Automatic Human Joint Detection Using Microsoft Kinect

Automatic human joint detection has been used in many application nowadays. In this paper, we propose an approach to detect full body human joint method using depth and color image. The proposed solution is divided into 3 stage, which is image preprocess stage, distance transform stage, and anthropometric constraint analysis stage. The output of our solution is a stickman model with the same pose as in the given input image. Our implementation is done by using a Microsoft Kinect RGB and depth camera with 480x640 image resolution. The performance of this solution is demonstrated on several human posture

Review of Local Descriptor in RGB-D Object Recognition

The emergence of an RGB-D (Red-Green-Blue-Depth) sensor which is capable of providing depth and RGB images gives hope to the computer vision community. Moreover, the use of local features began to increase over the last few years and has shown impressive results, especially in the field of object recognition. This article attempts to provide a survey of the recent technical achievements in this area of research. We review the use of local descriptors as the feature representation which is extracted from RGB-D images, in instances and category-level object recognition. We also highlight the involvement of depth images and how they can be combined with RGB images in constructing a local descriptor. Three different approaches are used in involving depth images into compact feature representation, that is classical approach using distribution based, kernel-trick, and feature learning. In this article, we show that the involvement of depth data successfully improves the accuracy of object recognition

Automatic Data Interpretation in Accounting Information Systems Based On Ontology

Financial transactions recorded into accounting journals based on the evidence of the transaction. There are several kinds of evidence of transactions, such as invoices, receipts, notes, memos and others.  Invoice as one of transaction receipt has many forms that it contains a variety of information.  The information contained in the invoice identified based on rules.  Identifiable information includes: invoice date, supplier name, invoice number, product ID, product name, quantity of product and total price.  In this paper, we proposed accounting ontology and Indonesian accounting dictionary. It can be used in intelligence accounting systems. Accounting ontology provides an overview of account mapping within an organization. The accounting dictionary helps in determining the account names used in accounting journals.  Accounting journal created automatically based on accounting evidence identification.  We have done a simulation of the 160 Indonesian accounting evidences, with the result of precision 86.67%, recall 92.86% and f-measure 89.67%

Optimization of Salient Object Segmentation by using the influence of color in Digital Image

Human attention is more likely to be interested indifferent objects or striking in image processing called salientobject. Existing approaches worked well in finding the salientobject in this image, but they have not been able to accuratelydetect where objects should stand out due to the influence of lightintensity, there are various object results of salient object detectionin which area is still cut off or do not appear because they do notinclude salient area. We offer solutions to fix these problems byoptimizing salient object detection prioritizing object area aftersalient area, through checking comparison of the color regionlocated around the area of the salient. This Optimization of theapplication is able to improve to 83% from 100 salient object whichhas this problem, and able to produce more natural Saliency Cut

DEWA: A Multiaspect Approach for Multiple Face Detection in Complex Scene Digital Image

A new approach for detecting faces in a digital image with unconstrained background has been developed. The approach is composed of three phases: segmentation phase, filtering phase and localization phase. In the segmentation phase, we utilized both training and non-training methods, which are implemented in user selectable color space. In the filtering phase, Minkowski addition-based objects removal has been used for image cleaning. In the last phase, an image processing method and a data mining method are employed for grouping and localizing objects, combined with geometric-based image analysis. Several experiments have been conducted using our special face database that consists of simple objects and complex objects. The experiment results demonstrated that the detection accuracy is around 90% and the detection speed is less than 1 second in average

Data Partition and Communication On Parallel Heuristik Model Based on Clonal Selection Algorithm

Researchers conducted experiments on parallel algorithms, which are inspired by the clonal selection, called Clonal Selection Algorithm (CSA). This algorithm is a population-based heuristic solution. Course-grained parallelism model applied to improve the execution time. Inter-process communication overhead is addressed by adjusting the communication frequencies and size of data communicated. In this research, conducted experiments on six parallel computing models represent all possible partitions and communications. Experiments conducted using data of NP-Problem, Traveling Salesman Problem (TSP). The algorithm is implemented using the model of message passing libraries using MPJExpress. Experiments conducted in a cluster computation environment. Result shows the best parallelism model is achieved by partitioning the initial population data at the beginning of communication and the end of generation. Communication frequency can be up to per 1% of the population size generated. Using four dataset from TSPLib, this reseache shows effect of the communication frequency that increased the best cost, from 44.16% to 87.01% for berlin52.tsp; from 9.61% to 53.43%  for kroA100.tsp, and from 12.22% to 17.18% for tsp225.tsp. With eight processors, using communication frequency will be reduced the execution time e.g 93.07%, 91.60%, 89.60%, 74.74% for burma14.tsp, berlin52.tsp, kroA100.tsp, and tsp225.tsp respectively. We conclude that frequency of communication greatly affects the execution time, and also the best cost. It improved execution time and best cost

PENELITIAN AWAL : OTOMATISASI INTERPRETASI DATA AKUNTANSI BERBASIS NATURAL LANGUAGE PROCESSING

Proses pencatatan transaksi akuntansi ke dalam sistem akuntansi terkadang terhambat karena lambatnya pemahaman terhadap transaksi yang terjadi.  Keterlambatan ini terjadi karena masih dominannya peran manusia dalam sistem akuntansi, padahal manusia memiliki keterbatasan.  Pemahaman terhadap transaksi akuntansi berkaitan dengan proses klasifikasi terhadap transaksi yang terjadi.  Bila terjadi kesalahan dalam proses klasifikasi maka akan mengakibatkan kesalahan dalam penyajian laporan keuangan.  Penelitian ini bertujuan untuk mengembangkan otomatisasi interpretasi terhadap data akuntansi, baik dalam hal pengenalan transaksi akuntansi, ekstraksi dan kemudian melakukan pengelompokkan terhadap transaksi akuntansi berdasarkan Natural Language Processing.  Langkah utama yang dilakukan dalam pencapaian tersebut melalui analisa dasar terhadap transaksi akuntansi berdasarkan interpretasi bahasa alami.  Simulasi yang dilakukan terhadap beberapa transaksi akuntansi menunjukkan sistem yang dibangun berdasarkan Natural Language Processing dapat meningkatkan kecepatan dan ketepatan dalam interpretasi data akuntansi. Kata kunci: otomatisasi, akuntansi, natural language processin

K Means Clustering and Meanshift Analysis for Grouping the Data of Coal Term in Puslitbang tekMIRA

Indonesian government agencies under the Ministry of Energy and Mineral Resources have problems in classifying data dictionary of coal. This research conduct grouping coal dictionary using K-Means and MeanShift algorithm. K-means algorithm is used to get cluster value on character and word criteria. The last iteration of Euclidian distance calculation data on k-means combine with Meanshift algorithm. The meanshift calculates centroid by selecting different bandwidths. The result of grouping using k-means and meanshift algorithm shows different centroid to find optimum bandwidth value. The data dictionary of this research has sorted in alphabetically