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The requirements for the air reconnaissance system using unmanned aircraft systems are being studied. Problematic aspects of the air reconnaissance data processing process are analyzed from the point of view of ensuring the required level of operational efficiency and reliability of intelligence information. The possibility of automating the process of detecting aerial reconnaissance objects on video images is being investigated. A software-hardware module for automated detection and recognition of objects of interest is being developed to increase the level of processing efficiency and reliability of air reconnaissance data. Modern models of detection and recognition of objects of interest based on algorithms of the YOLO platform are studied. The software component of the module is being developed using the YOLOv8 algorithm architecture and a set of data formed in the conditions of air reconnaissance. The analysis of the results of experimental studies shows that: the developed model allows to ensure the required level of operational efficiency of video image processing and the reliability of aerial reconnaissance data; practical implementation of the developed model at ground command and control stations is possible without the use of additional computing equipment. The hardware component of the module is being developed using Raspberry Pi components. The analysis of quantitative evaluations of the efficiency of data processing using the developed software and hardware module on board the UAV of the studied class indicate an increase in time delays compared to the implementation at the command-and-control station, but it allows to ensure the required level of reliability. Further scientific research will be aimed at investigating the peculiarities of the implementation of the conceptual foundations of the developed module for various classes of UAV in order to increase the level of processing efficiency and reliability of aerial reconnaissance data.

Today, neural networks are gaining popularity and are increasingly used in various fields of life. They are used for data analysis, clustering, classification, object detection, or hidden patterns. Neural networks are increasingly used in business forecasting tasks. Computer vision systems are no exception. An important component of these processes is image preprocessing. It is known that images obtained from video cameras can exhibit perspective distortions originating from imperfect camera angles. Usually, the affine transform technique is used to correct geometric distortions or deformations, such as scaling, moving, shifting, rotating. In this paper, we study the features of affine transforms and their application with neural networks. Methods for recognizing geometric parameters in image transformation and moving are considered. Attention is paid to elementary transformations, which include transfer, scaling, shift, rotation. A mathematical model of image transformation for computer vision systems is developed. On the basis of the proposed method, an algorithm for perspective transformations of images obtained from video cameras located in parking lots or city parking lots is built, which greatly facilitates the further detection, segmentation and classification of objects. To improve the performance of the classical Mask R-CNN, a study was conducted in which a block with affine transformations was added to the convolutional neural network. Affine transformations are used to correct the perspective convergence of lines in the frame that are parallel in reality.

It is shown that trends in the development of information technologies for processing, coding and data transmission relate to the growing attractiveness of wireless infocommunication systems and networks. In modern conditions, such technologies are used to organize information support using unmanned ground-based and air-based systems. The significance of the use of wireless telecommunication technologies for the organization of management processes in crisis conditions is substantiated. The article substantiates an approach for constructing a technology for compression of fragments-containers using a preliminary transformation, which is based on: the formation of two constituent arrays of spectral components (ASC), namely: the vector of gradations of spectral space and the scale of spectral intervals; coding of the low-frequency component of the transformant based on taking into account statistical dependencies in the differential space. It is stated that this allows: to adapt to the structure of ASC, taking into account the different concentration of high-frequency components in fragments of video sequences and the different level of the quantization factor; identify additional structural patterns in the ASC; localize statistical redundancy due to the presence of correlation between adjacent fragments in the spectral-parametric description; reduce redundancy caused by the presence of uneven distribution of low-frequency components; create conditions for the use of parallel coding technology. A technology for encoding the scale of spectral intervals of spectral-parametric description ASC is being created. It is based on breaking down the ASC into two components: the length of the first sequence of non-significant elements; A sub-vector that consists of elements on a scale of spectral intervals with the exception of the first and last sequence of nonsignificant elements. It is proved that this allows: to identify additional structural regularities; localize statistical redundancy, create conditions for concealing confidential information using steganographic methods. The stages of the method of monotonic coding of a significant subvector of the scale of spectral intervals are described. [ABSTRACT FROM AUTHOR]

