Journal articles: 'Availability of images'

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Van Horn, John Darrell. "Online Availability of fMRI Results Images." Journal of Cognitive Neuroscience 15, no. 6 (August 1, 2003): 769–70. http://dx.doi.org/10.1162/089892903322370690.

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Pei, Jialong. "Bioprotein Spectrum Availability Analysis Based on ANN." Journal of Physics: Conference Series 2547, no. 1 (July 1, 2023): 012029. http://dx.doi.org/10.1088/1742-6596/2547/1/012029.

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Abstract The most common way to analysis protein components is using mass spectrum, however there are errors cause in the operating process and made by equipment, some mass spectrometry images cannot be used for research as the image loses its original features or the features are covered by noise. To exclude unavailable image the equipment can use the method of ANN. ANN uses neural networks to find features of images, and automatically classify images. It successively adjusts parameters in the neural network and produces an output increasingly similar to the target output. This paper analyses the pre-train mass spectrometry of proteins from mouse organs and uses machine learning methods to judge the availability of these mass spectrometry. By using different optimizers and network structures and trying various hyper parameters, this study trained a model that has 85% precision when judging the availability of the mass spectrogram. In particular, it is very effective in excluding noisy images. This greatly reduces the time cost of manually judging the availability of images and it has a good classification effect.

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Ahamed, Jarina Raihan, Pg Emeroylariffion Abas, and Liyanage Chandratilak De Silva. "An Image Synthesis Method Generating Underwater Images." Advances in Technology Innovation 7, no. 3 (March 7, 2022): 195–205. http://dx.doi.org/10.46604/aiti.2022.8938.

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The objective of this study is to convert normal aerial images into underwater images based on attenuation values for different water types by utilizing the image formation model (IFM) with Jerlov water types. Firstly, the depth values are derived from RGB-D images. If the depth information is not available, the values between 0.5 m to 10 m are chosen, and the transmission map is estimated by these values. Secondly, the statistical average background light values of Br = 0.6240, Bg = 0.805, and Bb = 0.7651 have been derived by analyzing 890 images using two methods, namely quad-tree decomposition and four-block division. Finally, the conversion of aerial-to-underwater images is done using the derived values, and the images are verified by computer simulation using MATLAB software. The result indicates that this method can easily generate underwater images from aerial images and makes it easier for the availability of ground truth.

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Kwan, Chiman. "Remote Sensing Performance Enhancement in Hyperspectral Images." Sensors 18, no. 11 (October 23, 2018): 3598. http://dx.doi.org/10.3390/s18113598.

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Hyperspectral images with hundreds of spectral bands have been proven to yield high performance in material classification. However, despite intensive advancement in hardware, the spatial resolution is still somewhat low, as compared to that of color and multispectral (MS) imagers. In this paper, we aim at presenting some ideas that may further enhance the performance of some remote sensing applications such as border monitoring and Mars exploration using hyperspectral images. One popular approach to enhancing the spatial resolution of hyperspectral images is pansharpening. We present a brief review of recent image resolution enhancement algorithms, including single super-resolution and multi-image fusion algorithms, for hyperspectral images. Advantages and limitations of the enhancement algorithms are highlighted. Some limitations in the pansharpening process include the availability of high resolution (HR) panchromatic (pan) and/or MS images, the registration of images from multiple sources, the availability of point spread function (PSF), and reliable and consistent image quality assessment. We suggest some proactive ideas to alleviate the above issues in practice. In the event where hyperspectral images are not available, we suggest the use of band synthesis techniques to generate HR hyperspectral images from low resolution (LR) MS images. Several recent interesting applications in border monitoring and Mars exploration using hyperspectral images are presented. Finally, some future directions in this research area are highlighted.

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Lulla, Kamlesh. "Availability of NASA's earth observation images via electronic media." Geocarto International 10, no. 1 (March 1995): 65–66. http://dx.doi.org/10.1080/10106049509354480.

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FULLER, R. M., G. B. GROOM, and S. M. WALLIS. "The Availability of Landsat TM images of Great Britain." International Journal of Remote Sensing 15, no. 6 (April 1994): 1357–62. http://dx.doi.org/10.1080/01431169408954170.

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Herron, Donald A. "Thoughts and observations on interpreting depth-imaged data." Interpretation 1, no. 1 (August 1, 2013): B1—B6. http://dx.doi.org/10.1190/int-2012-0020.1.

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Interpreting depth-imaged data requires attention to elements of uncertainty different from those associated with time-imaged data. In particular, an interpreter must visualize how the migration velocity model affects focusing and positioning of reflections in final images and, consequently, interpretation of the geology contained in those images. The availability of several versions of depth imaging based on a single velocity model but with different migration algorithms complicates interpretation in that the interpreter must decide which version provides the most accurate representation of the true geology.

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Mas, Jean-François, and Francisca Soares de Araújo. "Assessing Landsat Images Availability and Its Effects on Phenological Metrics." Forests 12, no. 5 (May 3, 2021): 574. http://dx.doi.org/10.3390/f12050574.

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Landsat imagery offers the most extended continuous land surface observation at 30 m spatial resolution and is widely used in land change studies. On the other hand, the recent developments on big data, such as cloud computing, give new opportunities for carrying out satellite-based continuous land cover monitoring including land use/cover change and more subtle changes as forest degradation, agriculture intensification and vegetation phenological patterns alterations. However, in the range 0–10∘ south latitude, especially in the summer and autumn, there is a high rainfall and high clouds presence. We hypothesise that it will be challenging to characterise vegetation phenology in regions where the number of valid (cloud-free) remotely-sensed observation is low or when the observations are unevenly distributed over the year. This paper aims to evaluate whether there is sufficient availability of Landsat 7 and 8 images over Brazil to support the analysis of phenodynamics of vegetation. We used Google Earth Engine to assess Landsat data availability during the last decades over the Brazilian territory. The valid observations (excluding clouds and shadow areas) from Landsat 4/5/7/8 during the period 1984–2017 were determined at pixel level. The results show a lower intensity of Landsat observations in the northern and northeastern parts of Brazil compared to the southern region, mainly due to clouds’ presence. Taking advantage of the overlapping areas between satellite paths where the number of observations is larger, we modelled the loss of information caused by a lower number of valid (cloud free) observations. We showed that, in the deciduous woody formations of the Caatinga dominium, the scarcity of valid observations has an adverse effect on indices’ performance aimed at describing vegetation phenology. However, the combination of Landsat data with satellite constellation such as Sentinel will likely permit to overcome many of these limitations.

