Interest Students X Class Of Audio Video Skills Competency SMK N 3 Yogyakarta In Participating Line Follower Robotic Extracurricular

The purpose of final project this thesis is: (1) determine the effect of intrinsic factors to the interest students X class of audio video skills competency SMK N 3 Yogyakarta in participating line follower robotic extracurricular, (2) determine the effect of extrinsic factors to the interest students X class of audio video skill competency SMK N 3 Yogyakarta in participating line follower robotic extracurricular, (3) determine the effect of intrinsic and extrinsic factors to the interest students X class of audio video skill competency SMK N 3 Yogyakarta in participating line follower robotic extracurricular. This study used survey research methods, variables in this study is interest students X class of audio video skill competency SMK N 3 Yogyakarta in participating line follower robotic extracurricular divided into intrinsic factors (X1), extrinsic factors (X2), and extracurricular (Y). The population in this study is X class of audio video skills competency force 2012/2013 which amounts to 68 students. Data collection techniques in this study is questionnaire likert scale measuring. The number of questions in the questionnaire is 60 items, and items that fall is 14 items, so that valid and reliable items totaling is 56 items. Techniques of analysis in this study is a multiple regression analysis. The results of this study are : (1) interest students X class of audio video skills competency SMK N 3 Yogyakarta in participating line follower robotic extracurricular that is equal to 0,372 then the value consulted with Rtable intrinsic factors bring low influence to interest students in participating line follower robotic extracurricular and the effective contribution of intrinsic factor is 23,41%, (2) interest students X class of audio video skills competency SMK N 3 Yogyakarta in participating line follower robotic extracurricular that is equal to 0,429 then the value consulted with Rtable extrinsic factors bring moderate influence to interest students in participating line follower robotic extracurricular and the effective contribution of extrinsic factor is 30,88%, (3) there is a significant effect between intrinsic and extrinsic factors on interest students X class of audio video skills competency SMK N 3 Yogyakarta in participating line follower robotic extracurricular, this is indicated with correlation coefficient is 0,737, coefficient of determination is 0,543, effective contribution of intrinsic factor is 23,41%, and effective contribution of extrinsic factor is 30,88%. Keyword : line follower robotics, audio video skill competenc

Live User-guided Intrinsic Video For Static Scenes

We present a novel real-time approach for user-guided intrinsic decomposition of static scenes captured by an RGB-D sensor. In the first step, we acquire a three-dimensional representation of the scene using a dense volumetric reconstruction framework. The obtained reconstruction serves as a proxy to densely fuse reflectance estimates and to store user-provided constraints in three-dimensional space. User constraints, in the form of constant shading and reflectance strokes, can be placed directly on the real-world geometry using an intuitive touch-based interaction metaphor, or using interactive mouse strokes. Fusing the decomposition results and constraints in three-dimensional space allows for robust propagation of this information to novel views by re-projection.We leverage this information to improve on the decomposition quality of existing intrinsic video decomposition techniques by further constraining the ill-posed decomposition problem. In addition to improved decomposition quality, we show a variety of live augmented reality applications such as recoloring of objects, relighting of scenes and editing of material appearance

RELATIONSHIP BETWEEN LEARNING MOTIVATION INTRINSIC AND EXTRINSIC STUDENTS WITH LEARNING STUDENT ACHIEVEMENT X CLASS VIDEO AUDIO ENGINEERING SKILLS COMPETENCY SMK MA'ARIF 1 WATES

This study aimed to determine whether there is a relationship between intrinsic and extrinsic motivation students individually or jointly with a class X student achievement competency skills SMK Techniques Audio Video Ma'arif 1 Wates. The research method used was ex post facto, a population of 36 students from all class X competency skills Techniques Audio Video SMK Ma'arif 1 Wates. The data were taken using a questionnaire for the independent variable to the dependent variable was the method of documentation. Content validity is obtained through expert judgment and analysis of grain Product Moment Correlation calculated for all the independent variables. Reliability of the instrument intrinsic and extrinsic motivation of students with Cronbach alpha coefficients. The data analysis technique used is descriptive statistics, correlation and regression at a significance level of 5% The results of class X competency skills Techniques Audio Video SMK Ma'arif 1 Wates found a positive and significant relationship between students' intrinsic motivation to student achievement indicated by the coefficient r of 0.446, and the significance of the relationship X1.1 variables with the variable Y can be seen the value of amounted to 12 558 t count> t table with (n-2) for 2042, and a large increase in intrinsic motivation by 1 point the achievement increased 0.454. The existence of a positive and significant relationship between extrinsic motivation of students with student achievement indicated by the coefficient r of 0.389 and significance of the relationship X1.2 variables with the variable Y can be seen t value of 12.841> t table with (n-2) of 2.042, and large increase in extrinsic motivation every 1 point increase the academic achievement 0.404. The existence of a positive and significant relationship between intrinsic and extrinsic motivation of students with student achievement indicated by the coefficient r of 0.461, and the significance of the relationship X1.2 variables with the variable Y can be seen t value of 10.837> t table with (n-2) for 2.024, and a large increase in intrinsic and extrinsic motivation every 1 point increase the academic achievement 0.539. Keywords: Motivation, Learning, Achievemen

