This document discusses using genetic algorithms for image enhancement and segmentation. It begins with an overview of genetic algorithms and how they can be applied to optimization problems like image processing. Specifically, it describes how genetic algorithms use operators like crossover and mutation to evolve solutions over generations. It then discusses how genetic algorithms can be used for two main image processing tasks: image enhancement to improve image quality, and image segmentation to partition an image into meaningful regions. The key steps of the genetic algorithm for these tasks are described, including initializing a population, defining a fitness function, and applying genetic operators to evolve better solutions across generations.
A DISCUSSION ON IMAGE ENHANCEMENT USING HISTOGRAM EQUALIZATION BY VARIOUS MET...pharmaindexing
This document summarizes several papers on image enhancement techniques using histogram equalization. It discusses papers that propose sub-region histogram equalization to improve contrast while preserving spatial relationships. It also discusses a 3D histogram equalization method that produces a uniform 1D grayscale histogram to overcome issues with previous color histogram methods. Another paper proposes using total variation minimization for cartoon-texture decomposition prior to histogram equalization to reduce intensity saturation effects. Further, a technique called gain controllable clipped histogram equalization is presented to enhance contrast while preserving original brightness. Finally, a method called bi-histogram equalization with neighborhood metrics is described which divides histograms to improve local contrast while maintaining brightness.
7 ijaems sept-2015-8-design and implementation of fuzzy logic based image fus...INFOGAIN PUBLICATION
The quality of image holds importance for both humans and machines. To fulfill the requirement of good quality images, image enhancement is needed. Application of a single contrast enhancement technique often does not produce desirable result and may lead to over enhanced images. To overcome this problem image fusion is performed so that better results with desired enhancement can be achieved. In the present paper an amalgamation of image enhancement, fusion and sharpening have been carried out in the candidate algorithm. The algorithm makes use of fuzzy logic for weight calculation. The results are compared with DACE/LIF approach and it is observed that the proposed algorithm improves the result in terms of quality parameters like PSNR (Peak Signal to Noise Ratio), AMBE (Absolute Mean Brightness Error) and SSIM (Structural Similarity Index) by 0.5 dB, 3 and 0.1 respectively from the existing technique.
Image enhancement is one of the challenging issues in image processing. The objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. Digital image enhancement techniques provide a lot of choices for improving the visual quality of images. Appropriate choice of such techniques is very important. This paper will provide an overview and analysis of different techniques commonly used for image enhancement. Image enhancement plays a fundamental role in vision applications. Recently much work is completed in the field of images enhancement. Many techniques have previously been proposed up to now for enhancing the digital images. In this paper, a survey on various image enhancement techniques has been done.
Image enhancement is a method of improving the quality of an image and contrast is a major aspect. Traditional methods of contrast enhancement like histogram equalization results in over/under enhancement of the image especially a lower resolution one. This paper aims at developing a new Fuzzy Inference System to enhance the contrast of the low resolution images overcoming the shortcomings of the traditional methods. Results obtained using both the approaches are compared.
IRJET - An Enhanced Approach for Extraction of Text from an Image using Fuzzy...IRJET Journal
This document presents an approach for extracting text from images using fuzzy logic. It involves preprocessing the image to remove noise, segmenting the image to extract individual characters, and then using fuzzy logic to identify the characters by comparing segmented characters to trained data and determining the degree of matching. The key steps are pre-processing, segmentation, feature extraction using techniques like statistical and geometrical features, classification using a convolutional neural network, and then using fuzzy logic to accurately identify characters by finding the highest matching value between segmented and trained characters. The goal is to recognize and extract text from the image in an editable format.
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.
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...IOSR Journals
Abstract: For enhancing an image various enhancement schemes are used which includes gray scale manipulation, filtering and Histogram Equalization, Where Histogram equalization is one of the well known image enhancement technique. It became a popular technique for contrast enhancement because it is simple and effective. The basic idea of Histogram Equalization method is to remap the gray levels of an image. Here using morphological segmentation we can get the segmented image. Morphological reconstruction is used to segment the image. Comparative analysis of different enhancement and segmentation will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameter is visual quality and objective parameters are Area, Perimeter, Min and Max intensity, Avg Voxel Intensity, Std Dev of Intensity, Eccentricity, Coefficient of skewness, Coefficient of Kurtosis, Median intensity, Mode intensity. Keywords: Histogram Equalization, Segmentation, Morphological Reconstruction .
Image Enhancement by Image Fusion for Crime InvestigationCSCJournals
This document proposes a method for image enhancement through image fusion for crime investigation applications. It summarizes existing image enhancement techniques like histogram equalization and presents their limitations. It then describes the proposed method which involves constructing an image pyramid and performing a wavelet transformation on input images. The pyramid and wavelet transformed images are then fused to generate an enhanced output image with improved contrast and information content. Experimental results on a surveillance camera image show that the proposed fusion scheme provides better perception for human visual analysis compared to traditional enhancement techniques.
A DISCUSSION ON IMAGE ENHANCEMENT USING HISTOGRAM EQUALIZATION BY VARIOUS MET...pharmaindexing
This document summarizes several papers on image enhancement techniques using histogram equalization. It discusses papers that propose sub-region histogram equalization to improve contrast while preserving spatial relationships. It also discusses a 3D histogram equalization method that produces a uniform 1D grayscale histogram to overcome issues with previous color histogram methods. Another paper proposes using total variation minimization for cartoon-texture decomposition prior to histogram equalization to reduce intensity saturation effects. Further, a technique called gain controllable clipped histogram equalization is presented to enhance contrast while preserving original brightness. Finally, a method called bi-histogram equalization with neighborhood metrics is described which divides histograms to improve local contrast while maintaining brightness.
7 ijaems sept-2015-8-design and implementation of fuzzy logic based image fus...INFOGAIN PUBLICATION
The quality of image holds importance for both humans and machines. To fulfill the requirement of good quality images, image enhancement is needed. Application of a single contrast enhancement technique often does not produce desirable result and may lead to over enhanced images. To overcome this problem image fusion is performed so that better results with desired enhancement can be achieved. In the present paper an amalgamation of image enhancement, fusion and sharpening have been carried out in the candidate algorithm. The algorithm makes use of fuzzy logic for weight calculation. The results are compared with DACE/LIF approach and it is observed that the proposed algorithm improves the result in terms of quality parameters like PSNR (Peak Signal to Noise Ratio), AMBE (Absolute Mean Brightness Error) and SSIM (Structural Similarity Index) by 0.5 dB, 3 and 0.1 respectively from the existing technique.
Image enhancement is one of the challenging issues in image processing. The objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. Digital image enhancement techniques provide a lot of choices for improving the visual quality of images. Appropriate choice of such techniques is very important. This paper will provide an overview and analysis of different techniques commonly used for image enhancement. Image enhancement plays a fundamental role in vision applications. Recently much work is completed in the field of images enhancement. Many techniques have previously been proposed up to now for enhancing the digital images. In this paper, a survey on various image enhancement techniques has been done.
Image enhancement is a method of improving the quality of an image and contrast is a major aspect. Traditional methods of contrast enhancement like histogram equalization results in over/under enhancement of the image especially a lower resolution one. This paper aims at developing a new Fuzzy Inference System to enhance the contrast of the low resolution images overcoming the shortcomings of the traditional methods. Results obtained using both the approaches are compared.
IRJET - An Enhanced Approach for Extraction of Text from an Image using Fuzzy...IRJET Journal
This document presents an approach for extracting text from images using fuzzy logic. It involves preprocessing the image to remove noise, segmenting the image to extract individual characters, and then using fuzzy logic to identify the characters by comparing segmented characters to trained data and determining the degree of matching. The key steps are pre-processing, segmentation, feature extraction using techniques like statistical and geometrical features, classification using a convolutional neural network, and then using fuzzy logic to accurately identify characters by finding the highest matching value between segmented and trained characters. The goal is to recognize and extract text from the image in an editable format.
