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APECE-505 Intelligent System Engineering Basics of Digital Image Processing! PowerPoint Presentation
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APECE-505 Intelligent System Engineering Basics of Digital Image Processing!

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APECE-505 Intelligent System Engineering Basics of Digital Image Processing!

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  1. APECE-505 Intelligent System Engineering Basics of Digital Image Processing! Md. Atiqur Rahman Ahad Reference books: – Digital Image Processing, Gonzalez & Woods. - Digital Image Processing, M. Joshi - Computer Vision – a modern approach, Forsyth & Ponce

  2. Syllabus: Expert system Neural networks Fuzzy logic Robot vision – Intro, 2-stages of robot vision, image processing, genetic/pattern discovery program, scene analysis, interpreting line & curves in the image, model-based vision Genetic Algorithm

  3. Computer / Robot / Machine vision • vs. • Human vision • Machine vs. Human • Camera vs. Eye • Computer/Processor vs. Brain • Artificial intelligence vs. Human brain… • - Very difficult for a machine – as object varies, number of object varies, dimensional issues, view-/illumination-/angle-/perspective-invariance, etc.

  4. Computer vision • Endowing machines with the means to “see” • Create an image of a scene and extract features • Very difficult problem for machines • Several different scenes can produce identical images. • Images can be noisy . • Cannot directly ‘invert’ the image to reconstruct the scene.

  5. CV  - creates a model of the real world from images • recovers useful information about a scene from its two dimensional projections • Finding out objects in the scene • Looking for “edges” in the image • Edge: a part of the image across which the image intensity or some other property of the image changes abruptly. • Attempting to segment the image into regions. • Region: a part of the image in which the image intensity or some other property of the image changes only gradually.

  6. Image processing stage – transform the original image into something that can be helpful for scene analysis • Interpreting lines  edge detection, edge accumulation, end-point identification • Curves analysis  junctions • 2. Scene Analysis stage – attempt to create an iconic [build a model] or a feature-based description of the original scene, providing a task-specific information

  7. Robot-player • Identify lines, corners • Identify the ball [ellipse or circle] • Identify players – opponents!

  8. MACHINE VISION Imaging device Scene Image Description Illumination Application feedback A typical CV-based control system

  9. Machine Vision Stages Analog to digital conversion Image Acquisition Remove noise, improve contrast… Image Processing Find regions (objects) in the image Image Segmentation Take measurements of objects/relationships Image Analysis Match the description with similar description of known objects (models) Pattern Recognition

  10. Model-based vision: • Considering various models and fit into it. • Cylindrical, stick model, etc. • e.g., Hierarchical representation through smaller cylinders to recreate a person

  11. Stereo vision & depth information: • Stereo vision has two or more cameras • Depth info from a single camera is difficult or almost impossible – though through texture analysis, it might be possible a bit • Depth  calculate the distance of foreground objects – far or closer! • Stereo vision – key constraint is correspondence problem or registration problem