This document discusses algorithms for automatically generating 3D video views from a single 2D image of both indoor and outdoor scenes. For indoor scenes, it segments the floor to determine the termination point for video generation. For outdoor scenes, it detects the vanishing point, which is used to calculate the distance to the termination point. The algorithms crop the input image to generate frames as it navigates up to the termination point, creating the effect of a 3D video view from a single 2D image with no need for human intervention. Experimental results on over 250 images demonstrated the effectiveness of the proposed methods.
Performance analysis on color image mosaicing techniques on FPGAIJECEIAES
Today, the surveillance systems and other monitoring systems are considering the capturing of image sequences in a single frame. The captured images can be combined to get the mosaiced image or combined image sequence. But the captured image may have quality issues like brightness issue, alignment issue (correlation issue), resolution issue, manual image registration issue etc. The existing technique like cross correlation can offer better image mosaicing but faces brightness issue in mosaicing. Thus, this paper introduces two different methods for mosaicing i.e., (a) Sliding Window Module (SWM) based Color Image Mosaicing (CIM) and (b) Discrete Cosine Transform (DCT) based CIM on Field Programmable Gate Array (FPGA). The SWM based CIM adopted for corner detection of two images and perform the automatic image registration while DCT based CIM aligns both the local as well as global alignment of images by using phase correlation approach. Finally, these two methods performances are analyzed by comparing with parameters like PSNR, MSE, device utilization and execution time. From the analysis it is concluded that the DCT based CIM can offers significant results than SWM based CIM.
A Review over Different Blur Detection Techniques in Image Processingpaperpublications3
Abstract: In last few years there is lot of development and attentions in area of blur detection techniques. The Blur detection techniques are very helpful in real life application and are used in image segmentation, image restoration and image enhancement. Blur detection techniques are used to remove the blur from a blurred region of an image which is due to defocus of a camera or motion of an object. In this literature review we represent some techniques of blur detection such as Blind image de-convolution, Low depth of field, Edge sharpness analysis, and Low directional high frequency energy. After studying all these techniques we have found that there are lot of future work is required for the development of perfect and effective blur detection technique.
Development of Human Tracking System For Video Surveillancecscpconf
Visual surveillance in dynamic scenes, especially for human and some objects is one of the
most active research areas. An attempt has been made to this issue in this work. It has wide
spectrum of promising application including human identification to detect the suspicious
behavior, crowd flux statistics, and congestion analysis using multiple cameras.
In this paper deals with the problem of detecting and tracking multiple moving people in a static
background. Detection of foreground object is done by background subtraction. Detected
objects are identified and analyzed through different blobs. Then tracking is performed by
matching corresponding features of blob. An algorithm has been developed in this perspective
using Angular Deviation of Center of Gravity (ADCG), which gives a satisfying result for segmentation of human object.
Digital Image Forgery Detection Using Improved Illumination Detection ModelEditor IJMTER
Image processing methods are widely used in advertisement, magazines, blogs, website,
television and more. When the digital images took their role, Happening of crimes and escaping from
the crimes happened becomes easier. To be with lawful, No one should be punished for not
commencing a crime, to help them this application can be used. The identification using color edge
method will give a exact detection of the crime and the forgeries that has been done in the digital
image.
Image composition or splicing methods are used to discover the image forgeries. The approach is
machine-learning- based and requires minimal user interaction and this technique is applicable to
images containing two or more people and requires no expert interaction for the tampering decision.
The obtained result by the classification performance using an SVM (Super Vector Machine) metafusion classifier and It yields detection rates of 86% on a new benchmark dataset consisting of 200
images, and 83% on 50 images that were collected from the Internet.
The further improvements can be achieved when more advanced illuminant color estimators become
available. Bianco and Schettini has proposed a machine-learning based illuminant estimator
particularly for faces which would help us in this for more accurate prediction. Effective skin
detection methods have been developed in the computer vision literature and this method also helps
us, in detecting pornography compositions which, according to forensic practitioners, have become
increasingly common nowadays.
Computer Vision Based 3D Reconstruction : A ReviewIJECEIAES
3D reconstruction are used in many fields starts from the object reconstruction such as site, and cultural artifacts in both ground and under the sea levels. The scientist are beneficial for these task in order to learn and keep the environment into 3D data due to the extinction. In this paper explained vision setup that is commonly used such as single camera, stereo camera, Kinect / Structured Light/ Time of Flight camera and fusion approach. The prior works also explained how the 3D reconstruction perform in many fields and using various algorithms.
An algorithm to quantify the swelling by reconstructing 3D model of the face with stereo images is presented. We
analyzed the primary problems in computational stereo, which include correspondence and depth calculation. Work has been carried out to determine suitable methods for depth estimation and standardizing volume estimations. Finally we designed software for reconstructing 3D images from 2D stereo images, which was built on Matlab and Visual C++. Utilizing
techniques from multi-view geometry, a 3D model of the face was constructed and refined. An explicit analysis of the stereo
disparity calculation methods and filter elimination disparity estimation for increasing reliability of the disparity map was
used. Minimizing variability in position by using more precise positioning techniques and resources will increase the accuracy of this technique and is a focus for future work
HUMAN COMPUTER INTERACTION ALGORITHM BASED ON SCENE SITUATION AWARENESScsandit
Implicit interaction based on context information is widely used and studied in the virtual scene.In context based human computer interaction, the meaning of action A is well defined. For instance, the right wave is defined turning paper or PPT in context B, And it mean volume up in context C. However, Select object in a virtual scene with multiple objects, context information is not fit. In view of this situation, this paper proposes using the least squares fitting curve beam to
predict the user's trajectory, so as to determine what object the user’s wants to operate .And fitting the starting position of the straight line according to the change of the discrete table. And
using the bounding box size control the Z variable to move in an appropriate location. Experimental results show that the proposed in this paper based on bounding box size to control
the Z variables get a good effect; by fitting the trajectory of a human hand, to predict the object that the subjects would like to operate. The correct rate is 88.6%.
Performance analysis on color image mosaicing techniques on FPGAIJECEIAES
Today, the surveillance systems and other monitoring systems are considering the capturing of image sequences in a single frame. The captured images can be combined to get the mosaiced image or combined image sequence. But the captured image may have quality issues like brightness issue, alignment issue (correlation issue), resolution issue, manual image registration issue etc. The existing technique like cross correlation can offer better image mosaicing but faces brightness issue in mosaicing. Thus, this paper introduces two different methods for mosaicing i.e., (a) Sliding Window Module (SWM) based Color Image Mosaicing (CIM) and (b) Discrete Cosine Transform (DCT) based CIM on Field Programmable Gate Array (FPGA). The SWM based CIM adopted for corner detection of two images and perform the automatic image registration while DCT based CIM aligns both the local as well as global alignment of images by using phase correlation approach. Finally, these two methods performances are analyzed by comparing with parameters like PSNR, MSE, device utilization and execution time. From the analysis it is concluded that the DCT based CIM can offers significant results than SWM based CIM.
