3D Vehicle Extraction and Tracking from Multiple Viewpoints for Traffic Monitoring by using Probability Fusion Map

This paper presents a novel solution of vehicle occlusion and 3D measurement for traffic monitoring by data fusion from multiple stationary cameras. Comparing with single camera based conventional methods in traffic monitoring, our approach fuses video data from different viewpoints into a common probability fusion map (PFM) and extracts targets. The proposed PFM concept is efficient to handle and fuse data in order to estimate the probability of vehicle appearance, which is verified to be more reliable than single camera solution by real outdoor experiments. An AMF based shadowing modeling algorithm is also proposed in this paper in order to remove shadows on the road area and extract the proper vehicle regions

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

Fast pornographic image recognition using compact holistic features and multi-layer neural network

The paper presents an alternative fast pornographic image recognition using compact holistic features and multi-layer neural network (MNN). The compact holistic features of pornographic images, which are invariant features against pose and scale, is extracted by shape and frequency analysis on pornographic images under skin region of interests (ROIs). The main objective of this work is to design pornographic recognition scheme which not only can improve performances of existing methods (i.e., methods based on skin probability, scale invariant feature transform, eigenporn, and Multilayer-Perceptron and Neuro-Fuzzy (MP-NF)) but also can works fast for recognition. The experimental outcome display that our proposed system can improve 0.3% of accuracy and reduce 6.60% the false negative rate (FNR) of the best existing method (skin probability and eigenporn on YCbCr, SEP), respectively. Additionally, our proposed method also provides almost similar robust performances to the MP-NF on large size dataset. However, our proposed method needs short recognition time by about 0.021 seconds per image for both tested datasets