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.
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...IOSR Journals
Abstract: For enhancing an image various enhancement schemes are used which includes gray scale manipulation, filtering and Histogram Equalization, Where Histogram equalization is one of the well known image enhancement technique. It became a popular technique for contrast enhancement because it is simple and effective. The basic idea of Histogram Equalization method is to remap the gray levels of an image. Here using morphological segmentation we can get the segmented image. Morphological reconstruction is used to segment the image. Comparative analysis of different enhancement and segmentation will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameter is visual quality and objective parameters are Area, Perimeter, Min and Max intensity, Avg Voxel Intensity, Std Dev of Intensity, Eccentricity, Coefficient of skewness, Coefficient of Kurtosis, Median intensity, Mode intensity. Keywords: Histogram Equalization, Segmentation, Morphological Reconstruction .
Image Enhancement by Image Fusion for Crime InvestigationCSCJournals
This document proposes a method for image enhancement through image fusion for crime investigation applications. It summarizes existing image enhancement techniques like histogram equalization and presents their limitations. It then describes the proposed method which involves constructing an image pyramid and performing a wavelet transformation on input images. The pyramid and wavelet transformed images are then fused to generate an enhanced output image with improved contrast and information content. Experimental results on a surveillance camera image show that the proposed fusion scheme provides better perception for human visual analysis compared to traditional enhancement techniques.
Image Contrast Enhancement Approach using Differential Evolution and Particle...IRJET Journal
This document presents a method for enhancing the contrast of gray-scale images using differential evolution optimization. It proposes using a parameterized intensity transformation function to modify pixel gray levels, with the goal of maximizing image contrast. The differential evolution algorithm is used to optimize the parameters of the transformation function. Experimental results applying this method are compared to other contrast enhancement techniques like histogram equalization and particle swarm optimization. The document provides background on image enhancement techniques, a literature review of previous work applying evolutionary algorithms like particle swarm optimization to image enhancement, and details of the proposed differential evolution approach, including the transformation function and fitness function used to evaluate contrast.
Learning Based Single Frame Image Super-resolution Using Fast Discrete Curvel...CSCJournals
High-resolution (HR) images play a vital role in all imaging applications as they offer more details. The images captured by the camera system are of degraded quality due to the imaging system and are low-resolution (LR) images. Image super-resolution (SR) is a process, where HR image is obtained from combining one or multiple LR images of same scene. In this paper, learning based single frame image super-resolution technique is proposed by using Fast Discrete Curvelet Transform (FDCT) coefficients. FDCT is an extension to Cartesian wavelets having anisotropic scaling with many directions and positions, which forms tight wedges. Such wedges allow FDCT to capture the smooth curves and fine edges at multiresolution level. The finer scale curvelet coefficients of LR image are learnt locally from a set of high-resolution training images. The super-resolved image is reconstructed by inverse Fast Discrete Curvelet Transform (IFDCT). This technique represents fine edges of reconstructed HR image by extrapolating the FDCT coefficients from the high-resolution training images. Experimentation based results show appropriate improvements in MSE and PSNR.
This document discusses various techniques for image contrast enhancement, including contrast stretching, grey level slicing, histogram equalization, local enhancement equalization, image subtraction, and spatial filtering. It provides details on how each technique works and compares their performance both qualitatively and quantitatively using metrics like SNR and PSNR. The conclusion is that contrast stretching generally provides the best enhancement among the techniques compared, but other techniques may be better suited for specific applications.
Mr image compression based on selection of mother wavelet and lifting based w...ijma
Magnetic Resonance (MR) image is a medical image technique required enormous data to be stored and
transmitted for high quality diagnostic application. Various algorithms have been proposed to improve the
performance of the compression scheme. In this paper we extended the commonly used algorithms to image
compression and compared its performance. For an image compression technique, we have linked different
wavelet techniques using traditional mother wavelets and lifting based Cohen-Daubechies-Feauveau
wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7) wavelet transform with Set Partition in
Hierarchical Trees (SPIHT) algorithm. A novel image quality index with highlighting shape of histogram
of the image targeted is introduced to assess image compression quality. The index will be used in place of
existing traditional Universal Image Quality Index (UIQI) “in one go”. It offers extra information about
the distortion between an original image and a compressed image in comparisons with UIQI. The proposed
index is designed based on modelling image compression as combinations of four major factors: loss of
correlation, luminance distortion, contrast distortion and shape distortion. This index is easy to calculate
and applicable in various image processing applications. One of our contributions is to demonstrate the
choice of mother wavelet is very important for achieving superior wavelet compression performances based
on proposed image quality indexes. Experimental results show that the proposed image quality index plays
a significantly role in the quality evaluation of image compression on the open sources “BrainWeb:
Simulated Brain Database (SBD) ”.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
MODIFIED HISTOGRAM EQUALIZATION FOR IMAGE CONTRAST ENHANCEMENT USING PARTICLE...ijcseit
A novel Modified Histogram Equalization (MHE) technique for contrast enhancement is proposed in this
paper. This technique modifies the probability density function of an image by introducing constraints prior
to the process of histogram equalization (HE). These constraints are formulated using two parameters
which are optimized using swarm intelligence. This technique of contrast enhancement takes control over
the effect of HE so that it enhances the image without causing any loss to its details. A median adjustment
factor is then added to the result to normalize the change in the luminance level after enhancement. This
factor suppresses the effect of luminance change due to the presence of outlier pixels. The outlier pixels of
highly deviated intensities have greater impact in changing the contrast of an image. This approach
provides a convenient and effective way to control the enhancement process, while being adaptive to
various types of images. Experimental results show that the proposed technique gives better results in
terms of Discrete Entropy and SSIM values than the existing histogram-based equalization methods.
Review on Image Enhancement in Spatial Domainidescitation
With the proliferation in electronic imaging devices
like in mobiles, computer vision, medical field and space field;
image enhancement field has become the quite interesting
and important area of research. These imaging devices are
viewed under a diverse range of viewing conditions and a huge
loss in contrast under bright outdoor viewing conditions; thus
viewing condition parameters such as surround effects,
correlated color temperature and ambient lighting have
become of significant importance. Therefore, Principle
objective of Image enhancement is to adjust the quality of an
image for better human visual perception. Appropriate choice
of enhancement techniques is greatly influenced by the
imaging modality, task at hand and viewing conditions.
Basically, image enhancement techniques have been classified
into two broad categories: Spatial domain image enhancement
and Frequency domain image enhancement. This survey report
gives an overview of different methodologies have been used
for enhancement under the spatial domain category. It is noted
that in this field still more research is to be done.
COMPRESSION BASED FACE RECOGNITION USING DWT AND SVMsipij
The biometric is used to identify a person effectively and employ in almost all applications of day to day
activities. In this paper, we propose compression based face recognition using Discrete Wavelet Transform
(DWT) and Support Vector Machine (SVM). The novel concept of converting many images of single person
into one image using averaging technique is introduced to reduce execution time and memory. The DWT is
applied on averaged face image to obtain approximation (LL) and detailed bands. The LL band coefficients
are given as input to SVM to obtain Support vectors (SV’s). The LL coefficients of DWT and SV’s are fused
based on arithmetic addition to extract final features. The Euclidean Distance (ED) is used to compare test
image features with database image features to compute performance parameters. It is observed that, the
proposed algorithm is better in terms of performance compared to existing algorithms.