A Review over Different Blur Detection Techniques in Image Processingpaperpublications3
Abstract: In last few years there is lot of development and attentions in area of blur detection techniques. The Blur detection techniques are very helpful in real life application and are used in image segmentation, image restoration and image enhancement. Blur detection techniques are used to remove the blur from a blurred region of an image which is due to defocus of a camera or motion of an object. In this literature review we represent some techniques of blur detection such as Blind image de-convolution, Low depth of field, Edge sharpness analysis, and Low directional high frequency energy. After studying all these techniques we have found that there are lot of future work is required for the development of perfect and effective blur detection technique.
Development of Human Tracking System For Video Surveillancecscpconf
Visual surveillance in dynamic scenes, especially for human and some objects is one of the
most active research areas. An attempt has been made to this issue in this work. It has wide
spectrum of promising application including human identification to detect the suspicious
behavior, crowd flux statistics, and congestion analysis using multiple cameras.
In this paper deals with the problem of detecting and tracking multiple moving people in a static
background. Detection of foreground object is done by background subtraction. Detected
objects are identified and analyzed through different blobs. Then tracking is performed by
matching corresponding features of blob. An algorithm has been developed in this perspective
using Angular Deviation of Center of Gravity (ADCG), which gives a satisfying result for segmentation of human object.
Digital Image Forgery Detection Using Improved Illumination Detection ModelEditor IJMTER
Image processing methods are widely used in advertisement, magazines, blogs, website,
television and more. When the digital images took their role, Happening of crimes and escaping from
the crimes happened becomes easier. To be with lawful, No one should be punished for not
commencing a crime, to help them this application can be used. The identification using color edge
method will give a exact detection of the crime and the forgeries that has been done in the digital
image.
Image composition or splicing methods are used to discover the image forgeries. The approach is
machine-learning- based and requires minimal user interaction and this technique is applicable to
images containing two or more people and requires no expert interaction for the tampering decision.
The obtained result by the classification performance using an SVM (Super Vector Machine) metafusion classifier and It yields detection rates of 86% on a new benchmark dataset consisting of 200
images, and 83% on 50 images that were collected from the Internet.
The further improvements can be achieved when more advanced illuminant color estimators become
available. Bianco and Schettini has proposed a machine-learning based illuminant estimator
particularly for faces which would help us in this for more accurate prediction. Effective skin
detection methods have been developed in the computer vision literature and this method also helps
us, in detecting pornography compositions which, according to forensic practitioners, have become
increasingly common nowadays.
Computer Vision Based 3D Reconstruction : A ReviewIJECEIAES
3D reconstruction are used in many fields starts from the object reconstruction such as site, and cultural artifacts in both ground and under the sea levels. The scientist are beneficial for these task in order to learn and keep the environment into 3D data due to the extinction. In this paper explained vision setup that is commonly used such as single camera, stereo camera, Kinect / Structured Light/ Time of Flight camera and fusion approach. The prior works also explained how the 3D reconstruction perform in many fields and using various algorithms.
An algorithm to quantify the swelling by reconstructing 3D model of the face with stereo images is presented. We
analyzed the primary problems in computational stereo, which include correspondence and depth calculation. Work has been carried out to determine suitable methods for depth estimation and standardizing volume estimations. Finally we designed software for reconstructing 3D images from 2D stereo images, which was built on Matlab and Visual C++. Utilizing
techniques from multi-view geometry, a 3D model of the face was constructed and refined. An explicit analysis of the stereo
disparity calculation methods and filter elimination disparity estimation for increasing reliability of the disparity map was
used. Minimizing variability in position by using more precise positioning techniques and resources will increase the accuracy of this technique and is a focus for future work
HUMAN COMPUTER INTERACTION ALGORITHM BASED ON SCENE SITUATION AWARENESScsandit
Implicit interaction based on context information is widely used and studied in the virtual scene.In context based human computer interaction, the meaning of action A is well defined. For instance, the right wave is defined turning paper or PPT in context B, And it mean volume up in context C. However, Select object in a virtual scene with multiple objects, context information is not fit. In view of this situation, this paper proposes using the least squares fitting curve beam to
predict the user's trajectory, so as to determine what object the user’s wants to operate .And fitting the starting position of the straight line according to the change of the discrete table. And
using the bounding box size control the Z variable to move in an appropriate location. Experimental results show that the proposed in this paper based on bounding box size to control
the Z variables get a good effect; by fitting the trajectory of a human hand, to predict the object that the subjects would like to operate. The correct rate is 88.6%.
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.
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.
Analysis and Detection of Image Forgery Methodologiesijsrd.com
"Forgery" is a subjective word. An image can become a forgery based upon the context in which it is used. An image altered for fun or someone who has taken a bad photo, but has been altered to improve its appearance cannot be considered a forgery even though it has been altered from its original capture. The other side of forgery are those who perpetuate a forgery for gain and prestige. They create an image in which to dupe the recipient into believing the image is real and from this they are able to gain payment and fame. Detecting these types of forgeries has become serious problem at present. To determine whether a digital image is original or doctored is a big challenge. To find the marks of tampering in a digital image is a challenging task. Now these marks of tampering can be done by various operations such as rotation, scaling, JPEG compression, Gaussian noise etc. called as attacks. There are various methods proposed in this field in recent years to detect above mentioned attacks. This paper provides a detailed analysis of different approaches and methodologies used to detect image forgery. It is also analysed that block-based features methods are robust to Gaussian noise and JPEG compression and the key point-based feature methods are robust to rotation and scaling.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
Soft Shadow Rendering based on Real Light Source Estimation in Augmented RealityWaqas Tariq
The most challenging task in developing Augmented Reality (AR) applications is to make virtual objects mixed harmoniously with the real scene. To achieve photorealistic AR environment, three key issues must be emphasized namely consistency of geometry, illumination and speed. Shadow is an essential element to improve visual perception and realism. Without shadow, virtual objects will appear like it is floating and thus will make the environment look unrealistic. However, many shadow algorithms still have drawbacks such as producing sharp and hard-edged outlines, which make the shadow’s appearance unrealistic. Thus, this paper will focus on generating soft shadow in AR scene render based on real light sources position, where reflective sphere is used to create environment map image to estimate the light source from the real scene and render the soft shadows.