Prediction error image coding using a modified stochastic vector quantization scheme

The objective of this paper is to provide an efficient and yet simple method to encode the prediction error image of video sequences, based on a stochastic vector quantization (SVQ) approach that has been modified to cope with the intrinsic decorrelated nature of the prediction error image of video signals. In the SVQ scheme, the codewords are generated by stochastic techniques instead of being generated by a training set representative of the expected input image as is normal use in VQ. The performance of the scheme is shown for the particular case of segmentation-based video coding although the technique can be also applied to motion-compensated hybrid coding schemes.Peer ReviewedPostprint (published version

Resting state connectivity between medial temporal lobe regions and intrinsic cortical networks predicts performance in a path integration task

Humans differ in their individual navigational performance, in part because successful navigation relies on several diverse abilities. One such navigational capability is path integration, the updating of position and orientation during movement, typically in a sparse, landmark-free environment. This study examined the relationship between path integration abilities and functional connectivity to several canonical intrinsic brain networks. Intrinsic networks within the brain reflect past inputs and communication as well as structural architecture. Individual differences in intrinsic connectivity have been observed for common networks, suggesting that these networks can inform our understanding of individual spatial abilities. Here, we examined individual differences in intrinsic connectivity using resting state magnetic resonance imaging (rsMRI). We tested path integration ability using a loop closure task, in which participants viewed a single video of movement in a circle trajectory in a sparse environment, and then indicated whether the video ended in the same location in which it started. To examine intrinsic brain networks, participants underwent a resting state scan. We found that better performance in the loop task was associated with increased connectivity during rest between the central executive network (CEN) and posterior hippocampus, parahippocampal cortex (PHC) and entorhinal cortex. We also found that connectivity between PHC and the default mode network (DMN) during rest was associated with better loop closure performance. The results indicate that interactions between medial temporal lobe (MTL) regions and intrinsic networks that involve prefrontal cortex (PFC) are important for path integration and navigation.This work was supported by the Office of Naval Research (ONR MURI N00014-10-1-0936 and MURI N00014-16-1-2832). fMRI scanning was completed at the Athinoula A. Martinos Center for Biomedical Imaging (Charlestown, MA, USA), which receives support from the National Center for Research Resources (NCRR P41RR14075). (ONR MURI N00014-10-1-0936 - Office of Naval Research; MURI N00014-16-1-2832 - Office of Naval Research; NCRR P41RR14075 - National Center for Research Resources)Published versio

DramaQA: Character-Centered Video Story Understanding with Hierarchical QA

Despite recent progress on computer vision and natural language processing, developing video understanding intelligence is still hard to achieve due to the intrinsic difficulty of story in video. Moreover, there is not a theoretical metric for evaluating the degree of video understanding. In this paper, we propose a novel video question answering (Video QA) task, DramaQA, for a comprehensive understanding of the video story. The DramaQA focused on two perspectives: 1) hierarchical QAs as an evaluation metric based on the cognitive developmental stages of human intelligence. 2) character-centered video annotations to model local coherence of the story. Our dataset is built upon the TV drama "Another Miss Oh" and it contains 16,191 QA pairs from 23,928 various length video clips, with each QA pair belonging to one of four difficulty levels. We provide 217,308 annotated images with rich character-centered annotations, including visual bounding boxes, behaviors, and emotions of main characters, and coreference resolved scripts. Additionally, we provide analyses of the dataset as well as Dual Matching Multistream model which effectively learns character-centered representations of video to answer questions about the video. We are planning to release our dataset and model publicly for research purposes and expect that our work will provide a new perspective on video story understanding research.Comment: 21 pages, 10 figures, submitted to ECCV 202

Real-World Repetition Estimation by Div, Grad and Curl

We consider the problem of estimating repetition in video, such as performing push-ups, cutting a melon or playing violin. Existing work shows good results under the assumption of static and stationary periodicity. As realistic video is rarely perfectly static and stationary, the often preferred Fourier-based measurements is inapt. Instead, we adopt the wavelet transform to better handle non-static and non-stationary video dynamics. From the flow field and its differentials, we derive three fundamental motion types and three motion continuities of intrinsic periodicity in 3D. On top of this, the 2D perception of 3D periodicity considers two extreme viewpoints. What follows are 18 fundamental cases of recurrent perception in 2D. In practice, to deal with the variety of repetitive appearance, our theory implies measuring time-varying flow and its differentials (gradient, divergence and curl) over segmented foreground motion. For experiments, we introduce the new QUVA Repetition dataset, reflecting reality by including non-static and non-stationary videos. On the task of counting repetitions in video, we obtain favorable results compared to a deep learning alternative