Enhancement of Medical Images using Histogram Based Hybrid TechniqueINFOGAIN PUBLICATION
Digital Image Processing is very important area of research. A number of techniques are available for image enhancement of gray scale images as well as color images. They work very efficiently for enhancement of the gray scale as well as color images. Important techniques namely Histogram Equalization, BBHE, RSWHE, RSWHE (recursion=2, gamma=No), AGCWD (Recursion=0, gamma=0) have been used quite frequently for image enhancement. But there are some shortcomings of the present techniques. The major shortcoming is that while enhancement, the brightness of the image deteriorates quite a lot. So there was need for some technique for image enhancement so that while enhancement was done, the brightness of the images does not go down. To remove this shortcoming, a new hybrid technique namely RESWHE+AGCWD (recursion=2, gamma=0 or 1) was proposed. The results of the proposed technique were compared with the existing techniques. In the present methodology, the brightness did not decrease during image enhancement. So the results and the technique was validated and accepted. The parameters via PSNR, MSE, AMBE etc. are taken for performance evaluation and validation of the proposed technique against the existing techniques which results in better outperform.
Statistical Feature based Blind Classifier for JPEG Image Splice Detectionrahulmonikasharma
Digital imaging, image forgery and its forensics have become an established field of research now days. Digital imaging is used to enhance and restore images to make them more meaningful while image forgery is done to produce fake facts by tampering images. Digital forensics is then required to examine the questioned images and classify them as authentic or tampered. This paper aims to design and implement a blind classifier to classify original and spliced Joint Photographic Experts Group (JPEG) images. Classifier is based on statistical features obtained by exploiting image compression artifacts which are extracted as Blocking Artifact Characteristics Matrix. The experimental results have shown that the proposed classifier outperforms the existing one. It gives improved performance in terms of accuracy and area under curve while classifying images. It supports .bmp and .tiff file formats and is fairly robust to noise.
A Novel Approach To Detection and Evaluation of Resampled Tampered ImagesCSCJournals
Most digital forgeries use an interpolation function, affecting the underlying statistical distribution of the image pixel values, that when detected, can be used as evidence of tampering. This paper provides a comparison of interpolation techniques, similar to Lehmann [1], using analyses of the Fourier transform of the image signal, and a quantitative assessment of the interpolation quality after applying selected interpolation functions, alongside an appraisal of computational performance using runtime measurements. A novel algorithm is proposed for detecting locally tampered regions, taking the averaged discrete Fourier transform of the zero-crossing of the second difference of the resampled signal (ADZ). The algorithm was contrasted using precision, recall and specificity metrics against those found in the literature, with comparable results. The interpolation comparison results were similar to that of [1]. The results of the detection algorithm showed that it performed well for determining authentic images, and better than previously proposed algorithms for determining tampered regions.
1) The document discusses various medical image fusion techniques including pixel level, feature level, and decision level fusion.
2) It proposes a novel pixel level fusion method called Iterative Block Level Principal Component Averaging fusion that divides images into blocks and calculates principal components for each block.
3) Experimental results on fusing noise free and noise filtered MR images show that the proposed method performs well in terms of average mutual information and structural similarity compared to other algorithms.
IRJET- Comparison and Simulation based Analysis of an Optimized Block Mat...IRJET Journal
This document compares an optimized block matching algorithm to the four step search algorithm. It first provides background on block matching algorithms and motion estimation techniques used in video compression. It then describes the existing four step search algorithm and its process of checking 17-27 points to find the best motion vector match. The document proposes a new simpler and more efficient four step search algorithm that separates the search area into quadrants. It checks 3 points in the first phase to select a quadrant, then finds the lowest cost point in the second phase to set as the new origin, reducing computational complexity compared to the standard four step search.
The document proposes a Modified Fuzzy C-Means (MFCM) clustering algorithm to segment chromosomal images. The MFCM includes preprocessing steps of median filtering and image enhancement to address noise sensitiveness and segmentation error problems in existing methods. It achieves improved segmentation accuracy of 61.6% compared to 56.4%, 55.47%, and 57.6% for Fuzzy C-Means, Adaptive Fuzzy C-Means, and Improved Adaptive Fuzzy C-Means respectively. The MFCM results in higher quality segmented images as indicated by its lower mean square error and higher peak signal-to-noise ratio values.
This document summarizes a paper presented at the 2nd International Conference on Current Trends in Engineering and Management. The paper proposes using discrete wavelet transform techniques for pixel-based fusion of multi-focus images. It discusses registering the images and then applying pixel-level fusion methods like average, minimum and maximum approaches. It also introduces a wavelet-based fusion method that decomposes images into different frequency bands for fusion. The goal is to produce a single fused image that has the maximum information and focus from the input images.
MAGE Q UALITY A SSESSMENT OF T ONE M APPED I MAGESijcga
This paper proposes an objective assessment method
for perceptual image quality of tone mapped images.
Tone mapping algorithms are used to display high dy
namic range (HDR) images onto standard display
devices that have low dynamic range (LDR). The prop
osed method implements visual attention to define
perceived structural distortion regions in LDR imag
es, so that a reasonable measurement of distortion
between HDR and LDR images can be performed. Since
the human visual system is sensitive to structural
information, quality metrics that can measure struc
tural similarity between HDR and LDR images are
used. Experimental results with a number of HDR and
tone mapped image pairs show the effectiveness of
the proposed method.
Image Quality Assessment of Tone Mapped Images ijcga
This paper proposes an objective assessment method for perceptual image quality of tone mapped images. Tone mapping algorithms are used to display high dynamic range (HDR) images onto standard display devices that have low dynamic range (LDR). The proposed method implements visual attention to define perceived structural distortion regions in LDR images, so that a reasonable measurement of distortion between HDR and LDR images can be performed. Since the human visual system is sensitive to structural information, quality metrics that can measure structural similarity between HDR and LDR images are used. Experimental results with a number of HDR and tone mapped image pairs show the effectiveness of the proposed method.
This document describes a simulation of a three-phase shunt active power filter using a fuzzy logic controller to compensate for current harmonics from a non-linear load. It discusses how active power filters can mitigate harmonics and reactive power issues. The simulation shows that the fuzzy logic controller is able to keep the source current balanced, sinusoidal and in phase with the voltage after compensation, reducing the total harmonic distortion from 28.61% to 3.85%. The fuzzy logic controller provides an effective control approach without requiring an accurate mathematical model of the system.
Tackling the Great Consumer Attention Deficit: SxSW Panel PreviewUnmetric
This is a preview of the SxSW Panel proposed by Unmetric, Elizabeth Arden, and Lippe Taylor. To vote for this panel now, visit: http://panelpicker.sxsw.com/vote/17844.
These days, communication is free: the problem is attention. As new technologies and platforms continue to emerge, marketers are consistently required to change the way they communicate. And, as audience attention spans become shorter and shorter, brands must harness new and creative forms of micro-content to evoke the same deep connections as the large campaigns of the past.
This document provides a comparative study of two-way finite automata and Turing machines. Some key points:
- Two-way finite automata are similar to read-only Turing machines in that they have a finite tape that can be read in both directions, but cannot write to the tape.
- Turing machines have an infinite tape that can be read from and written to, allowing them to recognize recursively enumerable languages.
- Both models are examined in their ability to accept the regular language L={anbm|m,n>0}. The two-way finite automata uses two states and transitions between them, while the Turing machine uses four states and transitions between tape symbols.
This document discusses data mining algorithms for clustering healthcare data streams. It provides an overview of the K-means and D-stream algorithms, and proposes a framework for comparing them on healthcare datasets. The framework involves feature extraction from physiological signals, calculating risk components, and applying the K-means and D-stream algorithms to cluster the data. The results would show the effectiveness and limitations of each algorithm for clustering streaming healthcare data.
The document presents an algorithm to optimize Hamilton path tournament scheduling by minimizing total transportation costs. It describes how all possible pair combinations for a round robin tournament are generated using a Hamilton path. For each combination, the transportation cost of getting between stadiums for each team over the tournament is calculated. The combination with the lowest total cost is considered optimized. The algorithm works by calculating costs for all combinations and selecting the one with the minimum cost. This approach finds a schedule where teams travel less, reducing expenses and potentially improving performance.
