5th International Conference on Signal and Image Processing (SIGL 2018)

February 24~25, 2018, Dubai, UAE

Accepted Papers

  • Generation Of Automatic Visual Timescaped Panoramas Of Landmarks
    Heider Ali 1 and Anthony Whitehead2;
    1Carleton University, Systems and Computer Engineering Dept., Ottawa, ON, Canada. 2Carleton University, School of Information Technology, Ottawa, ON, Canada F-59650Villeneuve d'Ascq, France

    Providing a panoramic view of famous landmarks around the world offers artistic and historic value for historians, tourists, and researchers. Exploring the history of famous landmarks by presenting a comprehensive view of a temporal panorama merged with geographical and historical information presents a unique challenge of dealing with images that span a long period, from the 1800's up to present. This work presents a new concept of temporal panorama through a timeline display of aligned historic and modern images for many famous landmarks. Utilization of this panorama requires a collection of hundreds of thousands of landmark images from the Internet comprised of historic images and modern images of the digital age. These images have to be classified for subset selection to keep the more suitable images that chronologically document a landmarks history. Processing of historic images captured using older analog technology under various different capturing conditions represents a big challenge when they have to be used with modern digital images. Successful processing of historic images to prepare them for next steps of temporal panorama creation represents an active contribution in cultural heritage preservation through the fulfilment of one of UNESCO goals in preservation and displaying famous worldwide landmarks..

  • A Proposed Hsv-Based Pseudo-Coloring Scheme For Enhancing Medical Images
    Noura A. Semary
    Faculty of Computers and Information, Menoufia University, Egypt

    Medical imaging is one of the most attractive topics of image processing and understanding research fields due to the similarity between the captured body organs colors. Most medical images come in grayscale with low contrast gray values; which makes it a challenge to discriminate between the region of interest (ROI) and the background (BG) parts. Pseudo-coloring is one of the solutions to enhance the visual appeal of medical images, most literature works suggest RGB-base color palettes. In this paper, pseudo-coloring methods of different medical imaging works are investigated and a highly discriminative colorization method is proposed. The proposed colorization method employs HSV/HSI color models to generate the desired color scale. Experiments have been performed on different medical images and different assessment methods have been utilized. The results show that the proposed methodology could clearly discriminate between near grayscale organs especially in case of tumor existence. Comparisons with other literary works were performed and the results are promising.

  • Kurdish Sign Language Recognition System (KuSL)
    Abdulla Dlshad and Fattah Alizadeh
    University of Kurdistan-Hewler

    Deaf people all around the world face difficulty to communicate with the others. So, they use their own language to communicate with each other. The language is known as Sign Language. Each nation may have its own sign language such as American Sign Language (ASL), British Sign Language (BSL) and Arabic Sign Language. Hence, it is also difficult for deaf people to get used to technological services such as websites, television, mobile applications, and so on. This work aims to design a prototype system for deaf people to help them communicate with other people and computers without relying on human interpreters. The proposed system is for letter-based Kurdish Sign Language which has not been introduced before. The system would be a real time system that takes actions immediately after detecting hand gestures. Three algorithms for detecting Kurdish Sign Language have been implemented and tested, two of them are well-known methods such as SIFT and SURF that have been implemented by other researchers, and the third one has been introduced in this paper for the first time. The new algorithm is named Grid-based gesture descriptor. It turned out to be the best method for the recognition of Kurdish hand signs; the result of the proposed algorithm is 67% accuracy of detecting hand gestures, while, the other well-known algorithms responded both with 42% of accuracy