Hand gesture recognition using support vector machinetheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
RECOGNITION OF RECAPTURED IMAGES USING PHYSICAL BASED FEATUREScsandit
With the development of multimedia technology and digital devices, it is very simple and easier to recapture a high quality images from LCD screens. In authentication, the use of such
recaptured images can be very dangerous. So, it is very important to recognize the recaptured images in order to increase authenticity. Image recapture detection (IRD) is to distinguish realscene images from the recaptured ones. An image recapture detection method based on set of physical based features is proposed in this paper, which uses combination of low-level features including texture, HSV colour and blurriness. Twenty six dimensions of features are xtracted to train a suppo rt vector machine classifier with linear kernel. The experimental results show that the proposed method is efficient with good recognition rate of distinguishing real scene images from the recaptured ones. The proposed method also possesses low dimensional features compared to the state-of-the-art recaptured methods.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/person-re-identification-and-tracking-at-the-edge-challenges-and-techniques-a-presentation-from-the-university-of-auckland/
Morteza Biglari-Abhari, Senior Lecturer at the University of Auckland, presents the “Person Re-Identification and Tracking at the Edge: Challenges and Techniques” tutorial at the May 2021 Embedded Vision Summit.
Numerous video analytics applications require understanding how people are moving through a space, including the ability to recognize when the same person has moved outside of the camera’s view and then back into the camera’s view, or when a person has passed from the view of one camera to the view of another. This capability is referred to as person re-identification and tracking. It’s an essential technique for applications such as surveillance for security, health and safety monitoring in healthcare and industrial facilities, intelligent transportation systems and smart cities. It can also assist in gathering business intelligence such as monitoring customer behavior in shopping environments. Person re-identification is challenging.
In this talk, Biglari-Abhari discusses the key challenges and current approaches for person re-identification and tracking, as well as his initial work on multi-camera systems and techniques to improve accuracy, especially fusing appearance and spatio-temporal models. He also briefly discusses privacy-preserving techniques, which are critical for some applications, as well as challenges for real-time processing at the edge.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Tracking Chessboard Corners Using Projective Transformation for Augmented Rea...CSCJournals
Augmented reality has been a topic of intense research for several years for many applications. It consists of inserting a virtual object into a real scene. The virtual object must be accurately positioned in a desired place. Some measurements (calibration) are thus required and a set of correspondences between points on the calibration target and the camera images must be found. In this paper, we present a tracking technique based on both detection of Chessboard corners and a least squares method; the objective is to estimate the perspective transformation matrix for the current view of the camera. This technique does not require any information or computation of the camera parameters; it can used in real time without any initialization and the user can change the camera focal without any fear of losing alignment between real and virtual object.
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.
A HYBRID COPY-MOVE FORGERY DETECTION TECHNIQUE USING REGIONAL SIMILARITY INDICESijcsit
Different methods have been experimented for processing and detecting forgery in digital images. Image forgery involves various activities like copy-move forgery, image slicing, retouching, morphing etc. In copy-move forgery a portion within the image is copied and pasted on another part of the same image,generally to conceal or enhance certain portions of the image. This paper proposes a copy-move forgery detection using local fractal dimension and structural similarity indices. The image is classified into different texture regions based on the local fractal dimension. Forgery checking is thus confined to be among the portions within a region. Structural similarity index measure is applied to each block pair in each region to localize the forged portion. Experimental results prove that this hybrid method can effectively detect such kind of image tampering with minimum false positives.
Virtual viewpoint three dimensional panoramaijcseit
Conventional panoramic images are known to provide for an enhanced field of view in which the scene
always has a fixed appearance. The idea presented in this paper focuses on the use of the concept of virtual
viewpoint creation to generate different panoramic images of the same scene with three-dimensional
component. Three-dimensional effect in a resultant panorama is realized by superimposing a stereo-pair of
panoramic images.
10 Insightful Quotes On Designing A Better Customer ExperienceYuan Wang
In an ever-changing landscape of one digital disruption after another, companies and organisations are looking for new ways to understand their target markets and engage them better. Increasingly they invest in user experience (UX) and customer experience design (CX) capabilities by working with a specialist UX agency or developing their own UX lab. Some UX practitioners are touting leaner and faster ways of developing customer-centric products and services, via methodologies such as guerilla research, rapid prototyping and Agile UX. Others seek innovation and fulfilment by spending more time in research, being more inclusive, and designing for social goods.
Experience is more than just an interface. It is a relationship, as well as a series of touch points between your brand and your customer. Here are our top 10 highlights and takeaways from the recent UX Australia conference to help you transform your customer experience design.
For full article, continue reading at https://yump.com.au/10-ways-supercharge-customer-experience-design/
http://inarocket.com
Learn BEM fundamentals as fast as possible. What is BEM (Block, element, modifier), BEM syntax, how it works with a real example, etc.
Content personalisation is becoming more prevalent. A site, it's content and/or it's products, change dynamically according to the specific needs of the user. SEO needs to ensure we do not fall behind of this trend.
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.
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.
Analysis and Detection of Image Forgery Methodologiesijsrd.com
"Forgery" is a subjective word. An image can become a forgery based upon the context in which it is used. An image altered for fun or someone who has taken a bad photo, but has been altered to improve its appearance cannot be considered a forgery even though it has been altered from its original capture. The other side of forgery are those who perpetuate a forgery for gain and prestige. They create an image in which to dupe the recipient into believing the image is real and from this they are able to gain payment and fame. Detecting these types of forgeries has become serious problem at present. To determine whether a digital image is original or doctored is a big challenge. To find the marks of tampering in a digital image is a challenging task. Now these marks of tampering can be done by various operations such as rotation, scaling, JPEG compression, Gaussian noise etc. called as attacks. There are various methods proposed in this field in recent years to detect above mentioned attacks. This paper provides a detailed analysis of different approaches and methodologies used to detect image forgery. It is also analysed that block-based features methods are robust to Gaussian noise and JPEG compression and the key point-based feature methods are robust to rotation and scaling.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
Soft Shadow Rendering based on Real Light Source Estimation in Augmented RealityWaqas Tariq
The most challenging task in developing Augmented Reality (AR) applications is to make virtual objects mixed harmoniously with the real scene. To achieve photorealistic AR environment, three key issues must be emphasized namely consistency of geometry, illumination and speed. Shadow is an essential element to improve visual perception and realism. Without shadow, virtual objects will appear like it is floating and thus will make the environment look unrealistic. However, many shadow algorithms still have drawbacks such as producing sharp and hard-edged outlines, which make the shadow’s appearance unrealistic. Thus, this paper will focus on generating soft shadow in AR scene render based on real light sources position, where reflective sphere is used to create environment map image to estimate the light source from the real scene and render the soft shadows.