Image Contrast Enhancement Approach using Differential Evolution and Particle...IRJET Journal
This document presents a method for enhancing the contrast of gray-scale images using differential evolution optimization. It proposes using a parameterized intensity transformation function to modify pixel gray levels, with the goal of maximizing image contrast. The differential evolution algorithm is used to optimize the parameters of the transformation function. Experimental results applying this method are compared to other contrast enhancement techniques like histogram equalization and particle swarm optimization. The document provides background on image enhancement techniques, a literature review of previous work applying evolutionary algorithms like particle swarm optimization to image enhancement, and details of the proposed differential evolution approach, including the transformation function and fitness function used to evaluate contrast.
Learning Based Single Frame Image Super-resolution Using Fast Discrete Curvel...CSCJournals
High-resolution (HR) images play a vital role in all imaging applications as they offer more details. The images captured by the camera system are of degraded quality due to the imaging system and are low-resolution (LR) images. Image super-resolution (SR) is a process, where HR image is obtained from combining one or multiple LR images of same scene. In this paper, learning based single frame image super-resolution technique is proposed by using Fast Discrete Curvelet Transform (FDCT) coefficients. FDCT is an extension to Cartesian wavelets having anisotropic scaling with many directions and positions, which forms tight wedges. Such wedges allow FDCT to capture the smooth curves and fine edges at multiresolution level. The finer scale curvelet coefficients of LR image are learnt locally from a set of high-resolution training images. The super-resolved image is reconstructed by inverse Fast Discrete Curvelet Transform (IFDCT). This technique represents fine edges of reconstructed HR image by extrapolating the FDCT coefficients from the high-resolution training images. Experimentation based results show appropriate improvements in MSE and PSNR.
This document discusses various techniques for image contrast enhancement, including contrast stretching, grey level slicing, histogram equalization, local enhancement equalization, image subtraction, and spatial filtering. It provides details on how each technique works and compares their performance both qualitatively and quantitatively using metrics like SNR and PSNR. The conclusion is that contrast stretching generally provides the best enhancement among the techniques compared, but other techniques may be better suited for specific applications.
Mr image compression based on selection of mother wavelet and lifting based w...ijma
Magnetic Resonance (MR) image is a medical image technique required enormous data to be stored and
transmitted for high quality diagnostic application. Various algorithms have been proposed to improve the
performance of the compression scheme. In this paper we extended the commonly used algorithms to image
compression and compared its performance. For an image compression technique, we have linked different
wavelet techniques using traditional mother wavelets and lifting based Cohen-Daubechies-Feauveau
wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7) wavelet transform with Set Partition in
Hierarchical Trees (SPIHT) algorithm. A novel image quality index with highlighting shape of histogram
of the image targeted is introduced to assess image compression quality. The index will be used in place of
existing traditional Universal Image Quality Index (UIQI) “in one go”. It offers extra information about
the distortion between an original image and a compressed image in comparisons with UIQI. The proposed
index is designed based on modelling image compression as combinations of four major factors: loss of
correlation, luminance distortion, contrast distortion and shape distortion. This index is easy to calculate
and applicable in various image processing applications. One of our contributions is to demonstrate the
choice of mother wavelet is very important for achieving superior wavelet compression performances based
on proposed image quality indexes. Experimental results show that the proposed image quality index plays
a significantly role in the quality evaluation of image compression on the open sources “BrainWeb:
Simulated Brain Database (SBD) ”.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
MODIFIED HISTOGRAM EQUALIZATION FOR IMAGE CONTRAST ENHANCEMENT USING PARTICLE...ijcseit
A novel Modified Histogram Equalization (MHE) technique for contrast enhancement is proposed in this
paper. This technique modifies the probability density function of an image by introducing constraints prior
to the process of histogram equalization (HE). These constraints are formulated using two parameters
which are optimized using swarm intelligence. This technique of contrast enhancement takes control over
the effect of HE so that it enhances the image without causing any loss to its details. A median adjustment
factor is then added to the result to normalize the change in the luminance level after enhancement. This
factor suppresses the effect of luminance change due to the presence of outlier pixels. The outlier pixels of
highly deviated intensities have greater impact in changing the contrast of an image. This approach
provides a convenient and effective way to control the enhancement process, while being adaptive to
various types of images. Experimental results show that the proposed technique gives better results in
terms of Discrete Entropy and SSIM values than the existing histogram-based equalization methods.
Review on Image Enhancement in Spatial Domainidescitation
With the proliferation in electronic imaging devices
like in mobiles, computer vision, medical field and space field;
image enhancement field has become the quite interesting
and important area of research. These imaging devices are
viewed under a diverse range of viewing conditions and a huge
loss in contrast under bright outdoor viewing conditions; thus
viewing condition parameters such as surround effects,
correlated color temperature and ambient lighting have
become of significant importance. Therefore, Principle
objective of Image enhancement is to adjust the quality of an
image for better human visual perception. Appropriate choice
of enhancement techniques is greatly influenced by the
imaging modality, task at hand and viewing conditions.
Basically, image enhancement techniques have been classified
into two broad categories: Spatial domain image enhancement
and Frequency domain image enhancement. This survey report
gives an overview of different methodologies have been used
for enhancement under the spatial domain category. It is noted
that in this field still more research is to be done.
COMPRESSION BASED FACE RECOGNITION USING DWT AND SVMsipij
The biometric is used to identify a person effectively and employ in almost all applications of day to day
activities. In this paper, we propose compression based face recognition using Discrete Wavelet Transform
(DWT) and Support Vector Machine (SVM). The novel concept of converting many images of single person
into one image using averaging technique is introduced to reduce execution time and memory. The DWT is
applied on averaged face image to obtain approximation (LL) and detailed bands. The LL band coefficients
are given as input to SVM to obtain Support vectors (SV’s). The LL coefficients of DWT and SV’s are fused
based on arithmetic addition to extract final features. The Euclidean Distance (ED) is used to compare test
image features with database image features to compute performance parameters. It is observed that, the
proposed algorithm is better in terms of performance compared to existing algorithms.
Enhancement of Medical Images using Histogram Based Hybrid TechniqueINFOGAIN PUBLICATION
Digital Image Processing is very important area of research. A number of techniques are available for image enhancement of gray scale images as well as color images. They work very efficiently for enhancement of the gray scale as well as color images. Important techniques namely Histogram Equalization, BBHE, RSWHE, RSWHE (recursion=2, gamma=No), AGCWD (Recursion=0, gamma=0) have been used quite frequently for image enhancement. But there are some shortcomings of the present techniques. The major shortcoming is that while enhancement, the brightness of the image deteriorates quite a lot. So there was need for some technique for image enhancement so that while enhancement was done, the brightness of the images does not go down. To remove this shortcoming, a new hybrid technique namely RESWHE+AGCWD (recursion=2, gamma=0 or 1) was proposed. The results of the proposed technique were compared with the existing techniques. In the present methodology, the brightness did not decrease during image enhancement. So the results and the technique was validated and accepted. The parameters via PSNR, MSE, AMBE etc. are taken for performance evaluation and validation of the proposed technique against the existing techniques which results in better outperform.
Statistical Feature based Blind Classifier for JPEG Image Splice Detectionrahulmonikasharma
Digital imaging, image forgery and its forensics have become an established field of research now days. Digital imaging is used to enhance and restore images to make them more meaningful while image forgery is done to produce fake facts by tampering images. Digital forensics is then required to examine the questioned images and classify them as authentic or tampered. This paper aims to design and implement a blind classifier to classify original and spliced Joint Photographic Experts Group (JPEG) images. Classifier is based on statistical features obtained by exploiting image compression artifacts which are extracted as Blocking Artifact Characteristics Matrix. The experimental results have shown that the proposed classifier outperforms the existing one. It gives improved performance in terms of accuracy and area under curve while classifying images. It supports .bmp and .tiff file formats and is fairly robust to noise.
