The School of Computer Science is organizing the “Computer Science Week” [29th Oct – 2nd Nov], in collaboration with the Careers Services, Incubation Centre, Students’ Computer Science Society.
A Range of activities, talks and field trip (to AirAsia HQ) are scheduled.
Have a nice week everyone…. (Thanks for the organizers for such a huge effort and interesting program)
Please see poster & program attached for more details.
Baâzaoui, A., Barhoumi, W., Ahmed, A. and Zagrouba, E. (2017). Modeling Clinician Medical-knowledge in Terms of Med-level Features for Semantic Content-based Mammogram Retrieval. Expert Systems with Applications. [Accepted for publication].
Based on the Journal Citation Reports 2016 (released in June 2017), the “Expert Systems with Applications” journal has an Impact Factor of 3.928
Explore, get in touch, collaborate, join PhDs, RAs,…etc.
You are very welcome to explore the pages/posts, see what Amr and his groups are doing, and even better, get in touch for potential collaboration and/or if we can be of any help. Potential PhDs and RAs, you are welcome to get in touch to discuss your proposals.
Summary: “Object and Action Recognition Assisted by Computational Linguistics”.
The aim of this project is to investigate how computer vision methods such as object and
action recognition may be assisted by computational linguistic models, such as WordNet.
The main challenge of object and action recognition is the scalability of methods from
dealing with a dozen of categories (e.g. PASCAL VOC) to thousands of concepts (e.g.
ImageNet ILSVRC). This project is expected to contribute to the application of automated
visual content annotation and more widely to bridging the semantic gap between
computational approaches of vision and language.
Videos, especially the compressed ones, became a major part of our daily life. With the amount of videos growing exponentially, Scientists are being pushed to develop robust tools that could efficiently index and retrieve videos in a way similar to human perception of similarity.
*Based on http://www.youtube.com/yt/press/statistics.html
PROBLEM
§Manual annotation is a hard work and annotations are not always available for utilization.
§We need more smarter tagging process for videos.
§With the increasing of compressed videos, more efficient techniques are required to work directly on compressed files, without need for decompression.
AIM
Our aim is to build a framework that will operate on compressed videos (utilizing the DC-images sequence),
CONCLUSION
•DC-IMAGE is suitable for cheaper computations and could be used as basic building block for real-time processing.
•Local features proved to be effective on DC-image, after our introduced modification.