Hand gesture recognition using support vector machinetheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
RECOGNITION OF RECAPTURED IMAGES USING PHYSICAL BASED FEATUREScsandit
With the development of multimedia technology and digital devices, it is very simple and easier to recapture a high quality images from LCD screens. In authentication, the use of such
recaptured images can be very dangerous. So, it is very important to recognize the recaptured images in order to increase authenticity. Image recapture detection (IRD) is to distinguish realscene images from the recaptured ones. An image recapture detection method based on set of physical based features is proposed in this paper, which uses combination of low-level features including texture, HSV colour and blurriness. Twenty six dimensions of features are xtracted to train a suppo rt vector machine classifier with linear kernel. The experimental results show that the proposed method is efficient with good recognition rate of distinguishing real scene images from the recaptured ones. The proposed method also possesses low dimensional features compared to the state-of-the-art recaptured methods.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/person-re-identification-and-tracking-at-the-edge-challenges-and-techniques-a-presentation-from-the-university-of-auckland/
Morteza Biglari-Abhari, Senior Lecturer at the University of Auckland, presents the “Person Re-Identification and Tracking at the Edge: Challenges and Techniques” tutorial at the May 2021 Embedded Vision Summit.
Numerous video analytics applications require understanding how people are moving through a space, including the ability to recognize when the same person has moved outside of the camera’s view and then back into the camera’s view, or when a person has passed from the view of one camera to the view of another. This capability is referred to as person re-identification and tracking. It’s an essential technique for applications such as surveillance for security, health and safety monitoring in healthcare and industrial facilities, intelligent transportation systems and smart cities. It can also assist in gathering business intelligence such as monitoring customer behavior in shopping environments. Person re-identification is challenging.
In this talk, Biglari-Abhari discusses the key challenges and current approaches for person re-identification and tracking, as well as his initial work on multi-camera systems and techniques to improve accuracy, especially fusing appearance and spatio-temporal models. He also briefly discusses privacy-preserving techniques, which are critical for some applications, as well as challenges for real-time processing at the edge.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Tracking Chessboard Corners Using Projective Transformation for Augmented Rea...CSCJournals
Augmented reality has been a topic of intense research for several years for many applications. It consists of inserting a virtual object into a real scene. The virtual object must be accurately positioned in a desired place. Some measurements (calibration) are thus required and a set of correspondences between points on the calibration target and the camera images must be found. In this paper, we present a tracking technique based on both detection of Chessboard corners and a least squares method; the objective is to estimate the perspective transformation matrix for the current view of the camera. This technique does not require any information or computation of the camera parameters; it can used in real time without any initialization and the user can change the camera focal without any fear of losing alignment between real and virtual object.
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.
A HYBRID COPY-MOVE FORGERY DETECTION TECHNIQUE USING REGIONAL SIMILARITY INDICESijcsit
Different methods have been experimented for processing and detecting forgery in digital images. Image forgery involves various activities like copy-move forgery, image slicing, retouching, morphing etc. In copy-move forgery a portion within the image is copied and pasted on another part of the same image,generally to conceal or enhance certain portions of the image. This paper proposes a copy-move forgery detection using local fractal dimension and structural similarity indices. The image is classified into different texture regions based on the local fractal dimension. Forgery checking is thus confined to be among the portions within a region. Structural similarity index measure is applied to each block pair in each region to localize the forged portion. Experimental results prove that this hybrid method can effectively detect such kind of image tampering with minimum false positives.
Virtual viewpoint three dimensional panoramaijcseit
Conventional panoramic images are known to provide for an enhanced field of view in which the scene
always has a fixed appearance. The idea presented in this paper focuses on the use of the concept of virtual
viewpoint creation to generate different panoramic images of the same scene with three-dimensional
component. Three-dimensional effect in a resultant panorama is realized by superimposing a stereo-pair of
panoramic images.
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Shadow Detection and Removal in Still Images by using Hue Properties of Color...ijsrd.com
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Goal location prediction based on deep learning using RGB-D camerajournalBEEI
In the navigation system, the desired destination position plays an essential role since the path planning algorithms takes a current location and goal location as inputs as well as the map of the surrounding environment. The generated path from path planning algorithm is used to guide a user to his final destination. This paper presents a proposed algorithm based on RGB-D camera to predict the goal coordinates in 2D occupancy grid map for visually impaired people navigation system. In recent years, deep learning methods have been used in many object detection tasks. So, the object detection method based on convolution neural network method is adopted in the proposed algorithm. The measuring distance between the current position of a sensor and the detected object depends on the depth data that is acquired from RGB-D camera. Both of the object detected coordinates and depth data has been integrated to get an accurate goal location in a 2D map. This proposed algorithm has been tested on various real-time scenarios. The experiments results indicate to the effectiveness of the proposed algorithm.
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.
Interactive full body motion capture using infrared sensor networkijcga
Traditional motion capture (mocap) has been
well
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the last decades
. However
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ld is mostly about capturing
precise animation to be used in
specific
application
s
after
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processing such as studying biomechanics or rigging models in movies. These data set
s are normally
captured in complex laboratory environments with
sophisticated
equipment thus making motion capture a
field that is mostly exclusive to professional animators.
In
addition
, obtrusive sensors must be attached to
actors and calibrated within t
he capturing system, resulting in limited and unnatural motion.
In recent year
the rise of computer vision and interactive entertainment opened the gate for a different type of motion
capture which focuses on producing
optical
marker
less
or mechanical sens
orless
motion capture.
Furtherm
ore a wide array of low
-
cost
device are released that are easy to use
for less mission critical
applications
.
This paper
describe
s
a new technique of using multiple infrared devices to process data from
multiple infrared sensors to enhance the flexibility and accuracy of the markerless mocap
using commodity
devices such as Kinect
. The method involves analyzing each individual sensor
data, decompose and rebuild
them into a uniformed skeleton across all sensors. We then assign criteria to define the confidence level of
captured signal from
sensor. Each sensor operates on its own process and communicates through MPI.
