Classifying seabed sediments using local auto-correlation features

Understanding the distribution of seafloor sediment using a side-scan sonar is very important to grasp the distribution of seabed resources. This task is traditionally carried out by a skilled human operator. However, with the appearance of Autonomous Underwater Vehicles, automated processing is now needed to tackle the large amount of data produced and to enable on the fly adaptation of the missions and near real time update of the operator. We propose in this paper a method that applies a higher-order local auto-correlation feature and a subspace method to the acoustic image provided by the side-scan sonar to classify seabed sediment automatically. In texture classification, the proposed method outperformed other methods such as a gray level co-occurrence matrix and a Local Binary Pattern operator. Experimental results show that the proposed method produces consistent maps of a seafloor

Nhận dạng cử chỉ của bàn tay người theo thời gian thực.

This paper proposes a new  method of hand gesture recognition using Microsoft’s Kinect in real-time. Our system includes detecting and recognizing hand gestures via combining shape, local auto-correlation information and multi-class support vector machine (SVM). Our evaluation shows that the system recognizes one-handed gestures with more than 93% accuracy in real-time. The efficiency of the system execution is good enough and we are encouraged to develop a natural human-machine interaction in the near future.Bài báo trình bày một số kết quả nhận dạng cử chỉ của bàn tay người theo thời gian thực sử dụng thông tin thu được từ cảm biến Kinect của hãng Microsoft. Một số kết quả chính của hướng nghiên cứu được trình bày như: kỹ thuật tách vùng bàn tay, nhận dạng tư thế của bàn tay, đề xuất thuật toán hiệu chỉnh kết quả nhận dạng từ chuỗi các tư thế. Kết quả nhận dạng cho độ chính xác khả quan (trên 93%) tạo tiền đề cho các ứng dụng tương tác người máy theo thời gian thực

