Akita University

Content／学習内容

• Introduction of Image Processing for Remote Sensing

  • Remote Sensing
  • Human Vision

  In the first lecture on remote sensing fundamentals, the basics of human vision (cones and rods) and image processing are introduced, emphasizing the need for probability, calculus, and linear algebra, and comparing cameras to clarify visual principles. The second lecture defines remote sensing, including the detection of electromagnetic waves beyond visible light, highlighting the significance of non-contact observation via satellites and drones, the importance of choosing sensors for specific purposes, and references to key literature.

  Videos

  ／学習動画

  • What is Vision?

    Introduces remote sensing–based physical exploration and basic image processing, noting the need for probability, calculus, and linear algebra. Also compares the human visual system (cones and rods) to camera principles.

  • What is Remote Sensing?

    This video outlines the definition and applications of remote sensing, focusing on sensors that detect electromagnetic waves beyond visible light. It explains the advantages of non-contact observation via satellites and drones, discusses sensor choices according to specific objectives, and introduces key references.

  Lecturers

  ／講師

  • TORIYA Hisatoshi

    Associate Professor, Graduate School of International Resource Sciences, Akita University

• Basic of Image Processing (1)

  • bit and Byte
  • pixel

  In “Basics of Image Processing (1),” this lecture covers the essentials of digitizing images through sampling and quantization, exploring fundamental concepts such as resolution, bit depth, color spaces, and coordinate systems. By examining how image quality and data size interact and referencing everyday examples like smartphone cameras, students gain practical skills applicable to image analysis and computer vision. Clear explanations using formulas and diagrams ensure beginners can build a solid foundation and prepare for further research or practical applications.

  Videos

  ／学習動画

  • Basic Knowledge og Image Processing

    This lecture focuses on the methods of digital image acquisition and the concept of color spaces, providing a clear explanation of fundamental image processing topics—such as pixels, coordinate systems, and overall workflows. Real-world examples are included to deepen understanding and guide learners toward practical analysis and everyday application of image processing.

  • Digital Image Acquisition

    In this lecture, we delve into the concepts of sampling and quantization that underpin converting analog information into digital form, exploring how real-world data is handled on computers. Using examples like pixels, resolution, and bit depth, we discuss the trade-off between image quality and data size. Drawing on practical cases such as smartphone camera capabilities and storage limitations, we also address the advantages and disadvantages of higher resolution and compare digital and analog domains. Lastly, we examine how changes in quantization precision affect image quality through real-world demonstrations.

  • Excercises

    To consolidate knowledge through small exercises.

  Lecturers

  ／講師

  • TORIYA Hisatoshi

    Associate Professor, Graduate School of International Resource Sciences, Akita University

• Basic of Image Processing (2)

  • additive and subtractive color mixing

  In this lecture, the principles of additive (RGB) and subtractive color mixing are explained using examples from human vision and camera sensors. It also covers methods of converting color images to grayscale, discusses multispectral and hyperspectral imaging for handling multiple wavelengths, and introduces histograms to understand the brightness distribution of each channel. Through these topics, students gain a foundational understanding of everything from basic color concepts to advanced spectral analysis, comprehensively covering essential knowledge for both academic research and practical applications in image processing.

  Videos

  ／学習動画

  • Image Properties and Color Spaces

    Continuing from the previous lecture, this video explains the mechanisms of RGB additive color mixing (as used by human vision and image sensors), subtractive color mixing (as used in printing), methods of grayscale conversion, multi- and hyperspectral imaging, and the basics of histograms.

  • Excercises

    To consolidate knowledge through small exercises.

  • FAQ and Comments

    Introduction of FAQ and comments of the class

  Lecturers

  ／講師

  • TORIYA Hisatoshi

    Associate Professor, Graduate School of International Resource Sciences, Akita University

• Pixel-by-Pixel Intensity Transformation

  • Intensity
  • Contrast
  • Histogram

  This lecture covers basic statistics and pixel-by-pixel intensity transformation in image processing. The first half introduces mean, variance, and standard deviation, while the second half focuses on brightness and contrast adjustments through intensity transformations. Topics include histogram analysis, non-linear transformations using tone curves, and the impact of clipping on image information loss.

