Video Images Research Articles

3D-Oculography: New Method of Determination of Human Gaze Point Position in Space

INTRODUCTION: Oculography — a method of recording movement of eyeballs of a human or animal by analyzing changes in electrical potentials recorded by two electrodes fixed on skin near the eye socket is used to solve various problems. Among them are the determination of pronounced nystagmus, of characteristic changes of oculomotor reactions in different conditions of observing video images. Of interest is the determination of the position of gaze point (GP) and of the area of increased attention in three-dimensional space. This information is associated with the cognitive system of the observer and is of interest not only to physiologists, but also to specialists of related fields. . AIM: To experimentally prove the effectiveness of the developed method of 3D-oculography providing determination of the GP position in space. MATERIALS AND METHODS: The possibility of restoring GP position based on the analysis of recorded oculograms even in the presence of additive noise induced by the external electromagnetic fields, is proven by the numeric modeling method. Direct experimental studies were conducted with recording oculograms of a human observer on a previously verified equipment. A ZB-2 oculograph was used, the data obtained were verified by the method of average values. RESULTS: The obtained modeling results permitted to determine the maximal values of dispersion of additive noise and amplitude shifts of signals, which make possible satisfactory restoration of the coordinates of the GP and parameters of its movement trajectory. The qualitative correspondence of the experimental results to the results of numerical modeling was proven. The correspondence between trajectories of the observed object moving in space and trajectories of GP movement synthesized from counts of recorded oculograms was confirmed. CONCLUSION: Multichannel record of oculographic signals permits restoring parameters of the GP trajectory. Creating systems for recording these signals requires minimization of the noise level. An increase in dispersion of the noise component of the signal leads to most significant errors in calculating the coordinates of trajectory points.

Sequential Behavior of Broiler Chickens in Enriched Environments under Varying Thermal Conditions Using the Generalized Sequential Pattern Algorithm: A Proof of Concept

Behavior analysis is a widely used non-invasive tool in the practical production routine, as the animal acts as a biosensor capable of reflecting its degree of adaptation and discomfort to some environmental challenge. Conventional statistics use occurrence data for behavioral evaluation and well-being estimation, disregarding the temporal sequence of events. The Generalized Sequential Pattern (GSP) algorithm is a data mining method that identifies recurrent sequences that exceed a user-specified support threshold, the potential of which has not yet been investigated for broiler chickens in enriched environments. Enrichment aims to increase environmental complexity with promising effects on animal welfare, stimulating priority behaviors and potentially reducing the deleterious effects of heat stress. The objective here was to validate the application of the GSP algorithm to identify temporal correlations between heat stress and the behavior of broiler chickens in enriched environments through a proof of concept. Video image collection was carried out automatically for 48 continuous hours, analyzing a continuous period of seven hours, from 12:00 PM to 6:00 PM, during two consecutive days of tests for chickens housed in enriched and non-enriched environments under comfort and stress temperatures. Chickens at the comfort temperature showed high motivation to perform the behaviors of preening (P), foraging (F), lying down (Ld), eating (E), and walking (W); the sequences <{Ld,P}>; <{Ld,F}>; <{P,F,P}>; <{Ld,P,F}>; and <{E,W,F}> were the only ones observed in both treatments. All other sequential patterns (comfort and stress) were distinct, suggesting that environmental enrichment alters the behavioral pattern of broiler chickens. Heat stress drastically reduced the sequential patterns found at the 20% threshold level in the tested environments. The behavior of lying laterally “Ll” is a strong indicator of heat stress in broilers and was only frequent in the non-enriched environment, which may suggest that environmental enrichment provides the animal with better opportunities to adapt to stress-inducing challenges, such as heat.