A Novel Approach To Detection and Evaluation of Resampled Tampered ImagesCSCJournals
Most digital forgeries use an interpolation function, affecting the underlying statistical distribution of the image pixel values, that when detected, can be used as evidence of tampering. This paper provides a comparison of interpolation techniques, similar to Lehmann [1], using analyses of the Fourier transform of the image signal, and a quantitative assessment of the interpolation quality after applying selected interpolation functions, alongside an appraisal of computational performance using runtime measurements. A novel algorithm is proposed for detecting locally tampered regions, taking the averaged discrete Fourier transform of the zero-crossing of the second difference of the resampled signal (ADZ). The algorithm was contrasted using precision, recall and specificity metrics against those found in the literature, with comparable results. The interpolation comparison results were similar to that of [1]. The results of the detection algorithm showed that it performed well for determining authentic images, and better than previously proposed algorithms for determining tampered regions.
1) The document discusses various medical image fusion techniques including pixel level, feature level, and decision level fusion.
2) It proposes a novel pixel level fusion method called Iterative Block Level Principal Component Averaging fusion that divides images into blocks and calculates principal components for each block.
3) Experimental results on fusing noise free and noise filtered MR images show that the proposed method performs well in terms of average mutual information and structural similarity compared to other algorithms.
IRJET- Comparison and Simulation based Analysis of an Optimized Block Mat...IRJET Journal
This document compares an optimized block matching algorithm to the four step search algorithm. It first provides background on block matching algorithms and motion estimation techniques used in video compression. It then describes the existing four step search algorithm and its process of checking 17-27 points to find the best motion vector match. The document proposes a new simpler and more efficient four step search algorithm that separates the search area into quadrants. It checks 3 points in the first phase to select a quadrant, then finds the lowest cost point in the second phase to set as the new origin, reducing computational complexity compared to the standard four step search.
The document proposes a Modified Fuzzy C-Means (MFCM) clustering algorithm to segment chromosomal images. The MFCM includes preprocessing steps of median filtering and image enhancement to address noise sensitiveness and segmentation error problems in existing methods. It achieves improved segmentation accuracy of 61.6% compared to 56.4%, 55.47%, and 57.6% for Fuzzy C-Means, Adaptive Fuzzy C-Means, and Improved Adaptive Fuzzy C-Means respectively. The MFCM results in higher quality segmented images as indicated by its lower mean square error and higher peak signal-to-noise ratio values.
This document summarizes a paper presented at the 2nd International Conference on Current Trends in Engineering and Management. The paper proposes using discrete wavelet transform techniques for pixel-based fusion of multi-focus images. It discusses registering the images and then applying pixel-level fusion methods like average, minimum and maximum approaches. It also introduces a wavelet-based fusion method that decomposes images into different frequency bands for fusion. The goal is to produce a single fused image that has the maximum information and focus from the input images.
MAGE Q UALITY A SSESSMENT OF T ONE M APPED I MAGESijcga
This paper proposes an objective assessment method
for perceptual image quality of tone mapped images.
Tone mapping algorithms are used to display high dy
namic range (HDR) images onto standard display
devices that have low dynamic range (LDR). The prop
osed method implements visual attention to define
perceived structural distortion regions in LDR imag
es, so that a reasonable measurement of distortion
between HDR and LDR images can be performed. Since
the human visual system is sensitive to structural
information, quality metrics that can measure struc
tural similarity between HDR and LDR images are
used. Experimental results with a number of HDR and
tone mapped image pairs show the effectiveness of
the proposed method.
Image Quality Assessment of Tone Mapped Images ijcga
This paper proposes an objective assessment method for perceptual image quality of tone mapped images. Tone mapping algorithms are used to display high dynamic range (HDR) images onto standard display devices that have low dynamic range (LDR). The proposed method implements visual attention to define perceived structural distortion regions in LDR images, so that a reasonable measurement of distortion between HDR and LDR images can be performed. Since the human visual system is sensitive to structural information, quality metrics that can measure structural similarity between HDR and LDR images are used. Experimental results with a number of HDR and tone mapped image pairs show the effectiveness of the proposed method.
This document describes a simulation of a three-phase shunt active power filter using a fuzzy logic controller to compensate for current harmonics from a non-linear load. It discusses how active power filters can mitigate harmonics and reactive power issues. The simulation shows that the fuzzy logic controller is able to keep the source current balanced, sinusoidal and in phase with the voltage after compensation, reducing the total harmonic distortion from 28.61% to 3.85%. The fuzzy logic controller provides an effective control approach without requiring an accurate mathematical model of the system.
Tackling the Great Consumer Attention Deficit: SxSW Panel PreviewUnmetric
This is a preview of the SxSW Panel proposed by Unmetric, Elizabeth Arden, and Lippe Taylor. To vote for this panel now, visit: http://panelpicker.sxsw.com/vote/17844.
These days, communication is free: the problem is attention. As new technologies and platforms continue to emerge, marketers are consistently required to change the way they communicate. And, as audience attention spans become shorter and shorter, brands must harness new and creative forms of micro-content to evoke the same deep connections as the large campaigns of the past.
This document provides a comparative study of two-way finite automata and Turing machines. Some key points:
- Two-way finite automata are similar to read-only Turing machines in that they have a finite tape that can be read in both directions, but cannot write to the tape.
- Turing machines have an infinite tape that can be read from and written to, allowing them to recognize recursively enumerable languages.
- Both models are examined in their ability to accept the regular language L={anbm|m,n>0}. The two-way finite automata uses two states and transitions between them, while the Turing machine uses four states and transitions between tape symbols.
This document discusses data mining algorithms for clustering healthcare data streams. It provides an overview of the K-means and D-stream algorithms, and proposes a framework for comparing them on healthcare datasets. The framework involves feature extraction from physiological signals, calculating risk components, and applying the K-means and D-stream algorithms to cluster the data. The results would show the effectiveness and limitations of each algorithm for clustering streaming healthcare data.
The document presents an algorithm to optimize Hamilton path tournament scheduling by minimizing total transportation costs. It describes how all possible pair combinations for a round robin tournament are generated using a Hamilton path. For each combination, the transportation cost of getting between stadiums for each team over the tournament is calculated. The combination with the lowest total cost is considered optimized. The algorithm works by calculating costs for all combinations and selecting the one with the minimum cost. This approach finds a schedule where teams travel less, reducing expenses and potentially improving performance.
1) The document discusses optimizing cross-layer interactions between the physical and MAC layers of wireless networks using genetic algorithms and smart antennas.
2) It proposes using a continuous genetic algorithm to optimize an array factor cost function for a smart antenna with varying element spacings.
3) The results show the continuous genetic algorithm can successfully optimize the array factor and minimize sidelobes for different element spacings, allowing nulls to be placed to improve the radiation pattern.
This document provides information about the 1952 film "Viva Zapata!" directed by Elia Kazan. It is a 121-minute American biographical and historical drama film about the life of Mexican revolutionary Emiliano Zapata. The main characters portrayed are Emiliano Zapata, his wife Josefina, and his brother Eufemio. The film depicts Zapata's role in helping Mexican farmers request land rights and his involvement in deposing dictator Porfirio Diaz alongside Pancho Villa and Francisco Madero in 1911 before being murdered in an ambush in 1917.
Wireless Communications courses and research at COMNET ProjectENhANCE
The document discusses wireless communications education and research at Aalto University. It describes the structure of the Master's Programme in Communications Engineering, including basic, intermediate and advanced modules in areas like radio communications. It also discusses the master's thesis process and examples. For doctoral education, it outlines the general structure including theoretical studies in the research field and scientific principles. It provides examples of completed doctoral theses. Finally, it gives an overview of the research areas and projects in the Comnet department, including communications and information theory, advanced radio systems, performance analysis and more.
