•The new algorithm is evaluated on Flavia dataset and on self-collected dataset of 625 leaf images.•A thorough review of different plant identification techniques is presented.•Different Classifier are compared for classification task.•Results show that the proposed algorithm can attain plant recognition accuracy of up to 98.75% with Flavia and 97.25% on self-collected dataset.

Technological innovation in additive manufacturing has enabled it to reach the consumer market very rapidly since the last decade. The word 'additive manufacturing' is now widely replaced with the word '3D Printing'. The 3D printing industry is evolving from desktop to Industrial 3D printers all over the world, which will fulfill the requirements in Industry 4.0. The current usage of 3D printing is in medical and dental applications, rapid prototyping, food industry, and Jewelry making. This emerging technology disrupts the $12 Trillion manufacturing sector. In 2017, there is a 21% increase in the industrial growth of Additive Manufacturing from $6.06 billion to $7.3 billion. This article explains the current global trends in 3D printing in technologically advanced countries and emphasizes the use of 3D printing in accelerating the research and development in emerging economies, in particular to Pakistan. Further, it helps to bring the success stories regarding 3D printing for startups and the challenges related to the adaptation of 3D printing technologies in Pakistan to limelight.

Diabetic Retinopathy (DR) is an eye disorder that progressively leads to vision loss due to high glucose causing impairment of retinal blood vessels (BVs). ‘Retinal Bright Lesions’ such as ‘Hard Exudates’ (HEs) are plasma leakages from rapture retinal capillaries. HEs appear as hard, waxy, yellowish deposits from tiny spots to fat patches and signify moderate-severe Non-Proliferative Diabetic Retinopathy (NPDR). This paper proposes a simple, compact and computationally inexpensive technique for detection and classification of HEs using Digital Image Processing Techniques on digital fundus images complements and Artificial Neural Networks (ANN). The proposed technique unfolds through five stages i.e. Pre-processing, coarse detection, optimization, features detection & extraction followed by classification. ‘Speed Up Robust Features’ (SURF) algorithm has been used for features detection & extraction while ‘Feed-Forward Back-propagation’ (FFBP) ANN has been used for classification. The proposed technique has yielded 98.7% ‘Sensitivity’ (SE), 97.5% ‘Specificity’ (SP) and 97.7% ‘Accuracy’ (AC) on ‘DIARETDB1’ fundus images.

In this paper, a generalized 2D trajectory models are developed for human motion during walking and running for a user-centered Human-Computer Interaction design. The mapping of the head motion trajectories has been done through monocular cues to compute instantaneous displacement and velocity. The novelty of the work lies in using non-contact sensor only which saves from the use of complex body-contact sensors as well as improves data storage and processing. The 2D trajectory models developed are tested on 30 subjects and that show the average head motion trajectory map of a running person differs from that of the walking person on a treadmill. The quantification of head velocity values as a function of walking and running velocities is also tested which can be used to identify different locomotion velocities from head velocities.

Machine vision technologies hold the promise of enabling rapid and accurate fruit crop yield predictions in the field. The key to fulfilling this promise is accurate segmentation and detection of fruit in images of tree canopies. This paper proposes two new methods for automated counting of fruit in images of mango tree canopies, one using texture-based dense segmentation and one using shape-based fruit detection, and compares the use of these methods relative to existing techniques:—(i) a method based on K-nearest neighbour pixel classification and contour segmentation, and (ii) a method based on super-pixel over-segmentation and classification using support vector machines. The robustness of each algorithm was tested on multiple sets of images of mango trees acquired over a period of 3 years. These image sets were acquired under varying conditions (light and exposure), distance to the tree, average number of fruit on the tree, orchard and season. For images collected under the same conditions as the calibration images, estimated fruit numbers were within 16 % of actual fruit numbers, and the F1 measure of detection performance was above 0.68 for these methods. Results were poorer when models were used for estimating fruit numbers in trees of different canopy shape and when different imaging conditions were used. For fruit-background segmentation, K-nearest neighbour pixel classification based on colour and smoothness or pixel classification based on super-pixel over-segmentation, clustering of dense scale invariant feature transform features into visual words and bag-of-visual-word super-pixel classification using support vector machines was more effective than simple contrast and colour based segmentation. Pixel classification was best followed by fruit detection using an elliptical shape model or blob detection using colour filtering and morphological image processing techniques. Method results were also compared using precision–recall plots. Imaging at night under artificial illumination with careful attention to maintaining constant illumination conditions is highly recommended.