This document presents a novel demosaicking approach to restore full-color images from single-sensor digital camera data. The approach improves existing directional filtering and weighting techniques to reduce common demosaicking artifacts. It analyzes the advantages and limitations of prior methods and proposes a new estimation scheme. The method interpolates the green color plane using directional filtering or weighted averaging depending on edge strength. It then interpolates the red and blue planes using weighted averaging on color difference planes. Experimental results showed the proposed approach outperformed six state-of-the-art demosaicking methods in subjective and objective quality measures.
An Efficient Filtering Technique for Denoising Colour Images IJECEIAES
Single-sensor digital cameras capture image with the aid of masking the sensor surface along a colour filter array(CFA) such that every sensor pixel solely samples certain of three primary colour values i.e., R (red), G (green) and B (blue). To get a full-colour image, an interpolation method commonly referred in conformity with CFA demosaicking is required to estimate the other two contributions for producing a full-colour image. But, the clutter in imaging sensors not only corrupts the colour filter array but also introduces artifacts at some stage in the colour interpolation step and affects the characteristics of image. To acquire high quality full-colour image, a kind of viable and effective interpolation algorithm based over gradient is used. This technique can remove the noise effectively by retaining image border and detail data clearly.
An Efficient Filtering Technique for Denoising Colour Images IJECEIAES
Single-sensor digital cameras capture image with the aid of masking the sensor surface along a colour filter array(CFA) such that every sensor pixel solely samples certain of three primary colour values i.e., R (red), G (green) and B (blue). To get a full-colour image, an interpolation method commonly referred in conformity with CFA demosaicking is required to estimate the other two contributions for producing a full-colour image. But, the clutter in imaging sensors not only corrupts the colour filter array but also introduces artifacts at some stage in the colour interpolation step and affects the characteristics of image. To acquire high quality full-colour image, a kind of viable and effective interpolation algorithm based over gradient is used. This technique can remove the noise effectively by retaining image border and detail data clearly.
RP BASED OPTIMIZED IMAGE COMPRESSING TECHNIQUEprj_publication
Colorization is a method that adds color components to a gray scale image using only
a few representative pixels (RP) by the user. A novel approach to image compression called
colorization-based coding is being done. It automatically extracts representative pixels from
an original color image at an encoder and restores a full color image by using colorization at
the decoder. However past studies on colorization based coding obtain redundant
representative pixels and do not remove the pixels required for suppressing coding errors.
The main issue in colorization based coding is how to extract representative pixels (RP) so
that compression rate is high and the reconstructed images have good visual quality. In this
work we formulate the colorization based coding into an optimization problem i.e., an
minimization problem. We use the colorization matrix that colorizes the image in a multi
scale manner and this is combined with RP extraction method allows us to choose a very
small set of RP. Simulation results revealed that implemented method compress the color
image with higher compression rate and has good quality and is comparable to conventional
JPEG standard.
FACE RECOGNITION ALGORITHM BASED ON ORIENTATION HISTOGRAM OF HOUGH PEAKSijaia
In this paper we propose a novel face recognition algorithm based on orientation histogram of Hough Transform Peaks. The novelty of the approach lies in utilizing Hough Transform peaks for determining the orientation angles and computing the histogram from it. For extraction of feature vectors first the images are divided into non overlapping blocks of equal size. Then for each of the blocks the orientation histograms are computed. The obtained histograms are combined to form the final feature vector set. Classification is done using k nearest neighbor classifier. The algorithm has been tested on the ORL
database, Yale B Database & the Essex Grimace Database.97% Recognition rates have been obtained for
ORL database, 100% for Yale B and 100% for Essex Grimace database
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/xnor/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-rastegari
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Mohammad Rastegari, Chief Technology Officer at Xnor.ai, presents the "Methods for Creating Efficient Convolutional Neural Networks" tutorial at the May 2019 Embedded Vision Summit.
In the past few years, convolutional neural networks (CNNs) have revolutionized several application domains in AI and computer vision. The biggest challenge with state-of-the-art CNNs is the massive compute demands that prevent these models from being used in many embedded systems and other resource-constrained environments.
