Analysis on the Development Direction of News Photography in the Digital Era

With the rapid rise of information technology and the continuous update of science and technology, society has stepped into the digital age, which accelerates the prosperity of visual culture, and news has entered the era of reading pictures. The development of digital technology not only brings convenience to news photography, but also brings great challenges to professional news photographers. The advantage is that the buttons of digital cameras have replaced the technical means of traditional film photography, thus greatly reducing the threshold of photography. This article will explain the two development directions of photojournalism in the digital age, evaluate the contribution of documentary photojournalism to society, and also consider some development constraints.

Developing Digital Photomicroscopy

(1) The need for efficient ways of recording and presenting multicolour immunohistochemistry images in a pioneering laboratory developing new techniques motivated a move away from photography to electronic and ultimately digital photomicroscopy. (2) Initially broadcast quality analogue cameras were used in the absence of practical digital cameras. This allowed the development of digital image processing, storage and presentation. (3) As early adopters of digital cameras, their advantages and limitations were recognised in implementation. (4) The adoption of immunofluorescence for multiprobe detection prompted further developments, particularly a critical approach to probe colocalization. (5) Subsequently, whole-slide scanning was implemented, greatly enhancing histology for diagnosis, research and teaching.

A Low-Cost and Ultralight Unmanned Aerial Vehicle-Borne Multicamera Imaging System Based on Smartphones

Newly developed oblique photogrammetry (OP) techniques based on unmanned aerial vehicles (UAVs) equipped with multicamera imaging systems are widely used in many fields. Smartphones cost less than the cameras commonly used in the existing UAV OP system, providing high-resolution images from a built-in imaging sensor. In this paper, we design and implement a novel low-cost and ultralight UAV OP system based on smartphones. Firstly, five digital cameras and their accessories detached from the smartphones are then fitted into a very small device to synchronously shoot images at five different perspective angles. An independent automatic capture control system is also developed to realize this function. The proposed smartphone-based multicamera imaging system is then mounted on a modified version of an existing lightweight UAV platform to form a UAV OP system. Three typical application examples are then considered to evaluate the performance of this system through practical experiments. Our results indicate that both horizontal and vertical location accuracy of the generated 3D models in all three test applications achieve centimeter-level accuracy with respect to different ground sampling distances (GSDs) of 1.2 cm, 2.3 cm, and 3.1 cm. The accuracy of the two types of vector maps derived from the corresponding 3D models also meet the requirements set by the surveying and mapping standards. The textural quality reflected by the 3D models and digital ortho maps (DOMs) are also distinguishable and clearly represent the actual color of different ground objects. Our experimental results confirm the quality and accuracy of our system. Although flight efficiency and the accuracy of our designed UAV OP system are lower than that of the commercial versions, it provides several unique features including very low-cost, ultralightweight, and significantly easier operation and maintenance.

Artificial intelligence technologies for more flexible recommendation in uniforms

PurposeThis research aims to collect human body variables via 2D images captured by digital cameras. Based on those human variables, the forecast and recommendation of the Digital Camouflage Uniforms (DCU) for Taiwan's military personnel are made.Design/methodology/approachA total of 375 subjects are recruited (male: 253; female: 122). In this study, OpenPose converts the photographed 2D images into four body variables, which are compared with those of a tape measure and 3D scanning simultaneously. Then, the recommendation model of the DCU is built by the decision tree. Meanwhile, the Euclidean distance of each size of the DCU in the manufacturing specification is calculated as the best three recommendations.FindingsThe recommended size established by the decision tree is only 0.62 and 0.63. However, for the recommendation result of the best three options, the DCU Fitting Score can be as high as 0.8 or more. The results of OpenPose and 3D scanning have the highest correlation coefficient even though the method of measuring body size is different. This result confirms that OpenPose has significant measurement validity. That is, inexpensive equipment can be used to obtain reasonable results.Originality/valueIn general, the method proposed in this study is suitable for applications in e-commerce and the apparel industry in a long-distance, non-contact and non-pre-labeled manner when the world is facing Covid-19. In particular, it can reduce the measurement troubles of ordinary users when purchasing clothing online.

Red Spot on the European Green Map: Will the Extra Catastrophic Phenomenon Take the Polish Poaching-Pressured Ospreys to the Brink of Extinction?

Poland is the only European country where the Osprey population is declining due to the mortality of adult birds from poaching, which impacts not only single breeding attempts but also the Lifetime Reproductive Success (LRS) of specimens. However, what if there came an extra mortality factor in the moment of the lowest numbers of Osprey, already vulnerable in the country? In the years 2018–2020, we installed 22 trail cameras and five digital cameras (live online video feeds) on the nests. The total failure level observed in cameras (18.5%) was high. We observed, using these cameras, the extra mortality of chicks (10.7% of potentially fledged chicks) and even adult birds by unexpected predation by Northern Goshawk and White-tailed Eagle. This phenomenon is also common in the national population, as we found a total of ten cases of total losses by predators (eight or nine of them were birds of prey), including nests not covered by camera monitoring. The extra adult-predation by Goshawks means an extra drop in LRS. Those adult and chick predations are an example of exceptional catastrophic phenomena, which have been described as the direct cause of the extinction of animal populations throughout history. Only active conservation and stop poaching of the Polish population could stop the decline and save the Polish Ospreys.

Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach

In the construction of new smart cities, traditional fire-detection systems can be replaced with vision-based systems to establish fire safety in society using emerging technologies, such as digital cameras, computer vision, artificial intelligence, and deep learning. In this study, we developed a fire detector that accurately detects even small sparks and sounds an alarm within 8 s of a fire outbreak. A novel convolutional neural network was developed to detect fire regions using an enhanced You Only Look Once (YOLO) v4network. Based on the improved YOLOv4 algorithm, we adapted the network to operate on the Banana Pi M3 board using only three layers. Initially, we examined the originalYOLOv4 approach to determine the accuracy of predictions of candidate fire regions. However, the anticipated results were not observed after several experiments involving this approach to detect fire accidents. We improved the traditional YOLOv4 network by increasing the size of the training dataset based on data augmentation techniques for the real-time monitoring of fire disasters. By modifying the network structure through automatic color augmentation, reducing parameters, etc., the proposed method successfully detected and notified the incidence of disastrous fires with a high speed and accuracy in different weather environments—sunny or cloudy, day or night. Experimental results revealed that the proposed method can be used successfully for the protection of smart cities and in monitoring fires in urban areas. Finally, we compared the performance of our method with that of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

Motion Analysis for Jumping Discus Throwing Correction

The purpose of this study was to explore the kinematical characteristics of jumping discus throwing. Eight male right-handed discus throwers who used to practice the jumping throwing technique were recruited as participants. Two high-speed digital cameras with 120 Hz sampling rate were synchronized to capture the movement. The captured images were processed using a motion analysis suite, and the markers attached to joints on images were digitized manually. Based on the results, throwers should keep smaller the shoulder–hip twisting and the right anterior superior iliac spine (abbreviated: ASIS) in front of the right acromion (for right-handed throwers) from the instant the right foot lands to the instant the left foot lands, before the instant of the right foot lands; keep the discus at a depressed position; and reduce the time before discus release, particularly the time of the non-support phase and the second single-support phase. Additionally, release velocity must be improved because throwing distance is directly proportional to squared release velocity. In conclusion, the current study demonstrated comprehensive kinematical analyses, which can be used to instruct the jumping discus throwing technique with duration and angle characteristics of throwing movement for athletes by coaches with videos.

Matters of choosing the pixel size in topographic aerial photography

The existing regulatory documents on photogrammetric works are technologically outdated. They neither take into account the peculiarities of aerial photography with digital cameras, the navigation equipment used and modern image processing methods, nor regulate the technique of calculating the pixel size on the ground. In order to select the pixel size in the terrain for aerial photography with topographic requirements concerning to the results, the method of multivariate analysis of the input data is proposed. It is supposed to ensure the minimum pixel size on the ground according to such criteria as the accuracy of the aerial triangulation results, the accuracy of building a digital elevation model for orthotransformation, the possibility of the objects interpretation with a specified minimum size and consideration of camera exposure parameters. To determine the accuracy criteria, we used formulas for pre- calculation of spatial phototriangulation accuracy with multiple choice of parameters. Examples of pixel size selection in the terrain at designing aerial photography by an amateur camera for the purposes of large-scale mapping are considered. Conclusions on the necessity of solving the issues of selecting parameters of large scale aerial photography, taking into account multiple input data and used aerial survey equipment are made.

Experimental 3-dimensional tracking of the dynamics of a single electron in the Fermilab Integrable Optics Test Accelerator (IOTA)

We present the results of experimental studies on the transverse and longitudinal dynamics of a single electron in the IOTA storage ring. IOTA is a flexible machine dedicated to beam physics experiments with electrons and protons. A method was developed to reliably inject and circulate a controlled number of electrons in the ring. A key beam diagnostic system is the set of sensitive high-resolution digital cameras for the detection of synchrotron light emitted by the electrons. With 60–130 electrons in the machine, we measured beam lifetime and derived an absolute calibration of the optical system. At exposure times of 0.5 s, the cameras were sensitive to individual electrons. Camera images were used to reconstruct the time evolution of oscillation amplitudes of a single electron in all 3 degrees of freedom. The evolution of amplitudes directly showed the interplay between synchrotron-radiation damping, quantum excitations, and scattering with the residual gas. From the distribution of measured single-electron oscillation amplitudes, we deduced transverse emittances, momentum spread, damping times, and beam energy. Estimates of residual-gas density and composition were calculated from the measured distributions of vertical scattering angles. Combining scattering and lifetime data, we also provide an estimate of the aperture of the ring. To our knowledge, this is the first time that the dynamics of a single electron are tracked in all three dimensions with digital cameras in a storage ring.

An Automated Auroral Detection System Using Deep Learning: Real-time Operation in Tromsø, Norway

The activity of citizen scientists who capture images of aurora borealis using digital cameras has recently been contributing to research regarding space physics by professional scientists. Auroral images captured using digital cameras not only fascinate us, but may also provide information about the energy of precipitating auroral electrons from space; this ability makes the use of digital cameras more meaningful. To support the application of digital cameras, we have developed artificial intelligence that monitors the auroral appearance in Tromsø, Norway, instead of relying on the human eye, and implemented a web application, “Tromsø AI”, which notifies the scientists of the appearance of auroras in real-time. This “AI” has a double meaning: artificial intelligence and eyes (instead of human eyes). Utilizing the Tromsø AI, we also classified large-scale optical data to derive annual, monthly, and UT variations of the auroral occurrence rate for the first time. The derived occurrence characteristics are fairly consistent with the results obtained using the naked eye, and the evaluation using the validation data also showed a high F1 score of over 93%, indicating that the classifier has a performance comparable to that of the human eye classifying observed images