  Videos

  ／学習動画

  • Review of Basic Statistics

    This lecture reviews basic statistical concepts, including mean (μ), variance (σ²), and standard deviation (σ). Practical examples, such as dice rolls and coin flips, illustrate differences in data dispersion. Additionally, a past entrance exam question from Akita University is used for a hands-on exercise in calculating data variability.

  • Pixel-by-Pixel Intensity Transformation

    This lecture explains pixel-by-pixel intensity transformation. It covers the calculation of image mean and variance, the impact of brightness and contrast changes on histograms, non-linear transformations using tone curves, and the effects of clipping-induced data loss.

  • Excersises, and FAQ and Comments

    Knowledge acquisition through small exercises; FAQs and comments will be presented.

  Lecturers

  ／講師

  • TORIYA Hisatoshi

    Associate Professor, Graduate School of International Resource Sciences, Akita University

• Spatial Filtering

  • edge detection
  • sharpening filter

  This lecture explains the fundamentals and applications of spatial filtering. It introduces the concept of filtering using neighboring pixel values, presents linear filtering equations, and covers smoothing (averaging, Gaussian) and edge detection filters (differential, Prewitt, Sobel). Exercises help in understanding practical implementation, noise reduction, and edge enhancement techniques.

  Videos

  ／学習動画

  • Basic of Spacial Filtering

    This lecture introduces spatial filtering, explains its difference from pixel-wise intensity transformation, presents its mathematical formulation, and demonstrates the filtering process through exercises.

  • Smoothing Filters and Edge Detection

    This lecture introduces smoothing filters (averaging, Gaussian) and edge detection filters (differential, Prewitt, Sobel), explaining their characteristics and applications.

  • Sharpening Filters, FAQ and Comments

    In this lecture, the sharpening filter will be explained.FAQs and comments will also be presented.

  Lecturers

  ／講師

  • TORIYA Hisatoshi

    Associate Professor, Graduate School of International Resource Sciences, Akita University

• Filtering Based on the Frequency Domain

  • Low-pass filter

  This lecture covers the theory and practice of frequency domain filtering. It explains how signals are transformed from the spatial domain to the frequency domain using the Fourier transform, demonstrating effects such as smoothing via low-pass filters and high-frequency enhancement. The lecture also introduces the FFT2D tool for visual frequency analysis of images. Additionally, it explores practical applications of frequency transformation in sound perception and data compression, reinforcing concepts through hands-on exercises.

  Videos

  ／学習動画

  • Basic of Frequency Dmain Filtering

    This lecture introduces the fundamentals of frequency domain filtering. It explains the transformation of signals into the frequency domain using the Fourier transform, how to interpret the results, the effects of low-pass filters that allow low-frequency components to pass, and applications such as JPEG compression.

  • Practical Training of Frequency Domain Filtering

    This lecture introduces practical applications of frequency domain filtering using Kobe University’s FFT2D tool. It demonstrates the effects of low-pass filtering and high-frequency enhancement, teaching how to visually analyze image frequency components.

  Lecturers

  ／講師

  • TORIYA Hisatoshi

    Associate Professor, Graduate School of International Resource Sciences, Akita University

• Geometric Transformation (1)

  This lecture explains the basic concepts of geometric transformation and provides a detailed discussion on coordinate transformations using linear algebra. The first session introduces fundamental matrix-based linear transformations, including scaling, reflection, and rotation. The second session reviews the exercise answers and explains how to derive transformation matrices in detail. Special emphasis is placed on deriving the rotation matrix, followed by an introduction to shearing transformation. The lecture aims to enhance understanding of fundamental image processing and spatial transformations.

  Videos

  ／学習動画

  • Introduction of Geometric Transformation, Excersices

    This lecture video introduces the basics of geometric transformation in images and explains coordinate transformations using linear algebra. It covers linear transformations such as scaling, rotation, and reflection through matrix operations and discusses how to determine transformation matrices.

  • Basic Geometric Transformation

    This video reviews the answers from the previous exercise and explains how to derive transformation matrices for scaling, reflection, and rotation. It also demonstrates the derivation of the rotation matrix and introduces shearing transformation at the end.

