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.

Large Scale Multimedia Management: A Comprehensive Review

Multimedia applications deal, in most cases, with an extremely high volume of multimedia data (2D and 3D images, sounds, videos). That is why efficient algorithms should be developed to analyze and process these large datasets. On the other hand, multimedia management is based on efficient representation of knowledge which allows efficient data processing and retrieval. The main challenge in this era is to achieve clever and quick access to these huge datasets to allow easy access to the data and in a reasonable time. In this context, large-scale image retrieval is a fundamental task. Many methods have been developed in the literature to achieve fast and efficient navigating in large databases by using the famous content-based image retrieval (CBIR) methods associated with these methods allowing a decrease in the computing time, such as dimensional reduction and hashing methods. More recently, these methods based on convolutional neural networks (CNNs) for feature extraction and image classification are widely used. In this paper, we present a comprehensive review of recent multimedia retrieval methods and algorithms applied to large datasets of 2D/3D images and videos. This editorial paper discusses the mains challenges of multimedia retrieval in a context of large databases.

Three-Dimensional Sparse SAR Imaging with Generalized Lq Regularization

Three-dimensional (3D) synthetic aperture radar (SAR) imaging provides complete 3D spatial information, which has been used in environmental monitoring in recent years. Compared with matched filtering (MF) algorithms, the regularization technique can improve image quality. However, due to the substantial computational cost, the existing observation-matrix-based sparse imaging algorithm is difficult to apply to large-scene and 3D reconstructions. Therefore, in this paper, novel 3D sparse reconstruction algorithms with generalized Lq-regularization are proposed. First, we combine majorization–minimization (MM) and L1 regularization (MM-L1) to improve SAR image quality. Next, we combine MM and L1/2 regularization (MM-L1/2) to achieve high-quality 3D images. Then, we present the algorithm which combines MM and L0 regularization (MM-L0) to obtain 3D images. Finally, we present a generalized MM-Lq algorithm (GMM-Lq) for sparse SAR imaging problems with arbitrary q0≤q≤1 values. The proposed algorithm can improve the performance of 3D SAR images, compared with existing regularization techniques, and effectively reduce the amount of calculation needed. Additionally, the reconstructed complex image retains the phase information, which makes the reconstructed SAR image still suitable for interferometry applications. Simulation and experimental results verify the effectiveness of the algorithms.

Protocols for Generating Surfaces and Measuring 3D Organelle Morphology Using Amira

High-resolution 3D images of organelles are of paramount importance in cellular biology. Although light microscopy and transmission electron microscopy (TEM) have provided the standard for imaging cellular structures, they cannot provide 3D images. However, recent technological advances such as serial block-face scanning electron microscopy (SBF-SEM) and focused ion beam scanning electron microscopy (FIB-SEM) provide the tools to create 3D images for the ultrastructural analysis of organelles. Here, we describe a standardized protocol using the visualization software, Amira, to quantify organelle morphologies in 3D, thereby providing accurate and reproducible measurements of these cellular substructures. We demonstrate applications of SBF-SEM and Amira to quantify mitochondria and endoplasmic reticulum (ER) structures.

USING UAV-BASED 3D IMAGES OF INDIVIDUAL TREE SPECIES IN DISTANCE EDUCATION IN FORESTRY

Distance education has been offered for years, but the integration of technological developments and opportunities into education has recently increased its popularity and event it became an indispensable method during the Covid-19 pandemic period. In distance education, accessing all class materials such as lecture presentations, class notes, reading materials, videos, live chats or class hours, and archive records allow students (participants) to learn without being in the same environments with teachers or learners. Technology has made vast contributions to the field of education. For instance, 3D as a teaching tool for the class attracts students’ attention, makes the learning process more enjoyable, and increases participation. In particular, for the disciplines, such as forestry, earth, and environmental sciences, which require laboratory exercises, field observation, field trips, and in-situ measurements, 3D modeling has provided many benefits in distance education. It enables 3D demonstration of the individual tree species to develop a virtual field laboratory. This study focused on the data sources and techniques to generate a 3D model of the individual tree species that forestry students used for distance education. The capabilities of the method in the generation of 3D models were evaluated by using UAV-based SfM photogrammetry. The results indicated that implementing 3D images of individual tree species can be a promising method that may increase the interest, interaction and satisfaction of the students in distance education in forestry.

Prevalence of favourable anatomy for palatal emergence of an immediate implant in the maxillary central incisor post-extraction site.

The purpose of this study was to determine the prevalence of favourable anatomy for palatal emergence of an immediate flapless implant in the maxillary central incisor post-extraction site. Implants were virtually placed into maxillary central incisor sites using 3D implant planning software. Following a strict implant placement criteria to keep a safety distance to the buccal plate and other anatomical structures, sockets where assessed to determine their suitability for a palatally emerging implant. From 321 patients included in this study, 62.3% presented a suitable socket anatomy for an immediate implant to be placed with the angulation for a screw retained crown. In 29% of the cases, the implants had to be labially tilted to keep a minimum distance to the buccal plate. 8.7% were unsuitable for immediate implants due to anatomic limitations. The position and angulation for an implant in the maxillary central incisor socket should be carefully assessed preoperatively with 3D images, as many sites will not be candidates for a palatal emergence and thus, a screw retained restoration.

Natural Hydrocarbon Samples Classification by Topological Analysis Method

Nowadays material science involves powerful 3D imaging techniques such as X-ray computed tomography that generates high-resolution images of different structures. These methods are widely used to reveal information about the internal structure of geological cores; therefore, there is a need to develop modern approaches for quantitative analysis of the obtained images, their comparison, and classification. Topological persistence is a useful technique for characterizing the internal structure of 3D images. We show how persistent data analysis provides a useful tool for the classification of porous media structure from 3D images of hydrocarbon reservoirs obtained using computed tomography. We propose a methodology of 3D structure classification based on geometry-topology analysis via persistent homology.