Automatic Bruise Detection in Fruits Using Thermal Images

2017 ◽  
Vol 7 (5) ◽  
pp. 727-732 ◽  
Author(s):  
Manisha Satone ◽  
◽  
Samruddhi Diwakar ◽  
Vaishnavi Joshi ◽  
◽  
...  
Keyword(s):  
Thermal Images

ALGORITHMS OF TWO-STAGE RECOVERY OF RADIO THERMAL IMAGES

2017 ◽  
Vol 59 (1) ◽  
pp. 3-9
Author(s):  
V. K. Klochko ◽  
◽  
S. M. Gudkov ◽  
Keyword(s):  
Two Stage ◽  
Thermal Images

Evaluation of the heat changes in an ancient church because of restoration works: A microclimatic study supported by thermal images

2021 ◽  
pp. 1420326X2199462
Author(s):  
Stefano Ridolfi ◽  
Susanna Crescenzi ◽  
Fabiana Zeli ◽  
Stefano Perilli ◽  
Stefano Sfarra
Keyword(s):  
Methodological Approach ◽  
Thermal Capacity ◽  
Outdoor Environment ◽  
Heat Index ◽  
Heat Sources ◽  
Thermal Images ◽  
Roof Structure ◽  
Period 2 ◽  
Microclimate Monitoring ◽  
The Church

This work is centred on an ancient Italian church. Since 2011, a restoration plan has been undertaken by following sequential phases. The methodological approach to restoration was guided by environmental monitoring campaigns. In particular, two thermo-hygrometric campaigns were carried out during the warm months of the years 2015 and 2016. The first set of measurements was executed during the restoration of facades and roofs, making it possible to reach even areas that are usually difficult to access. The second set was performed to evaluate the indoor thermo-hygrometric conditions following the work of the previous year. This was intended to assess their differences in variability, the influence of the outdoor environment and any real and perceived improvement. Results demonstrate that thermal images helped in identifying both the heat sources causing thermal discomforts and the good thermal capacity of masonries. Concerning the heat index (HI), the church showed an improvement in the trend of malaise perceived by people during the second summer period (∼2°C lower than 2015). Finally, in the last microclimate monitoring, the roof structure no longer acted as an amplifier for daily temperature excursions.

scholarly journals Activity Recognition in Residential Spaces with Internet of Things Devices and Thermal Imaging

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 988
Author(s):  
Kshirasagar Naik ◽  
Tejas Pandit ◽  
Nitin Naik ◽  
Parth Shah
Keyword(s):  
Internet Of Things ◽  
Activity Recognition ◽  
Thermal Model ◽  
Thermal Image ◽  
Heat Radiation ◽  
Household Activity ◽  
Thermal Images ◽  
Gas Leakage ◽  
3D Space ◽  
Image Pairs

In this paper, we design algorithms for indoor activity recognition and 3D thermal model generation using thermal images, RGB images, captured from external sensors, and the internet of things setup. Indoor activity recognition deals with two sub-problems: Human activity and household activity recognition. Household activity recognition includes the recognition of electrical appliances and their heat radiation with the help of thermal images. A FLIR ONE PRO camera is used to capture RGB-thermal image pairs for a scene. Duration and pattern of activities are also determined using an iterative algorithm, to explore kitchen safety situations. For more accurate monitoring of hazardous events such as stove gas leakage, a 3D reconstruction approach is proposed to determine the temperature of all points in the 3D space of a scene. The 3D thermal model is obtained using the stereo RGB and thermal images for a particular scene. Accurate results are observed for activity detection, and a significant improvement in the temperature estimation is recorded in the 3D thermal model compared to the 2D thermal image. Results from this research can find applications in home automation, heat automation in smart homes, and energy management in residential spaces.

scholarly journals An Anatomical Thermal 3D Model in Preclinical Research: Combining CT and Thermal Images

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1200
Author(s):  
Franziska Schollemann ◽  
Carina Barbosa Pereira ◽  
Stefanie Rosenhain ◽  
Andreas Follmann ◽  
Felix Gremse ◽  
...  
Keyword(s):  
Image Processing ◽  
Temperature Profile ◽  
3D Models ◽  
Animal Suffering ◽  
Preclinical Research ◽  
Thermal Images ◽  
Animal Trials ◽  
Superficial Skin ◽  
Ct Data ◽  
3D Information

Even though animal trials are a controversial topic, they provide knowledge about diseases and the course of infections in a medical context. To refine the detection of abnormalities that can cause pain and stress to the animal as early as possible, new processes must be developed. Due to its noninvasive nature, thermal imaging is increasingly used for severity assessment in animal-based research. Within a multimodal approach, thermal images combined with anatomical information could be used to simulate the inner temperature profile, thereby allowing the detection of deep-seated infections. This paper presents the generation of anatomical thermal 3D models, forming the underlying multimodal model in this simulation. These models combine anatomical 3D information based on computed tomography (CT) data with a registered thermal shell measured with infrared thermography. The process of generating these models consists of data acquisition (both thermal images and CT), camera calibration, image processing methods, and structure from motion (SfM), among others. Anatomical thermal 3D models were successfully generated using three anesthetized mice. Due to the image processing improvement, the process was also realized for areas with few features, which increases the transferability of the process. The result of this multimodal registration in 3D space can be viewed and analyzed within a visualization tool. Individual CT slices can be analyzed axially, sagittally, and coronally with the corresponding superficial skin temperature distribution. This is an important and successfully implemented milestone on the way to simulating the internal temperature profile. Using this temperature profile, deep-seated infections and inflammation can be detected in order to reduce animal suffering.

scholarly journals A Novel Building Temperature Simulation Approach Driven by Expanding Semantic Segmentation Training Datasets with Synthetic Aerial Thermal Images

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 353
Author(s):  
Yu Hou ◽  
Rebekka Volk ◽  
Lucio Soibelman
Keyword(s):  
Semantic Segmentation ◽  
Sensor Data ◽  
University Campus ◽  
City Center ◽  
Thermal Images ◽  
Complex Envelope ◽  
Virtual Camera ◽  
Image Translation ◽  
Rgb Images ◽  
Synthetic Datasets

Multi-sensor imagery data has been used by researchers for the image semantic segmentation of buildings and outdoor scenes. Due to multi-sensor data hunger, researchers have implemented many simulation approaches to create synthetic datasets, and they have also synthesized thermal images because such thermal information can potentially improve segmentation accuracy. However, current approaches are mostly based on the laws of physics and are limited to geometric models’ level of detail (LOD), which describes the overall planning or modeling state. Another issue in current physics-based approaches is that thermal images cannot be aligned to RGB images because the configurations of a virtual camera used for rendering thermal images are difficult to synchronize with the configurations of a real camera used for capturing RGB images, which is important for segmentation. In this study, we propose an image translation approach to directly convert RGB images to simulated thermal images for expanding segmentation datasets. We aim to investigate the benefits of using an image translation approach for generating synthetic aerial thermal images and compare those approaches with physics-based approaches. Our datasets for generating thermal images are from a city center and a university campus in Karlsruhe, Germany. We found that using the generating model established by the city center to generate thermal images for campus datasets performed better than using the latter to generate thermal images for the former. We also found that using a generating model established by one building style to generate thermal images for datasets with the same building styles performed well. Therefore, we suggest using training datasets with richer and more diverse building architectural information, more complex envelope structures, and similar building styles to testing datasets for an image translation approach.

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