3D object retrieval and segmentation: various approaches including 2D poisson histograms and 3D electrical charge distributions.

Nowadays 3D models play an important role in many applications: viz. games, cultural heritage, medical imaging etc. Due to the fast growth in the number of available 3D models, understanding, searching and retrieving such models have become interesting fields within computer vision. In order to search and retrieve 3D models, we present two different approaches: one is based on solving the Poisson Equation over 2D silhouettes of the models. This method uses 60 different silhouettes, which are automatically extracted from different viewangles. Solving the Poisson equation for each silhouette assigns a number to each pixel as its signature. Accumulating these signatures generates a final histogram-based descriptor for each silhouette, which we call a SilPH (Silhouette Poisson Histogram). For the second approach, we propose two new robust shape descriptors based on the distribution of charge density on the surface of a 3D model. The Finite Element Method is used to calculate the charge density on each triangular face of each model as a local feature. Then we utilize the Bag-of-Features and concentric sphere frameworks to perform global matching using these local features. In addition to examining the retrieval accuracy of the descriptors in comparison to the state-of-the-art approaches, the retrieval speeds as well as robustness to noise and deformation on different datasets are investigated. On the other hand, to understand new complex models, we have also utilized distribution of electrical charge for proposing a system to decompose models into meaningful parts. Our robust, efficient and fully-automatic segmentation approach is able to specify the segments attached to the main part of a model as well as locating the boundary parts of the segments. The segmentation ability of the proposed system is examined on the standard datasets and its timing and accuracy are compared with the existing state-of-the-art approaches

COVID-19 Diagnosis System using SimpNet Deep Model

بعد تفشي COVID-19 ، تحول على الفور من وباء إلى جائحة. تم استخدام الصور الإشعاعية للأشعة المقطعية والأشعة السينية على نطاق واسع للكشف عن مرض COVID-19 من خلال مراقبة عتامة الأشعة تحت الحمراء في الرئتين. اكتسب التعلم العميق شعبية في تشخيص العديد من الأمراض الصحية بما في ذلك COVID-19 وانتشاره السريع يتطلب اعتماد التعلم العميق في تحديد حالات COVID-19. في هذه الدراسة ، تم اقتراح نموذج التعلم العميق ، بناءً على بعض المبادئ ، للكشف التلقائي عن COVID-19 من صور الأشعة السينية. تم اعتماد بنية SimpNet في دراستنا و تدريبها باستخدام صور الأشعة السينية. تم تقييم النموذج على كل من التصنيف الثنائي (COVID-19 و No-findings) ومهام التصنيف متعددة الفئات (COVID-19 ، No-findings ،  و  التهاب رئوي). حقق نموذجنا قيمة دقة بلغت 98.4٪ للثنائي و 93.8٪ للتصنيف متعدد الفئات. عدد معلمات نموذجنا هو 11 مليون معلمة وهي أقل من بعض الطرق الحديثة مع تحقيق نتائج أعلى.After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings, and Pneumonia) classification tasks. Our model has achieved an accuracy value of 98.4% for binary and 93.8% for the multi-class classification. The number of parameters of our model is 11 Million parameters which are fewer than some state-of-the-art methods with achieving higher results

Kurdish Sign Language Recognition System

Deaf people all around the world face difficulty to communicate with the others. Hence, they use their own language to communicate with each other. On the other hand, it is difficult for deaf people to get used to technological services such as websites, television, mobile applications, and so on. This project aims to design a prototype system for deaf people to help them to communicate with other people and computers without relying on human interpreters. The proposed system is for letter-based Kurdish Sign Language (KuSL) 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 KuSL have been implemented and tested, two of them are well-known methods that have been implemented and tested by other researchers, and the third one has been introduced in this paper for the 1st time. The new algorithm is named Gridbased gesture descriptor. It turned out to be the best method for the recognition of Kurdish hand signs. Furthermore, the result of the algorithm was 67% accuracy of detecting hand gestures. Finally, the other well-known algorithms are named scale invariant feature transform and speeded-up robust features, and they responded with 42% of accuracy