This document presents a new method for estimating motion in flotation froth images using mutual information as the similarity metric. It proposes using mutual information with a bin size of two (MI2) for block matching motion estimation of froth images. The paper finds that MI2 improves motion estimation accuracy in terms of peak signal-to-noise ratio compared to the commonly used mean absolute difference (MAD) metric, while having a similar computational cost. It tests the MI2 method on two froth video sequences using three-step search and new three-step search, and finds MI2 yields slightly better reconstructed image quality than MAD according to PSNR measurements.
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.
OBIA on Coastal Landform Based on Structure Tensor csandit
This paper presents the OBIA method based on structure tensor to identify complex coastal
landforms. That is, develop Hessian matrix by Gabor filtering and calculate multiscale structure
tensor. Extract edge information of image from the trace of structure tensor and conduct
watershed segment of the image. Then, develop texons and create texton histogram. Finally,
obtain the final results by means of maximum likelihood classification with KL divergence as
the similarity measurement. The study findings show that structure tensor could obtain
multiscale and all-direction information with small data redundancy. Moreover, the method
described in the current paper has high classification accuracy
Robust foreground modelling to segment and detect multiple moving objects in ...IJECEIAES
This document summarizes a research paper that proposes a robust foreground modeling method to segment and detect multiple moving objects in videos. The proposed method uses a running average technique to model the background and subtract it from video frames to detect foreground objects. Morphological operations like dilation and erosion are applied to reduce noise and merge connected regions. Convex hull processing is also used to define object boundaries more clearly. The method was tested on standard video datasets and achieved better performance than other techniques in segmenting objects under various challenging conditions like illumination changes and occlusion. Experimental results demonstrated high precision, recall and specificity based on comparisons with ground truth data.
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScsitconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the
global environment, and in analysing the target detection and recognition .But , segmentation
of (SAR) images is known as a very complex task, due to the existence of speckle noise.
Therefore, in this paper we present a fast SAR images segmentation based on between class
variance. Our choice for used (BCV) method, because it is one of the most effective thresholding
techniques for most real world images with regard to uniformity and shape measures. Our
experiments will be as a test to determine which technique is effective in thresholding
(extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScscpconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the global environment, and in analysing the target detection and recognition .But , segmentation of (SAR) images is known as a very complex task, due to the existence of speckle noise. Therefore, in this paper we present a fast SAR images segmentation based on between class variance. Our choice for used (BCV) method, because it is one of the most effective thresholding techniques for most real world images with regard to uniformity and shape measures. Our experiments will be as a test to determine which technique is effective in thresholding (extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
LEARNING FINGERPRINT RECONSTRUCTION: FROM MINUTIAE TO IMAGENexgen Technology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...ijcsit
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
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.
OBIA on Coastal Landform Based on Structure Tensor csandit
This paper presents the OBIA method based on structure tensor to identify complex coastal
landforms. That is, develop Hessian matrix by Gabor filtering and calculate multiscale structure
tensor. Extract edge information of image from the trace of structure tensor and conduct
watershed segment of the image. Then, develop texons and create texton histogram. Finally,
obtain the final results by means of maximum likelihood classification with KL divergence as
the similarity measurement. The study findings show that structure tensor could obtain
multiscale and all-direction information with small data redundancy. Moreover, the method
described in the current paper has high classification accuracy
Robust foreground modelling to segment and detect multiple moving objects in ...IJECEIAES
This document summarizes a research paper that proposes a robust foreground modeling method to segment and detect multiple moving objects in videos. The proposed method uses a running average technique to model the background and subtract it from video frames to detect foreground objects. Morphological operations like dilation and erosion are applied to reduce noise and merge connected regions. Convex hull processing is also used to define object boundaries more clearly. The method was tested on standard video datasets and achieved better performance than other techniques in segmenting objects under various challenging conditions like illumination changes and occlusion. Experimental results demonstrated high precision, recall and specificity based on comparisons with ground truth data.
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScsitconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the
global environment, and in analysing the target detection and recognition .But , segmentation
of (SAR) images is known as a very complex task, due to the existence of speckle noise.
Therefore, in this paper we present a fast SAR images segmentation based on between class
variance. Our choice for used (BCV) method, because it is one of the most effective thresholding
techniques for most real world images with regard to uniformity and shape measures. Our
experiments will be as a test to determine which technique is effective in thresholding
(extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScscpconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the global environment, and in analysing the target detection and recognition .But , segmentation of (SAR) images is known as a very complex task, due to the existence of speckle noise. Therefore, in this paper we present a fast SAR images segmentation based on between class variance. Our choice for used (BCV) method, because it is one of the most effective thresholding techniques for most real world images with regard to uniformity and shape measures. Our experiments will be as a test to determine which technique is effective in thresholding (extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
LEARNING FINGERPRINT RECONSTRUCTION: FROM MINUTIAE TO IMAGENexgen Technology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...ijcsit
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
This thesis applies Bayesian analysis and Markov random field (MRF) models with MCMC-Gibbs sampling to segment brain MR images into three classes: gray matter, white matter, and cerebrospinal fluid. Synthetic data is used to evaluate the MRF method under different noise conditions. Real brain MR images are also segmented. The MRF model characterizes the spatial dependencies between image pixels. MCMC sampling is used to estimate the posterior distribution and perform segmentation. The results demonstrate the MRF approach can accurately segment images, with better performance on lower noise images.
Paper 58 disparity-of_stereo_images_by_self_adaptive_algorithmMDABDULMANNANMONDAL
This document summarizes a research paper that proposes a new stereo matching algorithm called Self Adaptive Algorithm (SAA) to efficiently compute stereo correspondence or disparity maps from stereo images. SAA aims to improve matching speed by reducing the search zone and avoiding false matches through an adaptive search approach. It dynamically selects the search range based on previous matching results, reducing the range by 50% with each iteration. Experimental results on standard stereo datasets show that SAA outperforms other methods in terms of speed while maintaining accuracy, with processing speeds of 535 fps and 377 fps for different image pairs. SAA reduces computational time by 70.53-99.93% compared to other state-of-the-art methods.
FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHMAM Publications
The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.
Performance Analysis of CRT for Image Encryption ijcisjournal
With the fast advancements of information technology, the security of image data transmitted or stored over
internet is become very difficult. To hide the details, an effective method is encryption, so that only
authorized persons can decrypt the image with the keys available. Since the default features of digital
image such as high capacity data, large redundancy and large similarities among pixels, the conventional
encryption algorithms such as AES, , DES, 3DES, and Blow Fish, are not applicable for real time image
encryption. This paper presents the performance of CRT for image encryption to secure storage and
transmission of image over internet.
The document presents a new method for segmenting MR brain images that combines a hidden Markov random field (HMRF) model with a hybrid metaheuristic optimization algorithm. The HMRF model uses adaptive parameters to balance contributions from different tissue classes during segmentation. The hybrid metaheuristic algorithm improves the quality of solutions during HMRF optimization by combining the cuckoo search and particle swarm optimization algorithms. Experimental results on simulated and real MR brain images show the proposed method achieves satisfactory segmentation performance for images with noise and intensity inhomogeneity.