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Tayyab, Syed Muhammad, Steven Chatterton, and Paolo Pennacchi. "Image-Processing-Based Intelligent Defect Diagnosis of Rolling Element Bearings Using Spectrogram Images." Machines 10, no. 10 (October 8, 2022): 908. http://dx.doi.org/10.3390/machines10100908.

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Due to the excellent image recognition characteristics of convolutional neural networks (CNN), they have gained significant attention among researchers for image-processing-based defect diagnosis tasks. The use of deep CNN models for rolling element bearings’ (REBs’) defect diagnosis may be computationally expensive, and therefore may not be suitable for some applications where hardware and resources limitations exist. However, instead of using CNN models as end-to-end image classifiers, they can also be used to extract the deep features from images and those features can further be used as input to machine learning (ML) models for defect diagnosis tasks. In addition to extracting deep features using CNN models, there are also other methods for feature extraction from vibration characteristic images, such as the extraction of handcrafted features using the histogram of oriented gradients (HOG) and local binary pattern (LBP) descriptors. These features can also be used as input to classical ML models for image classification tasks. In this study, a performance comparison between all these image-processing-based defect diagnosis techniques was carried out in terms of fault detection accuracy and computational expense. Moreover, based upon the detailed comparison, a hybrid-ensemble method involving decision-level fusion is proposed, which is far less computationally expensive compared to CNN models while using them as end-to-end classifiers. The performance of all these models is also compared in the case of minimal training data availability and for diagnosis under slightly different operating conditions to ascertain their generalizability and ability to correctly diagnose despite the minimal availability of training data. The performance of the proposed hybrid-ensemble method remained outstanding for the REBs’ defect diagnosis despite the minimal of availability training data as well as the slight variation under operating conditions.

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Anderson, Nicholas V., Steven L. Petersen, Robert L. Johnson, Tyson J. Terry, and Val J. Anderson. "Detecting Floral Resource Availability Using Small Unmanned Aircraft Systems." Land 13, no. 1 (January 16, 2024): 99. http://dx.doi.org/10.3390/land13010099.

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Floral resources for native pollinators that live in wildland settings are diverse and vary across and within growing seasons. Understanding floral resource dynamics and management is becoming increasingly important as honeybee farms seek public land for summer pasture. Small Unmanned Aircraft Systems (sUASs) present a viable approach for accurate broad floristic surveys and present an additional solution to more traditional alternative methods of vegetation assessment. This methodology was designed as a simplified approach using tools frequently available to land managers. The images of three subalpine meadows were captured from a DJI Phantom 4 Pro drone platform three times over the growing season in 2019 in Sanpete County, Utah. The images were composited using Pix4D software 4.5.6and classified using a simple supervised approach in ENVI 4.8 and ArcGIS Pro 2.4.3 These same meadows were assessed using two traditional ocular methods of vegetation cover–meter-squared quadrats and macroplot estimation. The areas assessed with these methods were compared side by side with their classified counterparts from drone imagery. Classified images were not only found to be highly accurate when detecting overall floral cover and floral color groups (76–100%), but they were also strongly correlated with quadrat estimations, suggesting that these methods used in tandem may be a conducive strategy toward increased accuracy and efficiency when determining floral cover at broad spatial scales.

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Ogunbadewa, Ebenezer Yemi. "CHARACTERIZING CLOUD COVER AND SATELLITE REVISIT WITH CLOUD MASKS IN NORTH WEST ENGLAND." Geodesy and Cartography 38, no. 1 (April 12, 2012): 27–40. http://dx.doi.org/10.3846/20296991.2012.679803.

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The aim of this study is to determine the availability of cloud-free images in relation to satellite revisit periods for the UK and in particular for the North West of England. Cloud cover was analysed with cloud masks from AVHRR/APOLLO and TERRA/MODIS cloud products. Availability of cloud-free images was determined from revisit frequency and the numbers of monthly images from Landsat ETM+, ASTER, Quickbird and SPOT satellite sensor series. The average number of cloud-free days at Risley Moss and Charter's Moss is five days per month with a minimum of one cloud-free day. The results show that satellite revisit periods and cloud cover are major determinants of satellite data availability and in this study it was also found that contrary to popular opinion, satellite overpass does not necessarily mean imaging opportunity and additional cloud-free images could be obtained when there are no satellite overpasses. The numbers of cloud-free images at a given site may be increased by combining data from different satellite sensors.

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Steinebach, Martin, Sebastian Jörg, and Huajian Liu. "Checking the Integrity of Images with Signed Thumbnail Images." Electronic Imaging 2020, no. 4 (January 26, 2020): 118–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.4.mwsf-118.

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The integrity of images is an important and interesting field of research, since digital images are constantly encountered in everyday life today. The availability of image processing programs makes it possible for almost anyone to manipulate images without great effort. With the help of social media platforms, the hurdle for their distribution to a very large number of viewers has also been lowered. As a result, confidence in the integrity and authenticity of images, which was even stronger at the time of analogue photography, is dwindling. The aim of this work is to develop and investigate a concept that counteracts the lost trust and creates an opportunity to check the integrity of processed images. The concept is based on a combination of signed thumbnails and the logging of possible processing steps. We show that this combination has advantages compared to the existing approaches.

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Zealey, W. J., G. Melville, and M. Kreig. "Venus Unveiled: The Magellan Images." Publications of the Astronomical Society of Australia 10, no. 3 (1993): 200–202. http://dx.doi.org/10.1017/s1323358000025662.

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AbstractThe availability of Magellan radar images of Venus on CDROM allows small research groups to engage in planetary physics projects using simple image processing systems. The material lends itself to short projects for high school students or Masters students.Our initial investigations using the Magellan data have evolved into a Masters project which concentrates on the morphology of small scale structures which have terrestrial counterparts. These include studies of volcanic cone fields, lava tubes and other collapse features, and impact features.

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Tosi, Sébastien, Lídia Bardia, Maria Jose Filgueira, Alexandre Calon, and Julien Colombelli. "LOBSTER: an environment to design bioimage analysis workflows for large and complex fluorescence microscopy data." Bioinformatics 36, no. 8 (December 20, 2019): 2634–35. http://dx.doi.org/10.1093/bioinformatics/btz945.