In this talk, Rastegari explains and contrasts several recent techniques that enable CNN models with high accuracy to consume very little memory and processor resources. These methods include a variety of algorithmic and optimization approaches to deep learning models. Quantization, sparsification and compact model design are three of the major techniques for efficient CNNs, which are discussed in the context of computer vision applications including detection, recognition and segmentation.
Unconstrained Optimization Method to Design Two Channel Quadrature Mirror Fil...CSCJournals
This paper proposes an efficient method for the design of two-channel, quadrature mirror filter (QMF) bank for subband image coding. The choice of filter bank is important as it affects image quality as well as system design complexity. The design problem is formulated as weighted sum of reconstruction error in time domain and passband and stop-band energy of the low-pass analysis filter of the filter bank .The objective function is minimized directly, using nonlinear unconstrained method. Experimental results of the method on images show that the performance of the proposed method is better than that of the already existing methods. The impact of some filter characteristics, such as stopband attenuation, stopband edge, and filter length on the performance of the reconstructed images is also investigated.
Histogram Gabor Phase Pattern and Adaptive Binning Technique in Feature Selec...CSCJournals
The aim of this paper is to develop a robust system for face recognition by using Histogram Gabor Phase Pattern (HGPP) and adaptive binning technique. Gabor wavelet function is used for representing the features of the image both in frequency and orientation level. The huge feature space created by Gabor wavelet is classified by using adaptive binning technique. The unused bin spaces are used. As a result of which, the size of the space is drastically reduced and high quality HGPP created. It is due to this approach, the computation complexity and the time taken for the process is reduced and the recognition rate of the face improved. The significance of this system is its compatibility in yielding best results in the face recognition with major factors of a face image. The system is verified with FERET database and the results are compared with those of the existing methods.
Multiple patterning is a class of technologies for manufacturing integrated circuits (ICs), developed for photolithography to enhance the feature density. The simplest case of multiple patterning is double patterning, where a conventional lithography process is enhanced to produce double the expected number of features. The resolution of a photoresist pattern is believed to blur at around 45 nm half-pitch. For the semiconductor industry, therefore, double patterning was introduced for the 32 nm half-pitch node and below. This presentation gives us an insight of why multiple patterning is an important to give us a better resolution below 32nm.
Implementation of Low-Complexity Redundant Multiplier Architecture for Finite...ijcisjournal
In the present work, a low-complexity Digit-Serial/parallel Multiplier over Finite Field is proposed. It is
employed in applications like cryptography for data encryption and decryptionto deal with discrete
mathematical andarithmetic structures. The proposedmultiplier utilizes a redundant representation because
of their free squaring and modular reduction. The proposed 10-bit multiplier is simulated and synthesized
using Xilinx VerilogHDL. It is evident from the simulation results that the multiplier has significantly low
area and power when compared to the previous structures using the same representation.
A frequently used class of objects are the quadric surfaces, which are described with second-degree equations (quadratics). They include spheres, ellipsoids, tori, paraboloids, and hyperboloids.
Quadric surfaces, particularly spheres and ellipsoids, are common elements of graphics scenes
RP BASED OPTIMIZED IMAGE COMPRESSING TECHNIQUEprj_publication
Colorization is a method that adds color components to a gray scale image using only
a few representative pixels (RP) by the user. A novel approach to image compression called
colorization-based coding is being done. It automatically extracts representative pixels from
an original color image at an encoder and restores a full color image by using colorization at
the decoder. However past studies on colorization based coding obtain redundant
representative pixels and do not remove the pixels required for suppressing coding errors.
The main issue in colorization based coding is how to extract representative pixels (RP) so
that compression rate is high and the reconstructed images have good visual quality. In this
work we formulate the colorization based coding into an optimization problem i.e., an
minimization problem. We use the colorization matrix that colorizes the image in a multi
scale manner and this is combined with RP extraction method allows us to choose a very
small set of RP. Simulation results revealed that implemented method compress the color
image with higher compression rate and has good quality and is comparable to conventional
JPEG standard.