Copy Move Forgery Detection Using GLCM Based Statistical Features ijcisjournal
The features Gray Level Co-occurrence Matrix (GLCM) are mostly explored in Face Recognition and
CBIR. GLCM technique is explored here for Copy-Move Forgery Detection. GLCMs are extracted from all
the images in the database and statistics such as contrast, correlation, homogeneity and energy are
derived. These statistics form the feature vector. Support Vector Machine (SVM) is trained on all these
features and the authenticity of the image is decided by SVM classifier. The proposed work is evaluated on
CoMoFoD database, on a whole 1200 forged and processed images are tested. The performance analysis
of the present work is evaluated with the recent methods.
A new hybrid method for the segmentation of the brain mrissipij
The magnetic resonance imaging is a method which has undeniable qualities of contrast and tissue
characterization, presenting an interest in the follow-up of various pathologies such as the multiple
sclerosis. In this work, a new method of hybrid segmentation is presented and applied to Brain MRIs. The
extraction of the image of the brain is pretreated with the Non Local Means filter. A theoretical approach is
proposed; finally the last section is organized around an experimental part allowing the study of the
behavior of our model on textured images. In the aim to validate our model, different segmentations were
down on pathological Brain MRI, the obtained results have been compared to the results obtained by
another models. This results show the effectiveness and the robustness of the suggested approach.
A Novel Multiple-kernel based Fuzzy c-means Algorithm with Spatial Informatio...CSCJournals
Fuzzy c-means (FCM) algorithm has proved its effectiveness for image segmentation. However, still it lacks in getting robustness to noise and outliers, especially in the absence of prior knowledge of the noise. To overcome this problem, a generalized a novel multiple-kernel fuzzy cmeans (FCM) (NMKFCM) methodology with spatial information is introduced as a framework for image-segmentation problem. The algorithm utilizes the spatial neighborhood membership values in the standard kernels are used in the kernel FCM (KFCM) algorithm and modifies the membership weighting of each cluster. The proposed NMKFCM algorithm provides a new flexibility to utilize different pixel information in image-segmentation problem. The proposed algorithm is applied to brain MRI which degraded by Gaussian noise and Salt-Pepper noise. The proposed algorithm performs more robust to noise than other existing image segmentation algorithms from FCM family.
This document presents research on content-based image retrieval using color and texture features. It proposes using both quadratic distance based on color histograms to measure color similarity, and pyramid structure wavelet transforms and gray level co-occurrence matrix (GLCM) to measure texture. For color features, quadratic distance is calculated between color histograms to retrieve similar images based on color. For texture, pyramid structure wavelet transforms are used to decompose images into sub-bands and calculate energy levels, while GLCM extracts texture statistics. The methods are evaluated on a dataset of 10000 images and results show the integrated approach of color and texture features provides more accurate and faster retrieval compared to individual features.
A STOCHASTIC STATISTICAL APPROACH FOR TRACKING HUMAN ACTIVITYZac Darcy
This document summarizes a research paper on tracking human activity using a stochastic statistical approach. The paper proposes using covariance matrices to represent image regions for feature extraction, rather than traditional histogram-based methods. An improved mathematical model is developed using covariance matrices that is capable of more accurate and faster human/object tracking compared to existing histogram and other approaches. The accuracy of the new mathematical detection model is approximately 94.3% compared to 89.1% for conventional models, based on evaluation using publicly available datasets. The approach uses integral images to quickly compute covariances over regions of interest for efficient tracking.
Adaptive threshold for moving objects detection using gaussian mixture modelTELKOMNIKA JOURNAL
Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method outperforms the baseline method in term of error detection.
A Hybrid Technique for the Automated Segmentation of Corpus Callosum in Midsa...IJERA Editor
The corpus callosum (CC) is the largest white-matter structure in human brain. In this paper, we take two techniques to observe the results of segmentation of Corpus Callosum. The first one is mean shift algorithm and morphological operation. The second one is k-means clustering. In this paper, it is performed in three steps. The first step is finding the corpus callosum area using adaptive mean shift algorithm or k-means clustering . In second step, the boundary of detected CC area is then used as the initial contour in the Geometric Active Contour (GAC) mode and final step to remove unknown noise using morphological operation and evolved to get the final segmentation result. The experimental results demonstrate that the mean shift algorithm and k-means clustering has provided a reliable segmentation performance.
Robust Adaptive Threshold Algorithm based on Kernel Fuzzy Clustering on Image...cscpconf
The document presents a robust adaptive threshold algorithm based on kernel fuzzy clustering for image segmentation. It proposes using kernel fuzzy c-means clustering (KFCM) to generate adaptive thresholds for segmenting images. KFCM computes fuzzy membership values for pixels to cluster them. The algorithm was tested on MR brain images and showed good performance in detecting large and small objects while also enhancing low contrast images. Experimental results demonstrated the efficiency and accuracy of combining an adaptive threshold algorithm with KFCM for medical image segmentation.
A comprehensive study of different image super resolution reconstruction algo...IAEME Publication
This document discusses various algorithms for super-resolution image reconstruction. It begins by introducing the topic and defining super-resolution as producing a high-resolution image from multiple low-resolution images. It then presents a mathematical model to describe the super-resolution problem and shows how existing single-image restoration algorithms like maximum likelihood (ML), maximum a posteriori (MAP), and projection onto convex sets (POCS) can be applied to the super-resolution problem. Finally, it proposes a hybrid algorithm combining POCS and ML to improve performance and ensure convergence for super-resolution image reconstruction.
EXTENDED WAVELET TRANSFORM BASED IMAGE INPAINTING ALGORITHM FOR NATURAL SCENE...cscpconf
This paper proposes an exemplar based image inpainting using extended wavelet transform. The
Image inpainting modifies an image with the available information outside the region to be
inpainted in an undetectable way. The extended wavelet transform is in two dimensions. The
Laplacian pyramid is first used to capture the point discontinuities, and then followed by a
directional filter bank to link point discontinuities into linear structures. The proposed model
effectively captures the edges and contours of natural scene images
Automatic Segmentation of Brachial Artery based on Fuzzy C-Means Pixel Clust...IJECEIAES
Automatic extraction of brachial artery and measuring associated indices such as flow-mediated dilatation and Intima-media thickness are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose the basic but important component of such decision-assisting medical software development – noise tolerant fully automatic segmentation of brachial artery from ultrasound images. Pixel clustering with Fuzzy C-Means algorithm in the quantization process is the key component of that segmentation with various image processing algorithms involved. This algorithm could be an alternative choice of segmentation process that can replace speckle noise-suffering edge detection procedures in this application domain.
One approach to computerized histopathology image analysis is to leverage the multi-scale texture information resulting from single nuclei appearance to entire cell populations. In this talk, we will introduce a novel framework for learning highly adaptive texture-based local models of biomedical tissue. I will discuss our initial experience with the differentiation of brain tumor types in digital histopathology.
Computer apparition plays the most important role in human perception, which is limited to only the visual band of the electromagnetic spectrum. The need for Radar imaging systems, to recover some sources that
are not within human visual band, is raised. This paper present new algorithm for Synthetic Aperture Radar (SAR) images segmentation based on thresholding technique. Entropy based image thresholding has
received sustainable interest in recent years. It is an important concept in the area of image processing.