Construction Technology and Service Performance of Waterproof Curtain for Foundation Pit in Large-Particle Pebble Gravel Layer of Yangtze River Floodplain

A foundation pit is constructed in the floodplain of Yangtze River, and a deep and thick layer of large-particle pebble gravel exists below the base slab, thus forming a connected supply channel with the adjacent Yangtze River. The large water volume, high water pressure, and strong permeability of this layer bring great risks to the foundation pit construction. In view of the fact that conventional waterproof curtain construction technologies such as the deep mixing column and high-pressure jet grouting column cannot meet the engineering requirements under these kinds of geological and environmental conditions, a new waterproof curtain construction technology that combines the trenching technology of the diaphragm wall with the TRD (Trench cutting Remixing Deep wall) technology is proposed, i.e., the trenching-and-replacing-style TRD technology, as well as the construction process of this technology, is presented. After the waterproof curtain is built using the proposed technology, the strength, integrity, uniformity, and service performance of the waterproof curtain wall are tested and evaluated by the comprehensive methods of coring, borehole television imaging, resistivity CT, and a group well pumping test. The results show that the proposed technology overcomes the adverse effects of underlying large-particle pebble gravel layer, and the waterproof curtain built by it effectively cuts off the hydraulic connection inside and outside the pit. The technical proposal can provide useful references for similar projects.

P-203 Training of an artificial intelligence (AI) model to automatically detect nuclear errors during first cleavages

Abstract Study question Can an artificial intelligence (AI) algorithm be developed to automatically detect nuclear errors through dynamic images of first embryonic cleavages? Summary answer The AI model showed an accuracy and precision of above 60% in automatically detecting multinucleation in dynamic images. What is known already Implementation of dynamic monitoring by time-lapse (TL) incubators intends to enhance embryo selection, potentially leveraging algorithmic assistance. During embryonic development, certain morphological nuances, often overlooked in intermittent evaluations, become more apparent through TL monitoring. Some of these features are related to faulty karyokinesis in blastomeres, recognized as nuclear errors, which are linked to aneuploidy and reduced live-birth potential. Despite their significance in embryo selection, existing computer imaging AI models lack the ability to automatically detect these features. This research aims to develop the YOLO AI model’s ability to autonomously identify nuclear errors in dynamic embryonic cleavage imaging. Study design, size, duration Videos of 364 embryos recorded with four different TL incubators (Geri-Genea BiomedX, MIRI-ESCO, EmbryoScope/EmbryoScopePlus-Vitrolife) were randomly sampled from retrospective data. Videos started around the time of pronuclei fading (tPNf) and ceased at t5 (from 20hpi-50hpi). Each frame of each video was annotated by a senior embryologist. A total of 13246 frames were gathered with a resolution of 512x512 pixels, from which 9411 were used to train a YOLO model, 1977 for validation and test phases. Participants/materials, setting, methods For each frame, the embryologist outlined a box around the single nucleus observed in a blastomere and labelled it as “Normal”, and as “Multinucleated” if more than one nucleus or micronuclei were seen. 8% of the training dataset was composed of frames with no visible nucleus and 19% contained at least one multinucleated cell. The model thus further detected the presence of nuclei in a frame and classified it as “Normal” or “Multinucleated” when found. Main results and the role of chance Inferences and metrics were computed at the level of the embryo. No images of test videos were used in training. An embryo was considered “Normal” if no multinucleated cells were detected anywhere during the video. If at least one nuclear error was labelled, the embryo was considered as “Multinucleated”. To get a prediction at the embryo level, we considered the class predicted with the highest confidence during the video by the model. Another rule was to consider the embryo as “Multinucleated” if at least one cell was predicted as “Multinucleated” with a confidence above 0.5. When evaluated on the test dataset, the AI model reached 67% accuracy at detecting multinucleated embryos, with a precision of 63%, correctly identifying 61% of the multinucleated embryos. Conversely, when the model predicted that the embryo did not have any nuclear errors, it was right 61% of the time. Limitations, reasons for caution To reach the current set of results, the present model underwent previous multiple interfaces using several hundreds of embryos, all only trained by a single experienced embryologist. The model must undergo a cross-validation involving embryologists with varying levels of experience to assess its performance with a high degree of precision. Wider implications of the findings Continuous training and refinement of algorithms, informed by the accumulated knowledge of impacting features, play a crucial role in the evolution of automated systems. As these methods evolve, they will further refine embryo selection, improve outcomes, and contribute to the ongoing advancement of IVF. Trial registration number not applicable

