Hand Gesture of Recognition Pattern Analysis by Image Treatment Techniques

The goal of this project is to write a program in the C++ language that can recognize motions made by a subject in front of a camera. To do this, in the first place, a sequence of distance images has been obtained using a depth camera. Later, these images are processed through a series of blocks into which the program has been divided; each of them will yield a numerical or logical result, which will be used later by the following blocks. The blocks into which the program has been divided are three; the first detects the subject’s hands, the second detects if there has been movement (and therefore a gesture has been made), and the last detects the type of gesture that has been made accomplished. On the other hand, it intends to present to the reader three unique techniques for acquiring 3D images: stereovision, structured light, and flight time, in addition to exposing some of the most used techniques in image processing, such as morphology and segmentation.

Genome-Wide Analysis of the Peroxidase Gene Family and Verification of Lignin Synthesis-Related Genes in Watermelon

Watermelon (Citrullus lanatus) is an important horticultural crop worldwide, but peel cracking caused by peel hardness severely decreases its quality. Lignification is one of the important functions of class III peroxidase (PRX), and its accumulation in the plant cell wall leads to cell thickening and wood hardening. For in-depth physiological and genetical understanding, we studied the relationship between peel hardness and lignin accumulation and the role of PRXs affecting peel lignin biosynthesis using genome-wide bioinformatics analysis. The obtained results showed that lignin accumulation gradually increased to form the peel stone cell structure, and tissue lignification led to peel hardness. A total of 79 ClPRXs (class III) were identified using bioinformatics analysis, which were widely distributed on 11 chromosomes. The constructed phylogenetics indicated that ClPRXs were divided into seven groups and eleven subclasses, and gene members of each group had highly conserved intron structures. Repeated pattern analysis showed that deletion and replication events occurred during the process of ClPRX amplification. However, in the whole-protein sequence alignment analysis, high homology was not observed, although all contained four conserved functional sites. Repeated pattern analysis showed that deletion and replication events occurred during ClPRXs’ amplification process. The prediction of the promoter cis-acting element and qRT-PCR analysis in four tissues (leaf, petiole, stem, and peel) showed different expression patterns for tissue specificity, abiotic stress, and hormone response by providing a genetic basis of the ClPRX gene family involved in a variety of physiological processes in plants. To our knowledge, we for the first time report the key roles of two ClPRXs in watermelon peel lignin synthesis. In conclusion, the extensive data collected in this study can be used for additional functional analysis of ClPRXs in watermelon growth and development and hormone and abiotic stress response.

Land-use history impacts spatial patterns and composition of woody plant species across a 35-hectare temperate forest plot

Land-use history is the template upon which contemporary plant and tree populations establish and interact with one another and exerts a legacy on the structure and dynamics of species assemblages and ecosystems. We use the first census (2010–2014) of a 35-ha forest-dynamics plot at the Harvard Forest in central Massachusetts to describe the composition and structure of the woody plants in this plot, assess their spatial associations within and among the dominant species using univariate and bivariate spatial point-pattern analysis, and examine the interactions between land-use history and ecological processes. The plot includes 108,632 live stems ≥ 1 cm in diameter (2,215 individuals/ha) and 7,595 standing dead stems ≥ 5 cm in diameter. Live tree basal area averaged 42.25 m2/ha, of which 84% was represented by Tsuga canadensis (14.0 m2/ ha), Quercus rubra (northern red oak; 9.6 m2/ ha), Acer rubrum (7.2 m2/ ha) and Pinus strobus (eastern white pine; 4.4 m2/ ha). These same four species also comprised 78% of the live aboveground biomass, which averaged 245.2 Mg/ ha. Across all species and size classes, the forest contains a preponderance (> 80,000) of small stems (<10-cm diameter) that exhibit a reverse-J size distribution. Significant spatial clustering of abundant overstory species was observed at all spatial scales examined. Spatial distributions of A. rubrum and Q. rubra showed negative intraspecific correlations in diameters up to at least a 150-m spatial lag, likely indicative of crowding effects in dense forest patches following intensive past land use. Bivariate marked point-pattern analysis, showed that T. canadensis and Q. rubra diameters were negatively associated with one another, indicating resource competition for light. Distribution and abundance of the common overstory species are predicted best by soil type, tree neighborhood effects, and two aspects of land-use history: when fields were abandoned in the late 19th century and the succeeding forest types recorded in 1908. In contrast, a history of intensive logging prior to 1950 and a damaging hurricane in 1938 appear to have had little effect on the distribution and abundance of present-day tree species. Our findings suggest that current day composition and structure are still being influenced by anthropogenic disturbances that occurred over a century ago.

A Texture Features based Robust Facial Expression Recognition

Facial expression plays an important role in communicating emotions. In this paper, a robust method for recognizing facial expressions is proposed using the combination of appearance features. Traditionally, appearance features mainly divide any face image into regular matrices for the computation of facial expression recognition. However, in this paper, we have computed appearance features in specific regions by extracting facial components such as eyes, nose, mouth, and forehead, etc. The proposed approach mainly has five stages to detect facial expression viz. face detection and regions of interest extraction, feature extraction, pattern analysis using a local descriptor, the fusion of appearance features and finally classification using a Multiclass Support Vector Machine (MSVM). Results of the proposed method are compared with the earlier holistic representations for recognizing facial expressions, and it is found that the proposed method outperforms state-of-the-art methods.