Pal (1996) proposed a cross entropy thresholding method based on Gaussian distribution for bi-modal images. Our method is derived from Pal method that segment images using cross entropy thresholding based on Gamma distribution and can handle bi-modal and multimodal images. Our method is tested using
Synthetic Aperture Radar (SAR) images and it gave good results for bi-modal and multimodal images. The
results obtained are encouraging.
This document describes the design elements of a digipak for a music album, noting that the front and back covers as well as the interior images use a consistent urban, street art theme with digitally created images, blocky text styles, and spray painted effects to represent the genre of music and create a cohesive design. Key details like the track listings and parental advisory labels are also included. The design aims to portray a dark, rock-inspired vibe throughout the packaging in a way that matches the music.
SYSTEM AND METHOD FOR ACQUIRING OF STATIC IMAGES OF OBJECTS IN MOTIONUSP
This invention provides a system and method for acquiring static side images of vehicles in motion using video surveillance cameras along toll roads. The system detects vehicles in motion in video frames and stitches together relevant frames to create a single static image of each vehicle as it passes. This allows toll road operators to photograph vehicles for identification and monitoring purposes, without needing direct access or space alongside the road for stationary cameras. The invention is intended to assist toll road operators in Brazil with maintaining traffic order and vehicle supervision on highways.
The document describes a system and method for motion compensation that transforms a reference picture to a current picture for improved compression efficiency. It uses redacted tools to select the best practices for motion compensation by comparing differences between frames and determining transformations from prior motion compensated data. The system includes redacted tools comprising various redacted components and a redacted processor.
This thesis applies Bayesian analysis and Markov random field (MRF) models with MCMC-Gibbs sampling to segment brain MR images into three classes: gray matter, white matter, and cerebrospinal fluid. Synthetic data is used to evaluate the MRF method under different noise conditions. Real brain MR images are also segmented. The MRF model characterizes the spatial dependencies between image pixels. MCMC sampling is used to estimate the posterior distribution and perform segmentation. The results demonstrate the MRF approach can accurately segment images, with better performance on lower noise images.
Paper 58 disparity-of_stereo_images_by_self_adaptive_algorithmMDABDULMANNANMONDAL
This document summarizes a research paper that proposes a new stereo matching algorithm called Self Adaptive Algorithm (SAA) to efficiently compute stereo correspondence or disparity maps from stereo images. SAA aims to improve matching speed by reducing the search zone and avoiding false matches through an adaptive search approach. It dynamically selects the search range based on previous matching results, reducing the range by 50% with each iteration. Experimental results on standard stereo datasets show that SAA outperforms other methods in terms of speed while maintaining accuracy, with processing speeds of 535 fps and 377 fps for different image pairs. SAA reduces computational time by 70.53-99.93% compared to other state-of-the-art methods.
FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHMAM Publications
The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.
Performance Analysis of CRT for Image Encryption ijcisjournal
With the fast advancements of information technology, the security of image data transmitted or stored over
internet is become very difficult. To hide the details, an effective method is encryption, so that only
authorized persons can decrypt the image with the keys available. Since the default features of digital
image such as high capacity data, large redundancy and large similarities among pixels, the conventional
encryption algorithms such as AES, , DES, 3DES, and Blow Fish, are not applicable for real time image
encryption. This paper presents the performance of CRT for image encryption to secure storage and
transmission of image over internet.
The document presents a new method for segmenting MR brain images that combines a hidden Markov random field (HMRF) model with a hybrid metaheuristic optimization algorithm. The HMRF model uses adaptive parameters to balance contributions from different tissue classes during segmentation. The hybrid metaheuristic algorithm improves the quality of solutions during HMRF optimization by combining the cuckoo search and particle swarm optimization algorithms. Experimental results on simulated and real MR brain images show the proposed method achieves satisfactory segmentation performance for images with noise and intensity inhomogeneity.
Copy Move Forgery Detection Using GLCM Based Statistical Features ijcisjournal
The features Gray Level Co-occurrence Matrix (GLCM) are mostly explored in Face Recognition and
CBIR. GLCM technique is explored here for Copy-Move Forgery Detection. GLCMs are extracted from all
the images in the database and statistics such as contrast, correlation, homogeneity and energy are
derived. These statistics form the feature vector. Support Vector Machine (SVM) is trained on all these
features and the authenticity of the image is decided by SVM classifier. The proposed work is evaluated on
CoMoFoD database, on a whole 1200 forged and processed images are tested. The performance analysis
of the present work is evaluated with the recent methods.
A new hybrid method for the segmentation of the brain mrissipij
The magnetic resonance imaging is a method which has undeniable qualities of contrast and tissue
characterization, presenting an interest in the follow-up of various pathologies such as the multiple
sclerosis. In this work, a new method of hybrid segmentation is presented and applied to Brain MRIs. The
extraction of the image of the brain is pretreated with the Non Local Means filter. A theoretical approach is
proposed; finally the last section is organized around an experimental part allowing the study of the
behavior of our model on textured images. In the aim to validate our model, different segmentations were
down on pathological Brain MRI, the obtained results have been compared to the results obtained by
another models. This results show the effectiveness and the robustness of the suggested approach.
A Novel Multiple-kernel based Fuzzy c-means Algorithm with Spatial Informatio...CSCJournals
Fuzzy c-means (FCM) algorithm has proved its effectiveness for image segmentation. However, still it lacks in getting robustness to noise and outliers, especially in the absence of prior knowledge of the noise. To overcome this problem, a generalized a novel multiple-kernel fuzzy cmeans (FCM) (NMKFCM) methodology with spatial information is introduced as a framework for image-segmentation problem. The algorithm utilizes the spatial neighborhood membership values in the standard kernels are used in the kernel FCM (KFCM) algorithm and modifies the membership weighting of each cluster. The proposed NMKFCM algorithm provides a new flexibility to utilize different pixel information in image-segmentation problem. The proposed algorithm is applied to brain MRI which degraded by Gaussian noise and Salt-Pepper noise. The proposed algorithm performs more robust to noise than other existing image segmentation algorithms from FCM family.
This document presents research on content-based image retrieval using color and texture features. It proposes using both quadratic distance based on color histograms to measure color similarity, and pyramid structure wavelet transforms and gray level co-occurrence matrix (GLCM) to measure texture. For color features, quadratic distance is calculated between color histograms to retrieve similar images based on color. For texture, pyramid structure wavelet transforms are used to decompose images into sub-bands and calculate energy levels, while GLCM extracts texture statistics. The methods are evaluated on a dataset of 10000 images and results show the integrated approach of color and texture features provides more accurate and faster retrieval compared to individual features.
A STOCHASTIC STATISTICAL APPROACH FOR TRACKING HUMAN ACTIVITYZac Darcy
This document summarizes a research paper on tracking human activity using a stochastic statistical approach. The paper proposes using covariance matrices to represent image regions for feature extraction, rather than traditional histogram-based methods. An improved mathematical model is developed using covariance matrices that is capable of more accurate and faster human/object tracking compared to existing histogram and other approaches. The accuracy of the new mathematical detection model is approximately 94.3% compared to 89.1% for conventional models, based on evaluation using publicly available datasets. The approach uses integral images to quickly compute covariances over regions of interest for efficient tracking.
