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Abstract:Identifying individual trees and delineating their canopy structures from the forest point cloud data acquired by an airborne LiDAR (Light Detection And Ranging) has significant implications in forestry inventory. Once accurately identified, tree structural attributes such as tree height, crown diameter, canopy based height and diameter at breast height can be derived. This paper focuses on a novel computationally efficient method to adaptively calibrate the kernel bandwidth of a computational scheme based on mean shift-a non-parametric probability density-based clustering technique-to segment the 3D (three-dimensional) forest point clouds and identify individual tree crowns. The basic concept of this method is to partition the 3D space over each test plot into small vertical units (irregular columns containing 3D spatial features from one or more trees) first, by using a fixed bandwidth mean shift procedure and a small square grouping technique, and then rough estimation of crown sizes for distinct trees within a unit, based on an original 2D (two-dimensional) incremental grid projection technique, is applied to provide a basis for dynamical calibration of the kernel bandwidth for an adaptive mean shift procedure performed in each partition. The adaptive mean shift-based scheme, which incorporates our proposed bandwidth calibration method, is validated on 10 test plots of a dense, multi-layered evergreen broad-leaved forest located in South China. Experimental results reveal that this approach can work effectively and when compared to the conventional point-based approaches (e.g., region growing, k-means clustering, fixed bandwidth or multi-scale mean shift), its accuracies are relatively high: it detects 86 percent of the trees ("recall") and 92 percent of the identified trees are correct ("precision"), showing good potential for use in the area of forest inventory.

When people compare a computer-generated illustration to a hand-drawn illustration of the same object, they usually perceive differences. This seems to indicate that the two kinds of images follow different aesthetic principles. To explore and explain these differences, the authors compare texture stippling in hand-drawn and computer-generated illustrations, using image-processing analysis techniques.

Lifelogging tools aim to precisely capture daily experiences of people from the first-person perspective. Although there have been numerous lifelogging tools developed for users to record the external environment around them, the internal part of experience characterized by emotions seems to be neglected in the lifelogging field. However, the internal experiences of people are important and, therefore, lifelogging tools should be able to capture not only the environmental data, but also emotional experiences, thereby providing a more complete archive of past events. Moreover, there are implicit emotions that cannot be consciously experienced, but still influence human behaviors and memories. It has been proven that conscious emotions can be recognized from physiological signals of the human body. This fact may be used to enhance life-logs with information about unconscious emotions, which otherwise would remain hidden. On the other hand, it is not clear if unconscious emotions can be recognized from physiological signals and differentiated from conscious emotions. Therefore, an experiment was designed to elicit emotions (both conscious and unconscious) with visual and auditory stimuli and to record cardiovascular responses of 34 participants. The experimental results showed that heart rate responses to the presentation of the stimuli are unique for every category of the emotional stimuli and allow differentiation between various emotional experiences of the participants.

Zishi is a garment designed to support posture monitoring for the purposes of rehabilitation training. It has been designed with attention to presenting accurate and informative feedback to patients regarding their thoracic and shoulder posture as well as comfort, ease of use, wearability and aesthetics. Zishi can be useful during rehabilitation training for a variety of patient groups. So far, we have been concerned with two broad training scenarios a) for arm-hand (neurological) rehabilitation training after stroke or for MS and spinal cord injury patients. B) for shoulder patients. Zishi consists of a garment integrated with smart textiles and wearable electronics. It presents real-time feedback as a vibration delivered through the garment, visual and audio instructions through androidhand held device (smartphone or tablet).

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