This document summarizes a research paper that proposes a distributed Canny edge detection algorithm with the following key points:
1. The algorithm divides an input image into overlapping blocks that can be processed independently and in parallel to reduce memory requirements, latency, and increase throughput compared to the original Canny algorithm.
2. A novel method is proposed for calculating hysteresis thresholds based on an 8-bin non-uniform quantized gradient magnitude histogram to reduce computational complexity compared to previous methods.
3. An FPGA architecture is presented for implementing the proposed distributed Canny algorithm, along with simulation results demonstrating it can process an image 16 times faster than the original Canny algorithm with no loss in performance.
NMS and Thresholding Architecture used for FPGA based Canny Edge Detector for...idescitation
In this paper, an architecture designed for Non-
Maximal Suppression used in Canny edge detection algorithm
is presented in order to reduce memory requirements
significantly. The architecture also achieves decreased latency
and increased throughput with no loss in edge detection. The
new algorithm used has a low-complexity 8-bin non-uniform
gradient magnitude histogram to compute block-based
hysteresis thresholds that are used by the Canny edge detector.
Furthermore, the hardware architecture of the proposed
algorithm is presented in this paper and the architecture is
synthesized on the Xilinx Virtex 5 FPGA. The design
development is done in VHDL and simulated results are
obtained using modelsim 6.3 with Xilinx 12.2.
Video Stitching using Improved RANSAC and SIFTIRJET Journal
1. The document discusses techniques for stitching multiple video frames into a panoramic video using Scale-Invariant Feature Transform (SIFT) and an improved RANSAC algorithm.
2. Key points and feature descriptors are extracted from frames using SIFT to find correspondences between frames. The improved RANSAC algorithm is used to estimate homography matrices between frames and filter outlier matches.
3. Frames are blended together to compensate for exposure differences and misalignments before being mapped to a reference plane to create the panoramic video mosaic. The algorithm aims to produce a high quality panoramic video in real-time.
Performance Analysis of Iterative Closest Point (ICP) Algorithm using Modifie...IRJET Journal
This document discusses the Iterative Closest Point (ICP) algorithm, which is commonly used for 3D shape registration. It first provides background on ICP and describes some variants like Comprehensive ICP and Trimmed ICP. It then focuses on the Comprehensive ICP algorithm, explaining how it uses a lookup matrix to ensure unique point correspondences between shapes. Finally, it introduces the Modified Hausdorff Distance metric for evaluating similarity between registered shapes, which is more robust than other metrics. The document aims to analyze ICP variant performance using this distance metric.
Compressed sensing applications in image processing & communication (1)Mayur Sevak
This document discusses applications of compressed sensing in image processing and communication. It begins by explaining what compressed sensing is and how it works by acquiring signals using fewer samples than required by the Nyquist criterion through exploiting sparsity and incoherence. It then discusses how to implement compressed sensing using properties of sparsity and incoherence along with convex optimization for reconstruction. Applications discussed include image fusion, representation, denoising, remote sensing, MIMO and cognitive radio communications, and ultra-wideband channel estimation. Compressed sensing is shown to provide benefits like reducing data acquisition and transmission costs while enabling reliable signal reconstruction.
Implementation of Distributed Canny Edge Detection TechniqueIRJET Journal
The document describes a technique called distributed Canny edge detection that improves upon the standard Canny edge detection algorithm. The distributed technique divides an image into overlapping blocks and computes adaptive thresholds for each block based on its type, rather than computing global thresholds for the entire image. This allows edges to be detected more accurately at the block level for real-time applications. Experimental results show the distributed technique produces lower error rates compared to other edge detection methods.
This document provides an overview of various computer vision and image processing techniques including template matching, Hough transforms, image segmentation using watershed algorithms, feature detection using Harris corner detection. It outlines the stages of an assignment involving implementing and comparing Hough line and circle transforms, Harris corner detection and JPEG compression with OpenCV. It also describes a final group project to solve a real-world problem using computer vision techniques and building a mobile application.
This document proposes using dual back-to-back Kinect sensors mounted on a robot to capture a 3D model of a large indoor scene. Traditionally, one Kinect is slid across an area, but this requires prominent features and careful handling. The dual Kinect setup requires calibrating the relative pose between the sensors. Since they do not share a view, traditional calibration is not possible. The authors place a dual-face checkerboard on top with a mirror to enable each Kinect to view the same calibration object. This allows estimating the pose between the sensors using a mirror-based algorithm. After capturing local models, the two Kinect views can be merged into a combined 3D model with a larger field of view.
This paper presents an approach for image restoration in the presence of blur and noise. The image is divided into independent regions modeled with a Gaussian prior. Wavelet-based methods are used for image denoising, while classical Wiener filtering is used for deblurring. The algorithm finds the maximum a posteriori estimate at the intersection of convex sets generated by Wiener filtering. It provides efficient image restoration without sacrificing the simplicity of filtering, and generates a better restored image compared to previous methods.
NMS and Thresholding Architecture used for FPGA based Canny Edge Detector for...idescitation
In this paper, an architecture designed for Non-
Maximal Suppression used in Canny edge detection algorithm
is presented in order to reduce memory requirements
significantly. The architecture also achieves decreased latency
and increased throughput with no loss in edge detection. The
new algorithm used has a low-complexity 8-bin non-uniform
gradient magnitude histogram to compute block-based
hysteresis thresholds that are used by the Canny edge detector.
Furthermore, the hardware architecture of the proposed
algorithm is presented in this paper and the architecture is
synthesized on the Xilinx Virtex 5 FPGA. The design
development is done in VHDL and simulated results are
obtained using modelsim 6.3 with Xilinx 12.2.
Video Stitching using Improved RANSAC and SIFTIRJET Journal
1. The document discusses techniques for stitching multiple video frames into a panoramic video using Scale-Invariant Feature Transform (SIFT) and an improved RANSAC algorithm.
2. Key points and feature descriptors are extracted from frames using SIFT to find correspondences between frames. The improved RANSAC algorithm is used to estimate homography matrices between frames and filter outlier matches.
3. Frames are blended together to compensate for exposure differences and misalignments before being mapped to a reference plane to create the panoramic video mosaic. The algorithm aims to produce a high quality panoramic video in real-time.
Performance Analysis of Iterative Closest Point (ICP) Algorithm using Modifie...IRJET Journal
This document discusses the Iterative Closest Point (ICP) algorithm, which is commonly used for 3D shape registration. It first provides background on ICP and describes some variants like Comprehensive ICP and Trimmed ICP. It then focuses on the Comprehensive ICP algorithm, explaining how it uses a lookup matrix to ensure unique point correspondences between shapes. Finally, it introduces the Modified Hausdorff Distance metric for evaluating similarity between registered shapes, which is more robust than other metrics. The document aims to analyze ICP variant performance using this distance metric.
Compressed sensing applications in image processing & communication (1)Mayur Sevak
This document discusses applications of compressed sensing in image processing and communication. It begins by explaining what compressed sensing is and how it works by acquiring signals using fewer samples than required by the Nyquist criterion through exploiting sparsity and incoherence. It then discusses how to implement compressed sensing using properties of sparsity and incoherence along with convex optimization for reconstruction. Applications discussed include image fusion, representation, denoising, remote sensing, MIMO and cognitive radio communications, and ultra-wideband channel estimation. Compressed sensing is shown to provide benefits like reducing data acquisition and transmission costs while enabling reliable signal reconstruction.