Adaptive threshold for moving objects detection using gaussian mixture modelTELKOMNIKA JOURNAL
Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method outperforms the baseline method in term of error detection.
A Hybrid Technique for the Automated Segmentation of Corpus Callosum in Midsa...IJERA Editor
The corpus callosum (CC) is the largest white-matter structure in human brain. In this paper, we take two techniques to observe the results of segmentation of Corpus Callosum. The first one is mean shift algorithm and morphological operation. The second one is k-means clustering. In this paper, it is performed in three steps. The first step is finding the corpus callosum area using adaptive mean shift algorithm or k-means clustering . In second step, the boundary of detected CC area is then used as the initial contour in the Geometric Active Contour (GAC) mode and final step to remove unknown noise using morphological operation and evolved to get the final segmentation result. The experimental results demonstrate that the mean shift algorithm and k-means clustering has provided a reliable segmentation performance.
Robust Adaptive Threshold Algorithm based on Kernel Fuzzy Clustering on Image...cscpconf
The document presents a robust adaptive threshold algorithm based on kernel fuzzy clustering for image segmentation. It proposes using kernel fuzzy c-means clustering (KFCM) to generate adaptive thresholds for segmenting images. KFCM computes fuzzy membership values for pixels to cluster them. The algorithm was tested on MR brain images and showed good performance in detecting large and small objects while also enhancing low contrast images. Experimental results demonstrated the efficiency and accuracy of combining an adaptive threshold algorithm with KFCM for medical image segmentation.
A comprehensive study of different image super resolution reconstruction algo...IAEME Publication
This document discusses various algorithms for super-resolution image reconstruction. It begins by introducing the topic and defining super-resolution as producing a high-resolution image from multiple low-resolution images. It then presents a mathematical model to describe the super-resolution problem and shows how existing single-image restoration algorithms like maximum likelihood (ML), maximum a posteriori (MAP), and projection onto convex sets (POCS) can be applied to the super-resolution problem. Finally, it proposes a hybrid algorithm combining POCS and ML to improve performance and ensure convergence for super-resolution image reconstruction.
EXTENDED WAVELET TRANSFORM BASED IMAGE INPAINTING ALGORITHM FOR NATURAL SCENE...cscpconf
This paper proposes an exemplar based image inpainting using extended wavelet transform. The
Image inpainting modifies an image with the available information outside the region to be
inpainted in an undetectable way. The extended wavelet transform is in two dimensions. The
Laplacian pyramid is first used to capture the point discontinuities, and then followed by a
directional filter bank to link point discontinuities into linear structures. The proposed model
effectively captures the edges and contours of natural scene images
Automatic Segmentation of Brachial Artery based on Fuzzy C-Means Pixel Clust...IJECEIAES
Automatic extraction of brachial artery and measuring associated indices such as flow-mediated dilatation and Intima-media thickness are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose the basic but important component of such decision-assisting medical software development – noise tolerant fully automatic segmentation of brachial artery from ultrasound images. Pixel clustering with Fuzzy C-Means algorithm in the quantization process is the key component of that segmentation with various image processing algorithms involved. This algorithm could be an alternative choice of segmentation process that can replace speckle noise-suffering edge detection procedures in this application domain.
One approach to computerized histopathology image analysis is to leverage the multi-scale texture information resulting from single nuclei appearance to entire cell populations. In this talk, we will introduce a novel framework for learning highly adaptive texture-based local models of biomedical tissue. I will discuss our initial experience with the differentiation of brain tumor types in digital histopathology.
Computer apparition plays the most important role in human perception, which is limited to only the visual band of the electromagnetic spectrum. The need for Radar imaging systems, to recover some sources that
are not within human visual band, is raised. This paper present new algorithm for Synthetic Aperture Radar (SAR) images segmentation based on thresholding technique. Entropy based image thresholding has
received sustainable interest in recent years. It is an important concept in the area of image processing.
Pal (1996) proposed a cross entropy thresholding method based on Gaussian distribution for bi-modal images. Our method is derived from Pal method that segment images using cross entropy thresholding based on Gamma distribution and can handle bi-modal and multimodal images. Our method is tested using
Synthetic Aperture Radar (SAR) images and it gave good results for bi-modal and multimodal images. The
results obtained are encouraging.
This document describes the design elements of a digipak for a music album, noting that the front and back covers as well as the interior images use a consistent urban, street art theme with digitally created images, blocky text styles, and spray painted effects to represent the genre of music and create a cohesive design. Key details like the track listings and parental advisory labels are also included. The design aims to portray a dark, rock-inspired vibe throughout the packaging in a way that matches the music.
SYSTEM AND METHOD FOR ACQUIRING OF STATIC IMAGES OF OBJECTS IN MOTIONUSP
This invention provides a system and method for acquiring static side images of vehicles in motion using video surveillance cameras along toll roads. The system detects vehicles in motion in video frames and stitches together relevant frames to create a single static image of each vehicle as it passes. This allows toll road operators to photograph vehicles for identification and monitoring purposes, without needing direct access or space alongside the road for stationary cameras. The invention is intended to assist toll road operators in Brazil with maintaining traffic order and vehicle supervision on highways.
The document describes a system and method for motion compensation that transforms a reference picture to a current picture for improved compression efficiency. It uses redacted tools to select the best practices for motion compensation by comparing differences between frames and determining transformations from prior motion compensated data. The system includes redacted tools comprising various redacted components and a redacted processor.
This document summarizes the block matching based motion estimation algorithm. The algorithm estimates motion between two frames by comparing blocks in the reference frame to blocks in the target frame. It discusses key parameters like block size, search window, and matching criteria. Experimental results on example images demonstrate how varying these parameters affects the motion fields and computation time. The best results were found with a block size of 16x16, search window of 16, and threshold of 1.7.
Introduction to Digital Videos, Motion Estimation: Principles & Compensation. Learn more in IIT Kharagpur's Image and Video Communication online certificate course.
This document compares different block-matching motion estimation algorithms. It introduces block-matching motion estimation and describes popular distortion metrics like MSE and SAD. It then explains the full-search algorithm and more efficient algorithms like three-step search and four-step search that evaluate fewer candidate blocks to reduce computational cost. These algorithms are evaluated and compared using video test sequences to analyze their performance and quality.
Curved Wavelet Transform For Image Denoising using MATLAB.Nikhil Kumar
This document summarizes a student project on image denoising using wavelet analysis. It introduces wavelet transforms as a method to denoise digital images corrupted by noise. The project uses MATLAB to apply a discrete wavelet transform with a Haar wavelet, thresholds wavelet coefficients at different levels to compress and denoise the image, and demonstrates the results on an example image.
This document summarizes a presentation given by John Grant at the 2015 International Brand Conference on emerging markets and global brands. It discusses factors impacting China's economy, such as slowing growth rates and increasing wages. It also examines Japan's economic expansion in the 1970s-80s despite currency inflation. Additionally, it outlines MIT Sloan programs covering brand issues, the most valuable global brands, and observations on branding strategies of global and local enterprises in emerging markets, with key takeaways related to China.
