Blur Detection Methods for Digital Images-A SurveyEditor IJCATR
This paper described various blur detection methods along with proposed method. Digital photos are massively produced
while digital cameras are becoming popular; however, not every photo has good quality. Blur is one of the conventional image quality
degradation which is caused by various factors like limited contrast; inappropriate exposure time and improper device handling indeed,
blurry images make up a significant percentage of anyone's picture collections. Consequently, an efficient tool to detect blurry images
and label or separate them for automatic deletion in order to preserve storage capacity and the quality of image collections is needed.
There are various methods to detect the blur from the blurry images some of which requires transforms like DCT or Wavelet and some
doesn‟t require transform.
Analog signal processing approach for coarse and fine depth estimationsipij
Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the
market every day. Some of these have very severe Size, Weight and Power constraints whereas other
devices have to handle very high computational loads. Some require both these conditions to be met
simultaneously. Current imaging architectures and digital image processing solutions will not be able to
meet these ever increasing demands. There is a need to develop novel imaging architectures and image
processing solutions to address these requirements. In this work we propose analog signal processing as a
solution to this problem. The analog processor is not suggested as a replacement to a digital processor but
it will be used as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing two stereo
correspondence algorithms are implemented. We propose novel modifications to the algorithms and new
imaging architectures which, significantly reduces the computation time
Analog signal processing approach for coarse and fine depth estimationsipij
Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the
market every day. Some of these have very severe Size, Weight and Power constraints whereas other
devices have to handle very high computational loads. Some require both these conditions to be met
simultaneously. Current imaging architectures and digital image processing solutions will not be able to
meet these ever increasing demands. There is a need to develop novel imaging architectures and image
processing solutions to address these requirements. In this work we propose analog signal processing as a
solution to this problem. The analog processor is not suggested as a replacement to a digital processor but
it will be used as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing two stereo
correspondence algorithms are implemented. We propose novel modifications to the algorithms and new
imaging architectures which, significantly reduces the computation time
PARALLEL GENERATION OF IMAGE LAYERS CONSTRUCTED BY EDGE DETECTION USING MESSA...ijcsit
Edge detection is one of the most fundamental algorithms in digital image processing. Many algorithms have been implemented to construct image layers extracted from the original image based on selecting threshold parameters. Changing theses parameters to get a high quality layer is time consuming. In this paper, we propose two parallel technique, NASHT1 and NASHT2, to generate multiple layers of an input
image automatically to enable the image tester to select the highest quality detected edges. In addition, the
effect of intensive I/O operations and the number of parallel running processes on the performance of the proposed techniques have also been studied.
Imaging and Image sensors is a field that is continuously evolving. There are new products
coming into the market every day. Some of these have very severe Size, Weight and Power
constraints whereas other devices have to handle very high computational loads. Some require
both these conditions to be met simultaneously. Current imaging architectures and digital image
processing solutions will not be able to meet these ever increasing demands. There is a need to
develop novel imaging architectures and image processing solutions to address these
requirements. In this work we propose analog signal processing as a solution to this problem.
The analog processor is not suggested as a replacement to a digital processor but it will be used
as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing the highly
computational Normalized Cross Correlation algorithm is implemented. We propose two novel
modifications to the algorithm and a new imaging architecture which, significantly reduces the
computation time.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/qualcomm/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-mangen
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Michael Mangen, Product Manager for Camera and Computer Vision at Qualcomm, presents the "High-resolution 3D Reconstruction on a Mobile Processor" tutorial at the May 2016 Embedded Vision Summit.
Computer vision has come a long way. Use cases that were previously not possible in mass-market devices are now more accessible thanks to advances in depth sensors and mobile processors. In this presentation, Mangen provides an overview of how we are able to implement high-resolution 3D reconstruction – a capability typically requiring cloud/server processing – on a mobile processor. This is an exciting example of how new sensor technology and advanced mobile processors are bringing computer vision capabilities to broader markets.