A Study on the Recognition of Seabed Environments Employing Sonar Images

The ocean accounts for approximately 70% of the area on the earth, and the water as well as coastal areas sustain many species including humans. Ocean resources are used for fish farming, land reclamation, and a variety of other purposes. Seabed resources such as oil, natural gas methane hydrates, and manganese nodules are still largely unexploited on the bottom of the sea. Maps are critical to development activities such as construction, mining, offshore drilling, marine traffic control, security, environmental protection, and tourism. Accordingly, more topographic and others types of mapping information are needed for marine and submarine investigations. Both waterborne and airborne survey techniques show promise for collecting data on marine and submarine environments, and these techniques can be classified into four main categories. First, remote sensing by satellites or aircraft is a widely used technique that can yield important data such as information on sea levels and coastal sediment transport. Second, investigations may collect direct information by remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), and divers. While the quality of data obtained from these techniques is high, the data obtained are often limited to relatively shallow and small geographic areas. Third, sediment profile imagery can be used to collect photographs that contain detailed information about the seabed. Lastly, acoustic investigations that use sonar are popular in marine mapping studies, especially in coastal areas. In particular, acoustic investigations that employ ultrasound technology can yield rich information about variations in bathymetry. Unlike air, water has physical properties that make it difficult for light or electromagnetic waves to pass through. However, sound waves propagate readily in water. Therefore, sound waves are used in a wide range of technical applications to detect underwater structures that are difficult to observe with light-based techniques. In the dark depths of the ocean, the use of acoustic technology is essential. The development of marine acoustic technology is expanding in modern times. In addition to the basic physics related to acoustic waves, much research has been dedicated to other basic and applied fields such as electronics, physical oceanography, signal processing, and biology. The realization of new sonar systems that utilize advanced detection algorithms can be expected to contribute to major breakthroughs in oceanographic research that require deployment to novel marine environments and other areas of natural resource interest. In this study, the author focuses on side-scan sonar, which is one of the imaging technologies that employs sound to determine the seabed state, to conduct research on imaging algorithms for discrimination. The proposed method for discrimination was coupled to a high-speed detection method for installed reefs on the seabed. This method is also capable of detecting unknown objects with Haar-like features during object recognition of rectangular regions of a certain size via machine learning by AdaBoost and fast elimination of non-object regions on the cascade structure. Side-scan and forward looking sonars are some of the most widely used imaging systems for obtaining large-scale images of the seafloor, and their application continues to expand rapidly with their increasing deployment on AUVs. However, it can be difficult to extract quantitative information from the images generated from these processes, in particular, for the detection and extraction of information on the objects within these images. Hence, this study analyzes features that are common to most undersea objects projected in side-scan sonar images to improve information processing. By using a technique based on the k-means method to determine the Haar-like features, the number of patterns of Haar-like features was minimized and the proposed method was capable of detecting undersea objects faster than current methodology. This study demonstrates the effectiveness of this method by applying it to the detection of real objects imaged on the seabed (i.e., sandy ground and muddy ground). Attempts are made as well to automate the proposed method for discriminating objects lying on the seafloor from surficial sediments. During undersea exploration, a thorough understanding of the state of the seafloor surrounding objects of interest is important. Therefore, a method is proposed in this study to automatically determine seabed sediment characteristics. In traditional methods, a variety of techniques have been used to collect information about seabed sediments including depth measurements, bathymetry evaluations, and seabed image analyses using the co-occurrence direction of the gray values of the image. Unfortunately, such data cannot be estimated from the object image itself and it can take a long time to obtain the required information. Therefore, these techniques are not currently suitable for real-time identification of objects on the seafloor. For practical purposes, automatic techniques that are developed should follow a simple procedure that results in highly precise and accurate classifications. The technique proposed here uses the subspace method, which is a method that has been used for supervised pattern recognition and analyses of higher-order local autocorrelation features. The most important feature of this method is that it uses only acoustic images obtained from the side-scan sonar. This feature opens up the possibility of installing this technology in unmanned small digital devices. In this study, the classification accuracy of the proposed automation method is compared to the accuracy of traditional methods in order to show the usefulness of the technology. In addition, the proposed method is applied to real-world images of the seabed to evaluate its effectiveness in marine surveys. The thesis is organized as follows. In Chapter 1, the purpose of this study is presented and previous studies relevant to this research are reviewed. In Chapter 2, an overview of underwater sound is given and key principles of sound wave technology are explained. In Chapter 3, a new method for detecting and discriminating objects on the seafloor is proposed. In Chapter 4, the possibility of automating the discrimination method is explored. Finally, Chapter 5 summarizes the findings of this study and proposes new avenues for future research.九州工業大学博士学位論文 学位記番号:工博甲第364号　学位授与年月日:平成26年3月25日Chapter 1 Introduction|Chapter 2 Underwater acoustics|Chapter 3 Detection of underwater objects based on machine learning|Chapter 4 Automatic classification of seabed sediments using HLAC|Chapter 5 Conclusion九州工業大学平成25年

Detection of Ulcerative Colitis Severity and Enhancement of Informative Frame Filtering Using Texture Analysis in Colonoscopy Videos

There are several types of disorders that affect our colon’s ability to function properly such as colorectal cancer, ulcerative colitis, diverticulitis, irritable bowel syndrome and colonic polyps. Automatic detection of these diseases would inform the endoscopist of possible sub-optimal inspection during the colonoscopy procedure as well as save time during post-procedure evaluation. But existing systems only detects few of those disorders like colonic polyps. In this dissertation, we address the automatic detection of another important disorder called ulcerative colitis. We propose a novel texture feature extraction technique to detect the severity of ulcerative colitis in block, image, and video levels. We also enhance the current informative frame filtering methods by detecting water and bubble frames using our proposed technique. Our feature extraction algorithm based on accumulation of pixel value difference provides better accuracy at faster speed than the existing methods making it highly suitable for real-time systems. We also propose a hybrid approach in which our feature method is combined with existing feature method(s) to provide even better accuracy. We extend the block and image level detection method to video level severity score calculation and shot segmentation. Also, the proposed novel feature extraction method can detect water and bubble frames in colonoscopy videos with very high accuracy in significantly less processing time even when clustering is used to reduce the training size by 10 times