The document discusses digital image processing and provides an overview of key concepts. It defines digital and analog images and explains how digital images are represented by pixels. It outlines fundamental steps in digital image processing like image acquisition, enhancement, restoration, morphological processing, segmentation, representation, compression and object recognition. It also discusses applications in areas like remote sensing, medical imaging, film and video effects.
This document discusses image denoising techniques. It begins by defining image denoising as removing unwanted noise from an image to restore the original signal. It then discusses several types of noise like additive Gaussian noise, impulse noise, uniform noise, and periodic noise. For denoising, it covers spatial domain techniques like linear filters (mean, weighted mean), non-linear filters (median filter), and frequency domain techniques that apply a low-pass filter to the Fourier transform of the noisy image. The document provides examples of denoising noisy images using mean and median filters to remove different types of noise.
This document is a project report on noise reduction in images using filters. It was submitted by 4 students - Priya M, Dondla Leela Vasundhara, Inderpreet Kaur, and Nisha Mathew - to the Department of Computer Science at Mount Carmel College in Bengaluru, India. The report discusses image processing techniques including different types of noise, noise reduction methods, and the use of filters to reduce noise in digital images.
This is the basic introductory presentation for beginners. It gives you the idea about what is image processing means. The presentation consists of introduction to digital image processing, image enhancement, image filtering, finding an image edge, image analysis, tools for image processing and finally some application of digital image processing.
The document discusses various image denoising algorithms. It defines image denoising as removing noise from an image. Common denoising algorithms discussed include spatial domain filters like Gaussian filtering, anisotropic filtering, and total variation minimization. Neighborhood filtering and non-local means (NL-means) are also summarized. Gaussian filtering blurs edges and textures while anisotropic filtering degrades flat and texture regions. Total variation minimization can oversmooth textures. Neighborhood filtering is not robust to noisy pixel values. In contrast, NL-means compares neighborhood configurations and is more robust than neighborhood filtering alone.
1) Digital image processing involves processing digital images using computer software and algorithms. It includes techniques like image enhancement, restoration, compression, and segmentation.
2) The key stages in digital image processing are image acquisition, enhancement, restoration, morphological processing, segmentation, object recognition, representation and description, compression, and color image processing.
3) Digital image processing has various applications including medical imaging, space exploration, document processing, photography, remote sensing, and video/film special effects. It covers almost the entire electromagnetic spectrum from gamma to radio waves.
The document discusses image denoising techniques based on partial differential equations (PDEs). It begins by defining image noise and describing conventional denoising filters like averaging and median filters. It then focuses on diffusion-based denoising methods, particularly the influential 1987 work of Perona and Malik which introduced nonlinear anisotropic diffusion. Their approach uses an edge-stopping function to reduce diffusion near edges. The document outlines linear and nonlinear diffusion models, conditions for the diffusion coefficient function, and extensions of the Perona-Malik model. It summarizes a 2014 paper proposing a robust anisotropic diffusion scheme using novel variants of the edge-stopping function and diffusivity parameter computation.
This document provides an overview of real-time image processing. It begins with introducing real-time image processing and how it differs from ordinary image processing by having deadlines and predictable response times. The document then discusses the requirements for a real-time image processing system including high resolution video input, low latency, and high processing performance. It also covers applications such as mobile robots and human-computer interaction. In the end, it provides definitions of real-time image processing in both the perceptual and signal processing senses.
Introduction to Digital Image Processing Using MATLABRay Phan
This was a 3 hour presentation given to undergraduate and graduate students at Ryerson University in Toronto, Ontario, Canada on an introduction to Digital Image Processing using the MATLAB programming environment. This should provide the basics of performing the most common image processing tasks, as well as providing an introduction to how digital images work and how they're formed.
You can access the images and code that I created and used here: https://www.dropbox.com/sh/s7trtj4xngy3cpq/AAAoAK7Lf-aDRCDFOzYQW64ka?dl=0
CELL TRACKING QUALITY COMPARISON BETWEEN ACTIVE SHAPE MODEL (ASM) AND ACTIVE ...ijitcs
The aim of this paper is to introduce a comparison between cell tracking using active shape model (ASM)
and active appearance model (AAM) algorithms, to compare the cells tracking quality between the two
methods to track the mobility of the living cells. Where sensitive and accurate cell tracking system is
essential to cell motility studies. The active shape model (ASM) and active appearance model (AAM)
algorithms has proved to be a successful methods for matching statistical models. The experimental results
indicate the ability of (AAM) meth
Online Multi-Person Tracking Using Variance Magnitude of Image colors and Sol...Pourya Jafarzadeh
The document describes a multi-object tracking method that formulates tracking as a Short Minimum Clique Problem (SMCP). It uses three consecutive frames divided into three clusters, where each clique between clusters represents a tracklet (partial trajectory) of a person. Edges between clusters are weighted based on color histogram similarity and eigenvalue similarity of bounding boxes. Occlusion handling is performed by saving color histograms of occluded people in a buffer and comparing them to newly detected people. The method was evaluated on challenging datasets and shown to achieve promising results compared to state-of-the-art methods.
Hyperspectral Data Compression Using Spatial-Spectral Lossless Coding TechniqueCSCJournals
Hyperspectral imaging is widely used in many applications; especially in vegetation, climate changes, and desert studies. Such kind of imaging has a huge amount of data, which requires transmission, processing, and storage resources especially for space borne imaging. Compression of hyperspectral data cubes is an effective solution for these problems. Lossless compression of the hyperspectral data usually results in low compression ratio, which may not meet the available resources; on the other hand, lossy compression may give the desired ratio, but with a significant degradation effect on object identification performance of the hyperspectral data. Moreover, most hyperspectral data compression techniques exploits the similarities in spectral dimensions; which requires bands reordering or regrouping, to make use of the spectral redundancy. In this paper, we analyze the spectral cross correlation between bands for Hyperion hyperspectral data; spectral cross correlation matrix is calculated, assessing the strength of the spectral matrix, and finally, we propose new technique to find highly correlated groups of bands in the hyperspectral data cube based on "inter band correlation square", from the resultant groups of bands we propose a new predictor that can predict efficiently the whole bands within data cube based on weighted combination of spectral and spatial prediction, the results are evaluated versus other state of the art predictor for lossless compression.
Contour-based Pedestrian Detection with Foreground Distribution Trend Filteri...ITIIIndustries
In this work, we propose a real-time pedestrian detection method for crowded environments based on contour and motion information. Sparse contour templates of human shapes are first generated on the basis of a point distribution model (PDM), then a template matching step is applied to detect humans. To reduce the detecting time complexity and improve the detection accuracy, we propose to take the ratio and distribution trend of foreground pixels inside each detecting window into consideration. A tracking method is further applied to deal with the short-term occlusions and false alarms. The experimental results show that our method can efficiently detect pedestrians in videos of crowded scenes.