Implementation of Distributed Canny Edge Detection TechniqueIRJET Journal
The document describes a technique called distributed Canny edge detection that improves upon the standard Canny edge detection algorithm. The distributed technique divides an image into overlapping blocks and computes adaptive thresholds for each block based on its type, rather than computing global thresholds for the entire image. This allows edges to be detected more accurately at the block level for real-time applications. Experimental results show the distributed technique produces lower error rates compared to other edge detection methods.
This document provides an overview of various computer vision and image processing techniques including template matching, Hough transforms, image segmentation using watershed algorithms, feature detection using Harris corner detection. It outlines the stages of an assignment involving implementing and comparing Hough line and circle transforms, Harris corner detection and JPEG compression with OpenCV. It also describes a final group project to solve a real-world problem using computer vision techniques and building a mobile application.
This document proposes using dual back-to-back Kinect sensors mounted on a robot to capture a 3D model of a large indoor scene. Traditionally, one Kinect is slid across an area, but this requires prominent features and careful handling. The dual Kinect setup requires calibrating the relative pose between the sensors. Since they do not share a view, traditional calibration is not possible. The authors place a dual-face checkerboard on top with a mirror to enable each Kinect to view the same calibration object. This allows estimating the pose between the sensors using a mirror-based algorithm. After capturing local models, the two Kinect views can be merged into a combined 3D model with a larger field of view.
This paper presents an approach for image restoration in the presence of blur and noise. The image is divided into independent regions modeled with a Gaussian prior. Wavelet-based methods are used for image denoising, while classical Wiener filtering is used for deblurring. The algorithm finds the maximum a posteriori estimate at the intersection of convex sets generated by Wiener filtering. It provides efficient image restoration without sacrificing the simplicity of filtering, and generates a better restored image compared to previous methods.
This document provides an overview of various digital image processing techniques including morphological transformations, geometric transformations, image gradients, Canny edge detection, image thresholding, and a practical demo assignment. It discusses the basic concepts and algorithms for each technique and provides examples code. The document is presented as part of a practical course on digital image processing.
This document summarizes a research paper that proposes a novel background removal algorithm using fuzzy c-means clustering. It begins by introducing background subtraction and some of the challenges. It then describes the proposed algorithm which uses edge detection to locate regions of interest before applying fuzzy c-means clustering to segment the foreground object. The algorithm achieves significant computation time reduction compared to other methods. Experimental results show the proposed method has higher true positive rates and accuracy compared to other algorithms, though precision and similarity are slightly lower.
This document provides an overview of digital image processing techniques including feature detection, description, and matching. It discusses Harris corner detection, SIFT, SURF, FAST, BRIEF, and ORB feature detectors. It also covers brute force and FLANN-based feature matching as well as using feature matching and homography to find objects between images. Finally, it outlines an assignment on panorama stitching or bag-of-features image classification and a course project deadline.
NUMBER PLATE IMAGE DETECTION FOR FAST MOTION VEHICLES USING BLUR KERNEL ESTIM...paperpublications3
This document discusses a proposed method for detecting number plates on images of fast moving vehicles that have been blurred due to motion. It begins with an introduction to image processing and digital images. It then discusses estimating the blur kernel caused by vehicle motion in order to model it as a linear uniform blur with parameters for angle and length. Existing related works on image deblurring are reviewed. The proposed system estimates the blur kernel parameters using sparse representation and Radon transform methods, allows deblurring the image, and then uses artificial neural networks to identify numbers and characters in the deblurred image. The system is evaluated on real blurred images and shown to improve license plate recognition compared to previous methods.
The document compares frame difference and Kalman filter techniques for detecting moving vehicles in video surveillance. Frame difference is a simple but low accuracy method that uses thresholding on differences between frames. Kalman filtering provides better accuracy by modeling each pixel as a Kalman filter and updating estimates based on observations. The paper applies both methods to a vehicle video and finds that Kalman filtering produces cleaner detection with fewer false positives compared to frame difference.
The document provides an agenda for a practical session on digital image processing. It discusses stages of computer vision including stereo images, optical flow, and machine learning techniques like classification and clustering. Stereo vision and depth maps from stereo images are explained. Optical flow concepts like the Lucas-Kanade method are covered. Machine learning algorithms like KNN, SVM, and K-means clustering are also summarized. The document concludes with information about a project, assignment, and notable AI companies in Egypt.
A ROBUST BACKGROUND REMOVAL ALGORTIHMS USING FUZZY C-MEANS CLUSTERINGIJNSA Journal
Background subtraction is typically one of the first steps carried out in motion detection using static video cameras. This paper presents a novel method for background removal that processes only some pixels of each image. Some regions of interest of the objects in the image or frame are located with the help of edge detector. Once the region is detected only that area will be segmented instead of processing the whole image. This method achieves a significant reduction in computation time that can be used for subsequent image analysis. In this paper we detect the foreground object with the help of edge detector and combine the Fuzzy c-means clustering algorithm to segment the object by means of subtracting the current frame from the previous frame, the accurate background is identified.
Minimum image disortion of reversible data hidingIRJET Journal
1) The document presents a method for minimum image distortion in reversible data hiding. It aims to hide data in image files while maintaining high image quality after extraction.
2) The method assigns different weights to pixels for feature extraction in steganalysis based on their probability of being altered. It focuses on regions likely changed to reduce the effect of unchanged smooth areas.
3) Experimental results on four common mobile steganography techniques demonstrate the effectiveness of the proposed scheme, particularly at low embedding rates, in identifying areas containing hidden data while maintaining perceptual image quality.
SHORT LISTING LIKELY IMAGES USING PROPOSED MODIFIED-SIFT TOGETHER WITH CONVEN...ijfcstjournal
The paper proposes the modified-SIFT algorithm which will be a modified form of the scale invariant feature transform. The modification consists of considering successive groups of 8 rows of pixel, along the height of the image. These are used to construct 8 bin histograms for magnitude as well as orientation individually. As a result the number of feature descriptors is significantly less (95%) than the standard SIFT approach. Fewer feature descriptor leads to reduced accuracy. This reduction in accuracy is quite drastic when searching for a single (RANK1) image match; however accuracy improves if a band of likely (say tolerance of 10%) images is to be returned. The paper therefore proposes a two-stage-approach where
First Modified-SIFT is used to obtain a shortlisted band of likely images subsequently SIFT is applied within this band to find a perfect match. It may appear that this process is tedious however it provides a significant reduction in search time as compared to applying SIFT on the entire database. The minor reduction in accuracy can be offset by the considerable time gained while searching a large database. The
modified-SIFT algorithm when used in conjunction with a face cropping algorithm can also be used to find a match against disguised images.
PARALLEL GENERATION OF IMAGE LAYERS CONSTRUCTED BY EDGE DETECTION USING MESSA...ijcsit
Edge detection is one of the most fundamental algorithms in digital image processing. Many algorithms have been implemented to construct image layers extracted from the original image based on selecting threshold parameters. Changing theses parameters to get a high quality layer is time consuming. In this paper, we propose two parallel technique, NASHT1 and NASHT2, to generate multiple layers of an input
image automatically to enable the image tester to select the highest quality detected edges. In addition, the
effect of intensive I/O operations and the number of parallel running processes on the performance of the proposed techniques have also been studied.
Trajectory Based Unusual Human Movement Identification for ATM SystemIRJET Journal
This document summarizes a research paper on developing a system to identify unusual human movements at ATMs using trajectory analysis. The proposed system uses computer vision techniques like background subtraction and template matching to detect and track human movements. If a person's trajectory does not match expected patterns or if their face is covered, an alarm is triggered. The system is intended to prevent crimes and unauthorized access at ATMs by continuously monitoring movements and alerting administrators of any suspicious activity in real-time.