This document discusses data sharing in the cloud using distributed accountability. It proposes a Cloud Information Accountability (CIA) framework to provide end-to-end accountability in a highly distributed manner. The CIA framework uses an object-centered approach that enables automatic logging mechanisms to be enclosed with user data and policies. This improves security and privacy of data in the cloud. It also provides distributed auditing mechanisms and a secure Java virtual machine (JVM) for high security. The framework is evaluated against potential attacks like disassembly attacks and man-in-the-middle attacks to demonstrate its effectiveness.
This document summarizes various data aggregation techniques for wireless sensor networks. It discusses how data aggregation can help reduce transmission overhead and improve security by allowing computation on encrypted data through privacy homomorphism. Several data aggregation schemes are described, including concealed data aggregation (CDA) schemes based on privacy homomorphism that allow aggregation of encrypted data without decrypting it first. The document also reviews recoverable concealed data aggregation (RCDA) schemes, a concealed data aggregation scheme for multiple applications (CDAMA), and public key cryptosystems that can enable data aggregation functions to be performed on encrypted data in wireless sensor networks.
The document provides an overview of routing protocols for mobile ad hoc networks (MANETs). It discusses that MANETs are self-configuring networks without centralized control where nodes can act as routers to forward packets. The document classifies routing protocols as proactive (table-driven), reactive (on-demand), or hybrid. It describes examples of proactive routing protocols like DSDV, OLSR, and WRP that maintain up-to-date routing tables and share updates periodically or when changes occur. The document also discusses reactive protocols establish routes on demand and hybrid protocols that combine aspects of proactive and reactive approaches.
This document proposes an architecture called a pervasive public key infrastructure (pervasive-PKI) to provide authentication and authorization for mobile users across heterogeneous networks. The pervasive-PKI allows credential validation when centralized PKI services are unavailable due to disconnection or limited device capabilities. It includes three software components installed on user devices: 1) a Pervasive Trust Management component that handles trust information and certificate validation, 2) a Privilege Verifier that validates attribute certificates, and 3) an Access Control Engine that makes access decisions based on the other components. These components allow credential validation, authentication, and authorization to occur even when global connectivity and centralized services are lost.
Image enhancement plays an important role in vision applications. Recently a lot of work has been performed in the field of image enhancement. Many techniques have already been proposed till now for enhancing the digital images. This paper has presented a comparative analysis of various image enhancement techniques. This paper has shown that the fuzzy logic and histogram based techniques have quite effective results over the available techniques. This paper ends up with suitable future directions to enhance fuzzy based image enhancement technique further. In the proposed technique, an approach is made to enhance the images other than low-contrast images as well by balancing the stretching parameter (K) according to the color contrast. Proposed technique is designed to restore the degraded edges resulted due to contrast enhancement as well.
Image De-noising and Enhancement for Salt and Pepper Noise using Genetic Algo...IDES Editor
Image Enhancement through De-noising is one of
the most important applications of Digital Image Processing
and is still a challenging problem. Images are often received
in defective conditions due to usage of Poor image sensors,
poor data acquisition process and transmission errors etc.,
which creates problems for the subsequent process to
understand such images. The proposed Genetic filter is capable
of removing noise while preserving the fine details, as well as
structural image content. It can be divided into: (i) de-noising
filtering, and (ii) enhancement filtering. Image Denoising
and enhancement are essential part of any image processing
system, whether the processed information is utilized for visual
interpretation or for automatic analysis. The Experimental
results performed on a set of standard test images for a wide
range of noise corruption levels shows that the proposed filter
outperforms standard procedures for salt and pepper removal
both visually and in terms of performance measures such as
PSNR.Genetic algorithms will definitely helpful in solving
various complex image processing tasks in the future.
A Comparative Study on Image Contrast Enhancement TechniquesIRJET Journal
This document presents a comparative study of various image contrast enhancement techniques. It discusses techniques like histogram equalization, gamma correction, brightness preserving bi-histogram equalization (BBHE), brightness preserving dynamic histogram equalization (BPDHE), and region based adaptive contrast enhancement (RACE). The study evaluates the performance of these techniques on different color images using objective parameters like entropy, absolute contrast error, and peak signal to noise ratio. The results show that the BPDHE technique generally produces enhanced images with less color error, higher contrast-to-noise ratio, and entropy values indicating more details compared to the other techniques. BPDHE is therefore found to be the best technique for enhancing image contrast while preserving color and brightness.
This document compares image enhancement and analysis techniques using image processing and wavelet techniques on thermal images. It discusses various image enhancement methods such as converting images to grayscale, histogram equalization, contrast enhancement, linear and adaptive filtering, morphology, FFT transforms, and wavelet-based techniques including image fusion, denoising, and compression. Results showing enhanced, denoised, and compressed images are presented and analyzed. The document concludes that wavelet techniques provide better enhancement of thermal images compared to traditional image processing methods.
1) The document proposes a method for color image enhancement using Laplacian pyramid decomposition and histogram equalization. It separates an input image into red, green, and blue color channels.
2) Each color channel is decomposed into a Laplacian pyramid, and histogram equalization is applied to enhance the contrast in each band-pass image.
3) The enhanced band-pass images are then recombined using the Laplacian pyramid reconstruction equation to produce enhanced color channels, which are combined to generate the output enhanced color image. The method aims to improve both local and global contrast while maintaining natural image quality.
1) The document proposes a method for color image enhancement using Laplacian pyramid decomposition and histogram equalization. It separates an input image into red, green, and blue color channels.
2) Each color channel is decomposed into a Laplacian pyramid, and histogram equalization is applied to enhance the contrast in each level. The enhanced levels are then recombined to improve both local and global contrast.
3) The method aims to overcome issues with traditional histogram equalization like over-enhancement, by applying a smoothing technique before contrast adjustment in each level of the pyramid. The final enhanced image is reconstructed by combining the processed color channels.
A comprehensive method for image contrast enhancement based on global –local ...eSAT Publishing House
This document presents a method for image contrast enhancement based on combining global and local contrast techniques. It addresses issues with existing local standard deviation based methods when applied to constant image areas, which results in divided by zero errors. The proposed method modifies the local standard deviation calculation by adding a small value to avoid this, allowing contrast enhancement to be applied across the entire image without information loss. Experimental results on test images demonstrate improved contrast enhancement over existing methods, as measured by peak signal-to-noise ratio values.
IRJET- Low Light Image Enhancement using Convolutional Neural NetworkIRJET Journal
This document presents a study on enhancing low light images using a convolutional neural network. It begins with an introduction to the importance of image quality and challenges of low light images. It then describes the proposed system which uses a convolutional neural network with three layers - gamma correction, multiple convolutional layers, and color restoration. The results show that the convolutional layers help enhance edges in grayscale images. Finally, it concludes the CNN approach is effective for low light image enhancement.
Image Enhancement using Guided Filter for under Exposed ImagesDr. Amarjeet Singh
Image enhancement becomes an important step to
improve the quality of image and change in the appearance of
the image in such a way that either a human or a machine can
fetch certain information from the image after a change. Due
to low contrast images it becomes very difficult to get any
information out of it. In today’s digital world of imaging
image enhancement is a very useful in various applications
ranging from electronics printing to recognition. For highly
underexposed region, intensity bin are present in darken
region that’s by such images lacks in saturation and suffers
from low intensity. Power law transformation provides
solution to this problem. It enhances the brightness so as
image at least becomes visible. To modify the intensity level
histogram equalization can be used. In this we can apply
cumulative density function and probabilistic density function
so as to divide the image into sub images.
In proposed approach to provide betterment in
results guided filter has been applied to images after
equalization so that we can get better Entropy rate and
Coefficient of correlation can be improved with previously
available techniques. The guided filter is derived from local
linear model. The guided filter computes the filtering output
by considering the content of guidance image, which can be
the image itself or other targeted image.