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Background We recently reported the importance of deep learning (DL) of pelvic magnetic resonance imaging in predicting the degree of urinary incontinence (UI) following robot‐assisted radical prostatectomy (RARP). However, our results were limited because the prediction accuracy was approximately 70%. Aim To develop a more precise prediction model that can inform patients about UI recovery post‐RARP surgery using a DL model based on intraoperative video images. Methods and Results The study cohort comprised of 101 patients with localized prostate cancer undergoing RARP. Three snapshots from intraoperative video recordings showing the pelvic cavity (prior to bladder neck incision, immediately following prostate removal, and after vesicourethral anastomosis) were evaluated, including pre‐ and intraoperative parameters. We evaluated the DL model plus simple or ensemble machine learning (ML), and the area under the receiver operating characteristic curve (AUC) was analyzed through sensitivity and specificity. Of 101, 64 and 37 patients demonstrated “early continence (using 0 or 1 safety pad at 3 months post‐RARP)” and “late continence (others),” respectively, at 3 months postoperatively. The combination of DL and simple ML using intraoperative video snapshots with clinicopathological parameters had a notably high performance (AUC, 0.683–0.749) to predict early recovery from UI after surgery. Furthermore, combining DL with ensemble artificial neural network using intraoperative video snapshots had the highest performance (AUC, 0.882; sensitivity, 92.2%; specificity, 78.4%; overall accuracy, 85.3%) to predict early recovery from post‐RARP incontinence, with similar results by internal validation. The addition of clinicopathological parameters showed no additive effects for each analysis using DL, EL and simple ML. Conclusion Our findings suggest that the DL algorithm with intraoperative video imaging is a reliable method for informing patients about the severity of their recovery from UI after RARP, although it is not clear if our methods are reproducible for predicting long‐term UI and pad‐free continence.

Due to the limited network bandwidth and low-quality end-user devices, the images in distance education videos are transferred in low resolution. It results in low clarity, which badly affects video visibility and leads to poor learning effects. Therefore, a video-enhancing mechanism is required in distance education to ensure quality visualization. The quality is enhanced using a multi-scale Gabor filter, where a color image in the video is converted from the RGB space to the HSI space. A grey-level objective optimization function is constructed to attain a grey-scale image by the gradient conjugate solution method. The attained grey-scale image is used as the basis to obtain the grey-level change information of an image in different directions by using the multi-scale Gabor filter. The image texture is completed and enhanced by using the fractional order differential mask. The local information of the grey-scale images in educational videos is selected and a video image is obtained after the construction of the adaptive mask operator. The experimental results show that the proposed method can effectively grey-scale the images in distance education videos, which improves the sharpness and contrast, and effectively extract the local information. Moreover, the difference between the actual pixels is small, where the maximum difference is 144 and the minimum difference is 55, which shows the extraction results are more accurate. [ABSTRACT FROM AUTHOR]

Travel time estimation plays an essential role in the high-granular traffic control and management of urban roads with distinct lane-changing conditions among lanes. However, little attention has been given to the estimation of distributions of travel times among different lanes and different vehicle types in addition to their expected values. This paper proposes a new method for estimating lane-based travel time distributions with consideration of different vehicle types through matching low-resolution vehicle video images taken by conventional traffic surveillance cameras. The vehicle type classification is based on vehicle sizes and deep learning features extracted by densely connected convolutional neural networks, and the vehicle re-identification is conducted through a lane-based bipartite graph matching technique. A case study is carried out on a congested urban road in Hong Kong. Results show that the proposed method performs well in estimating the lane-level travel time distributions by vehicle type which can be very helpful for various lane-based and vehicle type-specific traffic management schemes. [ABSTRACT FROM AUTHOR]

The irrelevant background information in the personalized image is easy to be quantified into the same word as the main target, and the quantization process will inevitably cause the loss of a lot of visual information. This phenomenon will seriously reduce the quality of the generated theme when the personalized image content is complex. This paper proposes a Multi-Source Big Data Fusion Annotation (MSBDFA) model. The model obtains similar personalized images by analyzing the relevant multi-source information of the personalized images, and uses the annotations of the similar personalized images to label the personalized images. For the personalized images with complex background visual information, the personalized image retrieval based on complete information modeling uses the high-dimensional Gaussian distribution to directly model the continuous visual features of the personalized images, and uses the two-level spectral clustering algorithm to distribute the regional topics, so as to embed the complete local information contained in the visual features into the global features of the personalized image. Therefore, this method can completely retain visual information during the modeling process, so that the targets buried in the complex background can be better classified. The experimental results on the standard database show that the method proposed in this paper can generate high-quality personalized image subjects in complex scenes and has good retrieval performance. [ABSTRACT FROM AUTHOR]