Design and implementation of video tracking system based on camera field of viewsipij
The basic idea of this paper is to design and implement of video tracking system based on Camera Field of
View (CFOV), Otsu’s method was used to detect targets such as vehicles and people. Whereas most
algorithms were spent a lot of time to execute the process, an algorithm was developed to achieve it in a
little time. The histogram projection was used in both directional to detect target from search region,
which is robust to various light conditions in Charge Couple Device (CCD) camera images and saves
computation time.
Our algorithm based on background subtraction, and normalize cross correlation operation from a series
of sequential sub images can estimate the motion vector. Camera field of view (CFOV) was determined and
calibrated to find the relation between real distance and image distance. The system was tested by
measuring the real position of object in the laboratory and compares it with the result of computed one. So
these results are promising to develop the system in future.
A Literature Survey: Neural Networks for object detectionvivatechijri
Humans have a great capability to distinguish objects by their vision. But, for machines object
detection is an issue. Thus, Neural Networks have been introduced in the field of computer science. Neural
Networks are also called as ‘Artificial Neural Networks’ [13]. Artificial Neural Networks are computational
models of the brain which helps in object detection and recognition. This paper describes and demonstrates the
different types of Neural Networks such as ANN, KNN, FASTER R-CNN, 3D-CNN, RNN etc. with their accuracies.
From the study of various research papers, the accuracies of different Neural Networks are discussed and
compared and it can be concluded that in the given test cases, the ANN gives the best accuracy for the object
detection.
This document summarizes a research paper that proposes a new technique called Morphology based technique for Extraction and Detection of blinking Region from gif Images. It begins by introducing the goal of detecting blinking parts in gif images and issues with existing techniques. It then describes the proposed methodology which uses edge detection, morphological operations like closing, and precision/recall metrics to evaluate the technique. The methodology is tested on sample gif images and results show high precision and recall rates, indicating the model is effective at extracting blinking regions.
The document discusses image representation and feature extraction techniques. It describes how representation makes image information more accessible for computer interpretation using either boundaries or pixel regions. Feature extraction quantifies these representations by extracting descriptors like geometric properties, statistical moments, and textures. Desirable properties for descriptors include being invariant to transformations, compact, robust to noise, and having low complexity. Various boundary and regional descriptors are defined, such as chain codes, shape numbers, and moments.
FORGERY (COPY-MOVE) DETECTION IN DIGITAL IMAGES USING BLOCK METHODeditorijcres
AKHILESH KUMAR YADAV, DEENBANDHU SINGH, VIVEK KUMAR
Department of Computer Science and Engineering
Babu Banarasi Das University, Lucknow
akhi2232232@gmail.com, deenbandhusingh85@gmail.com, vivek.kumar0091@gmail.com
ABSTRACT- Digital images can be easily modified using powerful image editing software. Determining whether a manipulation is innocent of sharpening from those which are malicious, such as removing or adding parts to an image is the topic of this paper. In this paper we focus on detection of a special type of forgery-the Copy-Move forgery, in this part of the original image is copied moved to desired location in the same image and pasted. The proposed method compress images using DWT (discrete wavelet transform) and divided into blocks and choose blocks than perform feature vector calculation and lexicographical sorting and duplicated blocks are identified after sorting. This method is good at some manipulation/attack likes scaling, rotation, Gaussian noise, smoothing, JPEG compression etc.
INDEX TERMS- Copy-Move forgery, Wavelet Transform, Lexicographical Sorting, Region Duplication Detection.
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.
Visual Cryptography using Image ThresholdingIRJET Journal
This document proposes a novel visual cryptography algorithm using image thresholding that generates shares of an image without requiring a key. The technique thresholds the secret image to create two shares such that individual shares do not reveal information about the secret image. The original image can be reconstructed by performing an OR operation on the pixel values of the shares using the threshold value, without any loss of quality or information. The algorithm was tested on color images in MATLAB and successfully recovered the original image from the shares.
This document discusses different deep learning approaches for image segmentation. It covers using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). For CNNs, it describes fully convolutional networks, deconvolution networks, U-Net, and other models. For RNNs, it discusses multi-dimensional RNNs, scene labeling with LSTMs, turned and pyramid RNNs, and grid LSTMs. It also reviews Pix2Pix and other GAN-based models for image segmentation tasks.
M ESH S IMPLIFICATION V IA A V OLUME C OST M EASUREijcga
We develop a polygonal mesh simplification algorithm based on a novel analysis of the mesh
geometry.
Particularly, we propose first a characterization of vertices as hyperbolic or non
-
hyperbolic depend
-
ing
upon their discrete local geometry. Subsequently, the simplification process computes a volume cost for
each non
-
hyperbolic vertex, in anal
-
ogy with spherical volume, to capture the loss of fidelity if that vertex
is decimated. Vertices of least volume cost are then successively deleted and the resulting holes re
-
triangulated using a method based on a novel heuristic. Preliminary experiments i
ndicate a performance
comparable to that of the best known mesh simplification algorithms
Evaluate Combined Sobel-Canny Edge Detector for Image Procssingidescitation
Edge detection is one of the fundamental operation
of the computer vision to locate the sharp intensity changes to
find the edges in an image. The selection of detector depending
on the environment , especially in noisy background.In this
paper, presents a brief theory for the sobel kernel and canny
edge detector.Then propose an algorithm which combined
two detectors, the sobel detector which is widely used in digital
image processing and canny edge detector that is another
classical techniques. The design consists of three stages.Firstly
added salt & pepper noisy to the original free noisy image file
then compute the sobel detector for the file ,then apply canny
detector on the results of the second stage to filter the pixel
that signed out as an edge in the sobel detection by using
Gaussian filter. Test the algorithm by using various file which
contains salt & pepper noise with free noisy image with
different values of sigma to evaluate and specify the
performance, weakness.
Canny Edge Detection Algorithm on FPGA IOSR Journals
This document summarizes the implementation of the Canny edge detection algorithm on an FPGA. It begins with an introduction to edge detection and digital image processing. It then describes the Canny edge detection algorithm and its benefits. The document outlines the high-level implementation in Simulink and shows the input, grayscaled, and edge detected output images. It presents the system design with the FPGA reading in an image file and performing Canny edge detection. Simulation and synthesis results are shown verifying the design works as intended. The paper concludes the Canny edge detection algorithm was successfully designed, simulated, tested and realized on an FPGA.
This document provides an overview of various digital image processing techniques including morphological transformations, geometric transformations, image gradients, Canny edge detection, image thresholding, and a practical demo assignment. It discusses the basic concepts and algorithms for each technique and provides examples code. The document is presented as part of a practical course on digital image processing.
This document summarizes a research paper that proposes a novel background removal algorithm using fuzzy c-means clustering. It begins by introducing background subtraction and some of the challenges. It then describes the proposed algorithm which uses edge detection to locate regions of interest before applying fuzzy c-means clustering to segment the foreground object. The algorithm achieves significant computation time reduction compared to other methods. Experimental results show the proposed method has higher true positive rates and accuracy compared to other algorithms, though precision and similarity are slightly lower.
This document provides an overview of digital image processing techniques including feature detection, description, and matching. It discusses Harris corner detection, SIFT, SURF, FAST, BRIEF, and ORB feature detectors. It also covers brute force and FLANN-based feature matching as well as using feature matching and homography to find objects between images. Finally, it outlines an assignment on panorama stitching or bag-of-features image classification and a course project deadline.
