Texture Classification Based on Complex Network Model with Spatial Information

This paper proposes a method for image texture classification based on a complex network model. Finding relevant and valuable information in an image texture is an essential issue for image classification and remains a challenge. Recently, a complex network model has been used for texture analysis and classification. However, with current analysis methods, important empirical properties of image texture such as spatial information are discarded from consideration. Accordingly, we propose local spatial pattern mapping (LSPM) method for manipulating the spatial information in an image texture with multi-radial distance analysis to capture the texture pattern. In experiments, the feature properties under the traditional complex network model and those with the proposed method are analyzed by using the Brodatz, UIUC, and Outex databases. As results, the proposed method is shown to be effective for texture classification, providing an improved classification rate as compared to the traditional complex network model

A Modified Real-Coded Genetic Algorithm Considering with Fitness-based Variability

A genetic algorithm (GA) is a search algorithm based on the mechanism of natural genetics. In various GAs, a real-coded GA (RCGA) employing individuals represented by real valued-genes has been proposed to solve the optimization problem in the continuous searching space. However, the conventional RCGA yields ineffective searches due to insufficient genetic diversity in the selection process. In this paper, we propose a modified RCGA with variability operator maintaining the genetic diversity of the population. In the proposed method, a variability term is newly added to the individuals selected by the ordinary selection. The degree of the variability is decided considering the fitness value of the individual. The searching performance of the proposed method is better than the conventional methods. The effectiveness and the validity of the proposed method are verified by applying it to optimization problems of continuous benchmark functions and signal sources localization

Discrimination of Oral Mucosal Disease Inspired by Diagnostic Process of Specialist

A discrimination of oral mucosal diseases is very important in clinical site. Therefore, a development of a screening support system for oral mucosal diseases which supports the diagnosis of clinical dentist is required. In this paper, a discrimination method based on fuzzy inference using four attributes (existence of vitiligos, bulges, granular patterns, and reddening) for oral mucosal diseases is proposed. As the results of the experiment, the discrimination rates of squamous cell carcinoma, leukoplakia and lichen planus were 87%, 70% and 87%, respectively. The results suggest that the proposed method is effective in discriminating oral mucosal diseases

Tuna Nutriment Tracking using Trajectory Mapping in Application to Aquaculture Fish Tank

The cost of fish feeding is usually around 40 percent of total production cost. Estimating a state of fishes in a tank and adjusting an amount of nutriments play an important role to manage cost of fish feeding system. Our approach is based on tracking nutriments on videos collected from an active aquaculture fish farm. Tracking approach is applied to acknowledge movement of nutriment to understand more about the fish behavior. Recently, there has been increasing number of researchers focused on developing tracking algorithms to generate more accurate and faster determination of object. Unfortunately, recent studies have shown that efficient and robust tracking of multiple objects with complex relations remain unsolved. Hence, focusing to develop tracking algorithm in aquaculture is more challenging because tracked object has a lot of aquatic variant creatures. By following aforementioned problem, we develop tuna nutriment tracking based on the classical minimum cost problem which consistently performs well in real environment datasets. In evaluation, the proposed method achieved 21.32 pixels and 3.08 pixels for average error distance and standard deviation, respectively. Quantitative evaluation based on the data generated by human annotators shows that the proposed method is valuable for aquaculture fish farm and can be widely applied to real environment datasets

YOLOv8s-NE: Enhancing Object Detection of Small Objects in Nursery Environments Based on Improved YOLOv8

The primary objective of this research investigation is to examine object detection within the specific environment of a nursery. The nursery environment presents a complex scene with a multitude of objects, varying in size and background. To simulate real-world conditions, we gathered data from a nursery. Our study is centered around the detection of small objects, particularly in nursery settings where objects that include stationery, toys, and small accessories are commonly present. These objects are of significant importance in facilitating cognition of the activities and interactions taking place within the confines of the room. Due to their small size and the possibility of occlusion by other objects or children, precisely detecting these objects is regrettably fraught with inherent challenges. This study introduces YOLOv8s-NE in an effort to enhance the detection of small objects found in the nursery. We improve the standard YOLOv8 by incorporating an extra detection head to effectively for small objects. We replace the C2f module with C2f_DCN to further improve the model’s ability to detect objects of varying sizes that can be deformed or occluded within the image. Furthermore, we introduce NAM attention to focus on the important features and ignore less informative ones, thereby improving the accuracy of our proposed model. We used the five-fold cross-validation approach to split the dataset in order to evaluate the performance of YOLOv8s-NE, thereby facilitating a more comprehensive model evaluation. Our model achieves 34.1% of APs, 45.1% of mAP50:90, and 76.7% of mAP50 detection accuracy at 37.55 FPS on the nursery dataset. In terms of APs, mAP50:90, and mAP50 metrics, our proposed YOLOv8s-NE model outperforms the standard YOLOv8s model, with improvements of 4.6%, 4.7%, and 3.9%, respectively. We apply our proposed YOLOv8s-NE model as a safety system by developing an algorithm to detect objects on top of cabinets that could be potentially risky to children.journal articl