Our method emphasize
s on the need of minimum calculation overhead for better real time performance
while being able to maintain good scalability
Interactive Full-Body Motion Capture Using Infrared Sensor Network ijcga
Traditional motion capture (mocap) has been well-studied in visual science for the last decades. However the field is mostly about capturing precise animation to be used in specific applications after intensive post processing such as studying biomechanics or rigging models in movies. These data sets are normally captured in complex laboratory environments with sophisticated equipment thus making motion capture a
field that is mostly exclusive to professional animators. In addition, obtrusive sensors must be attached to actors and calibrated within the capturing system, resulting in limited and unnatural motion. In recent year the rise of computer vision and interactive entertainment opened the gate for a different type of motion capture which focuses on producing optical markerless or mechanical sensorless motion capture. Furthermore a wide array of low-cost device are released that are easy to use for less mission critical applications. This paper describes a new technique of using multiple infrared devices to process data from multiple infrared sensors to enhance the flexibility and accuracy of the markerless mocap using commodity
devices such as Kinect. The method involves analyzing each individual sensor data, decompose and rebuild
them into a uniformed skeleton across all sensors. We then assign criteria to define the confidence level of
captured signal from sensor. Each sensor operates on its own process and communicates through MPI.
Our method emphasizes on the need of minimum calculation overhead for better real time performance
while being able to maintain good scalability.
Conventional 2D to 3D rendering techniques involve a sequential process of grouping of the input images based on edge information and predictive algorithms to assign depth values to pixels with same hue. The iterative calculations and volume of data under scrutiny to assign „real-time‟ values raise latency issues and cost considerations. For commercial consumption, where speed and accuracy define the viability of a product, there is a need to reorient the approach used in the present methodologies. In predictive methodologies one of the core interests is achieving the initial approximation as close to the „real‟ value as possible. In this work, „synthetic‟ database is used to provide the first approximation through comparison techniques and fed to the predictive tool. It is believed that this work will provide a basis for developing an efficient 2D to 3D conversion methodology.
Motion Object Detection Using BGS TechniqueMangaiK4
Abstract--- The detection of moving object is an important in many applications such as a vehicle identification in a traffic monitoring system,human detection in a crime branch.In this paper we identify a vehicle in a video sequence.This paper briefly explain the detection of moving vehicle in a video.We introduce a new algorithm BGS for idntifying vehicle in a video sequence.First, we differentiate the foreground from background in frames by learning the background.Then, the image is divided into many small nonoverlapped frames. The candidates of the vehicle part can be found from the frames if there is some change in gray level between the current image and the background.The extracted background subtraction method is used in subsequent analysis to detect a vehicle and classify moving vehicle
Motion Object Detection Using BGS TechniqueMangaiK4
Abstract--- The detection of moving object is an important in many applications such as a vehicle identification in a traffic monitoring system,human detection in a crime branch.In this paper we identify a vehicle in a video sequence.This paper briefly explain the detection of moving vehicle in a video.We introduce a new algorithm BGS for idntifying vehicle in a video sequence.First, we differentiate the foreground from background in frames by learning the background.Then, the image is divided into many small nonoverlapped frames. The candidates of the vehicle part can be found from the frames if there is some change in gray level between the current image and the background.The extracted background subtraction method is used in subsequent analysis to detect a vehicle and classify moving vehicle.
Depth Estimation from Defocused Images: a SurveyIJAAS Team
An important step in 3D data generation is the generation of depth map. Depth map is a black and white image which has exactly the same size of the original captured 2D image that indicates the relative distance of each pixel from the observer to the objects in the real world. This paper presents a survey of Depth Perception from Defocused or blurs images as well as image from motion. The change of distance of the object from the camera has direct relation with the amount of blurring of object in the image. The amount of blurring will be calculated with a comparison in front of the camera directly and can be seen with the changes at gray level around the edges of objects.
Integration of poses to enhance the shape of the object tracking from a singl...eSAT Journals
Abstract In computer vision, tracking human pose has received a growing attention in recent years. The existing methods used multi-view videos and camera calibrations to enhance the shape of the object in 3D view. In this paper, tracking and partial reconstruction of the shape of the object from a single view video is identified. The goal of the proposed integrated method is to detect the movement of a person more accurately in 2D view. The integrated method is a combination of Silhouette based pose estimation and Scene flow based pose estimation. The silhouette based pose estimation is used to enhance the shape of the object for 3D reconstruction and scene flow based pose estimation is used to capture the size as well as the stability of the object. By integrating these two poses, the accurate shape of the object has been calculated from a single view video. Keywords: Pose Estimation, optical Flow, Silhouette, Object Reconstruction, 3D Objects
Wireless network implementation is a viable option for building network infrastructure in rural communities. Rural people lack network infrastructures for information services and socio-economic development. The aim of this study was to develop a wireless network infrastructure architecture for network services to rural dwellers. A user-centered approach was applied in the study and a wireless network infrastructure was designed and deployed to cover five rural locations. Data was collected and analyzed to assess the performance of the network facilities. The results shows that the system had been performing adequately without any downtime with an average of 200 users per month and the quality of service has remained high. The transmit/receive rate of 300Mbps was thrice as fast as the normal Ethernet transmit/receive specification with an average throughput of 1 Mbps. The multiple output/multiple input (MIMO) point-to-multipoint network design increased the network throughput and the quality of service experienced by the users.
3D reconstruction is a technique used in computer vision which has a wide range of applications in areas like object recognition, city modelling, virtual reality, physical simulations, video games and special effects. Previously, to perform a 3D reconstruction, specialized hardwares were required. Such systems were often very expensive and was only available for industrial or research purpose. With the rise of the availability of high-quality low cost 3D sensors, it is now possible to design inexpensive complete 3D scanning systems. The objective of this work was to design an acquisition and processing system that can perform 3D scanning and reconstruction of objects seamlessly. In addition, the goal of this work also included making the 3D scanning process fully automated by building and integrating a turntable alongside the software. This means the user can perform a full 3D scan only by a press of a few buttons from our dedicated graphical user interface. Three main steps were followed to go from acquisition of point clouds to the finished reconstructed 3D model. First, our system acquires point cloud data of a person/object using inexpensive camera sensor. Second, align and convert the acquired point cloud data into a watertight mesh of good quality. Third, export the reconstructed model to a 3D printer to obtain a proper 3D print of the model.