AbstractIntroductionGiven the rapid developments in the field of science and knowledge, one has to look for ways to learn how to learn, and it can learn at any time and place, and among these methods, methods that are closer to the learning style of learning. And causing more conflict with content. The purpose of this study was to investigate the effect of flipped learning through video and multimedia methods on learning in the mathematical science course of sixth grade.MethodThis quasi-experimental study was a post-test pre-test with control group. The statistical population of this study includes all sixth grade elementary school students in Shoush city and its sample consists of sixth grade pupils of the School of Pooshinebaf, Reyhaneh and Danyal Shoush sity. At first, three schools were selected purposefully. Then, from these three schools, a school was selected as a draw for a flipped learnin through video, and a flipped learning school through multimedia and other school method for traditional methods. The study volume was 81 students. 30 students were in the reverse training group using video images and 30 students in the multimedia reverse learning group and 21 students in the traditional education group. In this research, a learning test, a movie CD of teacher teaching, and a researcher-made multimedia CD were used to collect the data. In order to infer the data, covariance analysis were used.Results The findings showed that flipped learning, using video and multimedia methods, has a significant effect on learning mathematical lessons. As well as between the two methods of flipped learning, flipped learning by multimedia method has a significant effect on reverse learning by means of video images on learning.Discussion flipped learning because the students were ready in class, the teacher had enough time to give more and diverse questions. As well as more practical application of the students' lessons learned, which leads to more learning. In between the two methods of flipped learning, flipped learning by the multimedia method is due to multimedia features such as interactivity, Being attractive, using animations and pictures, and singing various exercises, taking tests and giving feedback on the series It has more impact on learning.

The provision of lane-level travel time information can enable accurate traffic control and route guidance in urban roads with distinctive traffic conditions among lanes. However, few studies in the literature have been conducted to estimate lane-level travel time distributions. This study proposes a new vehicle re-identification (V-ReID) method for estimating lane-level travel time distributions using video images from widely deployed surveillance cameras. In the proposed method, a lane-based bipartite graph matching is introduced to obtain optimal matches between upstream and downstream vehicles by considering lane-level traffic conditions and vehicles’ lane changing behaviors and visual features. A lane-based travel time estimation technique is introduced to real-time estimate full spectrum of lane-level distribution parameters, including not only the mean but also the standard deviation and the distribution type. A comprehensive case study is carried out on a congested urban road in Hong Kong. Results of case study show that the proposed method outperforms the state-of-the-art link-based V-ReID method and is capable for providing accurate lane-level travel time distribution information on congested urban roads. [ABSTRACT FROM AUTHOR]

Video images with measuring information in automated systems have a large volume. For example, the volume of video images of structures of natural origin is kilos and megabytes of information. Video images of natural structures have a high level of redundancy Reducing file volumes can be obtained through compression. Solving compression problems, or more generally, video image coding, uses advances and stimulates the development of many fields of engineering and mathematics. In the course of the research, a video compression using the structure of the zero-tree wavelet was selected. The results of the research have resulted in the development of existing wavelet compression techniques and the identification of optimal wavelet function for video images of this type. The best wavelet function for video images with measuring information in automated systems is Haar wavelet which characteristic indicators provide effective compression and fairly acceptable recovery. These indicators were the compression factor and the recovery error. The experimental results for five samples of video images of natural structures showed a pattern and a relationship between the appearance of the sample and its coefficients. You can see some equivalence of compression ratios, which indicates the similarity of video images, namely the identity of the outer surfaces of hard rock.

