Pembangkitan Cahaya Virtual Dinamis Pada Augmented Reality Menggunakan Canny Edge Detection, Contour Finding Dan Unity Light Renderer

ABSTRACT In Augmented Reality, the object lighting factor becomes a matter of concern. Lighting of virtual objects that have been manually generated is considered less realistic. Real time dynamic light generation system is needed to make an Augmented Reality application more realistic. With the generation of dynamic virtual light, AR objects lighting can be generated at the position and intensity of light colors that match the light source from the real environment around the AR object. In this study a light generation system was made with reference to the color intensity of light and the direction of light in the real environment. Retrieval of the light source color is done by retrieving the color value of a pixel with the highest intensity of brightness.Retrieval of the position of the light source is done by determining the axis of the pixel on the marker image which has the highest brightness level. From the results of 1st experiment through 4th experiment, the percentage of position equality is 92.10% from the actual position. From the results of the color experiment, it was found that the percentage of the light color of the results compared with the color of the source light was 66.66%.  Low percentage of color similarity caused by light reflection on high gray value on marker (> 180), and other light sources that affect the light output generated by the Unity3D game engine in the simulation

A Lvq-Based Temporal Tracking for Semi-Automatic Video Object Segmentation

This paper presents a Learning Vector Quantization (LVQ)-based temporal tracking method for semi-automatic video object segmentation. A semantic video object is initialized using user assistance in a reference frame to give initial classification of video object and its background regions. The LVQ training approximates video object and background classification and use them for automatic segmentation of the video object on the following frames thus performing temporal tracking. For LVQ training input, we sampling each pixel of a video frame as a 5-dimensional vector combining 2-dimensional pixel position (X,Y) and 3-dimensional HSV color space. This paper also demonstrates experiments using some MPEG-4 standard test video sequences to evaluate the accuracy of the proposed method

Practical Uses of A Semi-automatic Video Object Extraction System

Object-based technology is important for computer vision applications including gesture understanding, image recognition, augmented reality, etc. However, extracting the shape information of semantic objects from video sequences is a very difficult task, since this information is not explicitly provided within the video data. Therefore, an application for exttracting the semantic video object is indispensable and important for many advanced applications. An algorithm for semi-automatic video object extraction system has been developed. The performance measures of video object extraction system; including evaluation using ground truth and error metric is shown, followed by some practical uses of our video object extraction system. The principle at the basis of semi-automatic object extraction technique is the interaction of the user during some stages of the segmentation process, whereby the semantic information is provided directly by the user. After the user provides the initial segmentation of the semantic video objects, a tracking mechanism follows its temporal transformation in the subsequent frames, thus propagating the semantic information. Since the tracking tends to introduce boundary errors, the semantic information can be refreshed by the user at certain key frame locations in the video sequence. The tracking mechanism can also operate in forward or backward direction of the video sequence. The performance analysis of the results is described using single and multiple key frames; Mean Error and “Last_Error”, and also forward and backward extraction. To achieve best performance, results from forward and backward extraction can be merged

Keyframe Selection of Frame Similarity to Generate Scene Segmentation Based on Point Operation

Video segmentation has been done by grouping similar frames according to the threshold. Two-frame similarity calculations have been performed based on several operations on the frame: point operation, spatial operation, geometric operation and arithmatic operation. In this research, similarity calculations have been applied using point operation: frame difference, gamma correction and peak signal to noise ratio. Three-point operation has been performed in accordance with the intensity and pixel frame values. Frame differences have been operated based on the pixel value level. Gamma correction has analyzed pixel values and lighting values. The peak signal to noise ratio (PSNR) has been related to the difference value (noise) between the original frame and the next frame. If the distance difference between the two frames was smaller then the two frames were more similar. If two frames had a higher gamma correction factor, then the correction factor would have an increasingly similar effect on the two frames. If the value of PSNR was greater then the comparison of two frames would be more similar. The combination of the three point operation methods would be able to determine several similar frames incorporated in the same segmen