3D reconstruction is a technique used in computer vision which has a wide range of applications in areas like object recognition, city modelling, virtual reality, physical simulations, video games and special effects. Previously, to perform a 3D reconstruction, specialized hardwares were required. Such systems were often very expensive and was only available for industrial or research purpose. With the rise of the availability of high-quality low cost 3D sensors, it is now possible to design inexpensive complete 3D scanning systems. The objective of this work was to design an acquisition and processing system that can perform 3D scanning and reconstruction of objects seamlessly. In addition, the goal of this work also included making the 3D scanning process fully automated by building and integrating a turntable alongside the software. This means the user can perform a full 3D scan only by a press of a few buttons from our dedicated graphical user interface. Three main steps were followed to go from acquisition of point clouds to the finished reconstructed 3D model. First, our system acquires point cloud data of a person/object using inexpensive camera sensor. Second, align and convert the acquired point cloud data into a watertight mesh of good quality. Third, export the reconstructed model to a 3D printer to obtain a proper 3D print of the model.
COMPLETE END-TO-END LOW COST SOLUTION TO A 3D SCANNING SYSTEM WITH INTEGRATED...ijcsit
3D reconstruction is a technique used in computer vision which has a wide range of applications in
areas like object recognition, city modelling, virtual reality, physical simulations, video games and
special effects. Previously, to perform a 3D reconstruction, specialized hardwares were required.
Such systems were often very expensive and was only available for industrial or research purpose.
With the rise of the availability of high-quality low cost 3D sensors, it is now possible to design
inexpensive complete 3D scanning systems. The objective of this work was to design an acquisition and
processing system that can perform 3D scanning and reconstruction of objects seamlessly. In addition,
the goal of this work also included making the 3D scanning process fully automated by building and
integrating a turntable alongside the software. This means the user can perform a full 3D scan only by
a press of a few buttons from our dedicated graphical user interface. Three main steps were followed
to go from acquisition of point clouds to the finished reconstructed 3D model. First, our system
acquires point cloud data of a person/object using inexpensive camera sensor. Second, align and
convert the acquired point cloud data into a watertight mesh of good quality. Third, export the
reconstructed model to a 3D printer to obtain a proper 3D print of the model.
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Automatic 3D view Generation from a Single 2D Image for both Indoor and Outdoor Scenes
1. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013
DOI:10.5121/ijcsa.2013.3404 37
Automatic 3D view Generation from a Single 2D
Image for both Indoor and Outdoor Scenes
Geetha Kiran A1
and Murali S2
1
Malnad College of Engineering, Hassan, Karnataka, India
geethaamk@gmail.com
2
Maharaja Institute of Technology, Mysore, Karnataka, India
murali@mitmysore.in
ABSTRACT
Image based video generation paradigms have recently emerged as an interesting problem in the field of
robotics. This paper focuses on the problem of automatic video generation of both indoor and outdoor
scenes. Automatic 3D view generation of indoor scenes mainly consist of orthogonal planes and outdoor
scenes consist of vanishing point. The algorithm infers frontier information directly from the images using
a geometric context-based segmentation scheme that uses the natural scene structure. The presence of
floor is a major cue for obtaining the termination point for the video generation of the indoor scenes and
vanishing point plays an important role in case of outdoor scenes. In both the cases, we create the
navigation by cropping the image to the desired size upto the termination point. Our approach is fully
automatic, since it needs no human intervention and finds applications, mainly in assisting autonomous
cars, virtual walk through ancient time images, in architectural sites and in forensics. Qualitative and
quantitative experiments on nearly 250 images in different scenarios show that the proposed algorithms
are more efficient and accurate.
KEYWORDS
Floor segmentation,canny edge detector, hough transform, vanishing point, video generation
1. INTRODUCTION
Video generation from a single image is inherently a challenging problem. In Imaging devices,
there is a trade-off between the images (snapshots) and video because of the limitation in storage
capacity. Video clips need more storage space compared to images. This motivated to generate
the video from a single 2D image rather than storing video clips. Humans analyze variety of
single image cues and act accordingly, unlike robots. The work is an attempt to make robots
analyze similar to humans using single 2D image. The task of generating video from photographs
is receiving increased attention in many of the applications. We are addressing here the key case
where dimension of the real world object or measurement of object dimension in 2D plane is
unknown. However generating video using above methods is very difficult because of
perspective view. Alternatively, video could be generated using proper ground known i.e., floor
segmentation in case of indoor scenes. In the absence of accurate measurements, we wish to
exploit geometric characteristics (windows/doors) along with the color variations. Such
relationships are plentiful in man-made structures and often provide sufficient information to our
work. In case of Road scenes, video could be generated using proper ground known i.e.,
vanishing point. We describe a unified framework for navigation through a single 2D image in
lesser time. The input image may be easily acquired since no calibration target is needed or we
can download images from internet. The work is well-suited for navigation on Personal Digital
assistants(PDA’s) and personal computers, includes cases where buildings are destroyed and only
the archive images are available. It can also be applied in forensics and to assist autonomous cars
by generating video from a single 2D image and assessing in advance - how far there is a straight
2. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013
38
road? If there is any suspected person or item in our path of journey, it could be detected prior
and necessary action can be taken. We describe a unified framework for generating video from a
single 2D image. In the next section, a review on the related works is highlighted. Section 3 gives
description of floor segmentation from single view scene constraints along with the computation
of length of the floor. Section 4 gives description of finding the vanishing point from single view
scene constraints and computing the distance from the ground truth position to the vanishing
point. This is followed by the method of 3D view generation in section 5. Finally, some of the
experimental results are presented in section 6 followed by conclusion in section 7.