A Study on the Recognition of Seabed Environments Employing Sonar Images

The ocean accounts for approximately 70% of the area on the earth, and the water as well as coastal areas sustain many species including humans. Ocean resources are used for fish farming, land reclamation, and a variety of other purposes. Seabed resources such as oil, natural gas methane hydrates, and manganese nodules are still largely unexploited on the bottom of the sea. Maps are critical to development activities such as construction, mining, offshore drilling, marine traffic control, security, environmental protection, and tourism. Accordingly, more topographic and others types of mapping information are needed for marine and submarine investigations. Both waterborne and airborne survey techniques show promise for collecting data on marine and submarine environments, and these techniques can be classified into four main categories. First, remote sensing by satellites or aircraft is a widely used technique that can yield important data such as information on sea levels and coastal sediment transport. Second, investigations may collect direct information by remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), and divers. While the quality of data obtained from these techniques is high, the data obtained are often limited to relatively shallow and small geographic areas. Third, sediment profile imagery can be used to collect photographs that contain detailed information about the seabed. Lastly, acoustic investigations that use sonar are popular in marine mapping studies, especially in coastal areas. In particular, acoustic investigations that employ ultrasound technology can yield rich information about variations in bathymetry. Unlike air, water has physical properties that make it difficult for light or electromagnetic waves to pass through. However, sound waves propagate readily in water. Therefore, sound waves are used in a wide range of technical applications to detect underwater structures that are difficult to observe with light-based techniques. In the dark depths of the ocean, the use of acoustic technology is essential. The development of marine acoustic technology is expanding in modern times. In addition to the basic physics related to acoustic waves, much research has been dedicated to other basic and applied fields such as electronics, physical oceanography, signal processing, and biology. The realization of new sonar systems that utilize advanced detection algorithms can be expected to contribute to major breakthroughs in oceanographic research that require deployment to novel marine environments and other areas of natural resource interest. In this study, the author focuses on side-scan sonar, which is one of the imaging technologies that employs sound to determine the seabed state, to conduct research on imaging algorithms for discrimination. The proposed method for discrimination was coupled to a high-speed detection method for installed reefs on the seabed. This method is also capable of detecting unknown objects with Haar-like features during object recognition of rectangular regions of a certain size via machine learning by AdaBoost and fast elimination of non-object regions on the cascade structure. Side-scan and forward looking sonars are some of the most widely used imaging systems for obtaining large-scale images of the seafloor, and their application continues to expand rapidly with their increasing deployment on AUVs. However, it can be difficult to extract quantitative information from the images generated from these processes, in particular, for the detection and extraction of information on the objects within these images. Hence, this study analyzes features that are common to most undersea objects projected in side-scan sonar images to improve information processing. By using a technique based on the k-means method to determine the Haar-like features, the number of patterns of Haar-like features was minimized and the proposed method was capable of detecting undersea objects faster than current methodology. This study demonstrates the effectiveness of this method by applying it to the detection of real objects imaged on the seabed (i.e., sandy ground and muddy ground). Attempts are made as well to automate the proposed method for discriminating objects lying on the seafloor from surficial sediments. During undersea exploration, a thorough understanding of the state of the seafloor surrounding objects of interest is important. Therefore, a method is proposed in this study to automatically determine seabed sediment characteristics. In traditional methods, a variety of techniques have been used to collect information about seabed sediments including depth measurements, bathymetry evaluations, and seabed image analyses using the co-occurrence direction of the gray values of the image. Unfortunately, such data cannot be estimated from the object image itself and it can take a long time to obtain the required information. Therefore, these techniques are not currently suitable for real-time identification of objects on the seafloor. For practical purposes, automatic techniques that are developed should follow a simple procedure that results in highly precise and accurate classifications. The technique proposed here uses the subspace method, which is a method that has been used for supervised pattern recognition and analyses of higher-order local autocorrelation features. The most important feature of this method is that it uses only acoustic images obtained from the side-scan sonar. This feature opens up the possibility of installing this technology in unmanned small digital devices. In this study, the classification accuracy of the proposed automation method is compared to the accuracy of traditional methods in order to show the usefulness of the technology. In addition, the proposed method is applied to real-world images of the seabed to evaluate its effectiveness in marine surveys. The thesis is organized as follows. In Chapter 1, the purpose of this study is presented and previous studies relevant to this research are reviewed. In Chapter 2, an overview of underwater sound is given and key principles of sound wave technology are explained. In Chapter 3, a new method for detecting and discriminating objects on the seafloor is proposed. In Chapter 4, the possibility of automating the discrimination method is explored. Finally, Chapter 5 summarizes the findings of this study and proposes new avenues for future research.九州工業大学博士学位論文 学位記番号:工博甲第364号　学位授与年月日:平成26年3月25日Chapter 1 Introduction|Chapter 2 Underwater acoustics|Chapter 3 Detection of underwater objects based on machine learning|Chapter 4 Automatic classification of seabed sediments using HLAC|Chapter 5 Conclusion九州工業大学平成25年