Interior Batik Gallery Using Normal Mapping For Virtual Reality

We present an 3D Modelling  object for  batik Gallery  in virtual reality with textured material photo,  Lighting effect  and primitive mess with Normal Mapping Metthod. Normal Mapping is one of many method  to  give  depht  Impression texture and detail for object without  many   polycount, so we can get  efficiency of the computer work without loss of detail desired.  in this research  we try to explain  several techniques in making batik Gallery design textures with  modeling 3D,  The methods include stage Normal Mapping, Environment maps, bump maps, and shadow maps. The next proscess we can use Directional Light and Point Light for each object to get the  impression of depth and real dimension for each  texture.  The result of this research is  to improve  the modern batik consept and patterns with immersive  3D Room  in Virtual realit

Penerapan materi ilmu pengetahuan alam pada serious game sosialisasi mitigasi bencana berbasis model teori aktivitas dan taksonomi bloom

Penelitian ini merupakan kombinasi antara materi hiburan dan pendidikan untuk meningkatkan pemahaman tentang bencana alam, terutama bencana vulkanik. Desain yang diusulkan menggabungkan model Teori Aktivitas dan Taksonomi Bloom. Metode ini dapat menghemat biaya dan waktu. Titik fokus dari penelitian ini adalah materi Ilmu Pengetahuan Alam berdasarkan kurikulum 2013. Penelitian ini adalah langkah pertama untuk mengintegrasikan unsur-unsur pendidikan, hiburan, dan teknologi sebagai media pembelajaran untuk pengurangan risiko bencana. Kemampuan siswa dieksplorasi dengan menerapkan tiga aspek pembelajaran. Hasil tes menunjukkan bahwa kemampuan siswa meningkat 14,2% setelah bermain sepuluh kali dan meningkat menjadi 29,48% setelah siswa bermain 25 kali, dibandingkan dengan skor pretest.  This research is a combination of entertainment and education material to improve an understanding of natural disasters, especially volcanic eruptions. The proposed design combines Bloom's Taxonomy and Activity Theory models. The method reduces cost and time. The focal point of the research is the natural sciences material based on the 2013 curriculum. This research is the first step to integrate the elements of education, entertainment, and technology as a learning media for disaster risk reduction — students' abilities explored by applying three aspects of learning. The test results show that students' abilities are increased by 14.2% after play for ten times and increased to 29.48% after playing for 25 times, compared to the pretest scores

Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches

The natural process on plant growth system has a complex system and it has could be developed on characteristic studied using intelligent approaches conducting with artificial life system. The approaches on examining the natural process on soybean (Glycine Max L.Merr) plant growth have been analyzed and synthesized in these research through modeling using Artificial Neural Network (ANN) and Lindenmayer System (L-System) methods. Research aimed to design and to visualize plant growth modeling on the soybean varieties which these could help for studying botany of plant based on fertilizer compositions on plant growth with Nitrogen (N), Phosphor (P) and Potassium (K). The soybean plant growth has been analyzed based on the treatments of plant fertilizer compositions in the experimental research to develop plant growth modeling. By using N, P, K fertilizer compositions, its capable result on the highest production 2.074 tons/hectares. Using these models, the simulation on artificial life for describing identification and visualization on the characteristic of soybean plant growth could be demonstrated and applied

Determining the Standard Value of Acquisition Distortion of Fingerprint Images Based on Image Quality

This paper describes a novel procedure for determining the standard value of acquisition distortion of fingerprint images. Knowledge about the standard value of acquisition distortion of the fingerprint images is very important in determining the method for improving image quality. In this paper, we propose a model to determine the standard value that can be used in classifying the type of distortion of the fingerprint images based on the image quality. The results show that the standard value of acquisition distortion of the fingerprint images based on the image quality have values of the local clarity scores (LCS) follows: dry parameter values are in the range of 0.0127-0.0149, neutral parameter values are less than 0.0127, and oily parameter values are greater than 0.0149. Meanwhile, the global clarity scores (GCS) are as follows: dry parameter values are in the range of 0.0117-0.0120, neutral parameter values are less than 0.0117, and oily parameter values are greater than 0.0120; and ridge-valley thickness ratios (RVTR) are as follows: dry parameter values are less than 7.75E-05, neutral parameter values are 7.75E-05-5.94E-05, and oily parameter values are greater than 5.94E-05