Multimodal Interaction in Image and Video Applications by Angel D. Sappa, Jordi Vitrià

By Angel D. Sappa, Jordi Vitrià

Traditional development popularity (PR) and laptop imaginative and prescient (CV) applied sciences have often eager about complete automation, even supposing complete automation frequently proves elusive or unnatural in lots of purposes, the place the expertise is anticipated to aid instead of change the human brokers. even if, no longer the entire difficulties should be immediately solved being the human interplay the single method to take on these functions.

Recently, multimodal human interplay has develop into an incredible box of accelerating curiosity within the examine neighborhood. complex man-machine interfaces with excessive cognitive functions are a sizzling learn subject that goals at fixing not easy difficulties in snapshot and video purposes. really, the belief of laptop interactive structures was once already proposed at the early phases of laptop technology. these days, the ubiquity of photograph sensors including the ever-increasing computing functionality has open new and not easy possibilities for study in multimodal human interaction.

This ebook goals to teach how current PR and CV applied sciences can evidently evolve utilizing this new paradigm. The chapters of this ebook exhibit assorted winning case experiences of multimodal interactive applied sciences for either photograph and video functions. They disguise a large spectrum of purposes, starting from interactive handwriting transcriptions to human-robot interactions in actual environments.

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Extra resources for Multimodal Interaction in Image and Video Applications (Intelligent Systems Reference Library)

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Joost van de Weijer acknowledges support of Ramon y Cajal fellowship. 44 J. van de Weijer, F. Khan, and M. Masana References 1. : A divisive information-theoretic feature clustering algorithm for text classification. Journal of Machine Learning Research (JMLR) 3, 1265– 1287 (2003) 2. : Discriminative compact pyramids for object and scene recognition. Pattern Recognition (PR) 45(4), 1627–1636 (2012) 3. : The pascal visual object classes challenge 2007 results (2007) 4. : A discriminatively trained, multiscale, deformable part model.

Some clusters show constancy over color, whereas others are more constant over shape. following, we will shortly describe the Portmanteau representation which we apply in our prototype [12]. This representation combines the desired properties cue binding, cue weighting and scalability. A straightforward method to obtain the binding property is by considering a product vocabulary that contains a new word for every combination of shape and color terms. , cN } represent the visual shape and color vocabularies, respectively.

The visual feature vectors are finally L2-normalized. Runlength Descriptor (RLD) The Runlength descriptor [2] is based on a histogram of the length of the runs of consecutive pixels in the four main directions (horizontal, vertical and diagonals). The length of the runs is quantized in different intervals and thus, every bin of the histograms corresponds to one of these quantization intervals. The histograms in the four directions are concatenated to obtain the final descriptor. In addition, the image can be split into several regions at different levels of resolution (for instance, 2x2 and 4x4).

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