NUMBER PLATE IMAGE DETECTION FOR FAST MOTION VEHICLES USING BLUR KERNEL ESTIM...paperpublications3
This document discusses a proposed method for detecting number plates on images of fast moving vehicles that have been blurred due to motion. It begins with an introduction to image processing and digital images. It then discusses estimating the blur kernel caused by vehicle motion in order to model it as a linear uniform blur with parameters for angle and length. Existing related works on image deblurring are reviewed. The proposed system estimates the blur kernel parameters using sparse representation and Radon transform methods, allows deblurring the image, and then uses artificial neural networks to identify numbers and characters in the deblurred image. The system is evaluated on real blurred images and shown to improve license plate recognition compared to previous methods.
The document compares frame difference and Kalman filter techniques for detecting moving vehicles in video surveillance. Frame difference is a simple but low accuracy method that uses thresholding on differences between frames. Kalman filtering provides better accuracy by modeling each pixel as a Kalman filter and updating estimates based on observations. The paper applies both methods to a vehicle video and finds that Kalman filtering produces cleaner detection with fewer false positives compared to frame difference.
The document provides an agenda for a practical session on digital image processing. It discusses stages of computer vision including stereo images, optical flow, and machine learning techniques like classification and clustering. Stereo vision and depth maps from stereo images are explained. Optical flow concepts like the Lucas-Kanade method are covered. Machine learning algorithms like KNN, SVM, and K-means clustering are also summarized. The document concludes with information about a project, assignment, and notable AI companies in Egypt.
A ROBUST BACKGROUND REMOVAL ALGORTIHMS USING FUZZY C-MEANS CLUSTERINGIJNSA Journal
Background subtraction is typically one of the first steps carried out in motion detection using static video cameras. This paper presents a novel method for background removal that processes only some pixels of each image. Some regions of interest of the objects in the image or frame are located with the help of edge detector. Once the region is detected only that area will be segmented instead of processing the whole image. This method achieves a significant reduction in computation time that can be used for subsequent image analysis. In this paper we detect the foreground object with the help of edge detector and combine the Fuzzy c-means clustering algorithm to segment the object by means of subtracting the current frame from the previous frame, the accurate background is identified.
Minimum image disortion of reversible data hidingIRJET Journal
1) The document presents a method for minimum image distortion in reversible data hiding. It aims to hide data in image files while maintaining high image quality after extraction.
2) The method assigns different weights to pixels for feature extraction in steganalysis based on their probability of being altered. It focuses on regions likely changed to reduce the effect of unchanged smooth areas.
3) Experimental results on four common mobile steganography techniques demonstrate the effectiveness of the proposed scheme, particularly at low embedding rates, in identifying areas containing hidden data while maintaining perceptual image quality.
SHORT LISTING LIKELY IMAGES USING PROPOSED MODIFIED-SIFT TOGETHER WITH CONVEN...ijfcstjournal
The paper proposes the modified-SIFT algorithm which will be a modified form of the scale invariant feature transform. The modification consists of considering successive groups of 8 rows of pixel, along the height of the image. These are used to construct 8 bin histograms for magnitude as well as orientation individually. As a result the number of feature descriptors is significantly less (95%) than the standard SIFT approach. Fewer feature descriptor leads to reduced accuracy. This reduction in accuracy is quite drastic when searching for a single (RANK1) image match; however accuracy improves if a band of likely (say tolerance of 10%) images is to be returned. The paper therefore proposes a two-stage-approach where
First Modified-SIFT is used to obtain a shortlisted band of likely images subsequently SIFT is applied within this band to find a perfect match. It may appear that this process is tedious however it provides a significant reduction in search time as compared to applying SIFT on the entire database. The minor reduction in accuracy can be offset by the considerable time gained while searching a large database. The
modified-SIFT algorithm when used in conjunction with a face cropping algorithm can also be used to find a match against disguised images.
PARALLEL GENERATION OF IMAGE LAYERS CONSTRUCTED BY EDGE DETECTION USING MESSA...ijcsit
Edge detection is one of the most fundamental algorithms in digital image processing. Many algorithms have been implemented to construct image layers extracted from the original image based on selecting threshold parameters. Changing theses parameters to get a high quality layer is time consuming. In this paper, we propose two parallel technique, NASHT1 and NASHT2, to generate multiple layers of an input
image automatically to enable the image tester to select the highest quality detected edges. In addition, the
effect of intensive I/O operations and the number of parallel running processes on the performance of the proposed techniques have also been studied.
Trajectory Based Unusual Human Movement Identification for ATM SystemIRJET Journal
This document summarizes a research paper on developing a system to identify unusual human movements at ATMs using trajectory analysis. The proposed system uses computer vision techniques like background subtraction and template matching to detect and track human movements. If a person's trajectory does not match expected patterns or if their face is covered, an alarm is triggered. The system is intended to prevent crimes and unauthorized access at ATMs by continuously monitoring movements and alerting administrators of any suspicious activity in real-time.
Design and implementation of video tracking system based on camera field of viewsipij
The basic idea of this paper is to design and implement of video tracking system based on Camera Field of
View (CFOV), Otsu’s method was used to detect targets such as vehicles and people. Whereas most
algorithms were spent a lot of time to execute the process, an algorithm was developed to achieve it in a
little time. The histogram projection was used in both directional to detect target from search region,
which is robust to various light conditions in Charge Couple Device (CCD) camera images and saves
computation time.
Our algorithm based on background subtraction, and normalize cross correlation operation from a series
of sequential sub images can estimate the motion vector. Camera field of view (CFOV) was determined and
calibrated to find the relation between real distance and image distance. The system was tested by
measuring the real position of object in the laboratory and compares it with the result of computed one. So
these results are promising to develop the system in future.
A Literature Survey: Neural Networks for object detectionvivatechijri
Humans have a great capability to distinguish objects by their vision. But, for machines object
detection is an issue. Thus, Neural Networks have been introduced in the field of computer science. Neural
Networks are also called as ‘Artificial Neural Networks’ [13]. Artificial Neural Networks are computational
models of the brain which helps in object detection and recognition. This paper describes and demonstrates the
different types of Neural Networks such as ANN, KNN, FASTER R-CNN, 3D-CNN, RNN etc. with their accuracies.
From the study of various research papers, the accuracies of different Neural Networks are discussed and
compared and it can be concluded that in the given test cases, the ANN gives the best accuracy for the object
detection.
This document summarizes a research paper that proposes a new technique called Morphology based technique for Extraction and Detection of blinking Region from gif Images. It begins by introducing the goal of detecting blinking parts in gif images and issues with existing techniques. It then describes the proposed methodology which uses edge detection, morphological operations like closing, and precision/recall metrics to evaluate the technique. The methodology is tested on sample gif images and results show high precision and recall rates, indicating the model is effective at extracting blinking regions.
The document discusses image representation and feature extraction techniques. It describes how representation makes image information more accessible for computer interpretation using either boundaries or pixel regions. Feature extraction quantifies these representations by extracting descriptors like geometric properties, statistical moments, and textures. Desirable properties for descriptors include being invariant to transformations, compact, robust to noise, and having low complexity. Various boundary and regional descriptors are defined, such as chain codes, shape numbers, and moments.
FORGERY (COPY-MOVE) DETECTION IN DIGITAL IMAGES USING BLOCK METHODeditorijcres
AKHILESH KUMAR YADAV, DEENBANDHU SINGH, VIVEK KUMAR
Department of Computer Science and Engineering
Babu Banarasi Das University, Lucknow
akhi2232232@gmail.com, deenbandhusingh85@gmail.com, vivek.kumar0091@gmail.com
ABSTRACT- Digital images can be easily modified using powerful image editing software. Determining whether a manipulation is innocent of sharpening from those which are malicious, such as removing or adding parts to an image is the topic of this paper. In this paper we focus on detection of a special type of forgery-the Copy-Move forgery, in this part of the original image is copied moved to desired location in the same image and pasted. The proposed method compress images using DWT (discrete wavelet transform) and divided into blocks and choose blocks than perform feature vector calculation and lexicographical sorting and duplicated blocks are identified after sorting. This method is good at some manipulation/attack likes scaling, rotation, Gaussian noise, smoothing, JPEG compression etc.
INDEX TERMS- Copy-Move forgery, Wavelet Transform, Lexicographical Sorting, Region Duplication Detection.
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.
Visual Cryptography using Image ThresholdingIRJET Journal
This document proposes a novel visual cryptography algorithm using image thresholding that generates shares of an image without requiring a key. The technique thresholds the secret image to create two shares such that individual shares do not reveal information about the secret image. The original image can be reconstructed by performing an OR operation on the pixel values of the shares using the threshold value, without any loss of quality or information. The algorithm was tested on color images in MATLAB and successfully recovered the original image from the shares.
This document discusses different deep learning approaches for image segmentation. It covers using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). For CNNs, it describes fully convolutional networks, deconvolution networks, U-Net, and other models. For RNNs, it discusses multi-dimensional RNNs, scene labeling with LSTMs, turned and pyramid RNNs, and grid LSTMs. It also reviews Pix2Pix and other GAN-based models for image segmentation tasks.
M ESH S IMPLIFICATION V IA A V OLUME C OST M EASUREijcga
We develop a polygonal mesh simplification algorithm based on a novel analysis of the mesh
geometry.
Particularly, we propose first a characterization of vertices as hyperbolic or non
-
hyperbolic depend
-
ing
upon their discrete local geometry. Subsequently, the simplification process computes a volume cost for
each non
-
hyperbolic vertex, in anal
-
ogy with spherical volume, to capture the loss of fidelity if that vertex
is decimated. Vertices of least volume cost are then successively deleted and the resulting holes re
-
triangulated using a method based on a novel heuristic. Preliminary experiments i
ndicate a performance
comparable to that of the best known mesh simplification algorithms
Evaluate Combined Sobel-Canny Edge Detector for Image Procssingidescitation
Edge detection is one of the fundamental operation
of the computer vision to locate the sharp intensity changes to
find the edges in an image. The selection of detector depending
on the environment , especially in noisy background.In this
paper, presents a brief theory for the sobel kernel and canny
edge detector.Then propose an algorithm which combined
two detectors, the sobel detector which is widely used in digital
image processing and canny edge detector that is another
classical techniques. The design consists of three stages.Firstly
added salt & pepper noisy to the original free noisy image file
then compute the sobel detector for the file ,then apply canny
detector on the results of the second stage to filter the pixel
that signed out as an edge in the sobel detection by using
Gaussian filter. Test the algorithm by using various file which
contains salt & pepper noise with free noisy image with
different values of sigma to evaluate and specify the
performance, weakness.
Canny Edge Detection Algorithm on FPGA IOSR Journals
This document summarizes the implementation of the Canny edge detection algorithm on an FPGA. It begins with an introduction to edge detection and digital image processing. It then describes the Canny edge detection algorithm and its benefits. The document outlines the high-level implementation in Simulink and shows the input, grayscaled, and edge detected output images. It presents the system design with the FPGA reading in an image file and performing Canny edge detection. Simulation and synthesis results are shown verifying the design works as intended. The paper concludes the Canny edge detection algorithm was successfully designed, simulated, tested and realized on an FPGA.
Hardware software co simulation of edge detection for image processing system...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Seminar report on edge detection of video using matlab codeBhushan Deore
Edge detection is a key step in image analysis and object recognition. There are various methods of edge detection that operate by finding areas of rapid intensity change in an image. The document discusses several common edge detection techniques including the Robert and Sobel operators. The Robert operator uses simple 2x2 masks to find edges but can be noisy, while the Sobel operator uses 3x3 masks that are less susceptible to noise but produce thicker edges. Edge detection is important for tasks like image segmentation and is often used as an intermediate step for applications like video surveillance and medical imaging.
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is an open access journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.
Enhanced Optimization of Edge Detection for High Resolution Images Using Veri...ijcisjournal
dge Detection plays a crucial role in Image Processing and Segmentation where a set of algorithms aims
to identify various portions of a digital image at which a sharpened image is observed in the output or
more formally has discontinuities. The contour of Edge Detection also helps in Object Detection and
Recognition. Image edges can be detected by using two attributes such as Gradient and Laplacian. In our
Paper, we proposed a system which utilizes Canny and Sobel Operators for Edge Detection which is a
Gradient First order derivative function for edge detection by using Verilog Hardware Description
Language and in turn compared with the results of the previous paper in Matlab. The process of edge
detection in Verilog significantly reduces the processing time and filters out unneeded information, while
preserving the important structural properties of an image. This edge detection can be used to detect
vehicles in Traffic Jam, Medical imaging system for analysing MRI, x-rays by using Xilinx ISE Design
Suite 14.2.
Real Time Implementation of Ede Detection Technique for Angiogram Images on FPGAIRJET Journal
This document presents a new edge detection algorithm for angiogram images and its implementation on an FPGA. It begins with an introduction to angiography and importance of edge detection in analyzing angiogram images. It then describes the proposed algorithm which includes histogram equalization for enhancement followed by a modified Canny edge detection approach. The key steps of the modified Canny approach are also outlined. Experimental results on angiogram images demonstrate that the proposed FPGA implementation takes only 0.562ms for execution while maintaining accuracy. In conclusion, the algorithm is able to efficiently detect blood vessel edges in angiogram images making it useful for analyzing vascular diseases.
AN ENHANCED BLOCK BASED EDGE DETECTION TECHNIQUE USING HYSTERESIS THRESHOLDING sipij
Edge detection is a crucial step in various image processing systems like computer vision , pattern
recognition and feature extraction. The Canny edge detection algorithm even though exhibits high
accuracy, is computationally more complex compared to other edge detection techniques. A block based
distributed edge detection technique is presented in this paper, which adaptively finds the thresholds for
edge detection depending on block type and the distribution of gradients in each block. A novel method of
computation of high threshold has been proposed in this paper. Block-based hysteresis thresholds are
computed using a non uniform gradient magnitude histogram. The algorithm exhibits remarkably high
edge detection accuracy, scalability and significantly reduced computational time. Pratt’s Figure of Merit
quantifies the accuracy of the edge detector, which showed better values than that of original Canny and
distributed Canny edge detector for benchmark dataset. The method detected all visually prominent edges
for diverse block size.
This document summarizes 10 important AI research papers. It begins with a brief introduction on artificial intelligence and what the papers aim to provide information on. It then lists the 10 papers with their titles:
1. A Computational Approach to Edge Detection
2. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence
3. A Threshold Selection Method from Gray-Level Histograms
4. Deep Residual Learning for Image Recognition
5. Distinctive Image Features from Scale-Invariant Keypoints
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
7. Large-scale Video Classification with Convolutional Neural Networks
8. Probabilistic Reason
An Efficient Algorithm for Edge Detection of Corroded SurfaceIJERA Editor
Inspection process in industrial applications plays a vital role as it directly hinders the outages of industry. Thereby the inspection especially in case of corroded surfaces is to be fast, precised and accurate. Visual inspection has been very liable to mistakes because of numerous facts. The automatic inspection systems remove subjective aspects and can provide fast and accurate inspection. Inspection of corroded surfaces is very important concern, thus it is required to detect corroded surfaces. A new algorithm is developed by certain changes in mask and thresholding selection to detect corroded surfaces. The paper is about how we can amend the weak edges of input images and discarding false edges to overcome the problem of traditional techniques in this field. Proposed operator also compared with two commonly used edge detection algorithms which are Canny and Sobel.