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Abstract Summary Open source software such as ImageJ and CellProfiler greatly simplified the quantitative analysis of microscopy images but their applicability is limited by the size, dimensionality and complexity of the images under study. In contrast, software optimized for the needs of specific research projects can overcome these limitations, but they may be harder to find, set up and customize to different needs. Overall, the analysis of large, complex, microscopy images is hence still a critical bottleneck for many Life Scientists. We introduce LOBSTER (Little Objects Segmentation and Tracking Environment), an environment designed to help scientists design and customize image analysis workflows to accurately characterize biological objects from a broad range of fluorescence microscopy images, including large images exceeding workstation main memory. LOBSTER comes with a starting set of over 75 sample image analysis workflows and associated images stemming from state-of-the-art image-based research projects. Availability and implementation LOBSTER requires MATLAB (version ≥ 2015a), MATLAB Image processing toolbox, and MATLAB statistics and machine learning toolbox. Code source, online tutorials, video demonstrations, documentation and sample images are freely available from: https://sebastients.github.io. Supplementary information Supplementary data are available at Bioinformatics online.

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Kennedy, David. "Declassified satellite photographs and archaeology in the Middle East: case studies from Turkey." Antiquity 72, no. 277 (September 1998): 553–61. http://dx.doi.org/10.1017/s0003598x0008697x.

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Recent availability of declassified satellite images of landscapes and ancient cities in Turkey offer new and valuable material for archaeolgical research. Here David Kennedy explains the significance and use of some images in the Euphrates Valley.

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Darmanto, Darmakusuma, and Ahmad Cahyadi. "Effect of the Meteorological Conditions to Spring Water Availability in Some Karst Region at Gunungkidul Districts (Meteorologist Water Balance Analysis Study for Drought Mitigation)." Forum Geografi 27, no. 1 (July 20, 2013): 93. http://dx.doi.org/10.23917/forgeo.v27i1.5085.

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The objective of this study is to understand the influence of meteorological conditions of logva water availability in karst area, Gunungkidul Regency. Meteorological condition analysis was determined by creating meteorological water balance using Thornthwaite Mather method. Water availability condition was determined by using multi-temporal images. Then, the result of water balance was matched with the logva water availability derived from multi-temporal images. The result shows that meteorological conditions will influence logva water availability in karst area, Gunungkidul Regency. It was shown by comparing the amount of logva in surplus months and deficit months. The amount of logva in surplus months is more than in deficit months. In addition, the longer meteorological water deficit, the amounts of detected logva decreases. Based on that condition, it means that meteorological water balance analysis can be used to plan disaster mitigation based on the time and duration of deficit months.

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Ali, Syed Sadaf, Iyyakutti Iyappan Ganapathi, Ngoc-Son Vu, Syed Danish Ali, Neetesh Saxena, and Naoufel Werghi. "Image Forgery Detection Using Deep Learning by Recompressing Images." Electronics 11, no. 3 (January 28, 2022): 403. http://dx.doi.org/10.3390/electronics11030403.

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Capturing images has been increasingly popular in recent years, owing to the widespread availability of cameras. Images are essential in our daily lives because they contain a wealth of information, and it is often required to enhance images to obtain additional information. A variety of tools are available to improve image quality; nevertheless, they are also frequently used to falsify images, resulting in the spread of misinformation. This increases the severity and frequency of image forgeries, which is now a major source of concern. Numerous traditional techniques have been developed over time to detect image forgeries. In recent years, convolutional neural networks (CNNs) have received much attention, and CNN has also influenced the field of image forgery detection. However, most image forgery techniques based on CNN that exist in the literature are limited to detecting a specific type of forgery (either image splicing or copy-move). As a result, a technique capable of efficiently and accurately detecting the presence of unseen forgeries in an image is required. In this paper, we introduce a robust deep learning based system for identifying image forgeries in the context of double image compression. The difference between an image’s original and recompressed versions is used to train our model. The proposed model is lightweight, and its performance demonstrates that it is faster than state-of-the-art approaches. The experiment results are encouraging, with an overall validation accuracy of 92.23%.

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TORAL, GREGORIO M., DAVID ARAGONÉS, JAVIER BUSTAMANTE, and JORDI FIGUEROLA. "Using Landsat images to map habitat availability for waterbirds in rice fields." Ibis 153, no. 4 (July 5, 2011): 684–94. http://dx.doi.org/10.1111/j.1474-919x.2011.01147.x.

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Feathers, Karen M., and Poonam Arya. "Exploring Young Children’s Use of Illustrations in a Picturebook." Language and Literacy 17, no. 1 (January 23, 2015): 42. http://dx.doi.org/10.20360/g2630c.

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Using analysis of oral reading and eye movements, this study examined how third grade children used visual information as they orally read either the original or the adapted version of a picturebook. Eye tracking was examined to identify when and why students focused on images as well as what they looked at in the images. Results document children’s deliberate use of images and point to the important role of images in text processing. The content of images, availability and placement of text and images on a page, and children’s personal strategies affected the use of images.

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Kim, Juyeon, Jiyoung Son, Seungwon Jang, Dong-Hyun Nam, Gajin Han, Inkwon Yeo, Seok-Jae Ko, Jae-Woo Park, Bongha Ryu, and Jinsung Kim. "Availability of Tongue Diagnosis System for Assessing Tongue Coating Thickness in Patients with Functional Dyspepsia." Evidence-Based Complementary and Alternative Medicine 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/348272.

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Tongue diagnosis is an important procedure in traditional Korean medicine (TKM). In particular, tongue coating thickness (TCT) is deemed to show the progression of the disease. However, conventional tongue diagnosis has limitations because of various external factors. Therefore, it is necessary to investigate the availability of tongue diagnosis system (TDS) in the assessment of TCT. This study has been designed as a prospective clinical trial involving 60 patients with functional dyspepsia. Tongue images will be obtained by TDS twice with a 30 min interval. The system will measure the percentage of TCT and classify it as either no coating, thin coating, or thick coating according to the existing diagnostic criteria. After finishing the collection of 60 patients' tongue images, TCT on the images will be simultaneously evaluated by the conventional method to establish the gold standard for assessing TCT by 5 well-trained clinicians. The evaluation will be repeated by the same clinicians after 2 weeks, but the order of the images will be changed. This trial is expected to provide clinical evidence for the availability of TDS as a diagnostic tool and to contribute to the standardization of the diagnosis system used in TKM. This trial is registered with ClinicalTrials.govNCT01864837.

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Deependra, Kumar Shukla, Bansal Abhishek, and Singh Pawan. "Performance analysis of various copy-move forgery detection methods." i-manager's Journal on Digital Signal Processing 10, no. 2 (2022): 1. http://dx.doi.org/10.26634/jdp.10.2.19181.

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Analyzing digital images to reveal modifications is called image forensics. Digital images are now becoming incredibly popular due to the availability of several inexpensive image-capturing gadgets. These images are frequently altered, either unintentionally or intentionally, which causes the image to convey false information. Since digital images are frequently utilized as evidence in court proceedings, media, and for preserving visual records, approaches to detecting forgeries in these images should be designed. This paper thoroughly analyzes several image forgery detection strategies, including comparisons of the strategies, advantages, disadvantages, and experimental findings.

