http://cvssp-data.eps.surrey.ac.uk/Personal/AdrianHilton/Publications_files/survey_techrep.pdf

A survey of advances in vision-based human motion capture and analysis

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Flow The Psychology Of Optimal Experience

Quality evaluation of underwater images is a key goal of underwater video image retrieval and intelligent processing. To date, no metric has been proposed for underwater color image quality evaluation (UCIQE). The special absorption and scattering characteristics of the water medium do not allow direct application of natural color image quality metrics especially to different underwater environments. In this paper, subjective testing for underwater image quality has been organized. The statistical distribution of the underwater image pixels in the CIELab color space related to subjective evaluation indicates the sharpness and colorful factors correlate well with subjective image quality perception. Based on these, a new UCIQE metric, which is a linear combination of chroma, saturation, and contrast, is proposed to quantify the non-uniform color cast, blurring, and low-contrast that characterize underwater engineering and monitoring images. Experiments are conducted to illustrate the performance of the proposed UCIQE metric and its capability to measure the underwater image enhancement results. They show that the proposed metric has comparable performance to the leading natural color image quality metrics and the underwater grayscale image quality metrics available in the literature, and can predict with higher accuracy the relative amount of degradation with similar image content in underwater environments. Importantly, UCIQE is a simple and fast solution for real-time underwater video processing. The effectiveness of the presented measure is also demonstrated by subjective evaluation. The results show better correlation between the UCIQE and the subjective mean opinion score.

An Underwater Color Image Quality Evaluation Metric

Serious games are growing rapidly as a gaming industry as well as a field of academic research. There are many surveys in the field of digital serious games; however, most surveys are specific to a particular area such as education or health. So far, there has been little work done to survey digital serious games in general, which is the main goal of this paper. Hence, we discuss relevant work on serious games in different application areas including education, well-being, advertisement, cultural heritage, interpersonal communication, and health care. We also propose a taxonomy for digital serious games, and we suggest a classification of reviewed serious games applications from the literature against the defined taxonomy. Finally, the paper provides guidelines, drawn from the literature, for the design and development of successful serious games, as well as discussing research perspectives in this domain.

/pdf/an-overview-of-serious-games-2j2pkg83dx.pdf

An overview of serious games

Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

https://www.mdpi.com/1424-8220/20/11/3113/pdf

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Localization is the first step in osteoarthritis (OA) classification. Manual classification, however, is time-consuming, tedious, and expensive. The proposed system is designed as decision support system for medical doctors to classify the severity of knee OA. A method has been proposed here to localize a joint space area for OA and then classify it in 4 steps to classify OA into KL-Grade 0, KL-Grade 1, KL-Grade 2, KL-Grade 3 and KL-Grade 4, which are preprocessing, segmentation, feature extraction, and classification. In this proposed system, right and left knee detection was performed by employing the Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the template matching. The Gabor kernel, row sum graph and moment methods were used to localize the junction space area of knee. CLAHE is used for preprocessing step, i.e.to normalize the varied intensities. The segmentation process was conducted using the Gabor kernel, template matching, row sum graph and gray level center of mass method. Here GLCM (contrast, correlation, energy, and homogeinity) features were employed as training data. Overall, 50 data were evaluated for training and 258 data for testing. Experimental results showed the best performance by using gabor kernel with parameters α=8, θ=0, Ψ=[0 π/2], γ=0,8, N=4 and with number of iterations being 5000, momentum value 0.5 and α0=0.6 for the classification process. The run gave classification accuracy rate of 93.8% for KL-Grade 0, 70% for KL-Grade 1, 4% for KL-Grade 2, 10% for KL-Grade 3 and 88.9% for KL-Grade 4.

https://openbiomedicalengineeringjournal.com/contents/volumes/V7/TOBEJ-7-18/TOBEJ-7-18.pdf

Osteoarthritis Classification Using Self Organizing Map Based on Gabor Kernel and Contrast-Limited Adaptive Histogram Equalization

The need for water consumption not only for humans but also the other living things as natural supporting elements for continuity of life. Water consumption depends on the availability of water resources like rivers, lakes, and reservoirs. Certainly, water becomes limited natural resources most of them because of water pollutions. It is necessary to manage water quality to fulfil the sustainability of water functions as natural resources, we create an integrated system based on Internet of Things to measuring the water quality by developing environmental water management monitoring system using sensors. The use of raspberry pi as an embedded system will help in the manufacture of detecting sensors device and the use of remote communications technology can help the interaction of sending data between things. The result is the IoT water quality monitoring system can be operated as an automated water monitoring system for surface water and it’s real-time online.