P-402 Comparison of uterine contractions in artificial hormone treatment versus aromatase inhibitor stimulated frozen embryo transfer cycles

Abstract Study question does uterine contractions differ between aromatase inhibitor stimulated (AIS) or artificial hormone treatment (AHT) Frozen Embryo transfer (FET) cycles Summary answer uterine contraction activity did not show any difference between artificial hormone treatment and aromatase inhibitor stimulated FET cycles What is known already FET cycles are prepared by different protocols. aromatase inhibitor stimulated (AIS) or artificial hormone treatment (AHT) protocols are two common protocols among others. These protocols have different cycle dynamics and might differ in pregnancy rates and different effects on perinatal outcomes. Although embryo related factors are most important for implantation endometrial receptivity and therefore uterine contractions might have a role in implantation. English literature provides scarce data on uterine contractions and pregnancy outcomes. Presence of prominent contractions were related to poor pregnancy outcomes in some studies whereas other few studies failed to show any effect in this sense Study design, size, duration this study is an observational cohort study of successive frozen embryo transfer (FET) cycles of eligible patients between 1st of july 2023 to 31th of October 2023 in a tertiary university hospital in vitro fertilization clinic with ultrasonographic video recordings at transfer day. During study period of four months, 147 out of 265 consecutive FET cycles were included in this study and study is approved by local ethical committee of the affiliated university (KA23/388). Participants/materials, setting, methods Total study group was composed of 99 artificial hormone treatment cycles and 48 aromatase inhibitor stimulated FET cycles after exclusion of patients with uterine congenital malformations, uterine fibroids,adenomyosis patients and patient with previous uterine surgery. Implantation rates, pregnancy rates and clinical pregnancy rates were recorded. Transvaginal ultrasonographic sagittal uterine video image recordings were obtained at transfer day one hour before embryo transfer. Recordings were reviewed by two researchers and uterine contractions were documented by agreement. Main results and the role of chance This is the first study comparing different FET cycles and uterine contraction activities. Patient characteristics were similar between two groups. Artificial hormone treatment and aromatase inhibitor stimulated cycle groups showed similar endometrial thicknesses (11.47±1.92 and 11.24±1.02, respectively p = 0.42) and top quality embryo transfer rates (22.6 % and 26.8% p = 0.82). Although AIH group showed a statistically insignificant advantage, clinical and ongoing pregnancy rates were similar between protocols (supplementary data). Uterine peristaltic activity was not statistically different between aromataze inhibitor stimulated and artificial hormone treatment FET cycle (1.54±1.75 versus 1.63±2.15 p = 0.81 recpectively). Furthermore uterine persistalsizm were similar in pregnant and non-pregnant patients /1.52±2.07 vs 1.72±1.93 p = 0.57). However as far as Aromatase inhibitor stimulated FET cycles were considered; pregnant patients had less uterine peristaltic activity when compared ton non-pregnant patient (45.7% and 15.4%, respectively p = 0.054 ) although this difference was at the border of statistical significance. Limitations, reasons for caution This is an observational study of relatively short duration with limited number of patients. Our study in AIS cycles have a tendency to fewer contractions in cycles ended up in pregnancy. Larger series of FET cycles might prove differences in uterine contraction frequencies and pregnancy outcomes in AIS FET cycles. Wider implications of the findings Our study supported new information on uterine contractility in different FET cycles protocols. Further large scale studies might provide new insights to uterine factor and its role in implantation as far as uterine contraactions are concerned. Trial registration number (KA23/388) local ethic committee approval