The crowding in bus is an important factor affecting passenger satisfaction and bus dispatching level. However, how to use video images to detect crowding accurately is a difficult problem. In this paper, firstly, an image sample library is established based on the evaluation standard of crowding in bus, which contains 16346 sample images. Then, Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) are used to extract the texture features of the image in bus. Then, a rough classification method of crowding based on Support Vector Machine (SVM) is proposed. At the same time, in order to improve the accuracy of rough classification of crowding, the optimization effects of grid search algorithm, particle swarm optimization algorithm and genetic algorithm on SVM parameters are compared. The results show that the optimization effect of genetic algorithm is the best, and the accuracy rate is 93.20%. Finally, for the problem that the SVM method is not ideal in the fine classification of crowding, this paper proposes a new method based on ResNet. SGD, Adadelta and Adam are selected to optimize the parameters of ResNet model. The accuracy of the optimal Adam algorithm reaches 96.22%, which effectively solves the problem of the fine classification of crowding in bus. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a tone mapping algorithm for processing high-brightness video images. This method can maximally recover the information of high-brightness areas and preserve detailed information. Along with benchmark data, real-life and practical application data were taken to test the proposed method. The experimental objects were license plates. We reconstructed the image in the RGB channel, and gamma correction was carried out. After that, local linear adjustment was completed through a tone mapping window to restore the detailed information of the high-brightness region. The experimental results showed that our algorithm could clearly restore the details of high-brightness local areas. The processed image conformed to the visual effect observed by human eyes but with higher definition. Compared with other algorithms, the proposed algorithm has advantages in terms of both subjective and objective evaluation. It can fully satisfy the needs in various practical applications. [ABSTRACT FROM AUTHOR]

In computer terminology, mining is considered as extracting meaningful information or knowledge from a large amount of data/information using computers. The meaningful information can be extracted from normal text, and images obtained from different resources, such as natural scene images, video, and documents by deriving semantics from text and content of the images. Although there are many pieces of work on text/data mining and several survey/review papers are published in the literature, to the best of our knowledge there is no survey paper on mining textual information from the natural scene, video, and document images considering word spotting techniques. In this article, we, therefore, provide a comprehensive review of both the non‐spotting and spotting based mining techniques. The mining approaches are categorized as feature, learning and hybrid‐based methods to analyze the strengths and limitations of the models of each category. In addition, it also discusses the usefulness of the methods according to different situations and applications. Furthermore, based on the review of different mining approaches, this article identifies the limitations of the existing methods and suggests new applications and future directions to continue the research in multiple directions. We believe such a review article will be useful to the researchers to quickly become familiar with the state‐of‐the‐art information and progresses made toward mining textual information from natural scene and video images. This article is categorized under:Algorithmic Development > Text Mining [ABSTRACT FROM AUTHOR]

Rapid advances in artificial intelligence have made it possible to produce forgeries good enough to fool an average user. As a result, there is growing interest in developing robust methods to counter such forgeries. This study presents a new Fourier spectrum‐based method for detecting forged text in video images. The authors' premise is that brightness distribution and the spectrum shape exhibit irregular patterns (inconsistencies) for forged text, while appearing more regular for original text. The method divides the spectrum of an input image into sectors and tracks to highlight these effects. Specifically, positive and negative coefficients for sectors and tracks are extracted to quantify the brightness distribution. Variations in the shape of the spectrum are analysed by determining the angular relationship between the principal axes and the sectors/tracks of the spectrum. Next, it combines these two features to detect forged text in the images of IMEI (International Mobile Equipment Identity) numbers and document. For evaluation, the following datasets are used: own video dataset and standard datasets, namely, IMEI number, ICPR 2018 Fraud Document Contest, and a natural scene text dataset. Experimental results show that the proposed method outperforms existing methods in terms of average classification rate and F‐score. [ABSTRACT FROM AUTHOR]

The quality of Physical Education (PE) education in high schools is closely related to interactive educational efficiency in classrooms. Teachers and students can improve their interest in learning through classroom interaction. Teachers can adjust educational programs according to the existing shortcomings of physical education, stimulate students’ interests in sports, and reduce student tensions and learning pressures. Students can increase their enthusiasm and creativity in sports, thereby enhancing students’ sports skills. Therefore, in a practical teaching process, it’s important to emphasize enhancing the effectiveness of interactive instruction in the classroom. This makes it possible to develop sports instruction. This paper analyzes how to effectively improve the effects of classroom interactions in a lower secondary school, and proposes a concrete teaching method for physical education. First, this paper explains the importance of improving the effectiveness of classroom education for junior high school students, and analyzes the present state of PE classroom education, and proposes an improvement strategy including physical education, and rationalizes students’ physical and mental development to stimulate students’ interest in sports. The classroom is innovative education and means that students improve their classroom enthusiasm. [ABSTRACT FROM AUTHOR]