29 SETTEMBRE 2021 – Aula Magna – Corso Duca degli Abruzzi, 24 – Politecnico di Torino
Ricerca, trasferimento tecnologico e supporto alle aziende sui temi fondamentali dei Big Data, Intelligenza Artificiale, la robotica e la rivoluzione digitale
A Three-Dimensional Representation method for Noisy Point Clouds based on Gro...Sergio Orts-Escolano
Slides used for the thesis defense of the PhD candidate Sergio Orts-Escolano.
The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time.This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problems and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.
Service description in the nfv revolution trends, challenges and a way forwardieeepondy
Service description in the nfv revolution trends, challenges and a way forward
+91-9994232214,7806844441, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2016-2017
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Email: ieeeprojectchennai@gmail.com
More Related Content
Similar to The unknown spatial quality of dense point clouds derived from stereo images
Blur Detection Methods for Digital Images-A SurveyEditor IJCATR
This paper described various blur detection methods along with proposed method. Digital photos are massively produced
while digital cameras are becoming popular; however, not every photo has good quality. Blur is one of the conventional image quality
degradation which is caused by various factors like limited contrast; inappropriate exposure time and improper device handling indeed,
blurry images make up a significant percentage of anyone's picture collections. Consequently, an efficient tool to detect blurry images
and label or separate them for automatic deletion in order to preserve storage capacity and the quality of image collections is needed.
There are various methods to detect the blur from the blurry images some of which requires transforms like DCT or Wavelet and some
doesn‟t require transform.
Analog signal processing approach for coarse and fine depth estimationsipij
Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the
market every day. Some of these have very severe Size, Weight and Power constraints whereas other
devices have to handle very high computational loads. Some require both these conditions to be met
simultaneously. Current imaging architectures and digital image processing solutions will not be able to
meet these ever increasing demands. There is a need to develop novel imaging architectures and image
processing solutions to address these requirements. In this work we propose analog signal processing as a
solution to this problem. The analog processor is not suggested as a replacement to a digital processor but
it will be used as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing two stereo
correspondence algorithms are implemented. We propose novel modifications to the algorithms and new
imaging architectures which, significantly reduces the computation time
Analog signal processing approach for coarse and fine depth estimationsipij
Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the
market every day. Some of these have very severe Size, Weight and Power constraints whereas other
devices have to handle very high computational loads. Some require both these conditions to be met
simultaneously. Current imaging architectures and digital image processing solutions will not be able to
meet these ever increasing demands. There is a need to develop novel imaging architectures and image
processing solutions to address these requirements. In this work we propose analog signal processing as a
solution to this problem. The analog processor is not suggested as a replacement to a digital processor but
it will be used as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing two stereo
correspondence algorithms are implemented. We propose novel modifications to the algorithms and new
imaging architectures which, significantly reduces the computation time
PARALLEL GENERATION OF IMAGE LAYERS CONSTRUCTED BY EDGE DETECTION USING MESSA...ijcsit
Edge detection is one of the most fundamental algorithms in digital image processing. Many algorithms have been implemented to construct image layers extracted from the original image based on selecting threshold parameters. Changing theses parameters to get a high quality layer is time consuming. In this paper, we propose two parallel technique, NASHT1 and NASHT2, to generate multiple layers of an input
image automatically to enable the image tester to select the highest quality detected edges. In addition, the
effect of intensive I/O operations and the number of parallel running processes on the performance of the proposed techniques have also been studied.
Imaging and Image sensors is a field that is continuously evolving. There are new products
coming into the market every day. Some of these have very severe Size, Weight and Power
constraints whereas other devices have to handle very high computational loads. Some require
both these conditions to be met simultaneously. Current imaging architectures and digital image
processing solutions will not be able to meet these ever increasing demands. There is a need to
develop novel imaging architectures and image processing solutions to address these
requirements. In this work we propose analog signal processing as a solution to this problem.