Video-based diagnosis support system for pianists with Musician’s dystonia

BackgroundMusician’s dystonia is a task-specific movement disorder that deteriorates fine motor control of skilled movements in musical performance. Although this disorder threatens professional careers, its diagnosis is challenging for clinicians who have no specialized knowledge of musical performance.ObjectivesTo support diagnostic evaluation, the present study proposes a novel approach using a machine learning-based algorithm to identify the symptomatic movements of Musician’s dystonia.MethodsWe propose an algorithm that identifies the dystonic movements using the anomaly detection method with an autoencoder trained with the hand kinematics of healthy pianists. A unique feature of the algorithm is that it requires only the video image of the hand, which can be derived by a commercially available camera. We also measured the hand biomechanical functions to assess the contribution of peripheral factors and improve the identification of dystonic symptoms.ResultsThe proposed algorithm successfully identified Musician’s dystonia with an accuracy and specificity of 90% based only on video footages of the hands. In addition, we identified the degradation of biomechanical functions involved in controlling multiple fingers, which is not specific to musical performance. By contrast, there were no dystonia-specific malfunctions of hand biomechanics, including the strength and agility of individual digits.ConclusionThese findings demonstrate the effectiveness of the present technique in aiding in the accurate diagnosis of Musician’s dystonia.

Sensitivity Study of Feature Vectors Formed on the Basis of Multiple Scale Transformations of Processed Images

Introduction. Under the conditions of globalization, transportation of oil products by tanker fleets becomes one of the causes of man-made disasters in the water areas of seas and oceans. In this context, environmental monitoring acquires particular significance as a tool for ensuring timely detection of negative consequences of man-made disasters. This task is facilitated by recognition of images obtained from unmanned aerial vehicles with selection of those depicting the traces of man-made accidents or their consequences.Aim. To develop approaches for carrying out automatic selection of input data obtained from unmanned aerial vehicles in the form of photo and video images at the preliminary stage of image recognition.Materials and methods. The theoretical part of the study employed a classification method based on pattern recognition theory. Mathematical processing and calculation were carried out in the MATLAB environment. Simulation was conducted using the MathCAD environment.Results. A series of experiments was conducted to select a basis for discrete wavelet transforms. Modeling was conducted using the study results of the sensitivity of the feature vectors of images formed on the basis of different types of wavelet transforms. A concept of image construction for the purposes of feature vector formation was developed.Conclusion. An approach to the formalization of images for the purposes of feature vector formation is proposed. A metric of their contrast estimation is substantiated. It was established that the sensitivity of a recognition system based on the representation of images in the form of discrete wavelet transform matrices depends not only on the type of the mother wavelet, but also on the value of the scale parameter.

An Exploratory Study on the Application and Impact of Artificial Intelligence Technology in China’s Film and Television Industry: Centered on the Innovation and Development Strategy of AI in Film Production

Purpose - This paper discusses the historical traceability of artificial intelligence technology and its development lineage in the film and television industry. It analyzes the close connection between AI technology and the film and television industry. The text summarizes the challenges and impacts of AI technology on the film and television industry, along with suggestions for future development. Design/Methodology/Approach - In order to explore the impact of artificial intelligence on the film and television culture industry, this paper analyzes the impact of artificial intelligence on the film and television culture industry from the perspective of macro-industry and the perspective of application technology, starting from the overall technological impetus of artificial intelligence to the film and television culture industry and the application of artificial intelligence in film and television image processing. The case was analyzed in detail. Based on a summary of previous research, this study identifies key issues and proposes solutions. Findings - Through the research of this paper, the problems of artificial intelligence in film and television creation are summarized, and the potential problems are easily generated technical dependence, legal risks in creation, and reduced professional value of practitioners, etc. Countermeasure suggestions are put forward to break the technical dependence, provide a legal basis for the creation of artificial intelligence, and adjust the use of the positioning to realize the synergy between man and machine. Research Implications - This paper aims to enhance the application of AI in the film and television culture industry, thereby increasing the industry vitality of the film and television sector.

