Items related to Intelligent Machine Vision: Techniques, Implementations...
Synopsis
1 Machine vision for industrial applications.- 1.1 Natural and artificial vision.- 1.2 Artificial vision.- 1.3 Machine Vision is not Computer Vision.- 1.3.1 Applications.- 1.3.2 Does it matter what we call the subject?.- 1.4 Four case studies.- 1.4.1 Doomed to failure.- 1.4.2 A successful design.- 1.4.3 Moldy nuts.- 1.4.4 A system that grew and grew.- 1.5 Machine Vision is engineering, not science.- 1.6 Structure, design, and use of machine vision systems.- 1.6.1 Using an IVS for problem analysis.- 1.6.2 Structure of an IVS.- 1.6.3 Other uses for an IVS.- 1.7 Other design tools.- 1.7.1 Lighting Advisor.- 1.7.2 Prototyping kits for image acquisition.- 1.8 Outline of this book.- 2 Basic machine vision techniques.- 2.1 Representations of images.- 2.2 Elementary image-processing functions.- 2.2.1 Monadic pixel-by-pixel operators.- 2.2.2 Dyadic pixel-by-pixel operators.- 2.2.3 Local operators.- 2.2.4 Linear local operators.- 2.2.5 Nonlinear local operators.- 2.2.6 N-tupIe operators.- 2.2.7 Edge effects.- 2.2.8 Intensity histogram [hpi, hgi, hge, hgc].- 2.3 Binary images.- 2.3.1 Measurements on binary images.- 2.3.2 Shape descriptors.- 2.4 Binary mathematical morphology.- 2.4.1 Opening and closing operations.- 2.4.2 Structuring element decomposition.- 2.5 Grey-scale morphology.- 2.6 Global image transforms.- 2.6.1 Hough transform.- 2.6.2 Two-dimensional discrete Fourier transform.- 2.7 Texture analysis.- 2.7.1 Statistical approaches.- 2.7.2 Co-occurrence matrix approach.- 2.7.3 Structural approaches.- 2.7.4 Morphological texture analysis.- 2.8 Further remarks.- 3 Algorithms, approximations, and heuristics.- 3.1 Introduction.- 3.1.1 One algorithm, many implementations.- 3.1.2 Do not judge an algorithm by its accuracy alone.- 3.1.3 Reformulating algorithms to make implementation easier.- 3.1.4 Use heuristics in lieu of algorithms.- 3.1.5 Heuristics may actually be algorithmic.- 3.1.6 Primary and secondary goals.- 3.1.7 Emotional objections to heuristics.- 3.1.8 Heuristics used to guide algorithms.- 3.1.9 Heuristics may cope with a wider range of situations.- 3.2 Changing image representation.- 3.2.1 Converting image format.- 3.2.2 Processing a binary image as a grey-scale image.- 3.2.3 Using images as lookup tables.- 3.3 Redefining algorithms.- 3.3.1 Convolution operations.- 3.3.2 More on decomposition and iterated operations.- 3.3.3 Separated Kernel Image Processing using finite-State Machines.- 3.3.4 Binary image coding.- 3.3.5 Blob connectivity analysis.- 3.3.6 Convex hull.- 3.3.7 Edge smoothing.- 3.3.8 Techniques based on histograms.- 3.4 Approximate and heuristic methods.- 3.4.1 Measuring distance.- 3.4.2 Fitting circles and polygons to a binary object.- 3.4.3 Determining object/feature orientation.- 3.5 Additional remarks.- 3.5.1 Solving the right problem.- 3.5.2 Democracy: no small subset of operators dominates.- 3.5.3 Lessons of this chapter.- 4 Systems engineering.- 4.1 Interactive and target vision systems.- 4.2 Interactive vision systems, general principles.- 4.2.1 Speed of operation.- 4.2.2 Communication between an IVS and its user.- 4.2.3 Image and text displays.- 4.2.4 Command-line interfaces.- 4.2.5 How many images do we need to store in RAM?.- 4.2.6 How many images do we need to display?.- 4.3 Prolog image processing (PIP).- 4.3.1 Basic command structure.- 4.3.2 Dialog box.- 4.3.3 Pull-down menus.- 4.3.4 Extending the pull-down menus.- 4.3.5 Command keys.- 4.3.6 Journal window.- 4.3.7 Natural language input via speech.- 4.3.8 Speech output.- 4.3.9 Cursor.- 4.3.10 On-line documentation.- 4.3.11 Generating test images.- 4.3.12 PIP is not just an image-processing system.- 4.4 Advanced aspects of PIP.- 4.4.1 Programmable color filter.- 4.4.2 Mathematical morphology.- 4.4.3 Multiple-image-processing paradigms.- 4.4.4 Image stack and backtracking.- 4.4.5 Programming generic algorithms.- 4.4.6 Batch processing of images.- 4.4.7 Simulating a tine-scan camera.- 4.4.8 Range maps.- 4.4.9 Processing image sequences.- 4.4.10 Interf
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- PublisherSpringer
- Publication date2012
- ISBN 10 1447102401
- ISBN 13 9781447102403
- BindingPaperback
- LanguageEnglish
- Number of pages454
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