Tata Consultancy Services Limited

Rokkam Krishna Kanth

Jayavardhana Rama Gubbi Lakshminarasimha

Achanna Anil Kumar

Arpan Pal

Balamuralidhar Purushothaman

Tapas Chakravarty

Akshaya Ramaswamy

Subhasri Chatterjee

Existing Mueller Matrix polarization techniques that rely only on polarization properties are insufficient for accurate characterization of transparent objects. Embodiments of the present disclosure provide a method and system for Mueller Matrix polarimetric characterization of transparent object using optical properties along with the polarization properties to accurately characterize the transparent object. The polarization properties of are derived from a decomposed Mueller matrix element. Additionally, the method derives the optical properties by further subjecting the decomposed Mueller matrix element to Fresnel's law-based analysis and a reverse Monte Carlo analysis to extract optical properties such as a material refractive index and a material attenuation index. Optical properties vary with changes in the material property caused due to several factors such as manufacturing defect, aberration, inclusion of an impurity such as bubble or dust etc. Thus, considering the optical properties along with the polarization properties enables enhanced, accurate characterization of the transparent object.

Polarimetric characterization of transparent objects

This relates generally to a method and a system for spatio-temporal polarization video analysis. The spatio-temporal polarization data is analyzed for a computer vision application such as object detection, image classification, image captioning, image reconstruction or image inpainting, face recognition and action recognition. Numerous classical and deep learning methods have been applied on polarimetric data for polarimetric imaging analysis, however, the available pre-trained models may not be directly suitable on polarization data, as polarimetric data is more complex. Further compared to analysis of the polarimetric images, a significant number of actions can be detected by polarimetric videos, hence analyzing polarimetric videos is more efficient. The disclosure is a spatio-temporal analysis of polarization video. The disclosed techniques include configuring a set of parameters from the polarization video to train a spatio-temporal deep network architecture for analyzing polarimetric videos for computer vision applications.

Method and a system for spatio-temporal polarization video analysis

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Bengaluru, India

Rokkam Krishna Kanth