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Lee, Jennifer J., Mavra Ahmed, Tianyi Zhang, Madyson V. Weippert, Alyssa Schermel, and Mary R. L’Abbé. "The Availability and Quality of Food Labelling Components in the Canadian E-Grocery Retail Environment." Nutrients 13, no. 8 (July 29, 2021): 2611. http://dx.doi.org/10.3390/nu13082611.

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Background: Although packaged foods sold in retail stores must follow food labelling regulations, there are no e-grocery food labelling regulations to mandate and standardize the availability and presentation of product information. Therefore, the objective of the study was to evaluate the availability and quality of food labelling components in the Canadian e-grocery retail environment. Methods: A sample of fresh and pre-packaged products was identified on eight leading grocery retail websites in Canada, to assess the availability and quality of food labelling components. Results: Out of 555 product searches, all products were accompanied by product images with front-of-pack images more readily available (96.0%) than back-of-pack (12.4%) and other side panel images (3.1%). The following mandatory nutrition information was available for 61.1% of the products: nutrition facts table (68.8%), ingredient (73.9%), and allergen (53.8%) information. The majority of the nutrition information was available after scrolling down, clicking additionally on the description page, or viewing only as an image. Date markings were not available; packaging material information was available for 2.0% of the products. Conclusions: There was wide variability and inconsistencies in the presentation of food labelling components in the e-grocery retail environment, which can be barriers in enabling Canadians to make informed purchasing decisions.

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Gage, Ryan, William Leung, James Stanley, Anthony Reeder, Christina Mackay, Tim Chambers, Moira Smith, Michelle Barr, and Louise Signal. "Studying third-parties and environments: New Zealand sun-safety research." Health Promotion International 34, no. 3 (December 15, 2017): 440–46. http://dx.doi.org/10.1093/heapro/dax094.

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Summary Wearable cameras have been used to study health behaviours, but their utility in assessing third-party behaviours and the built environment is uncertain. This paper reports on the feasibility of using wearable cameras for this purpose in a study of sun-protective behaviours and shade availability during school lunch-breaks. The Kids’Cam study provided 168 children (aged 11–13 years), recruited from 16 randomly selected schools in the Wellington region of New Zealand, with wearable cameras. The devices automatically captured images every 7 s from the child’s perspective. Images captured during school lunch-breaks by a random sample of 15 children who took part during terms 4 and 1 (October 2014–April 2015) were selected and assessed for usability. The feasibility of studying third-party sun-protective behaviours and school shade availability was assessed for a subset of 320 images. Of the 3492 eligible lunch-break images, 96.4% were useable; the remainders were excluded due to obstruction, blurriness or unsuitable camera position. Overall, 1278 children and 108 shade structures were observed in the sample images. The use of shade, hats, sleeves, collars and sunglasses could be determined for 97.0%, 77.2%, 74.4%, 47.6% and 54.9% of children, respectively. All shade structures could be classified according to type, and canopy composition could be assessed for 95.4% of structures. Wearable cameras are a feasible tool for assessing sun-safety, particularly shade availability, hat wearing and shade use. This methodology could be used to objectively study other third-party health-related behaviours, and other features of the built environment.

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McConnell, James. "(319) Addtional Features Possible with Electronic Publications." HortScience 41, no. 4 (July 2006): 1044F—1045. http://dx.doi.org/10.21273/hortsci.41.4.1044f.

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An electronic document does not have to be limited to text and static images. Electronic publications greatly expand the potential of types of images that can be included compared to print media. With increased ease of accessing the internet and increased bandwidth availability, individuals have less limitations for downloading the enhanced documents. These documents include much more than color images. Options such as hypertext links, interactive graphics, movies, presentations, and audio are discussed.

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d'Andrimont, Raphaël, Momchil Yordanov, Guido Lemoine, Janine Yoong, Kamil Nikel, and Marijn van der Velde. "Crowdsourced Street-Level Imagery as a Potential Source of In-Situ Data for Crop Monitoring." Land 7, no. 4 (October 22, 2018): 127. http://dx.doi.org/10.3390/land7040127.

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New approaches to collect in-situ data are needed to complement the high spatial (10 m) and temporal (5 d) resolution of Copernicus Sentinel satellite observations. Making sense of Sentinel observations requires high quality and timely in-situ data for training and validation. Classical ground truth collection is expensive, lacks scale, fails to exploit opportunities for automation, and is prone to sampling error. Here we evaluate the potential contribution of opportunistically exploiting crowdsourced street-level imagery to collect massive high-quality in-situ data in the context of crop monitoring. This study assesses this potential by answering two questions: (1) what is the spatial availability of these images across the European Union (EU), and (2) can these images be transformed to useful data? To answer the first question, we evaluated the EU availability of street-level images on Mapillary—the largest open-access platform for such images—against the Land Use and land Cover Area frame Survey (LUCAS) 2018, a systematic surveyed sampling of 337,031 points. For 37.78% of the LUCAS points a crowdsourced image is available within a 2 km buffer, with a mean distance of 816.11 m. We estimate that 9.44% of the EU territory has a crowdsourced image within 300 m from a LUCAS point, illustrating the huge potential of crowdsourcing as a complementary sampling tool. After artificial and built up (63.14%), and inland water (43.67%) land cover classes, arable land has the highest availability at 40.78%. To answer the second question, we focus on identifying crops at parcel level using all 13.6 million Mapillary images collected in the Netherlands. Only 1.9% of the contributors generated 75.15% of the images. A procedure was developed to select and harvest the pictures potentially best suited to identify crops using the geometries of 785,710 Dutch parcels and the pictures’ meta-data such as camera orientation and focal length. Availability of crowdsourced imagery looking at parcels was assessed for eight different crop groups with the 2017 parcel level declarations. Parcel revisits during the growing season allowed to track crop growth. Examples illustrate the capacity to recognize crops and their phenological development on crowdsourced street-level imagery. Consecutive images taken during the same capture track allow selecting the image with the best unobstructed view. In the future, dedicated crop capture tasks can improve image quality and expand coverage in rural areas.

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Zhao, Maofan, Qingyan Meng, Lifeng Wang, Linlin Zhang, Xinli Hu, and Wenxu Shi. "Towards robust classification of multi-view remote sensing images with partial data availability." Remote Sensing of Environment 306 (May 2024): 114112. http://dx.doi.org/10.1016/j.rse.2024.114112.

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Wang, Yizhi, Congchao Wang, Petter Ranefall, Gerard Joey Broussard, Yinxue Wang, Guilai Shi, Boyu Lyu, et al. "SynQuant: an automatic tool to quantify synapses from microscopy images." Bioinformatics 36, no. 5 (October 9, 2019): 1599–606. http://dx.doi.org/10.1093/bioinformatics/btz760.

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Abstract Motivation Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses. Results We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods. Availability and implementation Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant. Supplementary information Supplementary data are available at Bioinformatics online.

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Fabian, Miroslav, and Stanislav Darula. "Simple Conversion Method for Determination of the Global Illuminance from Satellite Images." Advanced Materials Research 1041 (October 2014): 377–80. http://dx.doi.org/10.4028/www.scientific.net/amr.1041.377.

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To better utilize natural sources in the performance of buildings during whole year the availability of detail data in time and for specific location plays key role. Such data offers ground measurements or databases of satellite images. Establishment and operation of CIE IDMP ground stations is quite expensive and there are a few active stations on the ground at this time. To study of irradiance availability in specific locality the data describing relevant local radiative conditions are needed. For this reason the satellite images offer sufficient information. The first part of this paper is focused on short introduction to the remote sensing and the Heliosat method. In the second part are presented results of the solar irradiance availability on the horizontal plane and their time changes based on the processing satellite images. The illuminance and irradiance data obtained at ground CIE IDMP station in Bratislava were used to proposed verify model for estimation of the global illuminance from irradiance. Cities Bratislava and Brno are relative close with distance 130 km and similar daylight climate of Central European climatic zone, therefore can be assumed that data from CIE IDMP station Bratislava can represent relation between photometric and radiometric variables and daylighting changes in Brno.

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Ingole, Mr Shubham. "Vehicle Vacant Seat Identification and Mask Detection using Image processing." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 10, 2021): 118–21. http://dx.doi.org/10.22214/ijraset.2021.36253.

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This article describes the technique of real-time face detection, mask detection, and vacant seat available in the vehicle. There are so many technologies for finding seat availability in the vehicle. But image processing technology is very popular today. Face detection is part of image processing. It is used to find the face of a human being in a certain area. Face detection is used in many applications, such as facial recognition, people tracking or photography. In this paper, the face detection technique is used to detect the vacant seat availability in the vehicle and also to detect whether the passenger wear the mask on his face or not. The webcam is installed in the vehicle and connected with the Raspberry Pi 3 model B. When the vehicle leaves the station, the webcam will capture images of the passengers in the seating area. The webcam will be mounted on the vehicle. The images will be adjusted and enhanced to reduce noise made by the software application. The system obtains the maximum number of passengers in the vehicle that processes the images and then calculates the availability of seats in the vehicle. In covid-19 situation mask detection is necessary. so this system also used to detect the mask on face.

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Bhatti, Nabi Bux, Altaf Ali Siyal, Abdul Latif Qureshi, and Imtiaz Ali Bhatti. "Land Covers Change Assessment After Small Dam’s Construction Based on the Satellite Data." Civil Engineering Journal 5, no. 4 (April 27, 2019): 810–18. http://dx.doi.org/10.28991/cej-2019-03091290.

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The small dams were constructed in the study area for storing the rainwater. The present study was conducted to assess the impact of small dams on the LCC (Land Cover Change) in Nangarparkar, Pakistan based on the satellite data. The ENVI (Environment for Visualizing Images) software was used for classification of the four year’s images and three classes viz. water, vegetation, and soil were taken for detection of LCC. The MLH (Maximum Likelihood) supervised method was used to classify the multispectral satellite images. The classified results of the classes were found different each year before and after dam construction. Average results of the two years before dam’s construction revealed that water availability, vegetation cover and soil cover was 3.02%, 18.52%, and 32.30% respectively. However, after the dam construction, the water availability, vegetation cover and soil cover was 8.49%, 34.33%, and 17.15% respectively. Overall results revealed that water availability and vegetation cover were increased by 5.47 % and 15.18% respectively while soil cover decreased 15.15% after the construction of dams. Hence, based on the results, it is confirmed that the constructions of small dams have a direct and indirect positive impact on the land cover changes and it can play an important role in the resettlement of the communities of the arid areas.

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Hermens, Frouke, Marius Golubickis, and C. Neil Macrae. "Eye movements while judging faces for trustworthiness and dominance." PeerJ 6 (October 11, 2018): e5702. http://dx.doi.org/10.7717/peerj.5702.

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Past studies examining how people judge faces for trustworthiness and dominance have suggested that they use particular facial features (e.g. mouth features for trustworthiness, eyebrow and cheek features for dominance ratings) to complete the task. Here, we examine whether eye movements during the task reflect the importance of these features. We here compared eye movements for trustworthiness and dominance ratings of face images under three stimulus configurations: Small images (mimicking large viewing distances), large images (mimicking face to face viewing), and a moving window condition (removing extrafoveal information). Whereas first area fixated, dwell times, and number of fixations depended on the size of the stimuli and the availability of extrafoveal vision, and varied substantially across participants, no clear task differences were found. These results indicate that gaze patterns for face stimuli are highly individual, do not vary between trustworthiness and dominance ratings, but are influenced by the size of the stimuli and the availability of extrafoveal vision.

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Bai, B. X., Y. M. Tan, and P. Wu. "THE SPATIAL AND TEMPORAL AVAILABILITY DIFFERENCES OF CLOUD-FREE LANDSAT IMAGES OVER THREE GORGES RESERVOIR AREA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W9 (October 25, 2019): 1–8. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w9-1-2019.

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Abstract. Availability analysis of cloud-free optical remote sensing data is a prerequisite for remote sensing applications. In this study, spatio-temporal availability differences of cloud-free Landsat TM, ETM+, and OLI sensors images over Three Gorges Reservoir Area (TGRA) were analyzed from 1986 to 2019 based on the Google Earth Engine (GEE). The results show that: 1) in Summer, especially in August, the probabilities of obtaining Landsat images with no more than 30% cloud cover (CC) is higher. 2) the northeast of TGRA has higher probability of acquiring cloudless images than the southwest. 3) In TGRA, annual monitoring which require at least one cloud-free observation in a year largely unaffected by CC, but when considering seasonal monitoring, cloud contaminate will become a limitation, and monthly monitoring in this area is basically not feasible even if the three sensors data are combined. The results of this paper will provide important references for the research of using optical data in this area, and although the research area is relatively small, the analysis method and the program developed in this paper have no restrictions on the area.

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Sharma, Pranav, Pooja Santwani, and Rachit Narula. "Detecting Forged Images using Deep Learning." International Journal of Engineering and Advanced Technology 12, no. 1 (October 30, 2022): 6–8. http://dx.doi.org/10.35940/ijeat.a3792.1012122.

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This availability and requirement of data calls for the credibility and authenticity of the data. One such domain is images where tampering creates concern , leading to wide spread of misinformation and fake news. Images are transferred to initiate propagandas on social handles and other platforms. Most of these images are tampered from the authentic original content to allude people and miscommunicate malicious information. In this application, our main work is to modify the existing MobileNetV2 family of neural networks to a more relevant version, so that we can identify and differentiate tampered images from authentic images. We will further create our own convolutional neural network, to create an application which can help us to identify and differentiate tampered images from authentic images and compare our model with MobileNetV2.

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Bae, Chay, Michael Cheng, Christina N. Kraus, and Sheetal Desai. "Representation of Skin of Color in Rheumatology Educational Resources." Journal of Rheumatology 49, no. 4 (November 1, 2021): 419–23. http://dx.doi.org/10.3899/jrheum.210626.

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ObjectiveTo investigate the availability of images representing Black, Indigenous, and people of color in rheumatology educational resources.MethodsColor images were collected from 5 major educational resources and cataloged by the resources they came from, underlying rheumatic conditions, and skin type. Fitzpatrick skin type (FST) was used to categorize images into “light,” “dark,” or “indeterminate.” The images were initially scored by a fellow in the Division of Rheumatology and subsequently validated by a faculty member from the Department of Dermatology.ResultsOf the thousands of images reviewed, 1604 images met study criteria. FST validation from the Department of Dermatology resulted in the recoding of 111 images. The final scoring revealed 86% of the images to be light skin, 9% of images to be dark skin, and 5% of images to be indeterminate.ConclusionThe paucity of dark skin images in rheumatology resources is incongruent with current diversity estimates in the US. Significant efforts should be made to incorporate images of Black, Indigenous, and people of color into educational resources.

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Tongleamnak, Sakpod, and Masahiko Nagai. "Simulation of GNSS Availability in Urban Environments Using a Panoramic Image Dataset." International Journal of Navigation and Observation 2017 (January 26, 2017): 1–12. http://dx.doi.org/10.1155/2017/8047158.

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Performance of Global Navigation Satellite System (GNSS) positioning in urban environments is hindered by poor satellite availability because there are many man-made and natural objects in urban environments that obstruct satellite signals. To evaluate the availability of GNSS in cities, this paper presents a software simulation of GNSS availability in urban areas using a panoramic image dataset from Google Street View. Photogrammetric image processing techniques are applied to reconstruct fisheye sky view images and detect signal obstacles. Two comparisons of the results from the simulation and real world observation in Bangkok and Tokyo are also presented and discussed for accuracy assessment.

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Salazar, Addisson, Luis Vergara, and Gonzalo Safont. "Editorial for the Special Issue “New Advances on Sub-Pixel Processing: Unmixing and Mapping Methods”." Remote Sensing 13, no. 19 (September 23, 2021): 3807. http://dx.doi.org/10.3390/rs13193807.

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Innovative remote sensing image processing techniques have been progressively studied due to the increasing availability of remote sensing images, powerful techniques of data analysis, and computational power [...]

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Fynn, Iris E. M., and James Campbell. "Forest Fragmentation Analysis from Multiple Imaging Formats." Journal of Landscape Ecology 12, no. 1 (January 1, 2019): 1–15. http://dx.doi.org/10.2478/jlecol-2019-0001.

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Abstract In landscape ecology, forest fragmentation studies with emphasis on effects of scale on fragmentation patch metrics, is an important research area. With increasing availability of satellite data at multiple scales and varied resolutions, it has become important to understand effects of comparing fragmentation metrics acquired from coarse resolution images and those from finer resolution imagery. This is crucial because coarse resolution images such as Landsat imagery, are relatively easier to find because of their cheaper costs, availability and broad coverage, whereas finer resolution imagery is more expensive and therefore, spans only small areas. This paper examines effects of varied spatial resolutions on common fragmentation metrics using Landsat, Sentinel, National Agricultural Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) imagery obtained in November, 2017 of the Whitethorne area near Blacksburg, Virginia. The images are analyzed using FRAGSTATS and ArcGIS software programs. The results show significant differences in fragmentation metrics despite simultaneous acquisition of all images in the same area. Discussion of results obtained in this study centers on the reasons for this disparity, and examines uses of imagery of different resolutions for forest fragmentation analysis.

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Korfiati, Aigli, Giorgos Livanos, Christos Konstandinou, Sophia Georgiou, and George Sakellaropoulos. "SKIN LESION CLASSIFICATION FROM DERMOSCOPY AND CLINICAL IMAGES WITH A DEEP LEARNING APPROACH." International Journal of Advanced Research 9, no. 10 (October 31, 2021): 1294–300. http://dx.doi.org/10.21474/ijar01/13681.

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Computer-aided diagnosis (CAD) systems based on deep learning approaches are now feasible due to the availability of big data and the availability of powerful computational resources.The medical image-based CAD systems are of great interest in numerous diseases, but especially for skin cancer diagnosis, deep learning models have been mostly developed for dermoscopy images. Models for clinical images are few, mainly due to the unavailability of big volumes of relevant data. However, CAD systems able to classify skin lesions from clinical images would be of great valueboth for the population and clinicians as an initial early screening of lesions that would leadpatients to visiting a dermatologist in case of suspicious lesions. This is even more pronounced in areas where there is lack of dermoscopy instruments. Thus, in this paper, we aimed to build a classifier based on bothdermoscopy and clinical images able to discriminate skin cancer from skin lesions. The classification is made among three benign and two malignant categories, which include Nevus, Benign but not nevus, Benign but suspicious for malignancy, Melanoma and Non-Melanocytic Carcinoma.The proposed deep learning classifier achieves an Area Under Curve ranging between 0.75 and 0.9 for the five examined categories.

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Suresh, G., R. Gehrke, T. Wiatr, and M. Hovenbitzer. "SYNTHETIC APERTURE RADAR (SAR) BASED CLASSIFIERS FOR LAND APPLICATIONS IN GERMANY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 1187–93. http://dx.doi.org/10.5194/isprsarchives-xli-b1-1187-2016.

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Land cover information is essential for urban planning and for land cover change monitoring. This paper presents an overview of the work conducted at the Federal Agency for Cartography and Geodesy (BKG) with respect to Synthetic Aperture Radar (SAR) based land cover classification. Two land cover classification approaches using SAR images are reported in this paper. The first method involves a rule-based classification using only SAR backscatter intensity while the other method involves supervised classification of a polarimetric composite of the same SAR image. The LBM-DE has been used for training and validation of the SAR classification results. Images acquired from the Sentinel-1a satellite are used for classification and the results have been reported and discussed. The availability of Sentinel-1a images that are weather and daylight independent allows for the creation of a land cover classification system that can be updated and validated periodically, and hence, be used to assist other land cover classification systems that use optical data. With the availability of Sentinel-2 data, land cover classification combining Sentinel-1a and Sentinel-2 images present a path for the future.

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Suresh, G., R. Gehrke, T. Wiatr, and M. Hovenbitzer. "SYNTHETIC APERTURE RADAR (SAR) BASED CLASSIFIERS FOR LAND APPLICATIONS IN GERMANY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 1187–93. http://dx.doi.org/10.5194/isprs-archives-xli-b1-1187-2016.

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Land cover information is essential for urban planning and for land cover change monitoring. This paper presents an overview of the work conducted at the Federal Agency for Cartography and Geodesy (BKG) with respect to Synthetic Aperture Radar (SAR) based land cover classification. Two land cover classification approaches using SAR images are reported in this paper. The first method involves a rule-based classification using only SAR backscatter intensity while the other method involves supervised classification of a polarimetric composite of the same SAR image. The LBM-DE has been used for training and validation of the SAR classification results. Images acquired from the Sentinel-1a satellite are used for classification and the results have been reported and discussed. The availability of Sentinel-1a images that are weather and daylight independent allows for the creation of a land cover classification system that can be updated and validated periodically, and hence, be used to assist other land cover classification systems that use optical data. With the availability of Sentinel-2 data, land cover classification combining Sentinel-1a and Sentinel-2 images present a path for the future.

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Hussein, Hala, Ra’ad Muhajjar, and Bashar Mahdi. "Fragile Watermarks Detecting Forged Images." Iraqi Journal for Electrical and Electronic Engineering 19, no. 1 (January 24, 2023): 79–86. http://dx.doi.org/10.37917/ijeee.19.1.10.

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Technology and digital communications have advanced so that digital photos, videos, or text may be easily manipulated by those not authorized to do so. In addition, the availability of specialized picture editing programs like Photoshop has simplified the process of altering photographs. At first glance, there may seem to be no problem, especially when an image editing method is necessary to delete or add a certain scene that increases the picture’s beauty. But what about personal images or images with copyright? Attempts are constantly made to spoof these images using different approaches. Therefore, measures to reduce the likelihood of counterfeiting in digital and printed forms of media are required. The proposed approach aims to detect a counterfeit in images using a unique generator that conceals the data represented by the embedded watermark utilizing modern visual cryptography and hash algorithms. Image extractions may easily be analyzed for signs of forgery. As a result, our approach will detect and validate phony documents and images.

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Tikhonov, Alexey. "Preservation of Digital Images: Question of Fixity." Heritage 2, no. 2 (April 17, 2019): 1160–65. http://dx.doi.org/10.3390/heritage2020075.

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One of the most important aspects of the long-term digital-image preservation strategy is maintaining data fixity, i.e., assuring the integrity and authenticity of original data. This article aims to highlight the limitations of the approaches used to maintain the fixity of digital images in the digital preservation process and to offer perceptual hashing as a way to alleviate some of the limitations of current approaches, as well as discuss some non-technical implications of the described problems. This paper is exploratory, and while it includes a simple experiment description, it only outlines the problem and testing environment for a possible solution that could be elaborated on in further research. The most commonly used fixity maintaining techniques are immutability of data and file checksums/cryptographic hashes. On the other hand, planning for long-term preservation necessitates the need to migrate data into new future formats to maintain availability and sustainability, and the concept of the file itself should not be assumed to remain forever, which calls for other tools to ascertain the fixity of digital images. The problem goes beyond one that is exclusively technical: bitstream content is not ready for human perception, and the digital preservation strategy should include all the necessary technical steps to assure the availability of stored images to human eyes. This shifts the perspective on what should be considered the digital image in digital preservation. It is not the file, but a perceptible object, or, more specifically—instructions to create one. Therefore, it calls for additional tools to maintain fixity, such as perceptual hashing, transformation logging, and others.

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Ghannadi, M. A., M. Saadatseresht, and M. Motagh. "SENTINEL-1 IMAGE MATCHING USING STRONG SCATTERS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 233–35. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-233-2015.

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The availability of new radar spaceborne sensors offers new interesting potentialities for the geomatics application: spatial and temporal change detection, generation of Digital Elevation Model(DEM) using radargrametry and interferometry. Since the start of the sentinel-1 mission to take images from different regions all over the world, the ability to use these images in variety domains has been treasured. This paper suggests a method for image matching using strong scatters. all the experiments are done on sentinel-1 stereo images from Jam, Bushehr, Iran.

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Dumalag, J. B. L. C., K. L. S. Mariano, N. R. Cadiz, A. E. Retamar, J. C. Aceron, M. M. Felicen, and M. C. Gelido. "AUGMENTING THE PHILIPPINES’ DOST-ASTI’S POTENTIAL FLOOD EXTENTS MAPPING SERVICE WITH S-BAND NOVASAR-1 IMAGES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W8-2023 (April 24, 2024): 203–10. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w8-2023-203-2024.

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Abstract. The Philippines’ Advanced Science and Technology Institute under the Department of Science and Technology (DOST-ASTI) has developed an AI-based and near real-time flood extent mapping service that utilizes C-Band Sentinel-1 SAR images. However, this method is limited by the availability of the Sentinel-1 images during flooding events. To address this issue, the institute, through its SARwAIS Project, utilized the S-Band NovaSAR-1 satellite, which was designed and launched by Surrey Satellite Technology, Ltd. With a 10% share to NovaSAR-1’s imaging capacity, the country can task image acquisitions that could help augment the Sentinel-1 datasets. Successfully captured images are prepared using the institute’s developed pre-processing workflow. Afterwards, a thresholding method, adopted from UN-SPIDER’s recommended practices for flood mapping, is employed to identify potentially flooded areas from these images. Generated products are then assessed to determine their relative accuracy in detecting potential floods. Satisfactory products are then distributed to relevant disaster management agencies and are also published in the agency’s social media page for further information dissemination. Python scripts were then developed to automate the established workflows, which were initially done manually. These scripts also help expedite the generation of flood maps especially when processing multiple SAR images. The acquisition and utilization of NovaSAR-1 images substantially help the country address the gaps on the availability of workable data for a more informative disaster response especially during flooding events.

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Inocêncio, Marcos Vinicius, Almir Olivette Artero, Danilo Medeiros Eler, Francisco Assis da Silva, and Danillo Roberto Pereira. "EQUALIZAÇÃO E SEGMENTAÇÃO DE IMAGENS USANDO INFORMAÇÕES DE CORES." COLLOQUIUM EXACTARUM 12, no. 1 (June 15, 2020): 20–38. http://dx.doi.org/10.5747/10.5747/ce.2020.v12.n1.e306.

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Digital image processing techniques are generally applied to grayscale images, despitethe large availability of color images. Although the literature always devotes some topic to the study of processing using colors and, they claim that all processing with grayscale images can be extended to color images, using the three color channels, inpractice, the processing of color images still it is very little explored. Thus, this work presents some proposals for using color information in segmentation (using an edge detector) and in image equalization (presents a proposal that avoids the conversion between color models). The results obtained are evaluated through a qualitative and quantitative analysis of the images obtained with traditional processing, using only gray tones and images obtained using the proposed strategies. The results show that different ways of combining color information during the process lead to very different results.

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Landau, Anne M., Aage Kristian Olsen Alstrup, Ove Noer, Michael Winterdahl, Hélène Audrain, Arne Møller, Poul Videbech, Gregers Wegener, Albert Gjedde, and Doris J. Doudet. "Electroconvulsive stimulation differentially affects [11C]MDL100,907 binding to cortical and subcortical 5HT2A receptors in porcine brain." Journal of Psychopharmacology 33, no. 6 (March 19, 2019): 714–21. http://dx.doi.org/10.1177/0269881119836212.

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Background: Electroconvulsive therapy is an effective therapy of depression. We hypothesized that the beneficial effects are mediated partly by decreased serotonin receptor availability in the cortex. Aims: We used positron emission tomography with the serotonin 5HT2A receptor radioligand [11C]MDL100,907 to determine serotonin receptor availability in response to electroconvulsive stimulation (ECS). Methods: Seven Göttingen minipigs were deeply anaesthetized and imaged at baseline before the onset of ECS, and at 1–2 and 8–10 days after the end of a clinical course of ECS, consisting of 10 sessions over 3.5 weeks, and post-ECS values were compared to baseline. One additional minipig was anaesthetized over 10 sessions without ECS, as a control. We analysed images with the Ichise model for binding in cortex and hippocampus, followed by whole-brain analysis by statistical non-parametric mapping. Results: We found significantly increased binding potential of [11C]MDL100,907 in the cortex and hippocampus 1–2 days after ECS, consistent with increased serotonin receptor availability compared to baseline. By 8–10 days after the final ECS, the average tracer binding had returned towards baseline. However, we also found significantly decreased tracer binding in the subcortical regions of olfactory bulb, pons, thalamus and striatum. Conclusions: With ECS, minipigs, unlike primates but like rodents, have higher availability at cortical and hippocampal 5HT2A receptors. Decreased tracer binding is consistent with reduced serotonin receptor availability as a differential effect of ECS on 5HT2A receptors in subcortical regions of minipig brain.

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Plu, Daniel, and Guy Ducher. "Availability of aerial photography and space images; the photothèque at the IGN (F), France." Photogrammetria 43, no. 2 (November 1988): 83–100. http://dx.doi.org/10.1016/0031-8663(88)90024-5.

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Singhal, Prof Vikas, Dr Shivani Dubey, Dr Pankaj Gupta, and Dinesh Mishra. "A Study of Original and Tempered Images for Real-Fake Image." International Journal of Innovative Research in Advanced Engineering 11, no. 01 (January 27, 2024): 16–21. http://dx.doi.org/10.26562/ijirae.2024.v1101.03.

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The widespread use of image editing technologies in the digital age has raised concerns about the authenticity of visual content. This study delves into the field of image forensics, specifically analyzing original and tempered photos to determine their graphical behavior. The major goal is to develop solid algorithms for distinguishing between authentic and fraudulent photos based on an in-depth assessment of their visual properties. The study makes use of a large data collection that includes both original and manipulated photographs from a variety of sources and contexts. To reveal small differences between authentic and modified pictures, image processing techniques such as noise analysis, color profile investigation, and geometric feature extraction are used. Machine learning algorithms are critical in automating the analysis process and increasing the efficiency and scalability of the proposed methodology. Picture security is an issue for every company that employs digital images. Suspect data has long been used in forensics and public safety pictures, images from crime scenes, biometric photos, and other types of images. In this discipline, the usage of digital photographs has increased dramatically with the advancement of digital imaging. Digital image processing has made picture manipulation easier, but it has also aided in the development of several novel techniques in forensic investigation. Digital picture authenticity is becoming an issue due to the public availability of several programs for cropping and manipulating images. It serves as compelling evidence in many different types of crimes write for a variety of purposes. This development is picture processing or edits of two are also simplifies and editing photos. The most typical kinds of Conducted.

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Saini, Sujata, Hiroki Shibata, and Yasufumi Takama. "Construction of Handwritten Indus Signs Dataset Employing Social Approach." Journal of Advanced Computational Intelligence and Intelligent Informatics 28, no. 1 (January 20, 2024): 122–28. http://dx.doi.org/10.20965/jaciii.2024.p0122.

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This paper constructs a dataset of handwritten Indus signs employing a social approach. A writing system called the Indus script was created in the Indus civilization. It has been decoded numerous times throughout the years, but it has not yet been fully deciphered. Due to a lack of information and the scarcity of evidence, the mystery of the Indus signs has not yet been fully solved. Recently, there has been an increase in demand for huge datasets in order to use cutting-edge machine learning techniques. Considering the restricted availability of images of authentic Indus signs, this paper proposes creating an Indus signs dataset by asking participants to draw the Indus signs while referring to the image of the original Indus signs. A web application was developed and used to collect the 44 participants’ handwritten images of ten Indus signs. To show the availability of the constructed dataset, it is used to train convolutional neural networks. The experimental result demonstrates that the model can classify the images of original Indus script with 70% accuracy.

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Figueiredo, Estrela, Gideon F. Smith, and Jorge Paiva. "A checklist of Polygala (Polygalaceae) in the Flora of Southern Africa region with notes on types." Phytotaxa 155, no. 1 (December 19, 2013): 1. http://dx.doi.org/10.11646/phytotaxa.155.1.1.

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As part of a revision of Polygala in the Flora of Southern Africa region, the types of all the names relevant for the region were assessed. A catalogue of the names and their types is provided along with information on the availability of online images of these types. For 22 accepted taxa, no images of the types of the names are available online. Twenty lectotypes are designated here.

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Journal articles on the topic 'Availability of images'

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