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Digital Image Processing and Edge DetectionSeda Yalçın
This presentation is an introduction for digital image processing and edge detection which covers them on four topic; example of fields that use digital image processing, visibility that depends on human perception, fundamental definition of an image, analysis of edge detection algorithms such as Roberts, Prewitt, Sobel and Laplacian of a Gaussian.
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
Digital Image Processing and Edge DetectionSeda Yalçın
This presentation is an introduction for digital image processing and edge detection which covers them on four topic; example of fields that use digital image processing, visibility that depends on human perception, fundamental definition of an image, analysis of edge detection algorithms such as Roberts, Prewitt, Sobel and Laplacian of a Gaussian.
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionIJAEMSJORNAL
In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest. Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets. Many systems include detection, storage of video information, and human-computer interfaces. Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications. The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Multimodal Biometrics Recognition from Facial Video via Deep Learning cscpconf
Biometrics identification using multiple modalities has attracted the attention of many
researchers as it produces more robust and trustworthy results than single modality biometrics.
In this paper, we present a novel multimodal recognition system that trains a Deep Learning
Network to automatically learn features after extracting multiple biometric modalities from a
single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile
face, frontal face, right profile face, and right ear, present in the facial video clips, we train
supervised denosing autoencoders to automatically extract robust and non-redundant features.
The automatically learned features are then used to train modality specific sparse classifiers to
perform the multimodal recognition. Experiments conducted on the constrained facial video
dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a
99.17% and 97.14% rank-1 recognition rates, respectively. The multimodal recognition
accuracy demonstrates the superiority and robustness of the proposed approach irrespective of
the illumination, non-planar movement, and pose variations present in the video clips.
MULTIMODAL BIOMETRICS RECOGNITION FROM FACIAL VIDEO VIA DEEP LEARNINGcsandit
Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics.
In this paper, we present a novel multimodal recognition system that trains a Deep Learning Network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denosing autoencoders to automatically extract robust and non-redundant features.The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Experiments conducted on the constrained facial video
dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% rank-1 recognition rates, respectively. The multimodal recognition
accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips.
InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images
Abstract. We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view, where this capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene. To achieve this, we propose a novel self-supervised view generation training paradigm, where we sample and rendering virtual camera trajectories, including cyclic ones, allowing our model to learn stable view generation from a collection of single views. At test time, despite never seeing a video during training, our approach can take a single image and generate long camera trajectories comprised of hundreds of new views with realistic and diverse contents. We compare our approach with recent state-of-the-art supervised view generation methods that require posed multi-view videos and demonstrate superior performance and synthesis quality.
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5 ijaems sept-2015-9-video feature extraction based on modified lle using ada...INFOGAIN PUBLICATION
Locally linear embedding (LLE) is an unsupervised learning algorithm which computes the low dimensional, neighborhood preserving embeddings of high dimensional data. LLE attempts to discover non-linear structure in high dimensional data by exploiting the local symmetries of linear reconstructions. In this paper, video feature extraction is done using modified LLE alongwith adaptive nearest neighbor approach to find the nearest neighbor and the connected components. The proposed feature extraction method is applied to a video. The video feature description gives a new tool for analysis of video.
Ieee projects 2012 2013 - Digital Image ProcessingK Sundaresh Ka
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Measuring the Effects of Rational 7th and 8th Order Distortion Model in the R...IOSRJVSP
One of the biggest and important issues in the video watermarking is the distortion and attacks. The attacks and distortion affect the digital watermarking. Watermarking is an embedding process. With the help of watermarking, we insert the data into the digital objects. There are few methods are available for authentication of data, securing/protection of data. The watermarking technique also provides the data security, copyright protection and authentication of the data. Watermarking provides a comfortable life to authorized users. In my proposed work, we are working on distorted watermarked video. The distortion is present on the watermarked video is rational 7 th and 8 th order distortion model. In this paper, firstly we are embedding the watermark information into the original video and after that work on the distortion model which may be come into the watermarked video. We are also calculating the PSNR (Peak signal to noise ratio), SSIM (Structural similarity index measure), Correlation, BER (Bit Error Rate) and MSE (Mean Square Error) parameters for distorted watermarked video. We are showing the relationship between correlation and SSIM with BER, MSE and PSNR.
Coronary heart disease is a disease with the highest mortality rates in the world. This makes the development of the diagnostic system as a very interesting topic in the field of biomedical informatics, aiming to detect whether a heart is normal or not. In the literature there are diagnostic system models by combining dimension reduction and data mining techniques. Unfortunately, there are no review papers that discuss and analyze the themes to date. This study reviews articles within the period 2009-2016, with a focus on dimension reduction methods and data mining techniques, validated using a dataset of UCI repository. Methods of dimension reduction use feature selection and feature extraction techniques, while data mining techniques include classification, prediction, clustering, and association rules.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionIJAEMSJORNAL
In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest. Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets. Many systems include detection, storage of video information, and human-computer interfaces. Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications. The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Multimodal Biometrics Recognition from Facial Video via Deep Learning cscpconf
Biometrics identification using multiple modalities has attracted the attention of many
researchers as it produces more robust and trustworthy results than single modality biometrics.
In this paper, we present a novel multimodal recognition system that trains a Deep Learning
Network to automatically learn features after extracting multiple biometric modalities from a
single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile
face, frontal face, right profile face, and right ear, present in the facial video clips, we train
supervised denosing autoencoders to automatically extract robust and non-redundant features.
