Human Body Poses Recognition Using Neural Networks with Class Based Data Augmentation

Infotehnoloogia ja selle pidev arenemine on võimaldanud arvutitel näha ja õppida. Mida meie näeme oma silmadega, on võimalik jagada piksliteks ja anda pikslid sisendiks arvutile. Pikslite väärtuste põhjal saab arvuti näha ja õppida tundma ära erinevaid objekte, mida neile tundma õpetatakse. Arvutinägemisel ja õppimisel on palju võimalikke rakendusi. Antud lõputöös pakume välja raamistiku, mis suudab automaatselt tuvastada inimese keha poose traditsioonilise odava kaameraga tehtud pildilt. Meie lähenemisviis ühendab arvutinägemise ja neurovõrgud, et tuvastada inimene pildilt. Antud protsess algab silueti eraldamisega pildilt ning jätkub neurovõrgu kasutamisega. Neurovõrk tuvastab keha poosi eraldatud silueti põhjal. Suutmaks siluetti seostada keha poosiga, treeniti neurovõrku eelnevalt töödeldud piltidega siluettidest. Saadud tulemuste põhjal pakub antud raamistik paljulubavaid tulemusi aktsepteeritava täpsusega.Information technologies and continuous development of information technologies have made it possible for computers to see and learn. What we see with our eyes, can be divided into pixels and fed to a computer, giving the computer the ability to see and learn based on the pixel values. Based on the input values computers can learn to recognize different objects depending on the examples taught to them. There are many possible applications for computers to see and learn in order to solve new tasks. In this thesis, we propose a framework, capable of automatically recognizing human body poses from a single image, obtained with a traditional low-cost camera. Our approach combines computer vision with neural networks to detect a human from an image. This process starts by extracting the silhouette from an image and then using a neural network to recognize body poses based on the extracted silhouettes. In order to match detected silhouettes with body poses, the neural network was trained with an already classified augmented dataset of preprocessed images depicting silhouettes. According to our results, we show that the proposed method provides promising results with acceptable accuracy

Novel transcripts reveal a complex structure of the human TRKA gene and imply the presence of multiple protein isoforms

Publisher Copyright: © 2015 Luberg et al.Background: Tropomyosin-related kinase A (TRKA) is a nerve growth factor (NGF) receptor that belongs to the tyrosine kinase receptor family. It is critical for the correct development of many types of neurons including pain-mediating sensory neurons and also controls proliferation, differentiation and survival of many neuronal and non-neuronal cells. TRKA (also known as NTRK1) gene is a target of alternative splicing which can result in several different protein isoforms. Presently, three human isoforms (TRKAI, TRKAII and TRKAIII) and two rat isoforms (TRKA L0 and TRKA L1) have been described. Results: We show here that human TRKA gene is overlapped by two genes and spans 67 kb-almost three times the size that has been previously described. Numerous transcription initiation sites from eight different 5' exons and a sophisticated splicing pattern among exons encoding the extracellular part of TRKA receptor indicate that there might be a large variety of alternative protein isoforms. TrkA genes in rat and mouse appear to be considerably shorter, are not overlapped by other genes and display more straightforward splicing patterns. We describe the expression profile of alternatively spliced TRKA transcripts in different tissues of human, rat and mouse, as well as analyze putative endogenous TRKA protein isoforms in human SH-SY5Y and rat PC12 cells. We also characterize a selection of novel putative protein isoforms by portraying their phosphorylation, glycosylation and intracellular localization patterns. Our findings show that an isoform comprising mainly of TRKA kinase domain is capable of entering the nucleus. Conclusions: Results obtained in this study refer to the existence of a multitude of TRKA mRNA and protein isoforms, with some putative proteins possessing very distinct properties.publishersversionPeer reviewe