Significant physical attributes affecting quality of Indian black (CTC) tea

In this research work, an attempt was made to discriminate different grades of black crush-tear-curl (CTC) tea based upon their physical attributes such as color of brewed liquor, texture, size and shape of the tea granules obtained by machine vision technique. The principal component analysis (PCA) was applied over two types of data. First, tea samples with seven different quality grades but same mechanical grading and second, samples with same quality grade but nine different mechanical grades (Brokens, Fannings and Dust) were considered for analysis, respectively. The results of PCA showed that best discrimination (100%) in both types of data was given by color attributes only. Correlations among tea samples and physical attributes were determined. Based upon these results it may be concluded that color only attributes are the most significant and sufficient for quantification of tea quality whereas other physical attributes contribute so little to quality estimation that they may be ignored.     

An augmented image gradients based supervised regression technique for iris center localization

Multimedia Tools and Applications - This paper describes a robust and accurate technique for iris center localization by combining supervised regression based approach and image gradients. The...     

Discrimination analysis of Indian tea varieties based upon color under optimum illumination

The paper investigates optimum light illumination intensity for maximum discrimination of the Indian black tea varieties using machine vision. The technique involves acquisition and analysis of color information of brewed tea liquor after setting different light illumination intensities between 700 and 1,300 lux. The data involving color attributes was analyzed using principal component analysis. It was observed that best discrimination among these tea varieties was achieved between 1,000 and 1,050 lux. The correlation analysis was also performed to show the relationship between color attributes and tea varieties. The study reveals that color attributes are significant parameters for discriminating Indian black tea varieties. The proposed technique is fast, convenient and low cost over the methods involving chemical analysis. It is an important step towards standardization of quality evaluation of tea by instrumented means.     

Eye gaze tracking based directional control interface for interactive applications

This paper proposes an unobtrusive and calibration-free framework towards eye gaze tracking based interactive directional control interface for desktop environment using simple webcam under unconstrained settings. The proposed eye gaze tracking involved hybrid approach designed by combining two different techniques based upon both supervised and unsupervised methods wherein the unsupervised image gradients method computes the iris centers over the eye regions extracted by the supervised regression based algorithm. Experiments performed by the proposed hybrid approach to detect eye regions along with iris centers over challenging face image datasets exhibited exciting results. Similar approach for eye gaze tracking worked well in real-time by using a simple web camera. Further, PC based interactive directional control interface based upon iris position has been designed that works without needing any prior calibrations unlike other Infrared illumination based eye trackers.

The proposed work may be useful to the people with full body motor disabilities, who need interactive and unobtrusive eye gaze control based applications to live independently.

     

Quality evaluation of black CTC teas based upon seasonal variations

Effects of seasonal variations on the colour and chemical constituents of Indian black tea were investigated. The experimentation involved collection of different black tea samples that were cultivated at the same tea garden but harvested in three different seasons. Measurement of moisture content was done along with theaflavins (TFs), thearubigins (TRs) and colour (CIE La*b* values) of the brewed tea liquor using spectrophotometric approach. The correlation analysis between chemical constituents, that is, TFs and TRs, moisture content, colour and quality grading was done by using principal component analysis (PCA). The results suggest that quality of tea is dependent upon seasonal variations in terms of chemical changes such as TFs, TRs and moisture content, which also correlate well with colour. The efficacy of the colour of brewed tea liquor in explaining the changes in tea quality due to seasonal variations can be beneficial for scientific fixation of prices at tea auction centres.     

Classification of tea grains based upon image texture feature analysis under different illumination conditions

This paper discusses the role of illumination in discrimination of tea samples based upon textural features of tea granules. The images of tea granules were acquired using 3CCD color camera under Dual Ring light which consists of both Darkfield as well as Brightfield type of illumination. Ten graded tea samples were analyzed. Five textural features were ‘entropy’, ‘contrast’, ‘homogeneity’, ‘correlation’ and ‘energy’ obtained under both illuminations. The acquired textural features were subjected to principal component analysis (PCA). The results showed that best discrimination was obtained with Darkfield illumination with a variance of 96% whereas Brightfield illumination showed low discrimination with only 83% variance. Analysis of PCA biplot indicated correlations among graded tea samples and textural features. The study concludes that textural features may be used to estimate tea quality under Darkfield illumination being non-destructive and quick technique.