  Lecturers

  ／講師

  • TORIYA Hisatoshi

    Associate Professor, Graduate School of International Resource Sciences, Akita University

• Geometric Transformation (2)

  • Affine transformation
  • Homography transform

  This lecture explains linear, affine, and projective transformations. It begins by reviewing linear transformations and introducing homogeneous coordinates to handle transformations that cannot be expressed linearly. Using homogeneous coordinates, affine and projective transformations, including translation, can be represented with matrices. The lecture also discusses how transformations can be combined and emphasizes the importance of multiplication order in matrix operations. Finally, it presents examples of composite transformations using affine and projective transformations and touches on their applications.

  Videos

  ／学習動画

  • Affine/Homography Transformation

    This lecture reviews linear transformations and explains the need for homogeneous coordinates to handle transformations that cannot be expressed by linear transformations. It then defines and discusses the characteristics of affine and projective transformations using homogeneous coordinates. Finally, it explains how to combine multiple transformations and highlights the importance of matrix multiplication order.

  • Excercises, FAQ and Comments

    Small exercises will be used to consolidate understanding.FAQs and comments will also be presented.

  Lecturers

  ／講師

  • TORIYA Hisatoshi

    Associate Professor, Graduate School of International Resource Sciences, Akita University

• Geometric Transformation (3)

  • Resampling
  • Image registration

  This lecture introduces fundamental concepts of geometric transformation, focusing on resampling and interpolation methods. It explains the differences between nearest neighbor, bilinear, and bicubic interpolation, discussing the trade-offs between computational cost and image quality. The lecture also covers image mosaicking and registration, detailing feature point detection and homography transformation. Finally, it presents a case study on integrating different types of images, exploring applications in GIS and advancements in image processing technology.

  Videos

  ／学習動画

  • Basic Notion of Resampling and Interpolation

    This lecture explains resampling and interpolation in geometric transformations. It covers how to compensate for missing pixels when enlarging or transforming images using inverse transformation. The lecture also introduces interpolation methods, focusing on bilinear interpolation with practical calculations.

  • Image Mosaicking/Registration

    This lecture compares interpolation methods, including nearest neighbor, bilinear, and bicubic interpolation, discussing trade-offs between image quality and computational cost. It also covers image mosaicking and registration, explaining feature point detection and homography transformation.

  • Case study of Image Registration

    This lecture presents case studies of image registration, focusing on integrating different image types (SAR satellite, optical satellite, drone, and ground images). It discusses applications in GIS and advancements in interpolation and mosaicking technologies.

  Lecturers

  ／講師

  • TORIYA Hisatoshi

    Associate Professor, Graduate School of International Resource Sciences, Akita University

• Binalization

  This lecture covers binarization, a fundamental image processing technique. It first explains the basic concepts and applications, with a focus on its potential use in the mining industry. Then, a demonstration using image processing software is conducted, introducing the actual operation steps. Viewers are encouraged to try binarization using free software, with detailed instructions provided. By combining theoretical and practical knowledge, this lecture helps deepen understanding of binarization and its applications in the mining field.

  Videos

  ／学習動画

  • Introduction of Binalization

    This lecture introduces binarization, a fundamental image processing technique. It explains the basic concepts and applications of binarization, with a focus on its potential use in the mining industry. ​​

  • Demonstration Using Image Processing Software

    This lecture demonstrates binarization using image processing software. It also explains the steps so that viewers can try binarization themselves using free software. ​​

  Lecturers

  ／講師

  • TORIYA Hisatoshi

    Associate Professor, Graduate School of International Resource Sciences, Akita University

• Review of Image Processing (1)

  • A/D conversion
  • Resolution

  This lecture reviews image processing fundamentals and deepens understanding through practical exercises. The first part covers digital imaging concepts, A/D conversion, resolution, quantization, bits and bytes, image formats, and compression types. The second part introduces the image analysis software ImageJ, explaining how to download it, navigate its interface, display histograms, and perform binarization.

  Videos

  ／学習動画

  • Basic of Digital Imaging

    This lecture reviews image processing basics and includes ImageJ exercises, covering digital imaging, A/D conversion, resolution, quantization, bits and bytes, image formats, and compression types.

  • Image Analysis Software Introduction of “ImageJ”

    This lecture introduces ImageJ basics, covering its download, interface, histogram display, and binarization, leading to advanced processing in the next session.

