Control Systems

Download Computer Vision in Control Systems-2: Innovations in by Margarita N. Favorskaya, Lakhmi C. Jain PDF

By Margarita N. Favorskaya, Lakhmi C. Jain

The learn ebook is targeted at the fresh advances in laptop imaginative and prescient methodologies and options in perform. The Contributions include:

· Human motion acceptance: Contour-Based and Silhouette-based techniques.

· the appliance of computing device studying concepts to actual Time viewers research process.

· landscape building from Multi-view Cameras in outdoors Scenes.

· a brand new Real-Time approach to Contextual snapshot Description and Its software in robotic Navigation and clever keep watch over.

· notion of Audio visible details for cellular robotic movement keep an eye on structures.

· Adaptive Surveillance Algorithms according to the placement Analysis.

· more suitable, man made and mixed imaginative and prescient applied sciences for Civil Aviation.

· Navigation of self sufficient Underwater cars utilizing Acoustic and visible info Processing.

· effective Denoising Algorithms for clever popularity platforms.

· snapshot Segmentation according to Two-dimensional Markov Chains.

The booklet is directed to the PhD scholars, professors, researchers and software program builders operating within the parts of electronic video processing and machine imaginative and prescient technologies.

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Extra resources for Computer Vision in Control Systems-2: Innovations in Practice

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These shape properties or Poisson features are employed as a sequence for a number of frames in each video action. Gorelick et al. [2] created the Weizmann dataset, which contains ten different human actions done by nine actors, applied their recognition system on these dataset. The researchers recorded 100 % correct recognition rate using all sequence of frames in videos for classification using variant median Hausdroff distance. This distance is used because of the differences among videos in term of video length in frames.

Also, Dalal and Triggs [43] used another histogram feature, which is Histogram of Oriented Optical Flow (HOOF). By employing 18 S. Al-Ali et al. these features, the human object is detected. For classification, in both HOG and HOOF, a linear SVM is used. Both HOG and HOOF are mainly used in object recognition. In this chapter, both descriptors are employed as the features for human action recognition and achieved very good results in term of accuracy. Chaudhry et al. [1] employed the HOOF and the Binet-Cauchy kernels on nonlinear dynamical systems for recognition of human actions.

This feature is extracted based on the FDF. For the CDF feature, three experiments are conducted, based on the 38 S. Al-Ali et al. 3 Centroid-distance feature (CDF) experiments setting and results Exp. no. Feature parameters Classifier type Classifier parameters Correct recognition rate 1. No. of Fourier descriptors (FDs) = 18 No. of Fourier descriptors (FDs) = 18 No. 473 2. 3. 021 classifier. In these experiments, one parameter is only used for feature setting. This parameter is a number of the FDs used for boundary representation.

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