An Efficient Algorithm for Edge Detection of Corroded SurfaceIJERA Editor
Inspection process in industrial applications plays a vital role as it directly hinders the outages of industry. Thereby the inspection especially in case of corroded surfaces is to be fast, precised and accurate. Visual inspection has been very liable to mistakes because of numerous facts. The automatic inspection systems remove subjective aspects and can provide fast and accurate inspection. Inspection of corroded surfaces is very important concern, thus it is required to detect corroded surfaces. A new algorithm is developed by certain changes in mask and thresholding selection to detect corroded surfaces. The paper is about how we can amend the weak edges of input images and discarding false edges to overcome the problem of traditional techniques in this field. Proposed operator also compared with two commonly used edge detection algorithms which are Canny and Sobel.
Hardware Unit for Edge Detection with Comparative Analysis of Different Edge ...paperpublications3
Abstract: An edge in an image is a contour across which the brightness of the image changes abruptly. In image processing, an edge is often interpreted as one class of singularities. Edge detection is an important task in image processing. It is a main tool in pattern recognition, image segmentation, and scene analysis. An edge detector is basically a high pass filter that can be applied to extract the edge points in an image. This topic has attracted many researchers and many achievements have been made. Many researchers provided different approaches based on mathematical calculations which some of them are either robust or cost effective. A new algorithm will be proposed to detect the edges of image with increased robustness and throughput. Using this algorithm we will reduce the time complexity problem which is faced by previous algorithm. We will also propose hardware unit for proposed algorithm which will reduce the area, power and speed problem. We will compare our proposed algorithm with previous approach. For image quality measurement we will use some scientific parameters those are PSNR, SSIM, FSIM. Implementation of proposed algorithm will be done by Matlab and hardware implementation will be done by using of Verilog on Xilinx 14.1 simulator. Verification will be done on Model sim.
A PROJECT REPORT ON REMOVAL OF UNNECESSARY OBJECTS FROM PHOTOS USING MASKINGIRJET Journal
This document presents a project report on removing unnecessary objects from photos using masking techniques. It discusses using algorithms like Fast Marching and Navier-Stokes to fill in missing image data and maintain continuity across boundaries. The Fast Marching method begins at region boundaries and works inward, prioritizing completion of boundary pixels first. Navier-Stokes uses fluid dynamics equations to continue intensity value functions and ensure they remain continuous at boundaries. Color filtering can also be used to segment specific colored objects or regions. The project aims to implement these techniques to remove unwanted objects from images and fill the resulting gaps seamlessly.
Mislaid character analysis using 2-dimensional discrete wavelet transform for...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Conceptual and Practical Examination of Several Edge Detection StrategiesIRJET Journal
The document discusses various edge detection techniques for image segmentation. It begins with an introduction to edge detection and image segmentation. Edge detection aims to identify boundaries between different regions in an image and is an important step in image segmentation.
The document then covers the main approaches to edge detection - gradient-based and Laplacian-based. It explains these approaches and provides examples. Some commonly used edge detection techniques are also described - Roberts Cross, Sobel, Prewitt, Laplacian of Gaussian (LoG), and Canny. The steps involved in general edge detection processes like filtering, enhancement and detection are outlined.
Advantages and disadvantages of different edge detection techniques are analyzed. Comparisons of techniques are made through software experiments
IRJET- Image Feature Extraction using Hough Transformation PrincipleIRJET Journal
The document describes an image processing technique that uses Hough transformation and contour detection to extract features from images and count objects. It proposes an integrated method to detect circular objects, detach overlapping objects, and count objects of any shape. The method applies Canny edge detection, contour detection, and circular Hough transform to segment overlapping circular objects. It then uses contour detection to count all objects regardless of shape. Experimental results show the method can successfully segment and count overlapping circular and non-circular objects in test images.
2-Dimensional Wavelet pre-processing to extract IC-Pin information for disarr...IOSR Journals
Abstract: Due to higher processing power to cost ratio, it is now possible to replace the manual detection methods used in the IC (Integrated Circuit) industry by Image-processing based automated methods, to detect a broken pin of an IC connected on a PCB during manufacturing, which will make the process faster, easier and cheaper. In this paper an accurate and fast automatic detection method is used where the top view camera shots of PCBs are processed using advanced methods of 2-dimensional discrete wavelet pre-processing before applying edge-detection. Comparison with conventional edge detection methods such as Sobel, Prewitt and Canny edge detection without 2-D DWT is also performed. Keywords :2-dimensional wavelets, Edge detection, Machine vision, Image processing, Canny.
COMPOSITE IMAGELET IDENTIFIER FOR ML PROCESSORSIRJET Journal
The document proposes a composite imagelet identifier technique for machine learning processors that uses seam carving to manipulate edges and pixilation in images for processing. It discusses using existing algorithms like SSIM and Dijkstra's algorithm to calculate image energy and identify optimal seam locations for manipulation. The technique is evaluated using test images in MATLAB and is presented as having potential applications in areas like forestry, animal husbandry and safety monitoring.
ASIC Implementation for SOBEL AcceleratorIRJET Journal
This document summarizes an ASIC implementation of a Sobel edge detection accelerator. It begins with an introduction to accelerators and edge detection in video images. It then describes the proposed Sobel accelerator design, which uses a pipelined architecture to process pixels from an image in groups of four. The design includes registers to store pixel values, a multiplier array to calculate partial derivatives, and addition circuits to accumulate the results. Finally, it discusses the advantages of implementing the Sobel accelerator using an ASIC design over an FPGA, including higher density, cost savings, and faster fabrication for mass production.
IRJET-ASIC Implementation for SOBEL AcceleratorIRJET Journal
This document summarizes an ASIC implementation of a Sobel edge detection accelerator. It begins with an introduction to edge detection and accelerators. It then describes the proposed pipeline architecture of the Sobel accelerator, which takes in pixels from an image and processes them through multiplication, addition and other operations to produce output derivative pixels. The document discusses the ASIC design flow, including frontend steps like simulation, synthesis and DFT insertion, as well as backend steps such as floorplanning, placement, routing and timing analysis. It provides diagrams of the accelerator architecture and screenshots of synthesis reports.
Edge detection is one of the most frequent processes in digital image processing for various purposes, one of which is detecting road damage based on crack paths that can be checked using a Canny algorithm. This paper proposed a mobile application to detect cracks in the road and with customized threshold function in the requests to produce useful and accurate edge detection. The experimental results show that the use of threshold function in a canny algorithm can detect better damage in the road
Deep Convolutional Neural Networks (CNNs) have achieved impressive performance in
edge detection tasks, but their large number of parameters often leads to high memory and energy
costs for implementation on lightweight devices. In this paper, we propose a new architecture, called
Efficient Deep-learning Gradients Extraction Network (EDGE-Net), that integrates the advantages of Depthwise Separable Convolutions and deformable convolutional networks (DeformableConvNet) to address these inefficiencies. By carefully selecting proper components and utilizing
network pruning techniques, our proposed EDGE-Net achieves state-of-the-art accuracy in edge
detection while significantly reducing complexity. Experimental results on BSDS500 and NYUDv2
datasets demonstrate that EDGE-Net outperforms current lightweight edge detectors with only
500k parameters, without relying on pre-trained weights.