Corner Detection Using Mutual InformationCSCJournals
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This document summarizes an image compression algorithm called SAND. SAND compresses images without any loss of information by eliminating repeated pixels of the same color. It works in two steps: 1) Latitudinal compression, where rows are processed to absorb repeated pixels, and 2) Longitudinal compression, where the same is done for columns. The compressed image and data on the pixel absorptions are stored and transmitted. Decompression reconstructs the original image by interpreting the absorption data and referencing the compressed image as needed. SAND can achieve around 40% compression and is well-suited for applications where lossless compression is required, such as transmitting astronomical images.
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.
Geospatial Data Acquisition Using Unmanned Aerial SystemsIEREK Press
The Rivers State University campus in Portharcourt is one of the university campuses in the city of Portharcourt,
Nigeria covering over 21 square kilometers and housing a variety of academic, residential, administrative and other
support buildings. The University Campus has seen significant transformation in recent years, including the
rehabilitation of old facilities, the construction of new academic facilities and the most recent update on the creation
of new collages, faculties and departments. The current view of the transformations done within the University
Campus is missing from several available maps of the university. Numerous facilities have been constructed on the
University Campus that are not represented on these maps as well as the qualities associated with these facilities.
Existing information on the various landscapes on the map is outdated and it needs to be streamlined in light of
recent changes to the University's facilities and departments. This research article aims to demonstrate the
effectiveness of unmanned aerial systems (UAS) in geospatial data collection for physical planning and mapping of
infrastructures at the Rivers State University Port Harcourt campus by developing a UAS-based digital map and
tour guide for RSU's main campus covering all collages, faculties and departments and this offers visitors, staff and
students with location and attribute information within the campus.
Methodologically, Unmanned Aerial Vehicles were deployed to obtain current visible images of the campus
following the growth and increasing infrastructural development. At a flying height of 76.2m (250 ft), a DJI
Phantom 4 Pro UAS equipped with a 20-megapixel visible camera was flown around the campus, generating
imagery with 1.69cm spatial resolution per pixel. To obtain 3D modeling capabilities, visible imagery was acquired
using the flight-planning software DroneDeploy with a near nadir angle and 75 percent front and side overlap.
Vertical positions were linked to the World Geodetic System 1984 and horizontal positions to the 1984 World
Geodetic Datum universal transverse Mercator (UTM) (WGS 84). To match the UAS data, GCPs were transformed
to UTM zone 32 north.
Finally, dense point clouds, DSM, and an orthomosaic which is a geometrically corrected aerial image that provides
an accurate representation of an area and can be used to determine true distances, were among the UAS-derived
deliverables.
Blind Image Seperation Using Forward Difference Method (FDM)sipij
In this paper, blind image separation is performed, exploiting the property of sparseness to represent images. A new sparse representation called forward difference method is proposed. It is known that most of the independent component analysis (ICA) basis functions, extracted from images are sparse and gives unreliable sparseness measure. In the proposed method, the image mixture is first transformed to sparse images. These images are divided into blocks and for each block the sparseness measure ε0 norm is applied. The block having the most sparseness is considered to determine the separation matrix. The efficiency of the proposed method is compared with other sparse representation functions.
Fast Stereo Images Compression Method based on Wavelet Transform and Two dime...BRNSSPublicationHubI
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Sinusoidal Function for Population Size in Quantum Evolutionary Algorithm and...sipij
Fractal Image Compression is a well-known problem which is in the class of NP-Hard problems. Quantum Evolutionary Algorithm is a novel optimization algorithm which uses a probabilistic representation for solutions and is highly suitable for combinatorial problems like Knapsack problem. Genetic algorithms are widely used for fractal image compression problems, but QEA is not used for this kind of problems yet. This paper improves QEA whit change population size and used it in fractal image compression. Utilizing the self-similarity property of a natural image, the partitioned iterated function system (PIFS) will be found to encode an image through Quantum Evolutionary Algorithm (QEA) method Experimental results show that our method has a better performance than GA and conventional fractal image compression algorithms.
This document discusses tracking multiple objects in video using probabilistic distributions. It proposes using particle filters to represent object positions with random particles. The method initializes particles randomly, updates their positions each frame based on probabilistic distributions, and uses maximum likelihood estimation to compute the distribution parameters. It models object motion using a beta distribution and estimates the distribution's alpha and beta parameters from each frame to predict object positions. The results show this approach can effectively track multiple moving objects, especially when there are occlusions.
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.
This document summarizes a method for tracking deformable objects in images. It proposes casting the problem as finding optimal cyclic paths in a product space of the template shape and input image. A cost functional is introduced that considers data fidelity, shape consistency, and elastic deformation. The functional is optimized using a minimum ratio cycle algorithm on graphics cards, allowing real-time segmentation and tracking of deformable objects while guaranteeing a globally optimal solution. The method can be extended to track multiple deformable anatomical structures in medical images.
This document summarizes a method for tracking deformable objects in images. It proposes casting the problem as finding optimal cyclic paths in a product space of the template shape and input image. A cost functional is introduced that consists of three terms: data fidelity favoring strong edges, shape consistency favoring similar tangent angles, and an elastic penalty for stretching. Optimization is performed using simulated annealing for segmentation and iterated conditional modes for tracking. The algorithm provides optimal segmentation and point correspondences between template and image curve in linear time.
This document summarizes a research paper that proposes a new approach for tracking multiple deformable anatomical structures in medical images using geometrically deformable templates (GDTs). The GDTs can deform to match similar shapes based on image forces while minimizing a penalty function that measures deformation from the template's equilibrium shape. This allows simultaneous segmentation of multiple objects using intra- and inter-shape information. Simulated annealing is used for segmentation while iterated conditional modes is used for tracking. The paper also reviews previous work on image segmentation, tracking deformable objects, and shape-based image segmentation.
Quality Measurements of Lossy Image Steganography Based on H-AMBTC Technique ...AM Publications,India
This document presents a new technique called H-AMBTC (Hadamard-Absolute Moment Block Truncation Coding) for lossy image steganography. H-AMBTC combines Hadamard transformation with AMBTC (Absolute Moment Block Truncation Coding) to compress cover images and conceal secret data within them. The document describes the H-AMBTC encoding and decoding algorithms and compares its performance using different block sizes (2x2, 4x4, 8x8, 16x16) based on PSNR values. Results show that 16x16 blocks provided the best PSNR for test images, indicating higher quality of the stego-images produced by the H-AMBTC technique compared to smaller block sizes
This document introduces an R package called PSF that implements a Pattern Sequence based Forecasting (PSF) algorithm for univariate time series forecasting. The PSF algorithm clusters time series data and then predicts future values based on identifying repeating patterns of clusters. The PSF package contains functions that perform the main steps of the PSF algorithm, including selecting the optimal number of clusters, selecting the optimal window size, and making predictions for a given window size and number of clusters. The package aims to promote and simplify the use of the PSF algorithm for time series forecasting.
