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

By Margarita N. Favorskaya, Lakhmi C. Jain

The examine booklet is concentrated at the contemporary advances in machine imaginative and prescient methodologies and suggestions in perform. The Contributions include:

· Human motion popularity: Contour-Based and Silhouette-based methods.

· the applying of computer studying concepts to actual Time viewers research approach.

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

· a brand new Real-Time approach to Contextual photo Description and Its program in robotic Navigation and clever keep an eye on.

· conception of Audio visible info for cellular robotic movement keep an eye on structures.

· Adaptive Surveillance Algorithms in accordance with the location Analysis.

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

· Navigation of independent Underwater autos utilizing Acoustic and visible information Processing.

· effective Denoising Algorithms for clever acceptance structures.

· photo Segmentation in line with Two-dimensional Markov Chains.

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

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Example text

At the end, B normalized numbers are obtained. These numbers are called the HOOF feature descriptors of image. 10b shows the HOOF binned orientation of angles to 4 bins. The HOOF is similar to the HOG but with some differences. First, the HOOF does not require for sliding windows to be overlapping, because it represents the optical flow of motion in an image, though, this image can be divided into several equal non-overlapping windows [1, 42]. Second, the HOOF is binned symmetric angles over y-axis together to overcome problem of detection movement direction while the HOG does not.

Al-Ali and Milanova [44] employed an Aligned Motion Image (AMI) as a feature for human action recognition. Each video sample is represented by an AMI. Then, the Structural Similarity Index Measure (SSIM) is used to measure the distances among these images. In the last experiment of this chapter, the SSIM is employed but as a feature for representing each video. 924 % correct recognition rate. The contributions of this chapter are the following. A novel simple algorithm for human action recognition provides very good results in term of accuracy in contourbased features.

The computation of the HOG is based on magnitudes and angles of these gradients [42, 43]. This feature is extracted from image based on two parameters: number of overlapping windows on this image (N × N), and number of bins (B) for the gradients angles. Briefly, the HOG is computed through several steps. The gradients of an image are computed by filtering this image with horizontal kernel [–1, 0, 1] and vertical kernel [–1, 0, 1]−1. Then magnitudes and angles are computed based on the computed gradients.

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