Concise Computer Vision: An Introduction into Theory and Algorithms (Undergraduate Topics in Computer Science)
Format: PDF / Kindle (mobi) / ePub
Many textbooks on computer vision can be unwieldy and intimidating in their coverage of this extensive discipline. This textbook addresses the need for a concise overview of the fundamentals of this field.
Concise Computer Vision provides an accessible general introduction to the essential topics in computer vision, highlighting the role of important algorithms and mathematical concepts. Classroom-tested programming exercises and review questions are also supplied at the end of each chapter.
Topics and features:
* Provides an introduction to the basic notation and mathematical concepts for describing an image, and the key concepts for mapping an image into an image
* Explains the topologic and geometric basics for analysing image regions and distributions of image values, and discusses identifying patterns in an image
* Introduces optic flow for representing dense motion, and such topics in sparse motion analysis as keypoint detection and descriptor definition, and feature tracking using the Kalman filter
* Describes special approaches for image binarization and segmentation of still images or video frames
* Examines the three basic components of a computer vision system, namely camera geometry and photometry, coordinate systems, and camera calibration
* Reviews different techniques for vision-based 3D shape reconstruction, including the use of structured lighting, stereo vision, and shading-based shape understanding
* Includes a discussion of stereo matchers, and the phase-congruency model for image features
* Presents an introduction into classification and learning, with a detailed description of basic AdaBoost and the use of random forests
This concise and easy to read textbook/reference is ideal for an introductory course at third- or fourth-year level in an undergraduate computer science or engineering programme.
With Γ 1(p)=1 for and 0<Γ 1(p)<1 otherwise. For example, if Γ 1(p)<0.5, then reject the calculated disparities as being inconsistent. This confidence measure is based on an expectation that two consistent results support each other; but daily life often tells us different stories (just think about two newspapers telling different lies). Fitted Parabola It appears to be more appropriate to have confidence measures that are based on applied data cost functions, their values, or matching models.
Each descriptor is weighted uniformly by ω(i)=1/m for i=1,…,m. Fig. 10.15Five examples of descriptors in 2D descriptor space Corresponding events (e.g. images showing a human face), leading to those descriptors, have been classified. A bold blue circular line indicates the class number +1 and the thin red line class number −1. We assume two weak classifiers (i.e. w=2), denoted by h 1 and h 2. The classifier h 1 assigns the class number “+1” to any of the five descriptors, and the classifier h.
Probability estimates instead. For example, if r j,0
Topologic and geometric basics for analysing image regions, as well as two common ways for analysing distributions of image values. It also discusses line and circle detection as examples for identifying particular patterns in an image. 3.1 Basic Image Topology In Sect. 1.1.1 it was stated that pixels do not define a particular adjacency relation between them per se. It is our model that specifies a chosen adjacency relation. The selected adjacency relation has later significant impacts on.
Left to right, top to bottom). Right: Which order would result if the stack in the fill-algorithm in Fig. 5.8 is replaced by a first-in-first-out queue? The order shown on the right is used for visiting adjacent pixels when the algorithm is used for labelling a segment of white pixels. It would be a clockwise or counter-clockwise order of 8-adjacent pixels when labelling a segment of black pixels. Example 5.3 (Dependency on Seed Point if not Using an Equivalence Relation) Consider the.