Effect of sensor SNR and extinction ratio on polarimetric imaging error for nanowire-based systems.

High-sensor SNR and high extinction ratio (ER), which are often contradictory requirements for nanowire-filter-based polarimetric imaging systems, aid in attenuating polarimetric imaging system errors. Expressions were derived to analyze their attenuation effects and then simplified using photoelectronic numbers received by superpixels (PNRS). The first-derivative ratios of PNRS and ER were calculated to compare their attenuation effects. Mathematical models and experiments conducted using polarimetric imaging systems with various ERs and PNRSs indicate that systems with low PNRS and high ER exhibit a polarization error affected more by the attenuation effect of the PNRS than that of the ER. When the system ER is higher than 28, the attenuation effect of the PNRS is higher than that of the ER. Thus, system error attenuation is a trade-off between sensor SNR and ER.

Using an improved SIFT algorithm and fuzzy closed-loop control strategy for object recognition in cluttered scenes.

Partial occlusions, large pose variations, and extreme ambient illumination conditions generally cause the performance degradation of object recognition systems. Therefore, this paper presents a novel approach for fast and robust object recognition in cluttered scenes based on an improved scale invariant feature transform (SIFT) algorithm and a fuzzy closed-loop control method. First, a fast SIFT algorithm is proposed by classifying SIFT features into several clusters based on several attributes computed from the sub-orientation histogram (SOH), in the feature matching phase only features that share nearly the same corresponding attributes are compared. Second, a feature matching step is performed following a prioritized order based on the scale factor, which is calculated between the object image and the target object image, guaranteeing robust feature matching. Finally, a fuzzy closed-loop control strategy is applied to increase the accuracy of the object recognition and is essential for autonomous object manipulation process. Compared to the original SIFT algorithm for object recognition, the result of the proposed method shows that the number of SIFT features extracted from an object has a significant increase, and the computing speed of the object recognition processes increases by more than 40%. The experimental results confirmed that the proposed method performs effectively and accurately in cluttered scenes.