Development of IoT for Automated Water Quality Monitoring System

 Abstract— Dental radiographs are essential in diagnosing the pathology of the jaw. However, similar radiographic appearance of jaw lesions causes difficulties in differentiating cyst from tumor. Therefore, we conducted a development of computer-aided classification system for cyst and tumor lesions in dental panoramic images. The proposed system consists of feature extraction based on texture using the first-order statistics texture (FO), Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM). In this work, there were thirty three features which were classified using Support Vector Machine (SVM) based classification. The result shows that differentiation of cyst from tumor lesions can achieve accuracy up to 87.18% and Area Under the Receiver Operating Characteristic (AUC) curve up to 0.9444. When using the number of features used as predictors, the highest accuracy obtained were 8462% using FO, 61.54% using GLCM, 76.92% using GLRLM, 84.62% using the combination of FO and GLCM, 87.18% using the combination of FO and GLRLM, 75.56% using the combination of GLCM and GLRLM, and 87.18% using the combination of FO, GLCM and GLRLM. The highest AUC value was 0.9361 using FO, using GLCM was 0.8667, using GLRLM was 0.8722, using the combination of FO and GLCM was 0.9278, using the combination of FO and GLRLM was 0.9444, using the combination of GLCM and GLRLM was 0.8417, and using the combination of FO, GLCM and GLRLM was 0.9278. Based on the AUC value, the level of accuracy of this prediction can be categorized as 'Excellent'.

/pdf/classifying-cyst-and-tumor-lesion-using-support-vector-171edlskdr.pdf

Classifying Cyst and Tumor Lesion Using Support Vector Machine Based on Dental Panoramic Images Texture Features

Success of scale-invariant feature transform (SIFT) image registration is limited when attempted on camera footage taken under water. This is, largely due to the poor image quality inherent to imaging in aquatic environments. In this research we aim to overcome this shortcoming using a new method of pre-processing of true-color imagery taken under water based on the Contrast Limited Adaptive Histogram image Equalization (CLAHE) algorithm. CLAHE assumes that the distribution function of the pixel intensity values of an underwater-recorded image is dominated by Rayleigh scattering, and that the noise can be removed as a function hereof. Results showed that after applying the CLAHE image enhancement method registration success of SIFT increased by 41% compared to reference method (a straightforward contrast stretching enhancement). From the ANOVA results follows that the null hypothesis can be rejected and concluded that there is a significant difference among SIFT without enhancement, SIFT with contrast stretching and SIFT with CLAHE-Rayleigh at 5% level of significance (ANOVA, F=23.41, df=2, pvalue=7.34e-09). The finding concludes that CLAHE-Rayleigh is better compared to contrast stretching. As a follow-up study, CLAHE-Rayleigh should be compared with other image enhancement techniques (or a combination of techniques ‘hybrids’) to assess their relative impact on the success of SIFT-based image matching of underwater photography.

/pdf/underwater-image-enhancement-using-adaptive-filtering-for-1aa26q5glg.pdf

Underwater image enhancement using adaptive filtering for enhanced sift-based image matching

Their technology brings the ease of information for the public. Through an application, a variety of information about the city can be accessed quickly by society and government through the Program is called Smart City. Smart City is already implemented in many cities in the world and proved able to resolve the problems as quickly as well. The information can also be utilized Government to create comfort, safety and order and a better life. The purpose of writing this article is to obtain an overview of the smart city concept and its implementation. To that end, there should be an implementation strategy smart city proper and in accordance with the conditions of the city. However, of course there are some challenges faced by the people of the city and need to be resolved so that the construction of smart city can proceed smoothly.

Mochamad Hariadi

Papers

Osteoarthritis Classification Using Self Organizing Map Based on Gabor Kernel and Contrast-Limited Adaptive Histogram Equalization

Development of IoT for Automated Water Quality Monitoring System

Classifying Cyst and Tumor Lesion Using Support Vector Machine Based on Dental Panoramic Images Texture Features

Underwater image enhancement using adaptive filtering for enhanced sift-based image matching

Strategi Pembangunan Smart City dan Tantangannya bagi Masyarakat Kota