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
Multi fractal analysis of human brain mr imageeSAT Journals
Abstract In computer programming, code smell may origin of latent problems in source code. Detecting and resolving bad smells remain time intense for software engineers despite proposals on bad smell detecting and refactoring tools. Numerous code smells have been recognized yet the sequence in which the detection and resolution of different kinds of code smells are performed because software engineers do not know how to optimize sequence. In this paper, the novel refactoring approach is proposed to improve the performance of programs. In this recommended approach the code smells are automatically detected and refactored. The simulation results propose the reduction of time over the semi-automated refactoring are achieved when code smells are refactored by using multi-step automated refactoring. Keywords: Code smell, multi step refactoring, detection, code resolution, restructuring etc
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
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Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Robust Block-Matching Motion Estimation of Flotation Froth Using Mutual Information
1. Anthony Amankwah & Chris Aldrich
International Journal of Image Processing (IJIP), Volume (6) : Issue (5) : 2012 380
Robust Block-Matching Motion Estimation of Flotation Froth
Using Mutual Information
Anthony Amankwah anthony.amankwah@wits.ac.za
School of Computer Science
University of Witwatersrand
Johannesburg, 2050, South Africa
Chris Aldrich chris.aldrich@curtin.edu.au
Western Australian School of Mines
Curtin University of University, GPO Box U1987
Perth, WA, 6845, Australia
Abstract
In this paper, we propose a new method for the motion estimation of flotation froth using mutual
information with a bin size of two as the block matching similarity metric. We also use three-step
search and new-three-step-search as a search strategy. Mean sum of absolute difference (MAD)
is widely considered in blocked based motion estimation. The minimum bin size selection of the
proposed similarity metric also makes the computational cost of mutual information similar to
MAD. Experimental results show that the proposed motion estimation technique improves the
motion estimation accuracy in terms of peak signal-to-noise ratio of the reconstructed frame. The
computational cost of the proposed method is almost the same as the standard machine vision
methods used for the motion estimation of flotation froth.
Keywords: Mutual information, Mean sum of absolute difference, Motion Estimation, Froth
Flotation.
1. INTRODUCTION
Froth flotation is one of the most widely used separation methods in the mineral processing
industries. Despite being in use for more than a century, it is still not fully understood and remains
comparatively inefficient. Conventionally, the flotation process is controlled by trained operators
who visually inspect the froth and adjust the process control parameters based on their training. A
problematic situation is that different operators tend to interpret the visual characteristics of the
froth differently.
However, during the last couple of decades, froth image analysis has been studied widely as a
means to obviate these problems, as summarized in a recent review by Aldrich et al. [1]. Froth
images are a potentially rich source of information with regard to the state of the flotation process
and can be used as a basis for the development of advanced online control systems. Apart from
the appearance of the froth, the motion of flotation froth is also an important variable related to
the optimal recovery of minerals on industrial plants and has been used in industry among other
as a basis for mass pull control strategies [2].
The estimation of motion of froth using image processing techniques is not easy, since bubbles
collapse and merge, resulting in bubble deformations that may be difficult to track. Moreover,
although the froth as a whole has an average motion, it moves rapidly at the edges of the cell at
the launder overflow, while remaining almost stagnant at the centre of the cell [1].
Several approaches to froth motion estimation have been investigated, including bubble tracking
[3], cluster matching [4], and pixel tracing [5], but the most popular motion estimation technique is
2. Anthony Amankwah & Chris Aldrich
International Journal of Image Processing (IJIP), Volume (6) : Issue (5) : 2012 381
block matching [6-7] using the mean absolute difference (MAD) between successive images of
parts thereof [2]. In the block matching algorithm, the image frame is typically divided into
nonoverlapping rectangular blocks [8]. The best match to a given block of pixels is subsequently
searched in the previous frame of the video sequence within a search area about the location of
the current block. In principle, the best solution can be obtained with a full search, which
exhaustively searches for the best matched block within all locations of the search window.
However, this approach is computationally expensive and several fast search techniques have
been proposed to reduce the high computational cost of full search methods. These fast search
techniques select a subgroup of possible search candidate locations and thus reduce the number
of matching calculations per block image.
Examples of such fast search techniques are three-step-search (TSS) [9], new-three-step-search
(NTSS) [10], four-step-search [11], 2-D logarithmic search [12] and the diamond search [13]. The
mean square error (MSE) or mean absolute difference (MAD) are considered to be among the
best similarity metrics for motion estimation. In this paper, a new method is proposed for
estimating the motion of flotation froth using mutual information. As will be shown, estimation of
mutual information with a bin size of 2 as the block matching criterion improves the accuracy of
motion estimation in terms of the peak signal-to-noise ratio of the reconstructed frame, while the
computational complexity of the proposed similarity metric is also similar to MAD.
2. SIMILARITY METRICS
.
2.1 Mutual Information with Bin Size of Two
If X and Y are two image blocks to be matched, the mutual information (MI) can defined by
),()()(),( YXHYHXHYXMI −+= (1)
where H(X) and H(Y) are the Shannon entropies [14] of X and Y respectively, and H (X,Y) is the
Shannon entropy of the joint distribution of X and Y. If pX(x) and pY(y) are the marginal
probabilities, and pXY(xy) is defined as the joint probability density of X and Y. Then MI is defined
as
∑∑
∗
=
x y YX
XY
XY
ypxp
yxp
yxpYXMI
)()(
),(
log*),(),( .. (2).
In this work, we use the histogram method to estimate MI. Let HX(x) and HY(y) be the histograms
of X and Y respectively and HXY(x,y) their joint histogram. then MI can be defined as
∑∑
=
x y YX
XY
yHxH
yxEH
yxH
E
YXMI
)(*)(
),(
log*),(
1
),( (3)
where E is the number of entries [15-17].
Figure 1 shows the joint grey value histograms and corresponding joint entropies of an image
block with values shifted (a) 0 pixels, (b) 5 pixels, (c) 10 pixels and (d) 100 pixels. since the image
pairs are identical, all corresponding grey values lie on the diagonal in Figure 1(a). From the joint
histograms it can be deduced that i) diagonal features diminish with increasing dissimilarity. ii) the
spatial dispersion pattern increases in areas with increasing dissimilarity, and iii) joint entropy
increases with increasing similarity.
3. Anthony Amankwah & Chris Aldrich
International Journal of Image Processing (IJIP), Volume (6) : Issue (5) : 2012 382
(a) H(X,Y) = 0.53 (b) H(X,Y) = 0.98
(c) H(X,Y) = 1.05 (d) H(X,Y) = 1.20
Figure 1. Joint grey values of a block image of a flotation froth and a shifted version of the image.
(a) (b)
Figure 2. Example of (a) MAD surface of a block for a search area of 5 pixels (b) MI surface of the
same block over a search area of 5 pixels
Mutual information contains the term –H (A,B), which means that the minimization of the joint
entropy will maximize the mutual information. Since generally joint entropy increases with
4. Anthony Amankwah & Chris Aldrich
International Journal of Image Processing (IJIP), Volume (6) : Issue (5) : 2012 383
increasing image block mismatching, the mutual information decreases with increased
mismatching, i.e. similarity corresponds with the maximization of the mutual information. That is, if
images are aligned the amount of information they contain about each other is maximal.