The automatically learned features are then used to train modality specific sparse classifiers to
perform the multimodal recognition. Experiments conducted on the constrained facial video
dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a
99.17% and 97.14% rank-1 recognition rates, respectively. The multimodal recognition
accuracy demonstrates the superiority and robustness of the proposed approach irrespective of
the illumination, non-planar movement, and pose variations present in the video clips.
MULTIMODAL BIOMETRICS RECOGNITION FROM FACIAL VIDEO VIA DEEP LEARNINGcsandit
Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics.
In this paper, we present a novel multimodal recognition system that trains a Deep Learning Network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denosing autoencoders to automatically extract robust and non-redundant features.The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Experiments conducted on the constrained facial video
dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% rank-1 recognition rates, respectively. The multimodal recognition
accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips.
InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images
Abstract. We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view, where this capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene. To achieve this, we propose a novel self-supervised view generation training paradigm, where we sample and rendering virtual camera trajectories, including cyclic ones, allowing our model to learn stable view generation from a collection of single views. At test time, despite never seeing a video during training, our approach can take a single image and generate long camera trajectories comprised of hundreds of new views with realistic and diverse contents. We compare our approach with recent state-of-the-art supervised view generation methods that require posed multi-view videos and demonstrate superior performance and synthesis quality.
ieee projects download, base paper for ieee projects, ieee projects list, ieee projects titles, ieee projects for cse, ieee projects on networking,ieee projects 2012, ieee projects 2013, final year project, computer science final year projects, final year projects for information technology, ieee final year projects, final year students projects, students projects in java, students projects download, students projects in java with source code, students projects architecture, free ieee papers
5 ijaems sept-2015-9-video feature extraction based on modified lle using ada...INFOGAIN PUBLICATION
Locally linear embedding (LLE) is an unsupervised learning algorithm which computes the low dimensional, neighborhood preserving embeddings of high dimensional data. LLE attempts to discover non-linear structure in high dimensional data by exploiting the local symmetries of linear reconstructions. In this paper, video feature extraction is done using modified LLE alongwith adaptive nearest neighbor approach to find the nearest neighbor and the connected components. The proposed feature extraction method is applied to a video. The video feature description gives a new tool for analysis of video.
Ieee projects 2012 2013 - Digital Image ProcessingK Sundaresh Ka
ieee projects download, base paper for ieee projects, ieee projects list, ieee projects titles, ieee projects for cse, ieee projects on networking,ieee projects 2012, ieee projects 2013, final year project, computer science final year projects, final year projects for information technology, ieee final year projects, final year students projects, students projects in java, students projects download, students projects in java with source code, students projects architecture, free ieee papers
Measuring the Effects of Rational 7th and 8th Order Distortion Model in the R...IOSRJVSP
One of the biggest and important issues in the video watermarking is the distortion and attacks. The attacks and distortion affect the digital watermarking. Watermarking is an embedding process. With the help of watermarking, we insert the data into the digital objects. There are few methods are available for authentication of data, securing/protection of data. The watermarking technique also provides the data security, copyright protection and authentication of the data. Watermarking provides a comfortable life to authorized users. In my proposed work, we are working on distorted watermarked video. The distortion is present on the watermarked video is rational 7 th and 8 th order distortion model. In this paper, firstly we are embedding the watermark information into the original video and after that work on the distortion model which may be come into the watermarked video. We are also calculating the PSNR (Peak signal to noise ratio), SSIM (Structural similarity index measure), Correlation, BER (Bit Error Rate) and MSE (Mean Square Error) parameters for distorted watermarked video. We are showing the relationship between correlation and SSIM with BER, MSE and PSNR.
Coronary heart disease is a disease with the highest mortality rates in the world. This makes the development of the diagnostic system as a very interesting topic in the field of biomedical informatics, aiming to detect whether a heart is normal or not. In the literature there are diagnostic system models by combining dimension reduction and data mining techniques. Unfortunately, there are no review papers that discuss and analyze the themes to date. This study reviews articles within the period 2009-2016, with a focus on dimension reduction methods and data mining techniques, validated using a dataset of UCI repository. Methods of dimension reduction use feature selection and feature extraction techniques, while data mining techniques include classification, prediction, clustering, and association rules.
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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
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• Remote control system for accessing CCR and allied system over serial or TCP.
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Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Robust face recognition from multi view videos
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Robust Face Recognition from Multi-View
Videos
2. Abstract
Multiview face recognition has become an active research area in the last few years. In this
paper, we present an approach for video-based face recognition in camera networks. Our goal is
to handle pose variations by exploiting the redundancy in the multiview video data. However,
unlike traditional approaches that explicitly estimate the pose of the face, we propose a novel
feature for robust face recognition in the presence of diffuse lighting and pose variations. The
proposed feature is developed using the spherical harmonic representation of the face texture-mapped
onto a sphere; the texture map itself is generated by back-projecting the multiview video
data. Video plays an important role in this scenario. First, it provides an automatic and efficient
way for feature extraction. Second, the data redundancy renders the recognition algorithm more
robust. We measure the similarity between feature sets from different videos using the
reproducing kernel Hilbert space. We demonstrate that the proposed approach outperforms
traditional algorithms on a multiview video database.
Existing Method
3. In this paper, we present an approach for video-based face recognition in camera networks.
Our goal is to handle pose variations by exploiting the redundancy in the multiview video data.
Proposed Method
The proposed feature is developed using the spherical harmonic representation of the face
texture-mapped onto a sphere; the texture map itself is generated by back-projecting the
multiview video data. Video plays an important role in this scenario.
