Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.This study was supported by Grants-in-Aid for Scientifc Research (Grant Number H1805485) from the Japan Society for the Promotion of Science

On the 3D point cloud for human-pose estimation

This thesis aims at investigating methodologies for estimating a human pose from a 3D point cloud that is captured by a static depth sensor. Human-pose estimation (HPE) is important for a range of applications, such as human-robot interaction, healthcare, surveillance, and so forth. Yet, HPE is challenging because of the uncertainty in sensor measurements and the complexity of human poses. In this research, we focus on addressing challenges related to two crucial components in the estimation process, namely, human-pose feature extraction and human-pose modeling. In feature extraction, the main challenge involves reducing feature ambiguity. We propose a 3D-point-cloud feature called viewpoint and shape feature histogram (VISH) to reduce feature ambiguity by capturing geometric properties of the 3D point cloud of a human. The feature extraction consists of three steps: 3D-point-cloud pre-processing, hierarchical structuring, and feature extraction. In the pre-processing step, 3D points corresponding to a human are extracted and outliers from the environment are removed to retain the 3D points of interest. This step is important because it allows us to reduce the number of 3D points by keeping only those points that correspond to the human body for further processing. In the hierarchical structuring, the pre-processed 3D point cloud is partitioned and replicated into a tree structure as nodes. Viewpoint feature histogram (VFH) and shape features are extracted from each node in the tree to provide a descriptor to represent each node. As the features are obtained based on histograms, coarse-level details are highlighted in large regions and fine-level details are highlighted in small regions. Therefore, the features from the point cloud in the tree can capture coarse level to fine level information to reduce feature ambiguity. In human-pose modeling, the main challenges involve reducing the dimensionality of human-pose space and designing appropriate factors that represent the underlying probability distributions for estimating human poses. To reduce the dimensionality, we propose a non-parametric action-mixture model (AMM). It represents high-dimensional human-pose space using low-dimensional manifolds in searching human poses. In each manifold, a probability distribution is estimated based on feature similarity. The distributions in the manifolds are then redistributed according to the stationary distribution of a Markov chain that models the frequency of human actions. After the redistribution, the manifolds are combined according to a probability distribution determined by action classification. Experiments were conducted using VISH features as input to the AMM. The results showed that the overall error and standard deviation of the AMM were reduced by about 7.9% and 7.1%, respectively, compared with a model without action classification. To design appropriate factors, we consider the AMM as a Bayesian network and propose a mapping that converts the Bayesian network to a neural network called NN-AMM. The proposed mapping consists of two steps: structure identification and parameter learning. In structure identification, we have developed a bottom-up approach to build a neural network while preserving the Bayesian-network structure. In parameter learning, we have created a part-based approach to learn synaptic weights by decomposing a neural network into parts. Based on the concept of distributed representation, the NN-AMM is further modified into a scalable neural network called NND-AMM. A neural-network-based system is then built by using VISH features to represent 3D-point-cloud input and the NND-AMM to estimate 3D human poses. The results showed that the proposed mapping can be utilized to design AMM factors automatically. The NND-AMM can provide more accurate human-pose estimates with fewer hidden neurons than both the AMM and NN-AMM can. Both the NN-AMM and NND-AMM can adapt to different types of input, showing the advantage of using neural networks to design factors

Cytochemical studies of planetary microorganisms explorations in exobiology

Experiments to identify free living organisms in soils that may be substantially simpler in genetic content, and mirroring a more primitive stage of evolution than the species with which we are familiar to date, were designed. Organic chemical studies on the composition and disposition of elementary carbon leave nothing wanting as an aboriginal substrate for the original of life and early chemical evolution. Such studies were missed when it came to the interpretation of the Viking lander data, and needed for conceptual planning of future planetary missions

Purification and Characterisation of the Isocitrate Dehydrogenase From Streptomyces coelicolor and Cloning of Its Gene

As part of an effort to increase our understanding of central metabolic pathways in streptomycetes, the TCA cycle enzyme, isocitrate dehydrogenase (IDH), was studied from Streptomyces coelicolor. In Eschericia coli grown on acetate as a sole carbon source, IDH plays an important role in controlling the flux of carbon between the glyoxylate bypass and the TCA cycle. The study of any such control in streptomycetes is of interest because these pathways are important in providing energy and precursors for both primary and secondary metabolism

Seventeenth Annual Report of the Bureau of American Ethnology to the Secretary of the Smithsonian Institution 1895-96.

17th Annual Report of the Bureau of American Ethnology. (no date) HD 316 (pts. 1 and 2), 55-3, v94-95. 845p. [3836-3837] Research related to the American Indian; including the Seri Indians (McGee), the calendar history of the Kiowas (Mooney), Navajo houses (Mindeleff), and archaeology of Arizona (Fewkes); etc

Report of the Commissioner of Indian Affairs, 1886

Annual Message to Congress with Documents; Pres. Cleveland. 6 Dec. HED 1, 49-2, vl-12, 11440p. [2460-2471] Surrender of hostile Apaches under the leadership of Geronimo; recommends general legislation calling for allotment of Indian lands in severalty; land title issues involving railroads and settler encroachment need legislative action; annual report of the Sec. of War (Serials 2461-2465); annual report of the Sec. of Interior (Serials 2467-24 70): annual report of the Gen . Land Office (Serial 2468); annual report of the CIA (Serial 2467

Annual report of Commissioner of Indian Affairs, 1887

Annual Message to Congress with Documents; Pres. Cleveland. 6 Dec. HED 1, 50-1. vl-15. 14260p. [2532-2547] Annual report of the Sec. of War (Serials 2533-2538); annual report of the Sec. of Interior (Serials 2541-2546): annual report of the Gen. Land Office (Serial 2541); annual report of the CIA (Serial 2542): etc