2. RELATED WORK
It is observed that some methods have been developed for segmentation on a single image, few
which are directly relevant to the work are highlighted here. Erick Delage et al have used a graph
based segmentation algorithm to generate a partition of the image and assigned a unique
identifier to each partition output by the segmentation algorithm in [18]. Erick Delage et al [20]
have built a probabilistic model that incorporates a number of local image features and tries to
reason about the chroma of the floor in each column of the image. Ma Ling et al [22] has
segmented the floor region automatically by adopting clustering analysis and also have proposed
a PCA based improved version of the algorithm to remove negative effect of shadow for
segmented results. Xue – Nan Cui et al [23] have proposed detecting and segmenting the floor
by computing plane normals from motion fields in image sequences. A geometric characteristic
that objects are placed perpendicular to the ground floor can be utilized to find the floor in 2D
images. Surfaces often have fairly uniform appearances in texture and color and thus image
segmentation algorithms provide another set of useful features which can be used in many other
applications, including video generation. Some of the methods developed for detecting
vanishing point on a single image have been highlighted here. Techniques for estimating
vanishing points can be roughly divided into two categories. One requiring the
knowledge of the internal parameters of the camera and the other operates in an
uncalibrated setting. A large literature exists on automatic detection of vanishing points,
after Barnard [1] first introduced the use of the Gaussian Sphere as an accumulation
space. He suggested that the unbounded space can be mapped into the bounded surface of
the Gaussian sphere. Tuytelaars et al [2] mapped points into different bounded subspaces
according to their coordinates. Rother [3] pointed out these methods could not preserve
the original distances between lines and points. In this method, the intersections of all
pairs of non-collinear lines are considered as accumulator cells instead of a parameter
space. But these accumulator cells are difficult to index, searching for the maximal from
the accumulator cells is slow. The simple calculation of a weighted mean of pairwise
intersection is used by Caprile et al [4]. Researches [5-7] have used vanishing point as
global constraint for road. They compute the texture orientation for each pixel and select
the effective vote-points, then locate the vanishing point by using a voting procedure. Hui
Kong et al [8-11] have proposed an adaptive soft voting scheme which is based upon a
local voting region using high-confidence voters.
However, there are some redundancies during the voting process and the accuracy on
updating vanishing point. Murali S et al [12,13] have detected edges using canny edge
detector and hough transform is applied. The maximum votes of first N number of cells
in the hough space is used for computing the vanishing point. We use the similar
framework [12-13] in our work to decide the vanishing point.A very few Researchers
have proposed different methods for navigation through a Single 2D image. Shuqiang
3. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013
39
jiang et al [14] have proposed a method to automatically transform static images to
dynamic video clips in mobile devices. Xian-sheng Hua et al [15] developed a system
named photo2video to convert a photographic series into a video by simulating camera
motions. The camera motion pattern (both the key-frame sequencing scheme and
trajectory/ speed control strategy) is selected for each photograph to generate a
corresponding motion photograph clip. A region based method to generate a multiview
video from a conventional 2-dimensional video using color information to segment an
image has been proposed by Yun-Ki-Baek et al [16]. Na-Eun Yang et al [17] have
proposed method to generate depth map using local depth hypothesis and grouped
regions for 2D-to-3D conversion. The various methods of converting 2D to stereoscopic
3D images involves the fundamental, underlying principle of horizontal shifting of pixels
to create a new image so that there are horizontal disparities between the original image
and the new version. The extent of horizontal shift depends on the distance of the feature
of an object to the stereoscopic camera that the pixel represents. It also depends on the
inter-lens separation to determine the new image viewpoint.
The methods proposed by the authors for floor segmentation is time consuming and have made
certain assumptions specific to the application. These artifacts are not of much importance in our
work, this made us to propose a simple method for floor segmentation in lesser time. Using the
segmented image, length of the floor could be computed by distance method. This helps in video
generation. The methods proposed by the authors for detecting vanishing points have made
certain assumptions specific to the application. These artifacts are not of much importance in our
work, this made us to propose a new method as proposed in [12,13], which decides the vanishing
point in lesser time. Using the vanishing point, the distance from the ground truth position to the
vanishing point could be computed. This helps in navigating through the single Road image.
3. FLOOR SEGMENTATION
The goal is to obtain floor segmentation of a given single 2D indoor image. The crucial part of
the work is detecting the pixels belonging to the floor. There are methods available for floor
segmentation with known camera parameters. Requirements is to segment floor without having
knowledge of camera parameters. There is possibility to find the geometric relationship, may be
using color. The primary steps involves converting the given color image to gray, further convert
the gray image to binary image by computing a global threshold. Finally, segment the floor by
applying the dilation and erosion methods.
3.1. Segmentation
The floor path [19] is the major cue to generate video from a single 2D image of indoor scenes.
To segment the floor from the remaining parts of the indoor image scenes, dilation and erosion
techniques using the structuring elements are used.
Assuming E to be a Euclidean space or an integer grid, A a binary image in E, and B a structuring
element.
The dilation of A by B is defined by:
(1)
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The erosion of A by B is given by:
(2)
Structuring element is used for probing and expanding the shapes contained in the input image
yielding to floor segmentation (Figure 1).
(a) (b) (c) (d)
Figure 1. (a) Original Image (b) Gray Image
(c) Binary Image using Otsu’s method (d) Segmented Image
The segmented image obtained ( Figure 1(d) ) is used to find the length of the floor. The distance
between the start and end of the white pixel (row wise) from the floor segmented image is found
by using the Euclidean distance method. This length of the floor identified could directly be used
to decide the number of frames to be generated, generally 1:2 depends on the length and it can be
varied with requirements. These frames are incorporated in the video generation.
4. VANISHING POINT DETECTION
Images considered for modeling are perspective. In a perspective image, lines parallel in the
world space appear to meet at a point called Vanishing point. Vanishing points provide a strong
geometric cue for inferring information about 3 dimensional structure of a scene in almost all
kinds of man-made environment. There are methods available for detecting vanishing points
with known camera parameters and also with uncalibrated setting. The method described in this
section requires no knowledge of the camera parameters and proceeds directly from geometric
relationships. The step involves detecting edges using canny edge technique to identify the
straight lines depending upon the threshold fixed by the hough transform, compute the vanishing
point using the intersection points of the lines. The above steps have been explained in the
subsequent sections.
4.1. Line Determination
The given color image is converted to gray. Lines are edges of the objects and environment
present in an image. These lines may or may not contribute to form the actual vanishing point.
The existence of the lines are obtained by applying the canny edge detection algorithm. The
versatility of the canny algorithm is to adapt to various parameters like the sizes of the Gaussian
filter and the thresholds. This will allow it to be used in detecting edges of differing
characteristics.
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The input image (Figure 2(a)) is converted to gray image (Figure 2(b)), the edges are detected by
applying Canny edge detection algorithm. A set of white pixels containing edges are obtained
and the rest of the contents of the image are removed. A Canny edge detected image (Figure
2(c)) contains pixels contributing to straight lines and also other miscellaneous edges.
Considering all these pixels of the edges contributing to the straight lines, Hough transformation
is applied on the input image and the result (Figure 2(d)) is obtained as desired.