The analog processor is not suggested as a replacement to a digital processor but it will be used
as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing the highly
computational Normalized Cross Correlation algorithm is implemented. We propose two novel
modifications to the algorithm and a new imaging architecture which, significantly reduces the
computation time.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/qualcomm/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-mangen
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Michael Mangen, Product Manager for Camera and Computer Vision at Qualcomm, presents the "High-resolution 3D Reconstruction on a Mobile Processor" tutorial at the May 2016 Embedded Vision Summit.
Computer vision has come a long way. Use cases that were previously not possible in mass-market devices are now more accessible thanks to advances in depth sensors and mobile processors. In this presentation, Mangen provides an overview of how we are able to implement high-resolution 3D reconstruction – a capability typically requiring cloud/server processing – on a mobile processor. This is an exciting example of how new sensor technology and advanced mobile processors are bringing computer vision capabilities to broader markets.
29 SETTEMBRE 2021 – Aula Magna – Corso Duca degli Abruzzi, 24 – Politecnico di Torino
Ricerca, trasferimento tecnologico e supporto alle aziende sui temi fondamentali dei Big Data, Intelligenza Artificiale, la robotica e la rivoluzione digitale
A Three-Dimensional Representation method for Noisy Point Clouds based on Gro...Sergio Orts-Escolano
Slides used for the thesis defense of the PhD candidate Sergio Orts-Escolano.
The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time.This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problems and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.
Service description in the nfv revolution trends, challenges and a way forwardieeepondy
Service description in the nfv revolution trends, challenges and a way forward
+91-9994232214,7806844441, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2016-2017
-----------------------------------
Contact:+91-9994232214,+91-7806844441
Email: ieeeprojectchennai@gmail.com
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IEEE PROJECTS 2016-2017
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The unknown spatial quality of dense point clouds derived from stereo images
1. The Unknown Spatial Quality of Dense Point Clouds Derived From
Stereo Images
Abstract:
Is it possible to use stereo images to generate point clouds and to compute
rigorous uncertainty maps? Currently, neither modern commercial
photogrammetric software nor state-of-the-art algorithms are able to
provide a spatial distribution of uncertainty. In this letter, we explain why
this is the case, despite a high demand from the user community. Many
applications would indeed benefit from the availability of error bars on
each point, as uncertainties on derived models and quantities could be
accurately predicted. For instance, change detection could be performed
rigorously since the statistical significance of observed changes could be
computed. In this letter, we focus on dense stereo methods. We first
explain that it is not possible to derive reliable predictive uncertainties
mainly due to matching and modeling errors. Our research shows that
both intrinsic and practical limitations of the algorithms lead to
unpredictable artifacts. Then, we focus on the use of empirical errors,
showing that, despite the redundancy brought by multiview stereo, there is
a fundamental limitation due to the unknown density of independent
measurements. We think that these problems will represent a big challenge
2. for the future, as these limitations cannot be addressed by algorithmic
design, computational power, or imaging sensor technology.
Existing System:
Some users of 3-D data wish to have estimates of the accuracy for each
point, or each elevation in a gridded digital surface model (DSM). Usually,
one assumes a spatially uniform error model and performs an assessment
using reference data sets or ground control points (GCPs). It would allow
them to put an error bar on any inferred physical quantity, which is of
crucial importance in flood hazard modeling, or change detection, for
instance. For flood applications, we need to know the probability of a given
elevation to exceed a given threshold.
Proposed System:
One of the main issues with stereo algorithms is the presence of gross
errors (a few pixels at least), which are not simply fluctuations due to noise
but the result of matching failures. A good illustration is provided,
outlining some dramatic differences between various state-of-the-art
algorithms.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
3. • Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• RAM : 256 Mb.
Software Requirements:
• Operating system : - Windows XP.
• Front End : - JSP
• Back End : - SQL Server
Software Requirements:
• Operating system : - Windows XP.
• Front End : - .Net
• Back End : - SQL Server