Mechanisms of well iron clogging in groundwater heat pump systems: Insights from video imaging, hydrogeochemical analysis, and geochemical modeling

Groundwater heat pump (GWHP) systems are increasingly popular as low-carbon and environmentally friendly technologies, but well clogging induced by iron remains a significant issue. This study investigated the clogging characteristics and biogeochemistry of three typical wells (pumping, injection, and observation wells) in an operating GWHP system using video imaging, sampling, and analysis of hydrogeochemical and microbial data. The results revealed that iron-induced well clogging is a complex process involving physical, chemical, and microbial factors. Pumping wells experience clogging due to water mixing with varying redox conditions, resulting in hematite-based iron oxide deposits. Injection wells exhibit higher clogging severity, with transformed oxidation and accumulation of reduced iron minerals at the solid-liquid interface, resulting in darker colored clogs with magnetite. Clogging in both extraction and injection wells is closely related to iron-rich aquifer sections, where severe clogging occurs. Shallow clogging due to iron oxide is limited and attributed to the oxidation of zero-valent iron in well casing material. Iron-oxidizing bacteria and iron-reducing bacteria were detected in the consolidated deposits of clogged wells, indicating their involvement in the clogging formation process. Moreover, a strong correlation was observed between the presence of nitrate-reducing bacteria in the water phase and the severity of clogging, suggesting a possible link between iron oxidation and nitrate reduction in the system. Geochemical modeling results further supported the observed clogging severity in GWHP systems and confirmed varying clogging mechanisms in different wells and depths. These findings contribute to the understanding of clogging in GWHP operations, aiding in robust water utilization and energy-saving efforts, and supporting global carbon reduction initiatives.

Air bubbles crossing a stratified water-diesel fuel interface

This study examines the behavior of a single air bubble moving through a stratified interface between two liquid phases: purified water as the denser liquid and diesel fuel as the lighter counterpart. The study investigates the impact of the upper liquid's height on bubble behavior, particularly under low gas flow rates, using high-speed video imaging. The experimental findings unveil two distinct scenarios: stable and unstable regimes. In stable conditions, the bubble follows a linear trajectory, simultaneously transferring some of the denser liquid into the lighter phase. Conversely, under unstable conditions, the bubble undergoes oscillations, navigating a zigzag or spiral path. The study also explores the correlation between the upper liquid height and the deformation and division of the dragged volume. This comprehensive investigation sheds light on the intricate interplay between liquid heights and bubble dynamics at liquid-liquid interfaces.

DCM2Net: an improved face recognition model for panoramic stereoscopic videos.

The panoramic stereo video has brought a new visual experience for the audience with its immersion and stereo effect. In panoramic stereo video, the face is an important element. However, the face image in panoramic stereo video has varying degrees of deformation. This brings new challenges to face recognition. Therefore, this paper proposes a face recognition model DCM2Net (Deformable Convolution MobileFaceNet) for panoramic stereo video. The model mainly integrates the feature information between channels during feature fusion, redistributes the information between channels in the deeper part of the network, and fully uses the information between different channels for feature extraction. This paper also built a panoramic stereo video live system, using the DCM2Net model to recognize the face in panoramic stereo video, and the recognition results are displayed in the video. After experiments on different datasets, the results show that our model has better results on popular datasets and panoramic datasets.