(a) (b) (c) (d)
Figure 2. (a) Original Image (b) Gray Image (c) Edge detection (d) Hough transformation
As the outcome of the Hough transformation, a large number of straight lines are detected. These
straight lines depend upon the threshold fixed up for the Hough transformation. Points belonging
to the same straight line in the image plane have corresponding sinusoids which intersect in a
single point in the polar space (Figure 1(d)). The need for calculating the number of straight lines
is that there could be several straight lines in the image which intersects each other at different
points in the image plane. In such case there arises a situation that more than one peak value in
the polar space is obtained. Thus by selecting the number of peak values (in descending order of
their votes) equal to the number of straight lines ‘N’ present in the image we restrict the
unwanted lines which may not contribute to the real vanishing point. This reduces the
computational complexity of vanishing point detection to only the number of straight lines
contributing the possible vanishing point.
4.2. Intersection Point of any Two Lines
Lines drawn by Hough transformation are on edges of the object and environment in an image.
These lines may or may not contribute to form the actual vanishing point. Depending upon the
number of lines present in the image, the number of peaks in the Hough space is fixed up in a
descending order of their occurrences. Each peak in the hough space signifies the existence of a
longer edge in the image than any other points in the Hough space and hence a peak is formed.
These peaks of the voted points of the hough space are calculated to find the intersection between
two lines to calculate the vanishing point. Finding the intersection points for all combination of
lines selecting two at a time, corresponding one (x,y) pair is obtained. The number of pairs of x
and y values obtained for all combinations is given by the relation
(3)
where N is the number of peaks selected. These (x,y) pairs are the probable vanishing points. All
of them are within the vicinity of the actual vanishing point. We have taken the mean of the
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probable vanishing points (Figure 3(a)-blue color), since they are within the vicinity of the actual
vanishing point. In our work vanishing point is used to find the distance from the ground truth
position to the detected Vanishing point position. The distance obtained is used in the next
section to facilitate the termination point for the navigation. The distance of the Road identified
could directly be used to decide the number of frames to be generated, generally 1:2 depends on
the length and it can be varied with requirements.
(a) (b)
Figure 3.(a) Lines detected (green Color),Probable Vanishing Points(blue Color)
(b) Vanishing Point (Pink Color)
5. 3D VIEW GENERATION
Automatic 3D view generation from a single image is inherently a challenging problem. The
proposed method has attempted to generate the 3D view (Algorithm) for both indoor and outdoor
scenes.
Algorithm
Step 1: Read the Input Image
Step 2: Compute the termination point using
i ) Floor segmentation for indoor scenes
ii)Vanishing point for outdoor scenes
Step 3: Generate the frames based on the predefined rectangle
Step 4: Navigate through the single 2D Image upto the termination point
5.1 Indoor Scenes
The information obtained in the floor segmentation is used to generate the 3D view. The input for
the video generation are: single 2D image, computed termination point based on the distance
calculated using floor segmentation, the size of the rectangle based on which cropping takes
place. The input image is considered as the first frame and the image is cropped based on the
size of the predefined rectangle. The rectangle has to be clearly defined as it is the frame further
used for 3D view generation. The floor segmentation plays a vital role in detecting the
termination point to generate the frames. Then the cropped image is resized to the original image
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43
and stored in an array of images. An appropriate set of key-frames (Figure 4) are determined for
each image based on the distance computed by using floor segmentation.
(a) (b) (c)
(d) (e) (f)
Figure 4. (a) 20th
Frame (b) 40th
Frame (c) 60th
Frame(d) 80th
Frame (e) 100th
Frame
(f) 120th
Frame
5.2 Outdoor Scenes (Road Scenes)
The information obtained from section 4 is used to navigate through a single Road image. The
input for the navigation are - single 2D image, computed termination point based on the distance
from the ground truth position to the detected vanishing point. Based on this strategy, the frames
for navigation are generated by cropping the image based on the size of the image up to the
computed distance. The input image is considered as the first frame and the image is cropped
based on the size of the predefined rectangle. Then the cropped image is resized to the original
image and stored in an array of images. An appropriate set of key-frames (Figure 5) are
determined for each image based on the distance computed by using vanishing point.
(a) (b)
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(c) (d)
(e) (f)
Figure 5. (a) 10th
Frame (b) 20th
Frame (c) 40th
Frame(d) 70th
Frame (e) 100th
Frame
(f) 180th
Frame
6. EXPERIMENTAL RESULTS
The algorithm is applied to a test set of 250 images obtained from different buildings, all of them
are fairly different in interior decoration themes from each other. Since the indoor images
contained a diverse range of orthogonal geometries (wall posters, doors, windows, boxes,
cabinets etc.), we have observed that the results presented are indicative of the algorithm
performance on images of new buildings (interior) and scenes. We also have evaluated the
algorithm( Figure 6(a)) by manually detecting the floor path of a set of images and compared it
with the floor path generated by our method and the overall accuracy obtained from the result is
91.46%. In case of outdoor scenes obtained from different real-road images in different scenarios
that mainly consist of single vanishing point, we have observed that the results presented are
indicative of the algorithm performance. The images used in the experimentation are downloaded
from internet and few of them are self captured. The steps involves detecting edges using canny
edge technique, to identify the straight lines, compute the vanishing point using the intersection
points of the lines. All of them are within the vicinity of the actual vanishing point. Based on the
ground truth position, we compute the distance from the ground truth position to the computed
vanishing point. We also have evaluated the algorithm (figure 6(b)) by manually detecting the
distance from the ground truth value to the vanishing point and compared it with the distance
generated by our method and the overall accuracy obtained from the result is 97%.
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(a) (b)
Figure 6. (a) Comparison of the length of the floor computed manually with our method
(b) Comparison of vanishing point accuracy computed manually with our method
The first, intermediate and final frame generated by the methods for both Indoor and Road
Scenes (Figure 7) after deducing the termination point are clearly viewed. The frames give the
finer details in the intermediate and final frames that could be used in various applications
including virtual walk through ancient time images, in forensics, in architectural sites and in
automated vehicle.
7. CONCLUSION
An algorithm for automatic video generation from a single 2D image is proposed and
experimented for both indoor and outdoor images. This paper provides a solution to transform
static single 2D image into video clips. It not only helps the users to enjoy the important details of
the image but also provides a vivid viewing manner. The experimental results show that the
algorithm is performing well on a number of indoor and outdoor scenes. The work is
experimented on nearly 250 images in difficult scenarios. Further work can be extended to
produce videos including side view, working at planar level. This requires maintenance of
perspective view of the scene. Further work may be extended to include investigating on more
reliable Region Of Interest (ROI) detection techniques. Even finer details can be obtained from
the key frames used in video generation. The work is done in view of assisting the automated
vehicle and robots at low cost.
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Figure 7. (a)First Frame (b) Intermediate Frame (c) Final Frame