Deep Convolutional Neural Networks (CNNs) have achieved impressive performance in
edge detection tasks, but their large number of parameters often leads to high memory and energy
costs for implementation on lightweight devices. In this paper, we propose a new architecture, called
Efficient Deep-learning Gradients Extraction Network (EDGE-Net), that integrates the advantages of Depthwise Separable Convolutions and deformable convolutional networks (DeformableConvNet) to address these inefficiencies. By carefully selecting proper components and utilizing
network pruning techniques, our proposed EDGE-Net achieves state-of-the-art accuracy in edge
detection while significantly reducing complexity. Experimental results on BSDS500 and NYUDv2
datasets demonstrate that EDGE-Net outperforms current lightweight edge detectors with only
500k parameters, without relying on pre-trained weights.
Similar to IJCER (www.ijceronline.com) International Journal of computational Engineering research (20)
IJCER (www.ijceronline.com) International Journal of computational Engineering research
1. I nternational Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
A Distributed Canny Edge Detector and Its Implementation on F PGA
1,
Chandrashekar N.S.,2, Dr. K.R. Nataraj
1,
Department of ECE, Don Bosco Institute of Technology, Bangalore.
2,
Depart ment of ECE, SJB Institute of Technology, Bangalore.
Abstract
In this paper, we present a distributed Canny edge detection algorith m that results in significantly reduced
memo ry requirements decreased latency and increased throughput with no loss in edge detection performance as co mpared
to the original Canny algorith m. The new algorith m uses a low-co mplexity 8-bin non-uniform grad ient magnitude
histogram to compute block-based hysteresis thresholds that are used by the Canny edge detector. Furthermore, FPGA-
based hardware architecture of our proposed algorithm is presented in this paper and the architecture is synthesized on the
Xilin x Virtex 4 FPGA . The design development is done in VHDL and simulates the results in modelsim 6.3 using Xilin x
12.2.
Keywords: Canny Edge detector, Distributed Processing, Non-uniform quantization, FPGA .
1. Introduction
Edge detection is a very important first step in many algorithms used for segmentation, tracking and image/video
coding. The Canny edge detector is predominantly used due to its ability to extract significant edges [1]. Edge detection, as
a basic operation in image processing, has been researched extensively. A lot of edge detection algor ith ms, such as Robert
detector, Prewitt detector, Kirsch detector, Gauss -Laplace detector and Canny detector have been proposed. Among these
algorith ms, Canny algorith m has been used widely in the field of image processing because of its good performance [ 2].
The Canny edge detector is predominantly used in many real -world applications due to its ability to extract significant
edges with good detection and good localizat ion performance. Unfortunately, the Canny edge detection algorith m contains
extensive pre-processing and post-processing steps and is more computationally comp lex than other edge detection
algorith ms. Furthermo re, it performs hysteresis thresholding which requires co mputing high and low thresholds based on
the entire image statistics. This places heavy requirements on memory and results in large latency, hindering real-time
implementation of the Canny edge detection algorithm [3]. Imp lementing image processing algorithms on reconfigurable
hardware min imizes the time-to-market cost, enables rapid prototyping of co mplex algorith ms and simplifies debugging
and verification [4]. Edge detectors based on the first derivative do not guarantee to produce edge maps with continuous
edge contours nor unwanted branches. Edge detectors based on the second d erivatives, such as zero crossing; suffer fro m
generating erroneous edges in textured images because of its high sensitivity to noise. Being an effective edge detector
with single-p ixel response, Canny operator has been widely used in accurately abstractin g the edge information in image
processing. However, taking its 4-step process into account, its real-t ime imp lementation based on CPU has become a
significant problem, especially for the part of the edge tracing, which consumes a large amount of computing time. To
solve the problem, GPU will be used for sake of its powerful ability of parallel processing while a new Canny operator is
proposed with the introduction of parallel breakpoints detection and edge tracing without recursive operations [5] . The
original Canny algorith m computes the higher and lower thresholds for edge detection based on the entire image statistics,
which prevents the processing of blocks independent of each other [1]. In order to reduce memo ry requirements, decreased
latency and increased throughput, a distributed canny edge detection algorithm is proposed in [1]. The hysteresis threshold
calculation is a key element that greatly affects the edge detection results. In [3], it is proposed a new threshold selection
algorith m based on the distribution of pixel gradients in a block of pixels to overcome the dependency between the blocks.
However, in [1], the hysteresis thresholds calculation is based on a very finely and uniformly quantized 64 -bin gradient
magnitude histogram, which is computationally expensive and thereby, hinders the real-t ime imp lementation. In this paper,
a method based on non-uniform and coarse quantization of the gradient magnitude histogram is proposed. In addition, the
proposed algorithm is mapped onto reconfigurable hardware architecture. The threshold is calculated using the data of the
histogram of grad ient magnitude rather than is set manually in a failure -and-try fashion and can give quite good edge
detection results without the intervening of an operator [6]. Th is improved new Canny algorithm is also implemented on
FPGA (field programmab le gate array) to meet the needs of real time processing.
This paper is organized as fo llo ws : Section 2 gives a brief overview of the orig inal Canny edge detector
algorith m. Section 3 presents the proposed distributed Canny edge detection algorithm which includes a novel method for
the hysteresis thresholds computation based on a non -uniform quantized gradient magnitude histogram. The p roposed
hardware architecture and FPGA imple mentation algorithm are described in Section 4. Simu lation results are presented in
Section 5. A conclusion is given in Section 6.
Issn 2250-3005(online) November| 2012 Page 177
2. I nternational Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
2. Canny Edge Detector
The popular Canny edge detector uses the following steps to find contours presents in the image. The first stage is
achieved using Gaussian smoothing. The resulting image is sen t to the PC that sends it back to the gradient filter, but here
we modified our gradient filter a bit because this time we don't only need the gradient magnitude that is given by our
previous operator, but we need separately Gx and Gy. We also need the phase or orientation of our gradient which is
obtained using the following formu la:
θ = arctan
As we can see, this equation contains an arctan and a division. These operators are very difficult to implement using
hardware. We also don't need a high precision. The final θ has to give only one of the four following possible directions, as
we can see in Figure 1. The fourth direction is the horizontal d irection with zero degrees, not indicated in the figure.
Figure 1: Possible Direct ions for the Gradient Phase
Arctan and the division can be eliminated by simply co mparing Gx and Gy values. If they are of similar length,
we will obtain a diagonal d irect ion, if one is at least 2.5 times longer than the other, we will obtain a horizontal o r vert ical
direction. After the edge directions are known, non-maximu m suppression is applied. Nonmaximu m suppression is used to
trace pixels along the gradient in the edge direction and compare the value s perpendicular to the gradient. Two
perpendicular pixel values are compared with the value in the edge direction. If their value is lower than the pixel on the
edge, then they are suppressed i.e. their pixel value is changed to 0, else the higher pixel value is set as the edge and the
other two are suppressed with a pixel value of 0. Finally, hysteresis is used as a means of eliminating streaking . Streaking
is the breaking up of an edge contour caused by the operator output fluctuating above and below the threshold. If a single
threshold, T1 is applied to an image, and an edge has an average strength equal to T1, then due to noise, there will be
instances where the edge dips below the threshold. Equally it will also extend above the threshold making an edge look like
a dashed line. To avoid th is, hysteresis uses 2 thresholds, a high and a low. Any pixel in the image that has a value greater
than T1 is presumed to be an edge pixel, and is marked as such immediately. Then, any pixels that are connected to this
edge pixel and that have a value greater than T2 are also selected as edge pixels. If you think of fo llowing an edge, you
need a gradient of T2 to start but you don't stop till you hit a grad ient below T1 [7].
The original Canny algorith m [3] shown in Fig 2, consists of the following steps executed sequentially:
Low pass filtering the image with a Gaussian mask.
Co mputing horizontal and vertical gradients at each pixel location.
Co mputing the gradient magnitude at each pixel location.
