.
(13)
Completeness can be defined as the percentage of the ground truth region extracted by the
segmentation algorithm and can be calculated using Equation 14.
(14)
7. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.3,No.4,August 2013
17
where to obtain the true positive (TP) image a logical AND operation was performed between the
ground truth and the resultant image. The difference between the ground truth image and the true
positive image was taken as the false negative (FN) image of the respective segmented image.
The difference between the segmented image and the true positive image was taken as the false
positive (FP) image of the respective segmented image. The TP, FP, FN are illustrated in Figure
2. Here we consider the ground truth which is a manually segmented image. The ground truth
images are compared with the image segmented with the proposed algorithm. The Figure 3 to
Figure 8 shows that the segmented images using proposed algorithm are very similar to ground
truth. The objective analysis of the segment is evaluated using correctness and completeness
which is shown from Table 1 to Table 6. The correctness is above 80% in most of the cases and
completeness is above 90% in all cases.
Figure 2. Ground truth region of object and segmented region
Lilly.png
Figure 3. a. Shows the Lilly image. b. Shows the ground truth which is taken manually. c-f represent the
output of the image with varying structuring elements.
8. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.3,No.4,August 2013
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Table 1. Correctness and Completeness values for various sizes of structuring elements for Lilly.png.
Scissors.jpg
Figure 4. a. Shows the Scissors image. b. Shows the ground truth which is taken manually. c-f represent the
output of the image with varying structuring elements
Table 2. Correctness and Completeness values for various sizes of structuring elements for Scissors.jpg
Structuring Element
Size
Correctness Completeness
5x5 86.8629 90.5704
7x7 81.7156 93.7795
9x9 77.2008 95.2278
11x11 75.0547 96.3634
13x13 72.6632 97.4097
17x17 67.6016 98.7240
Structuring
Element
Size
Correctnes
s
Completenes
s
7x7 86.3627 79.4165
9x9 82.1526 85.8422
11x11 79.8946 88.6663
13x13 78.5122 91.3224
15x15 78.129 95.0174
17x17 74.5465 97.4122
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Mandolin.jpg
Figure 5. a. Shows the Mandolin image. b. Shows the ground truth which is taken manually. c-f represent
the output of the image with varying structuring elements.
Table 3. Correctness and Completeness values for various sizes of structuring elements for Mandolin.jpg
Structuring
Element
Size
Correctnes
s
Completenes
s
5x5 91.1111 90.4906
7x7 86.8741 94.5327
9x9 83.3252 96.9289
11x11 81.4175 98.4424
13x13 78.5678 99.1486
15x15 75.0938 99.6657
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Seahorse.jpg
Figure 6. a. Shows the Seahorse image. b. Shows the ground truth which is taken manually. c-f represent
the output of the image with varying structuring elements.
Table 4. Correctness and Completeness values for various sizes of structuring elements for Seahorse.jpg
Structuring
Element
Size
Correctnes
s
Completenes
s
7x7 90.0999 93.7143
9x9 85.1340 96.0395
11x11 83.0496 96.8145
13x13 81.0013 97.2830
15x15 78.7993 97.5981
17x17 76.5526 98.1602
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Flamingo.jpg
Figure 7. a. Shows the Flamingo image. b. Shows the ground truth which is taken manually. c-f represent
the output of the image with varying structuring elements
Table 5. Correctness and Completeness values for various sizes of structuring elements for Flamingo.jpg
Structuring
Element
Size
Correctnes
s
Completenes
s
5x5 85.456 85.3091
7x7 81.234 92.5348
9x9 77.6136 96.1200
11x11 74.9444 97.2496
13x13 73.3578 97.90656
15x15 71.6859 98.6985
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Dragonfly.jpg
Figure 8. a. Shows the Dragonfly image. b. Shows the ground truth which is taken manually. c-f represent
the output of the image with varying structuring elements.
Table 6. Correctness and Completeness values for various sizes of structuring elements for Dragonfly.jpg
5. CONCLUSIONS
A simple and regular image object segmentation method has been proposed to deal with large-
scale image databases. It performs dual multiscale morphological reconstruction operations on the
gray levels of entire images to identify the objects. Experiments have demonstrated that OSMM
yields better image object segmentation accuracy, both on shape region and boundary. The results
show that the correctness and completeness of the image increases as the structuring element
increases. The segmentation process can be used for identification of object in any database.
Structuring
Element
Size
Correctnes
s
Completenes
s
5x5 84.6530 97.1433
7x7 80.0787 99.3071
9x9 76.1462 99.6933
11x11 74.6419 99.7501
13x13 71.6884 99.8012
15x15 69.0193 99.8523
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ACKNOWLEDGEMENTS
The authors would like to express their gratitude to Dr.K.Basavapunnaiah, President, Sri. R.
Gopala Krishna, Secretary & Correspondent, and Dr. M. Gopala Krishna, Treasurer, RVR & JC
College of Engineering for providing necessary research infrastructure. They would like to thank
Dr. A. Sudhakar, Prinicipal for his invaluable suggestions and constant encouragement which led
to improvise the presentation of this paper.
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