FACE RECOGNITION ALGORITHM BASED ON ORIENTATION HISTOGRAM OF HOUGH PEAKSijaia
In this paper we propose a novel face recognition algorithm based on orientation histogram of Hough Transform Peaks. The novelty of the approach lies in utilizing Hough Transform peaks for determining the orientation angles and computing the histogram from it. For extraction of feature vectors first the images are divided into non overlapping blocks of equal size. Then for each of the blocks the orientation histograms are computed. The obtained histograms are combined to form the final feature vector set. Classification is done using k nearest neighbor classifier. The algorithm has been tested on the ORL
database, Yale B Database & the Essex Grimace Database.97% Recognition rates have been obtained for
ORL database, 100% for Yale B and 100% for Essex Grimace database
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/xnor/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-rastegari
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Mohammad Rastegari, Chief Technology Officer at Xnor.ai, presents the "Methods for Creating Efficient Convolutional Neural Networks" tutorial at the May 2019 Embedded Vision Summit.
In the past few years, convolutional neural networks (CNNs) have revolutionized several application domains in AI and computer vision. The biggest challenge with state-of-the-art CNNs is the massive compute demands that prevent these models from being used in many embedded systems and other resource-constrained environments.
In this talk, Rastegari explains and contrasts several recent techniques that enable CNN models with high accuracy to consume very little memory and processor resources. These methods include a variety of algorithmic and optimization approaches to deep learning models. Quantization, sparsification and compact model design are three of the major techniques for efficient CNNs, which are discussed in the context of computer vision applications including detection, recognition and segmentation.
Unconstrained Optimization Method to Design Two Channel Quadrature Mirror Fil...CSCJournals
This paper proposes an efficient method for the design of two-channel, quadrature mirror filter (QMF) bank for subband image coding. The choice of filter bank is important as it affects image quality as well as system design complexity. The design problem is formulated as weighted sum of reconstruction error in time domain and passband and stop-band energy of the low-pass analysis filter of the filter bank .The objective function is minimized directly, using nonlinear unconstrained method. Experimental results of the method on images show that the performance of the proposed method is better than that of the already existing methods. The impact of some filter characteristics, such as stopband attenuation, stopband edge, and filter length on the performance of the reconstructed images is also investigated.
Histogram Gabor Phase Pattern and Adaptive Binning Technique in Feature Selec...CSCJournals
The aim of this paper is to develop a robust system for face recognition by using Histogram Gabor Phase Pattern (HGPP) and adaptive binning technique. Gabor wavelet function is used for representing the features of the image both in frequency and orientation level. The huge feature space created by Gabor wavelet is classified by using adaptive binning technique. The unused bin spaces are used. As a result of which, the size of the space is drastically reduced and high quality HGPP created. It is due to this approach, the computation complexity and the time taken for the process is reduced and the recognition rate of the face improved. The significance of this system is its compatibility in yielding best results in the face recognition with major factors of a face image. The system is verified with FERET database and the results are compared with those of the existing methods.
Multiple patterning is a class of technologies for manufacturing integrated circuits (ICs), developed for photolithography to enhance the feature density. The simplest case of multiple patterning is double patterning, where a conventional lithography process is enhanced to produce double the expected number of features. The resolution of a photoresist pattern is believed to blur at around 45 nm half-pitch. For the semiconductor industry, therefore, double patterning was introduced for the 32 nm half-pitch node and below. This presentation gives us an insight of why multiple patterning is an important to give us a better resolution below 32nm.
Implementation of Low-Complexity Redundant Multiplier Architecture for Finite...ijcisjournal
In the present work, a low-complexity Digit-Serial/parallel Multiplier over Finite Field is proposed. It is
employed in applications like cryptography for data encryption and decryptionto deal with discrete
mathematical andarithmetic structures. The proposedmultiplier utilizes a redundant representation because
of their free squaring and modular reduction. The proposed 10-bit multiplier is simulated and synthesized
using Xilinx VerilogHDL. It is evident from the simulation results that the multiplier has significantly low
area and power when compared to the previous structures using the same representation.
