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... In general, calibration methods are based on points and lines, patterns and selfcalibration procedures. There are some studies related to calibration applied to fisheye lenses [73][74][75][76][77][78][79][80] and 360-degree cameras [51,69,70,[81][82][83], which have improved geometric results. Other studies showed comparisons between several types of cameras (perspective, fisheye, etc.) [34,84]. ...
This study shows the results of the analysis of the photogrammetric use of 360-degree cameras in complex heritage-related scenes. The goal is to take advantage of the large field of view provided by these sensors and reduce the number of images used to cover the entire scene compared to those needed using conventional cameras. We also try to minimize problems derived from camera geometry and lens characteristics. In this regard, we used a multi-sensor camera composed of six fisheye lenses, applying photogrammetric procedures to several funerary structures. The methodology includes the analysis of several types of spherical images obtained using different stitching techniques and the comparison of the results of image orientation processes considering these images and the original fisheye images. Subsequently, we analyze the possible use of the fisheye images to model complex scenes by reducing the use of ground control points, thus minimizing the need to apply surveying techniques to determine their coordinates. In this regard, we applied distance constraints based on a previous extrinsic calibration of the camera, obtaining results similar to those obtained using a traditional schema based on points. The results have allowed us to determine the advantages and disadvantages of each type of image and configuration, providing several recommendations regarding their use in complex scenes.
... Fisheye lenses utilize specific mapping (stereographic, equidistant, equisolid angle, orthogonal), which provides the images a characteristic convex non-rectilinear appearance. 28 These specific lenses have a wide range of applications due to their ability to provide rich visual information, including the genera-tion of augmented or virtual reality, 29 improving the performance of intelligent robot vision systems, 30 and simplifying the complexity of surveillance systems. 31 Various correction models have been proposed to rectify the distortion of fisheye lenses. ...
Incorporating information about the surroundings can have a significant impact on successfully determining the class of an object. This is of particular interest when determining the phenotypes of cells, for example, in the context of high-throughput screens. We hypothesized that an ideal approach would consider the fully featured view of the cell of interest, include its neighboring microenvironment, and give lesser weight to cells that are far from the cell of interest. To satisfy these criteria, we present an approach with a transformation similar to those characteristic of fisheye cameras. Using this transformation with proper settings, we could significantly increase the accuracy of single-cell phenotyping, both in the case of cell culture and tissue-based microscopy images, and we present improved results on a dataset containing images of wild animals.
... Fisheye lenses are ultra-wide angle lenses capable of taking wide panoramic or hemispheric images, however, they incorporate a significant optical distortion into the process. Fisheye lenses utilise specific mapping (stereographic, equidistant, equisolid angle, orthogonal) which lands the images a characteristic convex non-rectilinear appearance 28 . These specific lenses have a wide range of applications due to their ability to provide rich visual information, including the generation of augmented or virtual reality 29 , improving the performance of intelligent robot vision systems 30 , and simplifying the complexity of surveillance systems 31 . ...
Recently we have concluded that image-based features derived from the microenvironment have an enormous impact on successfully determining the class of an object. Here we demonstrate that deep learning-based phenotypic analysis of cells with a properly chosen microenvironment-size provides results comparable to our earlier neighbourhood-based methods that utilise hand-crafted image features. We hypothesised that treating cells with equal weight, regardless of their position within the cellular microenvironment, is suboptimal, and direct neighbours have a larger impact on the phenotype of the cell-of-interest than cells in its larger proximity. Hence we present a novel approach that (1) considers the fully featured view of the cell-of-interest, (2) includes the neighbourhood and (3) gives lesser weight to cells that are far from the cell. To achieve this, we present a transformation similar to those characteristic for fisheye cameras. Such a transformation satisfies all the above defined criteria, with a fast rate of transform for any images. Using the proposed transformation with proper settings we could significantly increase the accuracy of single-cell phenotyping, both in case of cell culture and tissue-based microscopy images. The range of potential applications of the proposed method goes beyond microscopy, as we present improved results on the iWildCam 2020 dataset containing images of wild animals.
