Segmentation of Moving Object with Uncovered Background, Temporary Poses and GMOB

AbstractVideo has to be segmented into objects for content-based processing. A number of video object segmentation algorithms have been proposed such as semiautomatic and automatic. Semiautomatic methods adds burden to users and also not suitable for some applications. Automatic segmentation systems are still a challenge, although they are required by many applications. The proposed work aims at contributing to identify the gaps that are present in the current segmentation system and also to give the possible solutions to overcome those gaps so that the accurate and efficient video segmentation system can be developed. The proposed system aims to resolve the issue of uncovered background, Temporary poses and Global motion of background

Background Subtraction Berbasis Algorithma K-Means Klastering untuk Deteksi Objek Bergerak

Background subtraction menjadi bagian yang sangat penting dari deteksi objek bergerak di video. Problem utamanya adalahketepatan dalam proses menentukan objek bergerak. Makalah ini mengusulkan metode klastering dengan k-means padabackground subtraction dalam mendeteksi objek bergerak. Untuk mengevaluasi performa dari k-means digunakan MeanSquare Error (MSE) dan Peak Signal Noise Ratio (PSNR). Hasil eksperimen menunjukkan bahwa k-means mampu untukmelakukan klasifikasi piksel latar depan atau latar belakang dalam mendeteksi objek.Keyword : k-means, background subtraction, objek bergera

Cast Shadow Removal with GMM for Surface Reflectance Component

Cast shadow on the background is generated by an object moving between a light source and the background. The position and illumination of the source always change with time, while the background is stable. Therefore, features connected with light source always change with time, such as geometry and color. In this paper, we present a shadow removal method by homomorphic model to extract surface reflectance component, which is only connected with background of the scene and is robust to change of light source. We assume that reflectance component fits Gaussian distribution, and then use GMM to model it. Experimental results show that, except dealing with shadow, our method is not sensitive to the change of illumination

Mechanical behaviour and compatibility analysis of thermoplastic polyurethane polycaprolactone-based new fused deposition modelling filament composite

The investigation focuses on the development of a Thermoplastic Poly Urethane(TPU), and Polycaprolactone (PCL) based new flexible polymer composite fused deposition modelling (FDM) filament feedstock. In this research study, the mechanical behaviour of the new polymer composite material is fabricated by injection moulding and tested. The mechanical behaviour of injection moulded TPU/PCL composite samples with various blend formulations was investigated experimentally using several mechanical testings. Several combinations of the blend formulations for the new TPU/PCL flexible feedstock was done by volume percentage (vol. %). Based on the experimental data obtained from the mechanical testing done which is the hardness and tensile of the new polymer composite of TPU/PCL has a high potential to be fabricated as the flexible filament feedstock. The blend ratio of 80:20 which as a medium hardness and a higher tensile strength proved to be a highly potential choice to be fabricated as the flexible filament feedstock. The research resulted in the success of extrusion of 1.75 mm of flexible filament for all three ratios of composites and testing it in FDM machine