Image Enlargement Based on Proportional Salient Feature

This paper proposes an image enlargement methodthat produces proportional salient content of imagemagnification. To obtain the proportional salient image content:first, we enlarge the source image to the high size of the targetimage using uniform enlarging. Second, we slice the image intosections from top to bottom following the minimum energy anddetect the salient feature of the image. Third, we enlarge the sliceof the image region that does not containthe salient feature of theimage to the full size of the target image. The proposed methodhas been tested in several images, such as akiyo, butterfly,cameraman, canoe, dolphin, and parrot. The experimentalresults show that the proposed method results in a proportionalcontent for image enlargement in the different ratios comparedwith the comparison method

Face Recognition Using Holistic Features and Within Class Scatter-Based PCA

The Principle Component Analysis (PCA) and itsvariations are the most popular approach for features clustering,which is mostly implemented for face recognition. The optimumprojection matrix of the PCA is typically obtained by eigenanalysisof global covariance matrix. However, the projection datausing the PCA are lack of discriminatory power. This problem iscaused by removing the null space of data scatter that containsmuch discriminant information. To solve this problem, we presentalternative strategy to the PCA called alternative PCA, whichobtains the optimum projection matrix from within class scatterinstead of global covariance matrix. This algorithm not onlyprovides better features clustering than that of common PCA(CPCA) but also can overcome the retraining problem of theCPCA. In this paper, this algorithm is applied for face recognitionwith the holistic features of face image, which has compact sizeand powerful energy compactness as dimensional reduction ofthe raw face image. From the experimental results, the proposedmethod provides better performance for both recognition rateand accuracy parameters than those of CPCA and its variationswhen the tests were carried out using data from several databasessuch as ITS-LAB., INDIA, ORL, and FERET

Modified Convolutional Neural Network Architecture for Batik Motif Image Classification

Batik is one of the cultural heritages of Indonesia that have many different motifs in each region as well as in its usage. However, the Indonesians sometimes not knowing the batik motif that they’re wearing every day, and sometimes they have a batik image without knowing batik information contained in their batik image. With the growing number of images of batik and batik motifs, a classification method that can classify various motifs of batik is required to automatically detect the motif from the batik image. Image processing using the Deep Learning especially for image classification is widely used recently because it has good results. The most popular method in deep learning is Convolutional Neural Network (CNN) which has been proved robust in natural images. This study offers a batik motif image classification system using CNN method with new network architecture developed by combining GoogLeNet and Residual Networks named IncRes. IncRes merges the Inception Module with Residual Network structure. With the 70.84% accuracy, the system can be used to classify the batik image motif accurately

Traffic Light Signal Parameters Optimization using Modification of Multielement Genetic Algorithm

A strategy to optimize traffic light signal parameters is presented for solving traffic congestion problem using modification of the Multielement Genetic Algorithm (MEGA). The aim of this method is to improve the lack of vehicle throughput (FF ) of the works called as traffic light signal parameters optimization using the MEGA and Particle Swarm Optimization (PSO). In this case, the modification of MEGA is done by adding Hash-Table for saving some best populations for accelerating the recombination process of MEGA which is shortly called as H-MEGA. The experimental results show that the H-MEGA based optimization provides better performance than MEGA and PSO based methods (improving the FF of both MEGA and PSO based optimization methods by about 10.01% (from 82,63% to 92.64%) and 6.88% (from 85.76% to 92.64%), respectively). In addition, the H-MEGA improve significantly the real FF of Ooe Toroku road network of Kumamoto City, Japan about 21.62%

Sensing Control Parameters of Flute from Microphone Sound Based on Machine Learning from Robotic Performer

When learning to play a musical instrument, it is important to improve the quality of self-practice. Many systems have been developed to assist practice. Some practice assistance systems use special sensors (pressure, flow, and motion sensors) to acquire the control parameters of the musical instrument, and provide specific guidance. However, it is difficult to acquire the control parameters of wind instruments (e.g., saxophone or flute) such as flow and angle between the player and the musical instrument, since it is not possible to place sensors into the mouth. In this paper, we propose a sensorless control parameter estimation system based on the recorded sound of a wind instrument using only machine learning. In the machine learning framework, many training samples that have both sound and correct labels are required. Therefore, we generated training samples using a robotic performer. This has two advantages: (1) it is easy to obtain many training samples with exhaustive control parameters, and (2) we can use the correct labels as the given control parameters of the robot. In addition to the samples generated by the robot, some human performance data were also used for training to construct an estimation model that enhanced the feature differences between robot and human performance. Finally, a flute control parameter estimation system was developed, and its estimation accuracy for eight novice flute players was evaluated using the Spearman’s rank correlation coefficient. The experimental results showed that the proposed system was able to estimate human control parameters with high accuracy