3D Position Estimation of Multiple Fish in a Small Tank Using DeepLabCut and Shortest Distance Calculation between Two Straight Lines

In a small tank, when fish approach the water surface or the sides of the tank, they are reflected like mirrors, and depending on the angle, there is a problem of seeing more fish than the actual number. To solve this problem, we propose a method to estimate the 3D positions of multiple fish and to obtain the actual positions of fish by using DeepLabCut and the calculation of the shortest distance between two straight lines. The experimental results indicate that the actual positions of the fish were generally estimated and that all reflected fish were positioned outside the tank.journal articl

Human Tracking Using Particle Filter with Reliable Appearance Model

In this paper, we present a human tracking algorithm that can work robustly in complex environments such that serious occlusion, various appearances and abrupt motion changes occur in the scenario. Our tracking framework is well known particle filter based on Condensation algorithm. In the observation model of the particle filter, we establish RAM(Reliable Appearance Model) which exhibits high discriminative performance in particular for human tracking. The RAM is to describe a target as features from local descriptors. In order to extract practical features from a larger number of local descriptors for robust tracking, the features were employed by boosting algorithm. The components of the features are utilized color and shape based-models. Experimental results demonstrate that our approach tracks the target accurately and reliably when position and scale are changing as well as occurrence of occlusion.SICE Annual Conference 2013 - International conference on Instrumentation, Control, Information Technology and System Integration, September 14-17, 2013, Nagoya University, Nagoya, Japa

Human Tracking Using Particle Filter with Reliable Appearance Model

In this paper, we present a human tracking algorithm that can work robustly in complex environments such that serious occlusion, various appearances and abrupt motion changes occur in the scenario. Our tracking framework is well known particle filter based on Condensation algorithm. In the observation model of the particle filter, we establish RAM(Reliable Appearance Model) which exhibits high discriminative performance in particular for human tracking. The RAM is to describe a target as features from local descriptors. In order to extract practical features from a larger number of local descriptors for robust tracking, the features were employed by boosting algorithm. The components of the features are utilized color and shape based-models. Experimental results demonstrate that our approach tracks the target accurately and reliably when position and scale are changing as well as occurrence of occlusion.SICE Annual Conference 2013 - International conference on Instrumentation, Control, Information Technology and System Integration, September 14-17, 2013, Nagoya University, Nagoya, Japa

Consideration of The Difference in Accuracy Between Synthetic and Non-Synthetic Speech in Diarization

In recent years, speech segments detection technique called speaker diarization is becoming increasingly important, mainly for meetings, news, telephone speech and so on. However, conventional speaker diarization methods using neural networks require a huge amount of training data. We have shown that speaker diarization is possible with realistic training data by dividing relatively short speech signals recorded individually for each target person and synthesizing them on a computer. In this paper, we investigate the robustness of the method to speech recorded by microphones from multiple sound sources, and also examine the difference in accuracy depending on the position of the sound sources.journal articl

Wave Parameters Prediction for Wave Energy Converter Site using Long Short-Term Memory

Forecasting the behaviour of various wave parameters is crucial for the safety of maritime operations as well as for optimal operations of wave energy converter (WEC) sites. For coastal WEC sites, the wave parameters of interest are significant wave height (Hs) and peak wave period (Tp). Numerical and statistical modeling, along with machine and deep learning models, have been applied to predict these parameters for the short and long-term future. For near-future prediction of Hs and Tp, this study investigates the possibility of optimally training a Long Short-Term Memory (LSTM) model on historical values of Hs and Tp only. Additionally, the study investigates the minimum amount of training data required to predict these parameters with acceptable accuracy. The Root Mean Square Error (RMSE) measure is used to evaluate the prediction ability of the model. As a result, it is identified that LSTM can effectively predict Hs and Tp given their historical values only. For Hs, it is identified that a 4-year dataset, 20 historical inputs, and a batch size of 256 produce the best results for three, six, twelve, and twenty-four-hour prediction windows at half-hourly step. It is also established that the future values of Tp can be optimally predicted using a 2-year dataset, 10 historical inputs, and a 128-batch size. However, due to the much dynamic nature of the peak wave period, it is discovered that the LSTM model yielded relatively low prediction accuracy as compared to Hs.journal articl