A frequently used class of objects are the quadric surfaces, which are described with second-degree equations (quadratics). They include spheres, ellipsoids, tori, paraboloids, and hyperboloids.
Quadric surfaces, particularly spheres and ellipsoids, are common elements of graphics scenes
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A lossless color image compression using an improved reversible color transfo...eSAT Journals
Abstract In case of the conventional lossless color image compression methods, the pixels are interleaved from each color component, and they are predicted and finally encoded. In this paper, we propose a lossless color image compression method using hierarchical prediction of chrominance channel pixels and encoded with modified Huffman coding. An input image is chosen and the R, G and B color channel is transform into YCuCv color space using an improved reversible color transform. After that a conventional lossless image coder like CALIC is used to compress the luminance channel Y. The chrominance channel Cu and Cv are encoded with hierarchical decomposition and directional prediction. The effective context modeling for prediction residual is adopted finally. It is seen from the experimental result the proposed method improves the compression performance than the existing method. Keywords: Lossless color image compression, hierarchical prediction, reversible color transform, modified Huffman coding.
Multiscale Gradient Based – Directional CFA Interpolation with RefinementIJTET Journal
Abstract—Single sensor digital cameras capture only one color value for every pixel location. The process of reconstructing a full color image from these incomplete color samples output from an image sensor overlaid with a color filter array (CFA) is called demosaicing or Color Filter Array (CFA) interpolation. The most commonly used CFA configuration is the Bayer filter. The proposed demosaicing method makes use of multiscale color gradients to adaptively combine color difference estimates from horizontal and vertical directions and determine the contribution of each direction to the green channel interpolation. This method does not require any thresholds and is non iterative. The red and blue channels are then refined using structural approximation.
Nowadays digital cameras are equipped with a
single sensor (CCD/CMOS), to reduce the size and cost of the
camera. The color filter array (CFA) is used to cover the sensor
and it consist of three primary colors such as red, green and blue
and it samples only one color component at each pixel location.
The process of estimating the other two missing color components
at each pixel location is known as demosaicing. The proposed
algorithm uses the directional color difference and multiscale
gradient method for green plane interpolation, this type of
interpolation method is used to reduce the artifacts and improve
the image quality. The red and blue plane are interpolated using
the estimated green plane, the bayer pattern is used for the
interpolation technique. The performance of the image is
measured using the CPSNR value
Novel algorithm for color image demosaikcing using laplacian maskeSAT Journals
Abstract Images in any digital camera is formed with the help of a monochrome sensor, which can be either a charge-coupled device(CCD) or complementary metal oxide semi-conductor(CMOS). Interpolation is the base for any demsoaicking process. The input for interpolation is the output of the Bayer Color Filter Array which is a mosaic like lattice structure. Bayer Color Filter Array samples the channel information of R,G and B values separately assigning only one channel component per pixel. To generate a complete color image, three channel values are required. In order to find those missing samples we use interpolation. It is a technique of estimating the missing values from the discrete observed samples scattered over the space. Thus Demosaicking or De-bayering is an algorithm of finding missing values from the mosaic patterned output of the Bayer CFA. Interpolation algorithm results in few artifacts such as zippering effect in the edges. This paper introduces an algorithm for demosaicking which outperforms the existing demosaciking algorithms. The main aim of this algorithm is to accurately estimate the Green component. The standard mechanism to compare the performance is PSNR(Peak Signal to Noise Ratio) and the image dataset for comparison was Kodak image dataset. The algorithm was implemented using Matlab2009B version. Keywords: Demosaicking, Interpolation, Bayer CFA, Laplacian Mask, Correlation.