Fire detection systems in ships are based on smoke and heat detection in accordance with safety regulations. The rapid advancement of machine vision technology has led to the development of video smoke detection (VSD) systems. In this study, a VSD system is applied to smoke detection within the engine room of the ship. A dataset for a range of scenarios was created with a smoke generator. The method for smoke detection was based on motion detection and a support vector machine classifier, which was used to make candidate regions and perform classification. A local binary pattern descriptor was used to extract the feature vector. A training set was made from a variety of video frames, randomly. Experimental results seldom produced false positive windows in the non‐smoke region. However, if the greyscale value of difference image between background and the smoke is lower than the setting value for motion detection, the system could not detect smoke. Processing time is sufficiently fast for use in real‐time smoke detection systems. To install a VSD system on‐board a vessel, the authors recommend a performance standard of the system which must be met. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a tone mapping algorithm for processing high-brightness video images. This method can maximally recover the information of high-brightness areas and preserve detailed information. Along with benchmark data, real-life and practical application data were taken to test the proposed method. The experimental objects were license plates. We reconstructed the image in the RGB channel, and gamma correction was carried out. After that, local linear adjustment was completed through a tone mapping window to restore the detailed information of the high-brightness region. The experimental results showed that our algorithm could clearly restore the details of high-brightness local areas. The processed image conformed to the visual effect observed by human eyes but with higher definition. Compared with other algorithms, the proposed algorithm has advantages in terms of both subjective and objective evaluation. It can fully satisfy the needs in various practical applications.

In order to solve the problem of difficult pre-processing of crop video image shadows, a probable learning pixel classification method is proposed to study its processing technology. The algorithm effectively detects the shadow area by performing intelligent video collaborative detection on the shaded parts of the crop video sequence. Firstly, the cloud collaborative detection algorithm that can be widely used in agriculture was proposed. The video key frame was obtained and the background modeling algorithm with strong adaptability to crop illumination was applied to realize real-time detection of the target, so as to construct the crop pixel model. Finally, the proposed algorithm and the constructed model are applied to the processing of shadows of agricultural plant video images for experimental verification. The results show that in video frames 47, 194 and 258, the probable learning pixel classification method can be used to determine the shaded part of each frame, which can greatly improve the detection accuracy of crop shadows. The research in this paper shows that the probability learning pixel classification method can better enhance the shadow robustness and accuracy of crop video images. [ABSTRACT FROM AUTHOR]

The video-recording of police interrogations of suspects has become widespread in criminal justice systems and is routinely regarded by legal professionals and lay people alike as a means of protecting the rights of suspects and reducing the likelihood of coerced or false confessions. This study, based on evidence from Japan and experiments conducted in Tokyo as well as cases and reinforced by studies from elsewhere, finds that the way visual images of suspects and their narratives are depicted on film can, on the contrary, be misleading. Not only lay participants in trials, but also legal professionals, may be misled into accepting unreliable confessions. Indeed the very power of visual images to convince viewers calls for great caution in their use. Possible solutions include the use of independent expert witnesses to evaluate the reliability of visual recordings and the restriction of taped evidence to audio tracks. [ABSTRACT FROM AUTHOR]

High-resolution video images contain huge amount of data so that the real-time capability of image registration and target localization algorithm is difficult to be achieved when operated on central processing units (CPU). In this paper, improved ORB (Oriented FAST and Rotated BRIEF, FAST, which means "Features from Accelerated Segment Test", is a corner detection method used for feature points extraction. BRIEF means "Binary Robust Independent Elementary Features", and it's a binary bit string used to describe features) based real-time image registration and target localization algorithm for high-resolution video images is proposed. We focus on the parallelization of three of the most time-consuming parts: improved ORB feature extraction, feature matching based on Hamming distance for matching rough points, and Random Sample Consensus algorithm for precise matching and achieving transformation model parameters. Realizing Compute Unified Device Architecture (CUDA)-based real-time image registration and target localization parallel algorithm for high-resolution video images is also emphasized on. The experimental results show that when the registration and localization effect is similar, image registration and target localization algorithm for high-resolution video images achieved by CUDA is roughly 20 times faster than by CPU implementation, meeting the requirement of real-time processing. [ABSTRACT FROM AUTHOR]

