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This paper proposes a new methodology to extract biometric features of plant leaf structures. Combining computer vision techniques and plant taxonomy protocols, these methods are capable of identifying plant species. The biometric measurements are concentrated in leaf internal forms, specifically in the veination system. The methodology was validated with real cases of plant taxonomy, and eleven species of passion fruit of the genus Passiflora were used. The features extracted from the leaves were applied to the neural network system to perform the classification of species. The results showed to be very accurate in correctly differentiating among species with 97% of success. The computer vision methods developed can be used to assist taxonomists to perform biometric measurements in plant leaf structures.

Purpose: The knowledge of the normal geometrical characteristics of the proximal humerus is crucial to the success of its arthroplasty. This important information, is however, limited for the South African population. Therefore, this study investigates the three-dimensional morphometric parameters, specifically examining the intra-ancestral differences within the South African population. Methods: With the aid of geometry extraction techniques, various morphometric characteristics were measured on South African cadaveric humeri originating from three ethnicities including whites, blacks, and mixed in the ratio 1:2:4. Results: There is a significant mean difference in humeral head diameter between blacks and whites and between mixed and whites with a mean difference of $-$4.86, 95% CI ($-9.40$, $-0.32$) and $-4.50$, 95% CI ($-8.56$, $-0.44$), respectively. Similarly, for articular surface diameter, a significant mean difference of $-4.58$, 95% CI ($-9.10$, $-0.0646$) and $-4.32$, 95% CI ($-8.36$, $-0.2854$) were recorded between blacks and whites and between mixed and whites, respectively. Conclusion: The outcome of our study showed that the shape of the South African proximal humerus varies distinctively within the different ethnicities that were measured. The findings from this study may provide the data required to design and develop a new shoulder implant appropriate for South African patients.

The patellofemoral joint is one of the parts of the knee extension mechanism that plays a role in the stability of the knee by enlarging the force arm of the quadriceps muscle and changing the direction of the muscle strength. For the entire knee joint to perform its task painlessly and functionally, the positions and strength of the muscles, the strength of the ligaments, and their reaction to movement must be compatible. The Insall–Salvati (Ins-Sal) index is useful for showing changes in patellar height produced by repositioning the tibial plateau, in other words, showing changes in patellar tendon length. Patella height is an important value to be taken into account in knee prosthesis surgery, tibial osteotomy, and anterior cruciate ligament reconstruction. The morphometric relationship between the reference measurements of the distal femur and proximal tibia and the position of the patella will be useful in determining the natural anatomy. In this study, we aimed to determine the relationship between patella height and distal femur and proximal tibia reference areas by using the artificial neural network method as an alternative approach method. In order to assess the performance of the estimation of the Ins-Sal index, the four ANN model with six input combinations which included age, gender and the reference measurements for the right and left sides have been constructed and tested. The MSE and $r$ values are calculated for every four models for the training and test phase. The results show that the proposed approach for modeling of relation between reference measurements and the Ins-Sal index is a powerful approach.

Sufficient information on slope characteristics is key in detailed mapping of landslides. On-site investigations are mainly conducted to closely identify landslide features and gather relevant geomorphological data. Remote sensing is also used as a supplementary tool as it offers a different perspective in landslide investigations, allowing additional insights that are not commonly observed with conventional in situ methods. We used unmanned aerial vehicles (UAVs) to survey deep-seated landslide sites in the Philippines, and generated digital elevation models (DEMs) from the acquired aerial images. Different morphometric parameters were extracted from the DEMs producing nine derivatives. These derivatives were consolidated through principal component analysis into a composite raster image that emphasized morphologically-distinct surface features. Five morphometric parameters, namely, slope, aspect, multiple shaded relief, roughness, and surface relief ratio, were found to be the most descriptive of the surface morphology of two deep-seated landslide sites. In the village of Parasanon, Pinabacdao, Samar Island, most of the previously mapped landslide scarps and cracks were highlighted. In addition, potential new features such as the upper boundary of the landslide, extensions of cracks, and a linear feature at the right flank were identified. In Manghulyawon village in La Libertad, Negros Island, the extent of the head scarp was recognized, and depletion and accumulation zones were revealed. These features require further field validation, as would be the case with every other remotely-sourced data. In addition, it should be noted that analysis from this method is only limited to the ground surface without significant vegetative cover and aboveground structures. The study demonstrated how remote sensing through UAV-derived DEMs supplemented findings from conventional approaches, providing a better understanding of landslides.