Co mputing a higher and lo wer threshold based on the histogram of the grad ients of the entire image.
Suppressing non-maximal strong (NMS) edges.
Co mputing the hysteresis high and low thresholds based on the histogram of the magnitudes of the gradients of the
entire image.
Performing hysteresis thresholding to determine the edge map.
Figure 2: Block Diagram of the Canny Edge Detection
In our imp lementation, the architecture is synthesized on the Xilin x VIRTEX 4 FPGA. The results show that a 16-core
architecture (3 X 3 block size fo r 256 X 256 image) leads to 16 t imes decrease in running time without performance
degradation when compared with the original frame -based Canny algorithm.
Issn 2250-3005(online) November| 2012 Page 178
3. I nternational Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
3. Proposed Distributed Canny Edge Detection Algorithm
The superior performance of the frame-based Canny algorith m is due to the fact that it computes the gradient
thresholds by analyzing the histogram of the gradients at all the pixel loca tions of an image. Though it is purely based on
the statistical distribution of the gradient values, it works well on natural images wh ich consist of a mix of smooth regions ,
texture reg ions and high-detailed reg ions [1]. Direct ly applying the frame-based Canny at a block-level would fail because
such a mix of reg ions may not be available locally in every block of the frame. Th is would lead to excessive edges in
texture reg ions and loss of significant edges in high detailed regions. The Canny edge detection algorithm operates on the
whole image and has a latency that is proportional to the size of the image. While perfo rming the original canny algorithm
at the block-level would speed up the operations, it would result in loss of significant edges in high -detailed regions and
excessive edges in texture regions. Natural images consist of a mix of smooth regions, texture regions and high -detailed
regions and such a mix of regions may not be available locally in every block o f the entire image. In [1], it is proposed a
distributed Canny edge detection algorithm, which removes the inherent dependency between the various blocks so that the
image can be d ivided into blocks and each block can be processed in parallel. The input image is div ided into m×m
overlapping blocks. The adjacent blocks overlap by (L − 1)/ 2 pixels for a L × L g radient mask. Ho wever, for each block,
only edges in the central n × n (where n = m + L − 1) non-overlapping reg ion are included in the final edge map. Steps 1 to
4 and Step 6 of the distributed Canny algorithm are the same as in the original Canny algorithm except that these are now
applied at the block level. Step 5, which is the hysteresis high and low thresholds calculation, is modified to enable parallel
processing. In [1], a parallel hysteresis thresholding algorith m was proposed based on the observation that a pixel with a
gradient magnitude of 2, 4 and 6 corresponds to blurred edges, psycho visually significant edges and very sharp edges,
respectively. In o rder to co mpute the high and low hysteresis thresholds, very finely and uniformly quantized 64-b in
gradient magnitude histograms are computed over overlapped blocks. If the 64-b in uniform d iscrete histogram is used for
the high threshold calculation, this entails performing 64 mult iplications and 64×Np co mparisons, where Np is the total
number of pixels in an image. Therefore, it is necessary to find a good way to reduce the complexity of the histogram
computation. As in [6], it was observed that the largest peak in the grad ient magnitude histograms after NM S of the
Gaussian smoothed natural images occurs near the origin and corresponds to low-frequency content, while edge pixels
form a series of s maller peaks where each peak corresponds to a class of edges having similar grad ient magnitudes. The
proposed distributed thresholds selection algorithm is shown in Fig.3. Let Gt be the set of pixels with gradient magnitudes
greater than a threshold t, and let NGt for t = 2, 4, 6, be the nu mber of corresponding gradient elements in the set Gt. Using
NGt, an intermed iate classification threshold C is calculated to indicate whether the considered block is high -detailed,
moderately edged, blurred or textured, as shown in Fig. 3. Consequently, the set Gt = Gt=c can be selected for co mputing
the high and low thresholds. The high threshold is calculated based on the histogram of the set Gc such that 20% of the total
pixels of the block would be identified as strong edges. The lower threshold is the 40% percentage of the higher threshold
as in the original Canny algorith m.
Figure 3: Pseudo-code of the proposed Distributed threshold Selection Scheme
We compared the high threshold value that is calculated using the proposed distributed algorithm based on an 8-
bin non-uniform g radient magnitude histogram with the value obtained when usin g a 16-b in non-uniform gradient
magnitude h istogram. These two h igh thresholds have similar values. Therefore, we use the 8-b in non-uniform g radient
magnitude histogram in our imp lementation.
4. Implementation Of The Proposed Distributed Canny Algorithm
In this section, we describe the hardware imp lementation of our proposed distributed canny edge detection
algorith m on the Xilin x VIRTEX 4 FPGA . We provide a h igh-level architecture diagram as follows.
Architecture: Depending on the available FPGA resources, the image needs to be partitioned into q sub-images and each
sub-image is further div ided into p m×m b locks. The proposed architecture, shown in Fig. 4, consists of q processing units
in the FPGA and some Static RAMs (SRAM) organized into q memory banks to store the image data, where q equals to
the image size divided by the SRAM size.
Issn 2250-3005(online) November| 2012 Page 179
4. I nternational Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
Figure 4: The Architecture of the proposed Distributed Canny A lgorithm
Figure 5: Block d iagram o f the CE (co mpute engine) fo r the proposed Distributed Canny edge Detection
Each processing unit processes a sub-image and reads/writes data fro m/to the SRAM through ping -pong buffers, which are
implemented with dualport Block RAMs (BRAM) on the FPGA. As shown in Fig.4, each processing unit (PU) consists of
p computing engines (CE), where each CE detects the edge map o f an m×m block image. Thus, p×q blocks can be
processed at the same t ime and the processing time for an N×N image is reduced, in the best case, by a factor of p×q. The
specific values of p and q depend on the processing time of each PE, the data loading t ime fro m the SRAM to the local
memo ry and the interface between FPGA and SRAM, such as total pins on the FPGA, the data bus width, the address bus
width and the maximu m system clock of the SRAM. In our applicat ion, we choose p = 2 and q = 8. In the proposed
architecture, each CE consists of the following 6 units, as shown in Fig.5:
1. Smoothening unit using Gaussian filter.
2. Vert ical and horizontal gradient calcu lation unit.
3. Magnitude calculat ion unit.
4. Directional non-maximu m suppression unit.
5. High and low threshold Calculation unit.
6. Th resholding with hysteresis unit.
5. Simulation Results And Analysis
Figure 6.shows the implementation software result and the FPGA imp lementation generated result. For the 256 X
256 camaramen image using the proposed distributed Canny edge detector with block size of 3 X 3 and a 3 × 3 gradient
mask. The FPGA result is obtained using ModelSim.
Figure 6: Simu lation waveform for canny Edge Detection system
(a) (b)
Issn 2250-3005(online) November| 2012 Page 180
5. I nternational Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
Figure 7:Co mparision results of 256 X 256 camaramen image (a) Original image (b ) Blu ring image
(a) (b)
Figure 8: Co mparision results of 256 X 256 camaramen image (a) orig inal image and (b)Edge map image
6. Conclusion
We proposed a novel nonuniform quantized histogram calcu lation method in order to reduce the co mputational
cost of the hysteresis threshold selection. As a result, the computational cost of the proposed algorithm is very low
compared to the orig inal Canny edge detection algorith m. The algorith m is mapped to onto a Xilin x Virtex-4 FPGA
platform and tested using ModelSim. It is capable of supporting fast real-time edge detection for images and videos with
various spatial and temporal resolutions including fu ll-HD content. For a 100 MHz clock rate, the total processing running
time using the FPGA imp lementation is 0.655 ms for a 256 X 256 image.
References
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