Lung Cancer Detection using Image Processing TechniquesIRJET Journal
This document presents a technique for detecting lung cancer in x-ray images using image processing. It involves enhancing images using Gabor filtering, segmenting images using marker-controlled watershed segmentation, and extracting features using binarization and masking. The key steps are collecting lung x-ray images, enhancing quality using Gabor filtering, segmenting regions of interest using watershed segmentation, extracting pixel counts and mask features, and classifying images as normal or abnormal based on these features. The goal is to enable early detection of lung cancer through automated analysis of medical images.
A Biometric Approach to Encrypt a File with the Help of Session KeySougata Das
The main objective of this work is to provide a two layer authentication system through biometric (face) and conventional session based password authentication. The encryption key for this authentication will be generated with the combination of the biometric key and session based password.
IRJET- Comparative Study of Artificial Neural Networks and Convolutional N...IRJET Journal
This document discusses and compares artificial neural networks and convolutional neural networks for crop disease detection using images. It first provides background on the importance of early crop disease detection in India. It then describes the image preprocessing, segmentation, and feature extraction steps involved, including converting to HSV color space and extracting texture features using GLCM. Artificial neural networks and convolutional neural networks are introduced for classification. The document conducts a literature review on previous work related to image preprocessing techniques, segmentation algorithms like K-means clustering, and feature extraction methods. In summary, it analyzes the process of detecting crop diseases from images using machine learning techniques.
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
Contrast Enhancement Techniques: A Brief and Concise ReviewIRJET Journal
The document discusses various contrast enhancement techniques for digital images. It provides an overview of techniques such as histogram equalization, which works by flattening the histogram and stretching the dynamic range of gray levels. Global histogram equalization uses the entire image histogram for contrast adjustment, while local histogram equalization processes the image in blocks to better preserve local details and brightness levels. The techniques aim to improve image quality by increasing luminance differences between foreground and background.
A Review Paper on Fingerprint Image Enhancement with Different MethodsIJMER
This document summarizes various techniques that have been used for fingerprint image enhancement in previous research. It discusses enhancement techniques in the spatial and frequency domains, as well as neural network-based and fuzzy-based approaches. Specifically, it reviews 12 different fingerprint enhancement algorithms proposed between 1994 and 2010. These algorithms use approaches such as directional filtering, Gabor filtering, median filtering, and genetic algorithms. The document evaluates each method and compares their performance based on metrics like minutiae extraction accuracy and false match rates. Overall, the document provides an overview of the state-of-the-art in fingerprint image enhancement techniques.
This document discusses a technique for enhancing mammogram images to aid in the detection of breast cancer. It proposes using a frequency domain smoothing-sharpening technique that combines the advantages of smoothing and sharpening processes. This technique aims to highlight changes in image intensity while removing random noise. The technique is tested on breast x-ray mammograms. Simulation results show the technique has potential to enhance image contrast, aiding radiologists in detecting and classifying breast cancer in mammograms. Key aspects of the technique include using Gabor filters in the frequency domain and optimizing parameters to improve contrast without losing relevant image information or introducing artifacts.
Image Steganography Using Wavelet Transform And Genetic AlgorithmAM Publications
This paper presents the application of Wavelet Transform and Genetic Algorithm in a novel
steganography scheme. We employ a genetic algorithm based mapping function to embed data in Discrete Wavelet
Transform coefficients in 4x4 blocks on the cover image. The optimal pixel adjustment process is applied after
embedding the message. We utilize the frequency domain to improve the robustness of steganography and, we
implement Genetic Algorithm and Optimal Pixel Adjustment Process to obtain an optimal mapping function to
reduce the difference error between the cover and the stego-image, therefore improving the hiding capacity with
low distortions. Our Simulation results reveal that the novel scheme outperforms adaptive steganography technique
based on wavelet transform in terms of peak signal to noise ratio and capacity, 39.94 dB and 50% respectively.
DCT based Steganographic Evaluation parameter analysis in Frequency domain by...IOSR Journals
This document analyzes DCT-based steganography using a modified JPEG luminance quantization table to improve evaluation parameters like PSNR, mean square error, and capacity. The authors propose modifying the default 8x8 quantization table by adjusting frequency values in 4 bands to increase image quality for the embedded stego image. Experimental results on test images show that using the modified table improves PSNR, decreases mean square error, and increases maximum embedding capacity compared to the default table. Therefore, the proposed method allows more secret data to be hidden with less distortion and improved image quality.
This document analyzes DCT-based steganography using a modified JPEG luminance quantization table to improve embedding capacity and image quality. The authors propose modifying the default 8x8 quantization table by changing frequency values to increase the peak signal-to-noise ratio and capacity while decreasing the mean square error of embedded images. Experimental results on test images show increased capacity, PSNR and reduced error when using the modified versus default table, indicating improved stego image quality. The proposed method aims to securely embed more data with less distortion than traditional DCT-based steganography.
Adaptive Image Resizing using Edge Contrastingijtsrd
Zooming is an important image processing operation. It can be termed as the process of enlarging or magnifying the image to a given factor. Indiscriminate application of a function to an image in order to resample it, produces aliasing, edge blurring. So the objective is to reduce these artifacts.This paper considers distinctive interpolation systems identified with versatile techniques with innate abilities to ensure sharp edges and subtleties. It is a versatile resampling calculation for zooming up pictures In this work, a versatile edge improvement procedure is proposed for two dimensional 2 D picture scaling application. The foreseen picture scaling calculation comprises of an edge identifier, bilinear interpolation and Sobel filter. The bilinear interpolation characterizes the power of the scaled pixel with the weighted normal of the four neighboring pixels, inserted pictures become smooth and loss of edge data. The versatile edge upgrade procedure is utilized to secure the edge includes successfully, to accomplish better picture quality and to dodge the edge data. The Sobel filter endeavors to decrease the commotion, in obscured and mutilated edges which is delivered by bilinear interpolation. A mathematical control and equipment sharing strategy are utilized to lessen registering asset of the bilinear interpolation. The examination shows that edges are very much safeguarded and interpolation artifacts obscuring, jaggies are decreased To contrast existing algorithms and proposed strategy calculation, we have taken original pictures and results for discussion. And we have gone to the choice that proposed calculation is superior to the current algorithms. We have looked at the images by two different ways – Mean Square Error MSE and Peak Signal to Noise Ratio PSNR . Mohd Sadiq Abdul Aziz | Dr. Bharti Chourasia "Adaptive Image Resizing using Edge Contrasting" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd35789.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/35789/adaptive-image-resizing-using-edge-contrasting/mohd-sadiq-abdul-aziz
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
This document provides a comparative study of two-way finite automata and Turing machines. Some key points:
- Two-way finite automata are similar to read-only Turing machines in that they have a finite tape that can be read in both directions, but cannot write to the tape.
- Turing machines have an infinite tape that can be read from and written to, allowing them to recognize recursively enumerable languages.
- Both models are examined in their ability to accept the regular language L={anbm|m,n>0}.
- The time complexity of a two-way finite automaton for this language is O(n2) due to making two passes over the
This document analyzes and compares the performance of the AODV and DSDV routing protocols in a vehicular ad hoc network (VANET) simulation. Simulations were conducted using NS-2, SUMO, and MOVE simulators for a grid map scenario with varying numbers of nodes. The results show that AODV performed better than DSDV in terms of throughput and packet delivery fraction, while DSDV had lower end-to-end delays. However, neither protocol was found to be fully suitable for the highly dynamic VANET environment. The document concludes that further work is needed to develop improved routing protocols optimized for VANETs.
This document discusses the digital circuit layout problem and approaches to solving it using graph partitioning techniques. It begins by introducing the digital circuit layout problem and how it has become more complex with increasing circuit sizes. It then discusses how the problem can be decomposed into subproblems using graph partitioning to assign geometric coordinates to circuit components. The document reviews several traditional approaches to solve the problem, such as the Kernighan-Lin algorithm, and discusses their limitations for larger circuit sizes. It also discusses more recent approaches using evolutionary algorithms and concludes by analyzing the contributions of various approaches.
