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Abstract Archaeozoology or zooarchaeology is the scientific field that aims to rebuild natural and cultural relations between human and animals. To reach that objective, archaeozoologists study faunal remains, such as bones. In this regard, a 3D representation is put into place. This would allow a better understanding of the initial spatial positions of the animals in the pits, and would make it possible to examine in a more rigorous manner the explanatory hypotheses by manipulating the 3D view of the pit and bones. This manipulation invokes the use of computer vision techniques, including 3D object recognition methods. In this work we present a novel approach to recognize 3D bones in occluded and cluttered scenes inspiring from a stat of the art method called spin images. We combine this method with saliency concept to improve the the robustness to occlusions and clutters significantly and also to decrease the complexity of the algorithm. [ABSTRACT FROM AUTHOR]

Human body anatomy becomes an important topic in Biology subject that must be understood since junior high school. Learning materials are mostly available in form of book and anatomy mannequin (puppet), but it is still insufficient enough to help students in understanding human body anatomy. Augmented Reality (AR) is a technology that combines a real thing into virtual environment interactively. This research purpose is to develop an AR application for human body anatomy learning to be more interesting and easier for student to understand. This application enables student to learn human body anatomy with 3D object interaction while previously using textbook and mannequin. Research method in for this study is by using quantitative method that collects data and then develops the prototype to prove the impact. Application development method is done by using waterfall method that includes planning (collect data and analysis), design (user interface and diagram), implementation, and testing. Research result is AR application for human body anatomy learning that contains 3D object, organ explanation and position that can be accessible on web. [ABSTRACT FROM AUTHOR]

Dewata Nawa Sanga in Hinduism has an important role as the foundation of the religious concept implemented by Hindus in Bali, Indonesia. It has the meaning of nine deities or manifestations of Ida Sang Hyang Widhi Wasa (God Almighty) that guard or control the nine wind direction. Dewata Nawa Sanga in terms of education in Bali has been learned by the elementary school students. However, in social life the concept of Dewata Nawa Sanga itself has not been attached and understood by all Balinese Hinduism yet. Based on the problem in social life, there is an idea to develop Markerless Augmented Reality Utilizing Gyroscope to Demonstrate the Position of Dewata Nawa Sanga. This application is designed to know, recognize, and understand the characteristics of Dewata Nawa Sanga using gyroscope. This sensor serves to fulfill the object of the deities in the coordinates to be determined. Furthermore, it serves to provide information about Dewata Nawa Sanga along with the innovative and informative 3D animation. The result of usefulness questionnaire of this application percentage value average is 88.4%. It can be concluded that AR Dewata Nawa Sanga is very useful and helpful for learners in exploring the Dewata Nawa Sanga position in a real-world environment. The result of usability and satisfaction questionnaire of this application percentage value average is 84.8%. It shows that the application is very useful for the participants to learn Dewata Nawa Sanga and very satisfied to use. [ABSTRACT FROM AUTHOR]

Context Cardiovascular disease remains the leading cause of death affecting the adult population. It involves disorders of myocardial perfusion which can lead to the disturbance in myocardial function and/or myocardial infarction. Finding an efficient method for the deformation analysis in the left ventricle of the heart (LV) is one of the major concerns in cardiac imaging. Objective In literature, most models provide global representation of the LV surface, that cannot specify the affected area. The present work is aimed at quantifying the local deformations in the LV by developing accurate computational approaches in order to refine the diagnosis and specify the ischemic territory. Method The proposed approach advocates the application of three different methods: (1) the numerical quantification of regional deformation based on SPHARM shape descriptors, (2) volume evolution, and (3) the Hotelling metric computation by use of the relative curvature at the vertices of the triangulation. In order to progress to a regional analysis, we carried out a division into 17 regions according to the AHA standard of LV object. It is worth noting that our two first methods are based on the application of the cloud closing process by generating new points on the cutting planes. Results The different approaches were tested on 44 patients. The obtained results show its effectiveness and validity by giving the degree and the location of the pathological deformation in the LV. We compared the obtained results with diagnostic offered by qualified nuclear medicine physicians. Also, we compared the obtained results of each methods in order to define the more robust approach. [ABSTRACT FROM AUTHOR]

In order to realize the image matching of three-dimensional object under different perspective, a method which is robust to affine transformation is proposed. Based on the fully affine invariant image comparison method, named Affine-SIFT (ASIFT), mASIFT (multi ASIFT) was put forward, in which a geometric transformation constraint method named mRANSAC(multi-plane RANSAC) was used to extract the right matching points from the coarse matches. It is corroborated that, compared to the singular homography constraint, the multi- plane constraint excluding mismatching method is more proper to the multi-plane characteristics of 3D objects. It could increase the number of matching pairs up to 5~9 times of ASIFT under the same condition. Further, the mutual error threshold of every homography would lower 3 pixels or 2 pixels. As a result, the proposed method could remove mismatches more efficiently, calculate out more accurate transformations and obtain more exact matching. [ABSTRACT FROM AUTHOR]