DETEKSI API MELALUI DATA VISUAL MENGGUNAKAN LBP-TOP

ABSTRAKSI: Fire is one of disaster that often happens in the daily life. Some of preventive action that usually conducted is by using smoke or heat detector. Nowadays, fire detection system can be deployed on CCTV. Some methods has been developed to detect fire on the video file, with the main focus is to extract the fire features such as color and moving pattern, so the accuracy can be increased and can accelerate the process. Some of research based on the serial filtering principle on the detection step. It makes the detection process become time consuming. To accelerate the process, it needs a new approach to analyze the fire features from spatial and temporal domain in the same timeOne of the feature extraction method on the dynamic texture domain is able to produce object features on the video that represents spatial and temporal features at once. The method is LBP-TOP (Local Binary Pattern-Three Orthogonal Plane). LBP-TOP can produce spatial and temporal features from object in video by analizing three planes: XY(spatial), XT and YT(temporal). The fire features produced by LBP-TOP was modeled by using clustering K-Means method as the reference model when the classification process was done by using K-NN method. By using LBP-TOP as the feature extraction method, K-Means as the modeling feature, and K-NN as the classificator, the accuracy of the detection process can reach 92% with less complexity.Kata Kunci : Keywords— LBP-TOP, feature extraction, fire detectionABSTRACT: Fire is one of disaster that often happens in the daily life. Some of preventive action that usually conducted is by using smoke or heat detector. Nowadays, fire detection system can be deployed on CCTV. Some methods has been developed to detect fire on the video file, with the main focus is to extract the fire features such as color and moving pattern, so the accuracy can be increased and can accelerate the process. Some of research based on the serial filtering principle on the detection step. It makes the detection process become time consuming. To accelerate the process, it needs a new approach to analyze the fire features from spatial and temporal domain in the same timeOne of the feature extraction method on the dynamic texture domain is able to produce object features on the video that represents spatial and temporal features at once. The method is LBP-TOP (Local Binary Pattern-Three Orthogonal Plane). LBP-TOP can produce spatial and temporal features from object in video by analizing three planes: XY(spatial), XT and YT(temporal). The fire features produced by LBP-TOP was modeled by using clustering K-Means method as the reference model when the classification process was done by using K-NN method. By using LBP-TOP as the feature extraction method, K-Means as the modeling feature, and K-NN as the classificator, the accuracy of the detection process can reach 92% with less complexity.Keyword: Keywords— LBP-TOP, feature extraction, fire detectio

A polynomial texture extraction with application in dynamic texture classification

International audienceGeometry and texture image decomposition is an important paradigm in image processing. Following to Yves Meyer works based on Total Variation (VT), the decomposition model has known a renewed interest. In this paper , we propose an algorithm which decomposes color image into geometry and texture component by projecting the image in a bivariate polynomial basis and considering the geometry component as the partial reconstruction and the texture component as the remaining part. The experimental results show the adequacy of using our method as a texture extraction tool. Furthermore, we integrate it into a dynamic texture classification process

Verification of Smoke Detection in Video Sequences Based on Spatio-temporal Local Binary Patterns

AbstractThe early smoke detection in outdoor scenes using video sequences is one of the crucial tasks of modern surveillance systems. Real scenes may include objects that are similar to smoke with dynamic behavior due to low resolution cameras, blurring, or weather conditions. Therefore, verification of smoke detection is a necessary stage in such systems. Verification confirms the true smoke regions, when the regions similar to smoke are already detected in a video sequence. The contributions are two-fold. First, many types of Local Binary Patterns (LBPs) in 2D and 3D variants were investigated during experiments according to changing properties of smoke during fire gain. Second, map of brightness differences, edge map, and Laplacian map were studied in Spatio-Temporal LBP (STLBP) specification. The descriptors are based on histograms, and a classification into three classes such as dense smoke, transparent smoke, and non-smoke was implemented using Kullback-Leibler divergence. The recognition results achieved 96–99% and 86–94% of accuracy for dense smoke in dependence of various types of LPBs and shooting artifacts including noise

Human Automotive Interaction: Affect Recognition for Motor Trend Magazine\u27s Best Driver Car of the Year

Observation analysis of vehicle operators has the potential to address the growing trend of motor vehicle accidents. Methods are needed to automatically detect heavy cognitive load and distraction to warn drivers in poor psychophysiological state. Existing methods to monitor a driver have included prediction from steering behavior, smart phone warning systems, gaze detection, and electroencephalogram. We build upon these approaches by detecting cues that indicate inattention and stress from video. The system is tested and developed on data from Motor Trend Magazine\u27s Best Driver Car of the Year 2014 and 2015. It was found that face detection and facial feature encoding posed the most difficult challenges to automatic facial emotion recognition in practice. The chapter focuses on two important parts of the facial emotion recognition pipeline: (1) face detection and (2) facial appearance features. We propose a face detector that unifies state‐of‐the‐art approaches and provides quality control for face detection results, called reference‐based face detection. We also propose a novel method for facial feature extraction that compactly encodes the spatiotemporal behavior of the face and removes background texture, called local anisotropic‐inhibited binary patterns in three orthogonal planes. Real‐world results show promise for the automatic observation of driver inattention and stress

Study on Co-occurrence-based Image Feature Analysis and Texture Recognition Employing Diagonal-Crisscross Local Binary Pattern

In this thesis, we focus on several important fields on real-world image texture analysis and recognition. We survey various important features that are suitable for texture analysis. Apart from the issue of variety of features, different types of texture datasets are also discussed in-depth. There is no thorough work covering the important databases and analyzing them in various viewpoints. We persuasively categorize texture databases ? based on many references. In this survey, we put a categorization to split these texture datasets into few basic groups and later put related datasets. Next, we exhaustively analyze eleven second-order statistical features or cues based on co-occurrence matrices to understand image texture surface. These features are exploited to analyze properties of image texture. The features are also categorized based on their angular orientations and their applicability. Finally, we propose a method called diagonal-crisscross local binary pattern (DCLBP) for texture recognition. We also propose two other extensions of the local binary pattern. Compare to the local binary pattern and few other extensions, we achieve that our proposed method performs satisfactorily well in two very challenging benchmark datasets, called the KTH-TIPS (Textures under varying Illumination, Pose and Scale) database, and the USC-SIPI (University of Southern California ? Signal and Image Processing Institute) Rotations Texture dataset.九州工業大学博士学位論文 学位記番号:工博甲第354号　学位授与年月日:平成25年9月27日CHAPTER 1 INTRODUCTION|CHAPTER 2 FEATURES FOR TEXTURE ANALYSIS|CHAPTER 3 IN-DEPTH ANALYSIS OF TEXTURE DATABASES|CHAPTER 4 ANALYSIS OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 5 CATEGORIZATION OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 6 TEXTURE RECOGNITION BASED ON DIAGONAL-CRISSCROSS LOCAL BINARY PATTERN|CHAPTER 7 CONCLUSIONS AND FUTURE WORK九州工業大学平成25年