The computational complexity between two block images depends not only on the number of
pixels in each block image (M) but also the number of bins used the estimate MI. The
computational cost of the histogram is )(MO . The computational cost relative to the number of
histogram bins is )( 2
RO , where R is the number of bins. In this work only two bins were used
(R = 2), with a block size of 40 x 40 pixels, so that the computational cost of MAD and MI were
approximately the same. The principle of maximization of mutual information is applied to the
motion vector of blocks. The motion vector is obtained by equation
),(maxarg
1,
nmMImv
pnmp −≤≤−
= (4)
Figures 2(a) and 2(b) respectively show the MAD surface and MI2 surface of same block over a
search area of 5 pixels in each of the vertical and horizontal directions. The MI2 surface is
sharper than the MAD surface, suggesting that MI2 can be more robust than MAD, since both
surfaces are smooth.
2.2 Mean Absolute Difference (MAD)
The full MAD search of an image frame divided into NN × blocks, can be expressed as
∑∑
−
=
−
−=
=
++−=
1
0
1
1
0
,,(),(),(
N
j
tt
Ni
i
njmiFjiFnmMAD
pnmp ≤≤− , (5)
where MAD(m,n) is the MAD computed for the displacement of (m,n) pixels. F
t
(i,j) and F
t-1
(i,j) are
the grey level values of the current and previous frame respectively. The search area or the
maximum displacement, p, allowed in the vertical and horizontal directions. The motion vector for
a block is obtained by the equation
),(minarg
1,
nmMADmv
pnmp −≤≤−
= (6)
where mv is the motion vector of the vertical and horizontal displacements. The computational
complexity of MAD is )(MO additions, where M is the number of pixels in each frame
3. SEARCH METHODS
The three-step-search and the new-three-step-search algorithms were used in this investigation.
3.1 Three-Step-Search (TSS)
The TSS algorithm begins with search location considered as the centre and sets a step size
depending on the search window. It searches eight locations around the centre. From the nine
locations searched, the one with the best similarity metric is selected as the new centre. A new
step size is selected, which is half of the original step size and a similar search is repeated for two
more iterations. Figure 3(a) illustrates the TSS algorithm.
5. Anthony Amankwah & Chris Aldrich
International Journal of Image Processing (IJIP), Volume (6) : Issue (5) : 2012 384
3.2 New-Three-Step-Search (NTSS)
The NTSS is a modified version of TSS and a centre biased search method. In the first step, 16
points are checked in addition to the search origin for the lowest or highest weight of the cost
function. If the optimal cost is at the origin, the search is stopped and the motion vector is (0,0). If
the optimal cost is at any of the 8 locations nearest to the origin, then the origin of the search is
changed to that point and points adjacent to it are searched. Depending on which point it is, 5
points or 3 points night be checked, as shown in Figure 3(b). The location that gives the optimal
cost is the best match and the motion vector is set to that location. On the other hand, if the
optimal cost is associated with one of the 8 locations further away from the origin, then the TSS
search method is followed. Thus although the NTSS method might need a minimum of 17 points
for every macroblock in the image, it has a worst case scenario of 33 points that have to be
searched.
First Step Second Step Third Step Out 1st
Step Inner 1st
Step Three Step Five Step
(a) (b)
Figure 3(a). The Three Step Search algorithm with rectangles, triangles and circles representing
first step search, second step search and third step search respectively, and (b) NTSS with
rectangles as first outer step, triangle as inner first step, circles and squares as three step and
five step respectively.
4. EXPERIMENTS AND RESULTS
The performance of the proposed similarity metric and MAD were evaluated using two video
sequences “Flot1” and “Flot2” from the Anglo Platinum Centre of Excellence laboratory in
Stellenbosch. The TSS and NTSS search strategies were used. The frame rate was 30 Hz and
the resolution of both sequences was 720 x 1280 pixels. A 4 dB Gaussian was added to Flot2 to
test the robustness of the algorithms, while a block size of 40 x 40 and a search area of 5 pixels
were used in the vertical and horizontal directions. The peak signal to noise ratio (PSNR) of the
image given in equation 6 is used to evaluate the performance of the algorithms
=
MSE
PSNR
2
10
255
log10
. (7)
Figures 4(a) and 4(b) show the motion vector estimate of frame 6 in the Flot1 sequence using
MAD with TSS and MI2 with TSS respectively. Figures 4(c) and 4(d) also show the motion vector
6. Anthony Amankwah & Chris Aldrich
International Journal of Image Processing (IJIP), Volume (6) : Issue (5) : 2012 385
estimate of frame 7 in the Flot2 sequence using MAD with TSS and MI2 with TSS respectively.
Figure 5(a) and 5(b) show the comparisons of the PSNR of MI2-with-NTSS, MI2-with-TSS, MAD-
with-NTSS, and MAD-with-TSS of the Flot1 and Flot2 sequences respectively. The performance
of the algorithms in terms of the PSNR of the reconstructed frame for the Flot1 sequence is
almost the same. The performance of the MI2 algorithms (MI2-with-NTSS and MI2-with-TSS) is
better than the MAD algorithms (MAD-with-NTSS and MAD-with-TSS). The overall average
PSNR of the experiments for the MI2 algorithms and the MAD algorithms are 66.6 dB and 66.0
dB respectively.
(a) (b)
(c) (d)
Figure 4(a). Motion vector of frame 6 in the Flot1 sequence estimated by conventional MAD using
TSS, (b) Motion vector of frame 6 in the Flot1 sequence estimated by conventional MI2 using
TSS (c) Motion vector of frame 7 in the Flot2 sequence estimated by the conventional MAD using
TSS, and. (d) Motion vector of frame 2 in the Flot2 sequence estimated by the conventional MI2
using TSS.
Tabel.1 Average peak to signal noise ratio (PSNR) (dB) of the Flot1 and Flot2 seqeunces
reconstructed by various motion estimation algorithms.
Method Flot1 Flot2
MAD-with-TSS 67.34 64.67
MAD-with-NTSS 67.35 64.67
MI2-with-TSS 66.94 66.12
MI2-with-NTSS 67.00 66.24
7. Anthony Amankwah & Chris Aldrich
International Journal of Image Processing (IJIP), Volume (6) : Issue (5) : 2012 386
(a)
(b)
Figure 5(a). Comparisons of the peak signal to noise ratio (PSNR) of sequences reconstructed by
various motion estimation algorithms. Using the Flot1 sequence with a resolution and frame rate
of 720 x 1280 and 30Hz respectively, and (b) using the Flot2 sequence with a resolution and
frame rate of 720 x 1280 and 30Hz respectively.
8. Anthony Amankwah & Chris Aldrich
International Journal of Image Processing (IJIP), Volume (6) : Issue (5) : 2012 387
5. CONCLUSIONS
In this paper, a new motion estimation method for flotation froth is proposed by using mutual
information with a bin size of two (MI2) as a similarity criterion. The computational cost is similar
to the popular MAD approach. The three-step-search and the new-three-step-search algorithms
were used to search the images. MI2 yielded better results in terms of the PSNR of the
reconstructed image frames than the classical minimum absolute difference (MAD). It reduces the
amount of bad motion vectors especially for noisy images compared the MAD approach. In the
future, the algorithm will tested with more video sequences in combination with different search
strategies. We will also investigate optimization algorithms such as the Spall’s algorithm [18] to
reduce computational cost and the lightening systems for the experimental data.
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