  Lecturers

  ／講師

  • TORIYA Hisatoshi

    Associate Professor, Graduate School of International Resource Sciences, Akita University

• Review of Image Processing (2)

  • Filtering
  • Particle analysis

  This lecture reviews fundamental image processing using ImageJ, focusing on particle analysis techniques. The first part calculates the area ratio of aggregate to cement in concrete, utilizing binarization and histogram analysis. The second part analyzes Kaki-no-Tane and peanuts, applying preprocessing, filtering, and classification based on shape and color. The final result successfully identified 32 Kaki-no-Tane and 8 peanuts out of 40 particles. Through this lecture, students gain practical insights into image analysis methodologies.

  Videos

  ／学習動画

  • Case study 1

    This lecture demonstrates particle analysis of concrete using ImageJ, performing grayscale conversion, binarization, and histogram analysis to estimate the aggregate-to-cement area ratio.

  • Case study 2

    This lecture demonstrates particle analysis of Kaki-no-Tane and peanuts using ImageJ. Preprocessing, filtering, and binarization were performed, and classification was attempted based on shape and color features. The final result identified 32 Kaki-no-Tane and 8 peanuts out of 40 particles.

  Lecturers

  ／講師

  • TORIYA Hisatoshi

    Associate Professor, Graduate School of International Resource Sciences, Akita University

• Pattern Recognition and Machine Learning (1)

  • Machine learning
  • Distance calculation

  This lecture introduces pattern recognition and machine learning fundamentals. It explains pattern recognition concepts, distance-based methods (Euclidean, Manhattan, Mahalanobis), and k-NN techniques. It also discusses classification models and their applications.

  Videos

  ／学習動画

  • Basic of Pattern Recognition

    Explains the basics of pattern recognition, class-feature relationships, and two methods: distance-based and machine learning.

  • Distance Calculation-Based Pattern Recognition

    Explains distance-based pattern recognition, introduces distance metrics, and discusses k-NN methods and their improved accuracy with large datasets.

  Lecturers

  ／講師

  • TORIYA Hisatoshi

    Associate Professor, Graduate School of International Resource Sciences, Akita University

• Pattern Recognition and Machine Learning (2)

  • Unsupervised learning
  • Neural network

  This lecture explains pattern recognition using machine learning. It covers supervised and unsupervised learning, introducing classification, regression, clustering, and dimensionality reduction. It then explores neural networks, deep learning, CNNs, and factors driving machine learning advancements. The lecture highlights how increased computing power and automated feature extraction have accelerated deep learning adoption, enabling its application in image recognition, natural language processing, and more.

  Videos

  ／学習動画

  • Pattern Recognition Using Machine Learning (1)

    This lecture explains pattern recognition using machine learning. Machine learning is categorized into supervised and unsupervised learning. Unsupervised learning includes clustering and dimensionality reduction, while supervised learning consists of classification and regression tasks.

  • Pattern Recognition Using Machine Learning (2)

    This lecture explains the concepts of neural networks and deep learning, highlighting CNN features and the factors driving machine learning advancements. It also discusses how improved computing power and automated feature extraction have contributed to the widespread adoption of deep learning.

  Lecturers

  ／講師

  • TORIYA Hisatoshi

    Associate Professor, Graduate School of International Resource Sciences, Akita University

• Image Processing for Remote Sensing

  • Mineral resource exploration
  • Spectral analysis

  This lecture covers the basics of remote sensing image processing and its applications in mineral exploration. It explains Earth’s science advancements, mineral formation, remote sensing types, electromagnetic properties, and satellite observation techniques. It also explores multispectral and hyperspectral satellites, methods for identifying specific minerals, and practical applications in resource exploration.

  Videos

  ／学習動画

  • Basic of Image Processing for Remote Sensing

    This lecture explains the basics of remote sensing image processing, its types, electromagnetic properties, and satellite observation techniques, highlighting applications in mineral exploration.

  Lecturers

  ／講師

  • TORIYA Hisatoshi

    Associate Professor, Graduate School of International Resource Sciences, Akita University

Staff／スタッフ

• 
  TORIYA Hisatoshi

  Akita University Graduate School of International Resource Sciences

  Associate Professor

Competency／コンピテンシー

Contact／お問合せ先

Akita University, Graduate School of International Resource Sciences, International Strategy Division
kokusaisenryaku@jimu.akita-u.ac.jp

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