Use of horizontal and vertical edge processing technique to improve number pl...eSAT Journals
Abstract
Automatic Number Plate Recognition (ANPR) is a bulk investigation system that catches the image of vehicles and identifies their
license number. ANPR may be supported in the finding of taken vehicles. The recognition of taken vehicles may be done in an
effective means by means of the ANPR systems situated on the highways. This paper proposes a recognition technique in which
the vehicle plate image is found by the digital cameras and the image is processed to acquire the number plate data. A back image
of a vehicle is taken and administered using numerous algorithms.
Key Words: Image Processing, Histogram, Skew Correction, Segmentation
COMPARISON OF GPU AND FPGA HARDWARE ACCELERATION OF LANE DETECTION ALGORITHMsipij
The two fundamental components of a complete computer vision system are detection and classification.
The Lane detection algorithm, which is used in autonomous driving and smart vehicle systems, is within the
computer vision detection area. In a sophisticated road environment, lane marking is the responsibility of
the lane detection system. The warning system for a car that leaves its lane also heavily relies on lane
detection. The two primary stages of the implemented lane detection algorithm are edge detection and line
detection. In order to assess the trade-offs for latency, power consumption, and utilisation, we will
compare the state-of-the-art implementation performance attained with both FPGA and GPU in this work.
Our analysis highlights the benefits and drawbacks of the two systems.
Comparison of GPU and FPGA Hardware Acceleration of Lane Detection Algorithmsipij
The two fundamental components of a complete computer vision system are detection and classification.
The Lane detection algorithm, which is used in autonomous driving and smart vehicle systems, is within the
computer vision detection area. In a sophisticated road environment, lane marking is the responsibility of
the lane detection system. The warning system for a car that leaves its lane also heavily relies on lane
detection. The two primary stages of the implemented lane detection algorithm are edge detection and line
detection. In order to assess the trade-offs for latency, power consumption, and utilisation, we will
compare the state-of-the-art implementation performance attained with both FPGA and GPU in this work.
Our analysis highlights the benefits and drawbacks of the two systems.
In image processing colour segmentation is used to extract features of an object in both special and frequency domains. The objective of this paper is to use colour segmentation technique to identify the defected region of fruits and corresponding percentage of frequency components from its Spectrogram. Here we separate the defective portion of fruit using colour segmentation technique taking four images from four directions to get the appropriate result of 3D images. The percentage of the defective portion is determined using scatterplot of the colours of the image. Next, we apply the similar concept to spectrogram of an image (even applicable in speech signal) to extract the percentages of frequency components of the signal.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
APPLYING EDGE INFORMATION IN YCbCr COLOR SPACE ON THE IMAGE WATERMARKINGsipij
Copyright protection has currently become a difficult domain in reality situation. an honest quality watermarking scheme might to have high sensory activity transparency, and may even be robust enough against potential attacks. This paper tends to propose the special domain based mostly watermarking
scheme for color pictures. This scheme uses the Sobel and canny edge detection strategies to work out edge data of the luminance and chrominance elements of the colour image. The edge detection strategies are used to verify the embedding capability of every color element. The massive capacities of watermark bits are embedded into an element of enormous edge information. The strength of the projected scheme is analyzed considering differing kinds of image process attacks, like Blurring and adding noise.
Edge detection is one of the most powerful image analysis tools for enhancing and detecting edges. Indeed, identifying and localizing edges are a low level task in a variety of applications such as 3-D reconstruction, shape recognition, image compression, enhancement, and restoration. This paper introduces a new algorithm for detecting edges based on color space models. In this RGB image is taken as an input image and transforming the RGB image to color models such as YUV, YCbCr and XYZ. The edges have been detected for each component in color models separately and compared with the original image of that particular model. In order to measure the quality assessment between images, SSIM (Structural Similarity Index Method) and VIF (Visual Information Fidelity) has been calculated. The results have shown that XYZ color model is having high SSIM value and VIF value. In the previous papers, edge detection based on RGB color model has low SSIM and VIF values. So by converting the images into different color models shows a significant improvement in detection of edges. Keywords: Edge detection, Color models, SSIM, VIF.
Similar to IRJET- Effective Demosaicking for Bayer Color Filter Arrays with Directional Filtering and Weighting Technique (20)
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
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