The paper examines the particulars of individual and comparative analysis of video images portraying the physical appearance of human subjects, when conducted for the purposes of forensic facial recognition. It details the specifics of the expert's actions at these stages of the investigation. Presented guidelines on the use of recommended methods of forensic video analysis are based on a review of forensic practice. The author points out that when studying elements of human appearance, the expert should as a matter of priority take into account those factors that affect the display of features in the video.

Brain regions involved in small-animal phobia include subcortical and cortical areas. The present study explored the neuronal correlates of small-animal phobia through fMRI data to determine whether a manipulation of number and proximity parameters affects the neurobiology of the processing of feared stimuli. The participants were 40 individuals with phobia and 40 individuals without phobia (28.7% male and 71.3% female). They watched videos of real and virtual images of spiders, cockroaches and lizards in motion presented more or less nearby with one or three stimuli in the different conditions. The results suggested a differential brain activity between participants with and without phobia depending on the proximity and number of phobic stimuli. Proximity activated the motor response marked by the precentral gyrus and the cingulate gyrus. By contrast, the number of stimuli was associated with significant sensory activity in the postcentral gyrus and ventromedial prefrontal cortex. We also observed a greater activity in the occipital cortex when exploring the number compared to the proximity factor. Threatening stimuli presented nearby and those presented in greater numbers generated an intense phobic response, suggesting a different emotion regulation strategy. Based on these findings, exposure therapies might consider including proximity to the threat and number of stimuli as key factors in treatment.

In current era of technology, information acquisition from images and videos become most important task due to the rapid development of data mining and machine learning. The information can be either textual, visual, or combination of these. Text appearing in images or videos is a significant source of information and plays a vital role to perceive it. Developing a unified method to detect the text is hard, as textual properties (i.e. font, size, color, illumination, orientation, etc.) may vary with the complex background. So far, multimedia and computer vision community unable yet to standardize any ideal approach to extract the text smoothly. In this paper, a novel method is proposed to detect and localize artificial Urdu text in individual video frames. Firstly, Sobel and Canny edge detection operators are applied to input frame and are merged with MSER (Maximally Stable Extremal Region) detected regions. Next, geometric constraints are applied to eliminate obvious non-text regions with large and small variations. Further refining of non-text regions is achieved by stroke width transform. SVM (Support Vector Machine) classifier is trained to classify text and non-text objects. Finally, bounding boxes are used to localize the text. Experimental results show that the proposed method is robust and efficient than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

There was developed a computerized system for measuring the geometric parameters and the number of phytoplankton instances in the water samples from reservoirs used for household purposes. The basis of these measurements is formation and computer processing of video images of water samples from these reservoirs. The measurement results are used to develop the mathematical models of phytoplankton processes, monitoring and forecasting of the status of reservoirs for household purposes, which are a source of water supply. [ABSTRACT FROM AUTHOR]

This article considers the mathematical models and methods of identifying the video image dynamic errors. These movies contain information on measuring the geometric parameters and motion parameters of natural stone products and equipment used in their manufacture. The details of dynamic errors are required to develop the algorithmic procedures of their compensation and to improve the accuracy of measuring these geometrical and motion parameters of the measurement object. [ABSTRACT FROM AUTHOR]

