Implementasi Kinect Body Tracking pada Sistem Pemindai Biometrik

Biometrik merupakan salah satu identifikasi karakteristik fisiologis seseorang. Tinggi dan berat badan dapat digunakan untuk pengukuran biometrik. Kedua variabel tersebut dapat dikumulasikan untuk mengidentifikasi BMI (Body Mass Index) seseorang. Citra objek yang telah berhasil dicapture oleh Kinect terdiri dari depth image serta titik-titik joint. Depth image dari sebuah citra akan diekstraksi sehingga dihasilkan citra berupa gambar latar belakang (background) dengan warna hitam putih dan titiktitik joint objek akan terlihat dan bisa diproses untuk perhitungan tinggi. Depth sensor akan memetakan objek yang ditangkapnya berdasarkan jarak yang telah ditentukan. Gambar tersebut akan dibandingkan dengan data hasil training sebelumnya. Sensor Kinect diposisikan di depan objek yang diatur dengan jarak 180 cm. Untuk memastikan jarak sensor Kinect tidak berubah, maka sensor ultrasonik diletakkan di depan sensor Kinect agar sistem dapat menyesuaikan posisinya tidak kurang atau lebih dari jarak yang telah ditentukan. Pada pengukuran tersebut diperoleh data tinggi yang dapat diinputkan bersamaan dengan berat badan objek sehingga dapat dihitung BMI seseorang. Setelah dilakukan 8 kali pengujian pada objek dengan tinggi antara 154 – 180 cm diperoleh rata-rata error sebesar 1,35% dari tinggi yang sebenarnya

A Fisher-Rao Metric for curves using the information in edges

Two curves which are close together in an image are indistinguishable given a measurement, in that there is no compelling reason to associate the measurement with one curve rather than the other. This observation is made quantitative using the parametric version of the Fisher-Rao metric. A probability density function for a measurement conditional on a curve is constructed. The distance between two curves is then defined to be the Fisher-Rao distance between the two conditional pdfs. A tractable approximation to the Fisher-Rao metric is obtained for the case in which the measurements are compound in that they consist of a point x and an angle α which specifies the direction of an edge at x. If the curves are circles or straight lines, then the approximating metric is generalized to take account of inlying and outlying measurements. An estimate is made of the number of measurements required for the accurate location of a circle in the presence of outliers. A Bayesian algorithm for circle detection is defined. The prior density for the algorithm is obtained from the Fisher-Rao metric. The algorithm is tested on images from the CASIA Iris Interval database

The Mixed-Lineage Kinase DLK Is a Key Regulator of 3T3-L1 Adipocyte Differentiation

The mixed-lineage kinase (MLK) family member DLK has been proposed to serve as a regulator of differentiation in various cell types; however, its role in adipogenesis has not been investigated. In this study, we used the 3T3-L1 preadipocyte cell line as a model to examine the function of DLK in adipocyte differentiation.Immunoblot analyses and kinase assays performed on 3T3-L1 cells showed that the expression and activity of DLK substantially increase as differentiation occurs. Interestingly, DLK appears crucial for differentiation since its depletion by RNA interference impairs lipid accumulation as well as expression of the master regulators of adipogenesis C/EBPalpha and PPARgamma2 at both the mRNA and protein levels. In contrast, neither the expression nor the DNA binding activity of C/EBPbeta, an activator for C/EBPalpha and PPARgamma, is affected by DLK loss.Taken together, these results suggest that DLK is important for expression of mature adipocyte markers and that its action most likely takes place via regulation of C/EBPbeta transcriptional activity and/or initiation of C/EBPalpha and PPARgamma2 gene transcription