深層ニューラルネットワークによる病理診断支援のためのドメイン知識の段階的転移に関する研究

筑波大学 (University of Tsukuba)201

Time and frequency domain algorithms for speech coding

The promise of digital hardware economies (due to recent advances in VLSI technology), has focussed much attention on more complex and sophisticated speech coding algorithms which offer improved quality at relatively low bit rates. This thesis describes the results (obtained from computer simulations) of research into various efficient (time and frequency domain) speech encoders operating at a transmission bit rate of 16 Kbps. In the time domain, Adaptive Differential Pulse Code Modulation (ADPCM) systems employing both forward and backward adaptive prediction were examined. A number of algorithms were proposed and evaluated, including several variants of the Stochastic Approximation Predictor (SAP). A Backward Block Adaptive (BBA) predictor was also developed and found to outperform the conventional stochastic methods, even though its complexity in terms of signal processing requirements is lower. A simplified Adaptive Predictive Coder (APC) employing a single tap pitch predictor considered next provided a slight improvement in performance over ADPCM, but with rather greater complexity. The ultimate test of any speech coding system is the perceptual performance of the received speech. Recent research has indicated that this may be enhanced by suitable control of the noise spectrum according to the theory of auditory masking. Various noise shaping ADPCM configurations were examined, and it was demonstrated that a proposed pre-/post-filtering arrangement which exploits advantageously the predictor-quantizer interaction, leads to the best subjective performance in both forward and backward prediction systems. Adaptive quantization is instrumental to the performance of ADPCM systems. Both the forward adaptive quantizer (AQF) and the backward oneword memory adaptation (AQJ) were examined. In addition, a novel method of decreasing quantization noise in ADPCM-AQJ coders, which involves the application of correction to the decoded speech samples, provided reduced output noise across the spectrum, with considerable high frequency noise suppression. More powerful (and inevitably more complex) frequency domain speech coders such as the Adaptive Transform Coder (ATC) and the Sub-band Coder (SBC) offer good quality speech at 16 Kbps. To reduce complexity and coding delay, whilst retaining the advantage of sub-band coding, a novel transform based split-band coder (TSBC) was developed and found to compare closely in performance with the SBC. To prevent the heavy side information requirement associated with a large number of bands in split-band coding schemes from impairing coding accuracy, without forgoing the efficiency provided by adaptive bit allocation, a method employing AQJs to code the sub-band signals together with vector quantization of the bit allocation patterns was also proposed. Finally, 'pipeline' methods of bit allocation and step size estimation (using the Fast Fourier Transform (FFT) on the input signal) were examined. Such methods, although less accurate, are nevertheless useful in limiting coding delay associated with SRC schemes employing Quadrature Mirror Filters (QMF)

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity