Fixed Download Images Of Parrot
Penny Dale
Red-crowned Parrots usually announce themselves with throaty screeches, well before they're seen. They are native to a small region of northeastern Mexico and South Texas, and some escaped individuals have set up breeding populations in a few large cities. These large, leaf-green parrots fly with shallow, fluttery wingbeats and then abruptly disappear when they land in treetops. Like many parrot species, their numbers have been decimated by the illegal cage bird trade, and Red-crowned Parrots are on the Red Watch List.
Ok, thank you for this, so there is no hope at this time or near future, that ODM could process any Sequoia made images.
And, for the sake of convenience, is there any list of supported brands (meaning cameras and relevant instruments), which should process fine with ODM? This could also help in with the similar cases like that one.
I would also like to know, whether is a triangulation supported in ODM? And was this information ever used in mine case? Could we confirm that information somehow?
When launched as fast orthophoto with sfm_algorithm: triangulation, it came out as empty image with no apparent errors in the console ( =sharing), also full report is there.
But in the source images there are properties Camera:Yaw, Camera:Pitch, Camera:Roll, are not these used in triangulation?
Without sfm_algorithm: triangulation however, it came out good ( -dmAOqvQni3jblMgLXLr?usp=sharing). I do not understand, should we not use triangulation when we have the corresponding meta, or particular models are not supported?
The Parrot minidrones are equipped with a downward-facing camera that provides images measuring 120-by-160 pixels. The image is internally used by the drone to calculate the optical flow of the drone. The image data is also available to the user to develop vision-based algorithms.
The Simulink Support Package for Parrot Minidrones provides a Simulink template that contains an inport that obtains the images captured by the drone's camera. You can use this template to develop image-processing algorithms. The output of the image-processing algorithm can be used as an additional input to control the flight of the drone.
3. On the Parrot Flight Control Interface, click Start. The deployed model is now ready to analyze images obtained from the downward-facing camera of the drone, and the first motor starts running.
The two images above have one thing in common: they are opticallightimages. This is what the objects look like with our eyes (or, in thecase of the galaxy, with our telescope-aided eyes). But an image can be made out of any kind ofelectromagnetic radiation. You just have to have the right kind of detector to 'see' the kind of radiation you want to study.
The infrared image above shows what we would see if our eyes weresensitive to infrared light instead of optical light. Heat from ourbodies (and a parrot's body) is emitted as infrared light. The parrot iswarm-blooded, so it radiates it's own heat, making it much warmer thanit's surroundings. This can be seen in the image above, since the parrotshines much brighter in infrared light than its surroundings.
Astronomers do the same thing with light across the electromagneticspectrum. They can use detectors of radio, infrared, optical,ultraviolet, X-ray and gamma-ray light to create images ofstars and galaxies and other cosmic objects.
An image is just a way for scientists to plot or draw light. Most images show the brightness of an object in the spatial domain, i.e., how many photons are coming from a specific location in space. Three properties of an image size, brightness, and resolution are the most important properties of an image to ascientist. From size, we learn about astronomical scales, like how bigthe Moon is. From brightness, we learn the amount of energy that anobject is producing, and then we may be able to figure out HOW it isproducing that energy.The ability of a detector to tell one location from a nearby location is called spatial resolution. Higher resolution lets us know things like whether or nota planet has rings or if there are two stars close to each other, versus one star by itself.
Looking at images of the same object made with different parts of theelectromagnetic spectrum is a very important tool for scientists. Each different wavelength of light tells astronomers something unique about that object. By studying alldifferent wavelengths and creating models that explain everything those images show, astronomers can see the "the big picture" of what isreally going on with that object.
The figure above, shows four images of the Crab Nebula. The radio image tells about themagnetic fields and free electrons in the Nebula. The optical image tells about the hydrogen in the Nebula and more about the free electrons moving in the magnetic field of the pulsar. The UV image tells about the cooler electrons, while the X-ray image tells about the very hot electrons coming from the collapsed central object in the Nebula.
Parrot beak meniscal tear is a type of radial meniscal tear with a more oblique course, which on axial images gives the characteristic appearance of a curved V, similar to a parrot's beak. As it is obliquely oriented in relation to the coronal and sagittal plane, it results in a marching cleft sign on sagittal images. This type of tear is usually symptomatic, as the partially torn meniscal flap is unstable.
By default, images are saved in PNG format. For floating-point images like depthmaps, the file format can bet set toRadiance RGBE HDR topreserve the original values. This is done by setting the recording/formatparameter to hdr.
Unmanned aerial systems (UAS) carrying commercially sold multispectral sensors equipped with a sunshine sensor, such as Parrot Sequoia, enable mapping of vegetation at high spatial resolution with a large degree of flexibility in planning data collection. It is, however, a challenge to perform radiometric correction of the images to create reflectance maps (orthomosaics with surface reflectance) and to compute vegetation indices with sufficient accuracy to enable comparisons between data collected at different times and locations. Studies have compared different radiometric correction methods applied to the Sequoia camera, but there is no consensus about a standard method that provides consistent results for all spectral bands and for different flight conditions. In this study, we perform experiments to assess the accuracy of the Parrot Sequoia camera and sunshine sensor to get an indication if the quality of the data collected is sufficient to create accurate reflectance maps. In addition, we study if there is an influence of the atmosphere on the images and suggest a workflow to collect and process images to create a reflectance map. The main findings are that the sensitivity of the camera is influenced by camera temperature and that the atmosphere influences the images. Hence, we suggest letting the camera warm up before image collection and capturing images of reflectance calibration panels at an elevation close to the maximum flying height to compensate for influence from the atmosphere. The results also show that there is a strong influence of the orientation of the sunshine sensor. This introduces noise and limits the use of the raw sunshine sensor data to compensate for differences in light conditions. To handle this noise, we fit smoothing functions to the sunshine sensor data before we perform irradiance normalization of the images. The developed workflow is evaluated against data from a handheld spectroradiometer, giving the highest correlation (R-2 = 0.99) for the normalized difference vegetation index (NDVI). For the individual wavelength bands, R-2 was 0.80-0.97 for the red-edge, near-infrared, and red bands.
A detailed description of anatomy can provide clinicians and researchers invaluable information for the diagnosis and treatment of diseases for any species. Although long used for humans and selected animals, such anatomic references for commonly kept parrot species currently do not exist. The Grey Parrot Anatomy Research Project aims to create an accurate physical and digital anatomy reference, including a standardized basis for avian anatomy nomenclature, of a commonly kept parrot species, the grey parrot (Psittacus erithacus). The grey parrot was chosen because of its recognition worldwide in cognition and intelligence research, ability to talk, presence in the pet and aviculture trade, and conservation concerns as wild populations are declining. The approach being used to develop such an atlas, involving advanced small animal imaging on live animals, image analysis and visualization techniques, could be highly desirable and applied towards other animals to create similar references.
The goals of the Grey Parrot Anatomy Research Project are four-fold. One is to create a physical anatomy atlas book. The atlas is our first goal, and we hope to have it published by 2022. As the information being collected for the project goes into far greater detail than can be shown in a book, we are also working towards building an online reference. This reference would be available through a web platform and ultimately allow users to manipulate images in 3-D. We also hope to open the information to other researchers who wish to pursue evaluation of detailed anatomic features we are digitally recording. We foresee the online digital application as a long term and ongoing project.
While the focus is on the grey parrot, we have been using a number of bird species (primarily parrots) to help develop various imaging techniques. Some healthy birds are used in non-invasive imaging. Deceased birds are used for dissection and imaging. However, no healthy parrots are being sacrificed for the project.
OBJECTIVE To create an atlas of the normal CT anatomy of the head of blue-and-gold macaws (Ara ararauna), African grey parrots (Psittacus erithacus), and monk parakeets (Myiopsitta monachus). ANIMALS 3 blue-and-gold macaws, 5 African grey parrots, and 6 monk parakeets and cadavers of 4 adult blue-and-gold macaws, 4 adult African grey parrots, and 7 monk parakeets. PROCEDURES Contrast-enhanced CT imaging of the head of the live birds was performed with a 4-multidetector-row CT scanner. Cadaveric specimens were stored at -20C until completely frozen, and each head was then sliced at 5-mm intervals to create reference cross sections. Frozen cross sections were cleaned with water and photographed on both sides. Anatomic structures within each head were identified with the aid of the available literature, labeled first on anatomic photographs, and then matched to and labeled on corresponding CT images. The best CT reconstruction filter, window width, and window level for obtaining diagnostic images of each structure were also identified. RESULTS Most of the clinically relevant structures of the head were identified in both the cross-sectional photographs and corresponding CT images. Optimal visibility of the bony structures was achieved via CT with a standard soft tissue filter and pulmonary window. The use of contrast medium allowed a thorough evaluation of the soft tissues. CONCLUSIONS AND CLINICAL RELEVANCE The labeled CT images and photographs of anatomic structures of the heads of common pet parrot species created in this study may be useful as an atlas to aid interpretation of images obtained with any imaging modality.
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