Dada a importância da avicultura de corte para a economia brasileira, este trabalho buscou compreender e identificar novos indicadores de bem-estar inerentes ao animal que contribuíssem para o aumento da eficácia produtiva, estudando-se diferentes reações comportamentais de matrizes de frango de corte, em câmara climática. O experimento foi delineado como um Quadrado Latino 3x3x3, em que as variáveis temperatura do ar, ração e idade das aves foram controladas, sendo as aves de diferentes idades alojadas em boxes distintos. Foram feitas observações do comportamento das aves em dois horários do dia, sendo uma observação na parte da manhã e outra na parte da tarde, durante períodos de 15 minutos cada, por meio de câmeras de vídeo instaladas no teto da câmara climática, não havendo interferência humana no registro das informações. Constatou-se, nos dados, a influência das variáveis controladas em diversos comportamentos observados, concluindo-se que a presença do alimento é um fator experimental relevante, pois aumenta as reações comportamentais agressivas.Knowing the importance that the poultry industry represents for the Brazilian economy, this work, searched to understand and to identify new welfare pointers inherent to the animal that contributed for the increase of the productive effectiveness, studying different behavior reactions in broiler breeders, in climatic chamber. The experiment was delineated as a Latin Square 3x3x3, where the variable: temperature of air, birds ration and birds age had been controlled. The birds of different ages had been lodged in distinct boxes. Observations of the behavior of the birds in two schedules of the day had been made, being one in the morning and the other one in the afternoon, during a period of 15 minutes each through video cameras, installed in the ceiling of the climatic chamber, having no interference of human being in the register of the data. It was verified the influence of the controlled variables in diverse observed behaviors where it was concluded that the presence of food resulted in bigger occurrences of aggressiveness reactions.

Copyright of Theatrical Colloquia is the property of George Enescu University of Arts, Artes Publishing House and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research investigated the developmental relationship between young children's performance on a mark test and on an object-reaching test using video images. Forty-three young children (ages 30-43 months) participated in the experiment. After performing the mark test, children experienced three main conditions of the object-reaching test on which they were asked to find the real target doll only by looking at its televised image. The results showed that a video-elicited correct search for a target which appeared behind the children's back was far more difficult than a search for a mark on their heads. This was especially true of the results obtained for one of the front/back conditions, whereby two stands were presented in front and at the back of the child at the same time and that the target was put only on the front or back. In this condition, many of chilren who passed the mark test made an incorrect search toward the front stand. [ABSTRACT FROM AUTHOR]

This study presents a novel technique for the measurement of insertion depth of an epidural Tuohy needle in real‐time. A wireless camera is used which transmits video images during insertion to a host computer. The computer contains the image processing algorithm to detect the visible needle in the image and measures the length. The measurement is done by HSV background removal, colour comparison and using RGB intensity profile to locate 10 mm markings on the Tuohy needle shaft. The visible length is then subtracted from the known length of the needle to calculate the depth of the needle tip. The camera can be placed in the operating theatre between 50 and 100 cm away from the needle insertion site. Wireless camera is beneficial since it minimises disturbance to the anaesthetist and the patient and avoids ethical and sterility concerns which would be caused by bringing additional equipment into the hospital room. The speed of the image processing technique is 10 frames per second with a maximum error of ±3 mm. The image processing failure rate was 3 frames out of 150 which gave an overall reliability of 97.8% during insertion. Also this enables continuous needle depth monitoring during insertion by storing measurements into a data file. The purpose of measuring needle depth in real time is to precisely place the needle in the epidural space. [ABSTRACT FROM AUTHOR]

By applying direct linear transformation theory in photogrammetry and based on the marked points information in accident site, the 2D direct linear transformation coefficients of orthographic projection transformation are solved out. By frame decoding of accident surveillance video, a group of pictures are obtained, and then a series of contact points between wheel and road are marked on decoded picture group and an orthographic projection is conducted on the marked points on wheel. Based on these marked points the trajectory curve is fitted, and the length of trajectory is calculated by integration. Combined with frame rate information of video the speed of accident vehicle is finally obtained. The method is applied to a real traffic accident case with the data of driving trajectory length and vehicle speed calculated. [ABSTRACT FROM AUTHOR]

In this study, a technique for analyzing directional wave spectra in shallow water areas using video image sequences is presented. The video image was obtained and collected by a single digital video camera. The technique was based on time series of the pixel brightness on video images. The pixel can be treated as a fixed instruments through use of the rectification process. The extended maximum likelihood method and the Bayesian directional method were used to estimate directional wave spectra using two different configurations of arrays from pixel brightness on video images. The study examined video image data at HORS pier on Hasaki Beach, Japan. The results indicate that video image data could be used to estimate surface wave spectra in very shallow water areas. [ABSTRACT FROM AUTHOR]