This document summarizes various data mining techniques that have been used for intrusion detection systems. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using detection models, a data warehouse for storage, and a model generator. It then discusses supervised and unsupervised learning approaches that have been applied, including neural networks, support vector machines, K-means clustering, and self-organizing maps. Finally, it reviews several related works applying these techniques and compares their results, finding that combinations of approaches can improve detection rates while reducing false alarms.
This document provides an overview of speech recognition systems and recent progress in the field. It discusses different types of speech recognition including isolated word, connected word, continuous speech, and spontaneous speech. Various techniques used in speech recognition are also summarized, such as simulated evolutionary computation, artificial neural networks, fuzzy logic, Kalman filters, and Hidden Markov Models. The document reviews several papers published between 2004-2012 that studied speech recognition methods including using dynamic spectral subband centroids, Kalman filters, biomimetic computing techniques, noise estimation, and modulation filtering. It concludes that Hidden Markov Models combined with MFCC features provide good recognition results for large vocabulary, speaker-independent, continuous speech recognition.
This document discusses integrating two assembly lines, Line A and Line B, based on lean line design concepts to reduce space and operators. It analyzes the current state of the lines using tools like takt time analysis and MTM/UAS studies. Improvements are identified to eliminate waste, including methods improvements, workplace rearrangement, ergonomic changes, and outsourcing. Paper kaizen is conducted and work elements are retimed. The goal is to integrate the lines to better utilize space and manpower while meeting manufacturing standards.
This document summarizes research on the exposure of microwaves from cellular networks. It describes how microwaves interact with biological systems and discusses measurement techniques and safety standards regarding microwave exposure. While some studies have alleged health hazards from microwaves, independent reviews by health organizations have found no evidence that exposure to microwaves below international safety limits causes harm. The document concludes that with precautions like limiting exposure time and using phones with lower SAR ratings, microwaves from cell phones pose minimal health risks.
This document summarizes a research paper that examines the effect of feature reduction in sentiment analysis of online reviews. It uses principle component analysis to reduce the number of features (product attributes) from a dataset of 500 camera reviews labeled as positive or negative. Two models are developed - one using the original set of 95 product attributes, and one using the reduced set. Support vector machines and naive Bayes classifiers are applied to both models and their performance is evaluated to determine if classification accuracy can be maintained while using fewer features. The results show it is possible to achieve similar accuracy levels with less features, improving computational efficiency.
This document provides a review of multispectral palm image fusion techniques. It begins with an introduction to biometrics and palm print identification. Different palm print images capture different spectral information about the palm. The document then reviews several pixel-level fusion methods for combining multispectral palm images, finding that Curvelet transform performs best at preserving discriminative patterns. It also discusses hardware for capturing multispectral palm images and the process of region of interest extraction and localization. Common fusion methods like wavelet transform and Curvelet transform are also summarized.
This document describes a vehicle theft detection system that uses radio frequency identification (RFID) technology. The system involves embedding an RFID chip in each vehicle that continuously transmits a unique identification signal. When a vehicle is stolen, the owner reports it to the police, who upload the vehicle's information to a central database. Police vehicles are equipped with RFID receivers. If a stolen vehicle passes within range of a receiver, the receiver detects the vehicle's ID signal and displays its details on a tablet. This allows police to quickly identify and recover stolen vehicles. The system aims to make it difficult for thieves to hide a vehicle's identity and allows vehicles to be tracked globally wherever the detection system is implemented.
This document discusses and compares two techniques for image denoising using wavelet transforms: Dual-Tree Complex DWT and Double-Density Dual-Tree Complex DWT. Both techniques decompose an image corrupted by noise using filter banks, apply thresholding to the wavelet coefficients, and reconstruct the image. The Double-Density Dual-Tree Complex DWT yields better denoising results than the Dual-Tree Complex DWT as it produces more directional wavelets and is less sensitive to shifts and noise variance. Experimental results on test images demonstrate that the Double-Density method achieves higher peak signal-to-noise ratios, especially at higher noise levels.
This document compares the k-means and grid density clustering algorithms. It summarizes that grid density clustering determines dense grids based on the densities of neighboring grids, and is able to handle different shaped clusters in multi-density environments. The grid density algorithm does not require distance computation and is not dependent on the number of clusters being known in advance like k-means. The document concludes that grid density clustering is better than k-means clustering as it can handle noise and outliers, find arbitrary shaped clusters, and has lower time complexity.
This document proposes a method for detecting, localizing, and extracting text from videos with complex backgrounds. It involves three main steps:
1. Text detection uses corner metric and Laplacian filtering techniques independently to detect text regions. Corner metric identifies regions with high curvature, while Laplacian filtering highlights intensity discontinuities. The results are combined through multiplication to reduce noise.
2. Text localization then determines the accurate boundaries of detected text strings.
3. Text binarization filters background pixels to extract text pixels for recognition. Thresholding techniques are used to convert localized text regions to binary images.
The method exploits different text properties to detect text using corner metric and Laplacian filtering. Combining the results improves
This document describes the design and implementation of a low power 16-bit arithmetic logic unit (ALU) using clock gating techniques. A variable block length carry skip adder is used in the arithmetic unit to reduce power consumption and improve performance. The ALU uses a clock gating circuit to selectively clock only the active arithmetic or logic unit, reducing dynamic power dissipation from unnecessary clock charging/discharging. The ALU was simulated in VHDL and synthesized for a Xilinx Spartan 3E FPGA, achieving a maximum frequency of 65.19MHz at 1.98mW power dissipation, demonstrating improved performance over a conventional ALU design.
This document describes using particle swarm optimization (PSO) and genetic algorithms (GA) to tune the parameters of a proportional-integral-derivative (PID) controller for an automatic voltage regulator (AVR) system. PSO and GA are used to minimize the objective function by adjusting the PID parameters to achieve optimal step response with minimal overshoot, settling time, and rise time. The results show that PSO provides high-quality solutions within a shorter calculation time than other stochastic methods.
This document discusses implementing trust negotiations in multisession transactions. It proposes a framework that supports voluntary and unexpected interruptions, allowing negotiating parties to complete negotiations despite temporary unavailability of resources. The Trust-x protocol addresses issues related to validity, temporary loss of data, and extended unavailability of one negotiator. It allows a peer to suspend an ongoing negotiation and resume it with another authenticated peer. Negotiation portions and intermediate states can be safely and privately passed among peers to guarantee stability for continued suspended negotiations. An ontology is also proposed to provide formal specification of concepts and relationships, which is essential in complex web service environments for sharing credential information needed to establish trust.
This document discusses and compares various nature-inspired optimization algorithms for resolving the mixed pixel problem in remote sensing imagery, including Biogeography-Based Optimization (BBO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). It provides an overview of each algorithm, explaining key concepts like migration and mutation in BBO. The document aims to prove that BBO is the best algorithm for resolving the mixed pixel problem by comparing it to other evolutionary algorithms. It also includes figures illustrating concepts like the species model and habitat in BBO.
This document discusses principal component analysis (PCA) for face recognition. It begins with an introduction to face recognition and PCA. PCA works by calculating eigenvectors from a set of face images, which represent the principal components that account for the most variance in the image data. These eigenvectors are called "eigenfaces" and can be used to reconstruct the face images. The document then discusses how the system is implemented, including preparing a face database, normalizing the training images, calculating the eigenfaces/principal components, projecting the face images into this reduced space, and recognizing faces by calculating distances between projected test images and training images.
This document summarizes research on using wireless sensor networks to detect mobile targets. It discusses two optimization problems: 1) maximizing the exposure of the least exposed path within a sensor budget, and 2) minimizing sensor installation costs while ensuring all paths have exposure above a threshold. It proposes using tabu search heuristics to provide near-optimal solutions. The research also addresses extending the models to consider wireless connectivity, heterogeneous sensors, and intrusion detection using a game theory approach. Experimental results show the proposed mobile replica detection scheme can rapidly detect replicas with no false positives or negatives.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.