Posted in Compressed Video, Computer Vision, dcapi, DCAPI blog, I-Frame, PhD, Research, Semantic Video Annotation, Video Analysis, video information retrieval, Video Matching, Video search engine, viva

Dr Saddam Bekhet, successfully passed viva

Congratulations to Saddam who successfully passed his PhD VIVA on 23rd May 2016.

Examiners have commended Saddam’s work and contributions. They also emphasized how well written the thesis is.

A well-deserved achievement Saddam, well done.

And all the best for your future career.

Posted in Compressed Video, Computer Vision, dcapi, DCAPI blog, Research, Video Analysis, Video Matching

PGRs Showcase Event 2015: Well done DCAPI members

Members of DCAPI have presented and showed their research work in the third Annual Showcase Event for the School of Computer Science, University of Lincoln. (6th and 7th May). The event accompanied by wide attendances from  several companies (Google UK, Siemens, QinetiQ, mass, Artsgraphica, Heritage Lincolnshire, …).

Saddam showing a sample demo of his work.
Saddam showing a sample demo of his work on video matching and retrieval.

 

Saddam also won the “Best Presentation” prize for his video similarity detection presentation.

Best presentation for Saddam

 

Saddam next to his poster.
Saddam next to his poster.

 

Hussein Presenting his work.
Hussein Presenting his work about computer aided classification of liver diseases.
Head of DCAPI group Dr.Amr Ahmed with Saddam Bekhet and Hussein Alahmar at the showcase event
Head of DCAPI group Dr.Amr Ahmed with group members, Saddam Bekhet and Hussein Alahmar at the showcase event
Posted in Computer Vision, Conference, I-Frame, International, PhD, Research, Video Analysis, Video Matching

Conference paper presented in “ ICPR 2014”

Saddam Bekhet presented his accepted paper in  ICPR2014 , Stockholm, Sweden
The paper title is “Compact Signature-based Compressed Video Matching Using Dominant Colour Profiles (DCP)

Abstract— This paper presents a novel technique for efficient and generic matching of compressed video shots, through compact signatures extracted directly without decompression. The compact signature is based on the Dominant Color Profile (DCP); a sequence of dominant colors extracted and arranged as a sequence of spikes in analogy to the human retinal representation of a scene. The proposed signature represents a given video shot with ~490 integer values, facilitating for real-time processing to retrieve a maximum set of matching videos. The technique is able to work directly on MPEG compressed videos, without full decompression, as it utilizes the DC-image as a base for extracting color features. The DC-image has a highly reduced size, while retaining most of visual aspects, and provides high performance compared to the full I-frame. The experiments and results on various standard datasets show the promising performance, both the accuracy and the efficient computation complexity, of the proposed technique.

Congratulations and well done for Saddam and Amr.

DSC_0402  DSC_0409   DSC_0393   DSC_0283

 

Posted in Compressed Video, Computer Vision, Conference, Research, Video Analysis, Video Matching

New paper accepted in ICPR 2014 – “Compact Signature-based Compressed Video Matching Using Dominant Colour Profiles (DCP)”

The paper “Compact Signature-based Compressed Video Matching Using Dominant Colour Profiles (DCP)” has been accepted in the ICPR 2014 conference http://www.icpr2014.org/, and will be presented in August 2014, Stockholm, Sweden.

Abstract— This paper presents a novel technique for efficient and generic matching of compressed video shots, through compact signatures extracted directly without decompression. The compact signature is based on the Dominant Color Profile (DCP); a sequence of dominant colors extracted and arranged as a sequence of spikes in analogy to the human retinal representation of a scene. The proposed signature represents a given video shot with ~490 integer values, facilitating for real-time processing to retrieve a maximum set of matching videos. The technique is able to work directly on MPEG compressed videos, without full decompression, as it utilizes the DC-image as a base for extracting color features. The DC-image has a highly reduced size, while retaining most of visual aspects, and provides high performance compared to the full I-frame. The experiments and results on various standard datasets show the promising performance, both the accuracy and the efficient computation complexity, of the proposed technique.

Congratulations and well done for Saddam.

Posted in commonsense knowledgebases, Compressed Video, Computer Vision, dcapi, DCAPI blog, DHS, Elderly, EPSRC network, I-Frame, iNET Scooter, International, Knowledge Engineering, Language, NDA program, PhD, Research, research project, semantic gap, Semantic Video Annotation, Surgeon training, SUS-IT, Uncategorized, Video Analysis, video information retrieval, Video Matching, Video search engine, Virtual Reality, Virtual training
Interested in joining us as a “Research Fellow”?
Get in touch by emailing aahmed@lincoln.ac.uk

Welcome to the DCAPI research group.

Our multi-disciplinary research is mainly focused on the analysis and mining of digital contents (Visual; images and videos, and textual). This includes Computer Vision, Image/Video Processing and analysis, Semantic Analysis, annotation, Action recognition, Image/Video Matching and similarity (Copy & Near-Duplicate detection), and many others.

We welcome any discussion and potential collaboration. Please get in touch with us (contacts on the right side-bar).

Posted in commonsense knowledgebases, Compressed Video, Computer Vision, dcapi, DCAPI blog, I-Frame, Knowledge Engineering, PhD, Research, research project, semantic gap, Semantic Video Annotation, Video Analysis, video information retrieval, Video Matching, Video search engine

Featured Research topics

Posted in Computer Vision, Computer Visions, Conference, dcapi, DCAPI blog, PhD, Research, Uncategorized, Video Analysis, video information retrieval, Video search engine

Conference paper presented in “ World Congress on Engineering 2013”

Saddam Bekhet presented his accepted paper in “World Congress on Engineering 2013“.
The paper title is “Video Matching Using DC-image and Local Features ”

Abstract:

This paper presents a suggested framework for video matching based on local features extracted from the DC-image of MPEG compressed videos, without decompression. The relevant arguments and supporting evidences are discussed for developing video similarity techniques that works directly on compressed videos, without decompression, and especially utilising small size images. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and the corresponding computation complexity. The second experiment compares between using local features and global features in video matching, especially in the compressed domain and with the small size images. The results confirmed that the use of DC-image, despite its highly reduced size, is promising as it produces at least similar (if not better) matching precision, compared to the full I-frame. Also, using SIFT, as a local feature, outperforms precision of most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the real-time margin. There are also various optimisations that can be done to improve this computation complexity.

Well done and congratulations to Saddam Bekhet .
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Posted in Computer Vision, Computer Visions, Conference, dcapi, DCAPI blog, Video Analysis, video information retrieval

New Conference paper Accepted to the “ World Congress on Engineering”: Video Matching Using DC-image and Local Features

New Conference  paper Accepted to the “ World Congress on Engineering”

New Conference paper accepted for publishing in  “World Congress on Engineering 2013“.

The paper title is “Video Matching Using DC-image and Local Features ”

Abstract:

This paper presents a suggested framework for video matching based on local features extracted from the DC-image of MPEG compressed videos, without decompression. The relevant arguments and supporting evidences are discussed for developing video similarity techniques that works directly on compressed videos, without decompression, and especially utilising small size images. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and the corresponding computation complexity. The second experiment compares between using local features and global features in video matching, especially in the compressed domain and with the small size images. The results confirmed that the use of DC-image, despite its highly reduced size, is promising as it produces at least similar (if not better) matching precision, compared to the full I-frame. Also, using SIFT, as a local feature, outperforms precision of most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the real-time margin. There are also various optimisations that can be done to improve this computation complexity.

Well done and congratulations to Saddam Bekhet .