Abstract: Gas–solids circulating fluidized bed (CFB) experiments have been carried out, with a focus on understanding the impact of polydispersity on cluster characteristics in a dilute riser. Two categories of polydispersity were studied: continuous particle size distributions (PSDs) of varying distribution widths, and binary mixtures of varying compositions. Video images show that particle clusters exist even in these very dilute systems (solid loading m =0.03–0.29). Local measurements were acquired using two independent instruments – a fiber optic probe and a high-speed video camera – with the cluster trends obtained from both are in qualitative agreement with each other. Cluster characteristics extracted from the measurements include appearance probability, duration and frequency. Results show that: (i) axial position is the strongest influence on radial profiles of cluster duration and frequency, but has negligible effect on cluster appearance probability, (ii) profile shapes are invariant with height, although magnitudes of cluster duration and frequency changes with height, and (iii) effects of both the widths of continuous PSDs and the compositions of binary mixtures are observed at the riser bottom for cluster duration and at the riser top for cluster frequency, though are insignificant for the appearance probability. [Copyright &y& Elsevier]

Abstract: Introduction: Unwanted effects of drugs on neurobehavioural and cardiovascular functions are normally assessed in separate studies and using different animals. The purpose of this study was to validate, in the monkey, a model that incorporates the neurobehavioural assessment into the Safety Pharmacology cardiovascular study, allowing for an integrated evaluation of these two physiological systems. Methods: Conscious male cynomolgus (Macaca fascicularis) monkeys (n =4) were given single oral doses of vehicle, d-amphetamine (0.5, 1 and 2mg/kg) or diazepam (0.5, 1 and 2.5mg/kg) in a dose-escalation study design. Blood pressure, heart rate, electrocardiogram (ECG), body temperature, locomotor activity and behaviour (by video) were monitored continuously for 24h post-dose. Animals underwent a standardised neurobehavioural test battery which allowed the direct examination of 31 signs, including behavioural responses and neurological examinations, conducted the day before dose, at maximal plasma concentration time (T max), and 24h post-dose. The study was carried out in a first phase with telemetric cardiovascular recording only, and a second phase with telemetric cardiovascular recording and neurobehavioural observations. Results from the second phase of the study were used to evaluate the influence of the direct neurobehavioural examination on the telemetrically acquired cardiovascular parameters. Results: The expected cardiovascular and neurobehavioural changes, based on the pharmacological properties of the compounds tested, were accurately detected. In the second phase of the study the direct neurobehavioural examination caused fluctuations of the telemetric cardiovascular parameters for no more than 20min from the end of the procedure and this did not alter or jeopardise the analysis and interpretation of the cardiovascular parameters. Discussion: These results confirm the validity of this combined model capable of providing in the cynomolgus monkey a reliable and reproducible neurobehavioural and cardiovascular assessment of candidate drugs during the course of safety pharmacology evaluations. [Copyright &y& Elsevier]

Abstract: Introduction: The combined evaluation of physiology and behaviour allows a complete and more comprehensive pre-clinical assessment of central nervous system (CNS) function. An integrated video-telemetric electroencephalography (Video-tEEG) system, which allows the simultaneous and continuous recording of EEG and video images for long periods, was developed. Methods: This study focuses on the refinement of the surgical methodology for the combined recording of cortical, hippocampal EEG and electromyogram (EMG) waveforms in freely moving rats. The post-operative recovery of animals was monitored by recording EEGs by telemetry and the general activity by video, on days 1, 6 and 15 after surgery, for approximately 24h, on each occasion. Results: The results suggested that the applied surgical technique for the implantation of the telemetric transmitter, allowed for a gradual recovery of the animals within 15days. During the recovery period the behavioural and locomotor parameters measured, indicated that there were no changes to the light-dark circadian cycle, and these parameters gradually tended to reach background levels within a 15-day period. Using a mechanical connection between the deep and the telemetric electrodes, 15days after surgical implantation the recording system was able to acquire cortical and hippocampal EEG traces of good quality. Discussion: This present study is concerned with the refinement of the surgical technique, as well as the integration and synchronisation of the commercially available Dataquest telemetry system and the Noldus video system, in order to study cortical, hippocampal EEG waveforms, in combination with behaviour and locomotion. The application of this integrated Video-tEEG system could provide advantages in the ethical use of animals in different pre-clinical research areas. [ABSTRACT FROM AUTHOR]