No abstract

Handwriting analysis is a method to predict personality of an author and to better understand the writer. Allograph and allograph combination analysis is a scientific method of writer identification and evaluating the behavior. To make this computerized we considered six main different types of features: (i) size of letters, (ii) slant of letters and words, (iii) baseline, (iv) pen pressure, (v) spacing between letters and (vi) spacing between words in a document to identify the personality of the writer. Segmentation is used to calculate the features from digital handwriting and is trained to SVM which outputs the behavior of the writer. For this experiment 100 different writers were used for different handwriting data samples. The proposed method gives about 94% of accuracy rate with RBF kernel. In this paper an automatic method has been proposed to predict the psychological personality of the writer. The system performance is measured under two different conditions with the same sample.

No abstract

No abstract

Close monitoring, proper control and management of plant diseases are essential in the efficient cultivation of crops. This paper presents a scheme that uses mobile phones for real-time on-field imaging of diseased plants followed by disease diagnosis via analysis of visual phenotypes. A threshold based offloading scheme is employed for judicious sharing of the computational load between the mobile device and a central server at the plant pathology laboratory, thereby offering a trade-off between the power consumption in the mobile device and the transmission cost. The part of the processing carried out in the mobile device includes leaf image segmentation and spotting of disease patch using improved k-means clustering. The algorithm is simple and hence suitable for Android based mobile devices. The segmented image is subsequently communicated to the central server. This ensures reduced transmission cost compared to that in transmitting full leaf image.

Abstract

In this paper, a novel approach for feature extraction from natural image such as plant leaf is proposed for automated living plant species recognition useful for botanical students in their research for plant species identification. A new multi-resolution and multidirectional Curvelet transform is applied on subdivided leaf images to extract leaf information, mathematically so that the orientation of the object in the image does not matter and which also increase the accuracy rate. These coefficients will be the input to a trained SVM classifier to classify the result. Compared to other exiting methods and tools in this field of plant species recognition the proposed system gives a higher accuracy rate of around 95.6% with 624 leaf dataset.

No abstract