An electric bicycle tracking algorithm for improved traffic management

This paper proposes an efficient electric bicycle tracking algorithm, EBTrack, utilizing the high-precision and lightweight YOLOv7 as the target detector to enhance the efficiency of illegal detection and recognition of electric bicycles. The EBTrack effectively captures the position and trajectory of electric bicycles in complex traffic monitoring scenarios. Firstly, we introduce the feature extraction network, ResNetEB, specifically designed for feature re-identification of electric bicycles. To maintain real-time performance, feature extraction is performed only when generating new object IDs, minimizing the impact on processing speed. Secondly, for accurate target trajectory prediction, we incorporate an adaptive modulated noise scale Kalman filter. Additionally, considering the uncertainty of electric bicycle entry directions in traffic monitoring scenarios, we design a specialized matching mechanism to reduce frequent ID switching. Finally, to validate the algorithm's effectiveness, we have collected diverse video image data of electric bicycle and urban road traffic in Hefei, Anhui Province, encompassing different perspectives, time periods, and weather conditions. We have trained the proposed detector and have evaluated its tracking performance on this comprehensive dataset. Experimental results demonstrate that EBTrack achieves impressive accuracy, with 89.8 % MOTA (Multiple Object Tracking Accuracy) and 94.2 % IDF1 (ID F1-Score). Furthermore, the algorithm effectively reduces ID switching, significantly improving tracking stability and continuity.

AI in evaluating ambulation of stroke patients: severity classification with video and functional ambulation category scale

ABSTRACT Background The evaluation of gait function and severity classification of stroke patients are important to determine the rehabilitation goal and the level of exercise. Physicians often qualitatively evaluate patients’ walking ability through visual gait analysis using naked eye, video images, or standardized assessment tools. Gait evaluation through observation relies on the doctor’s empirical judgment, potentially introducing subjective opinions. Therefore, conducting research to establish a basis for more objective judgment is crucial. Objective To verify a deep learning model that classifies gait image data of stroke patients according to Functional Ambulation Category (FAC) scale. Methods Gait vision data from 203 stroke patients and 182 healthy individuals recruited from six medical institutions were collected to train a deep learning model for classifying gait severity in stroke patients. The recorded videos were processed using OpenPose. The dataset was randomly split into 80% for training and 20% for testing. Results The deep learning model attained a training accuracy of 0.981 and test accuracy of 0.903. Area Under the Curve(AUC) values of 0.93, 0.95, and 0.96 for discriminating among the mild, moderate, and severe stroke groups, respectively. Conclusion This confirms the potential of utilizing human posture estimation based on vision data not only to develop gait parameter models but also to develop models to classify severity according to the FAC criteria used by physicians. To develop an AI-based severity classification model, a large amount and variety of data is necessary and data collected in non-standardized real environments, not in laboratories, can also be used meaningfully.

Flexspline Pitch Deviation Rapid Measurement Method Using Offset Point Laser Sensors

Flexsplines in harmonic gear reducers are usually characterized by a large number of teeth, small modulus, and poor stiffness, which makes them difficult to measure using conventional gear measuring centers. In order to efficiently evaluate the quality of flexsplines in harmonic gear reducers, a rapid measurement method for flexspline pitch using offset point laser sensors (PLS) is proposed. This paper investigates the principle of measuring the tooth flank of the flexspline under the offset of the PLS, establishes a model for collecting and analyzing gear surface data, builds an experimental system, calibrates the six pose parameters of the sensor using the geometric features of the flexspline’s outer circular surface, and completes the reconstruction of the left and right gear surfaces of the flexspline based on the measured data. In the experiment, the gear surface obtained by the proposed method is largely consistent with that measured by the video imaging method, and the repeatability of both single pitch deviation and cumulative pitch deviation is within ±3 µm.

Congestion and throughput optimization protocol for providing better quality of service and experience

<p>Multimedia traffic in Internet of Things applications is generated for various purposes and encompasses a wide range of multimedia data, including video streams, audio files, images, and sensor data. Network providers employ various strategies to handle multimedia traffic in IoT applications efficiently. But most of these methods have not considered optimizing the RTSP (Real-Time Streaming Protocol), RTP (Real-time Transport Protocol), and RTCP (Real-Time Control Protocol) to improve the throughput and QoS of the IoT applications. Hence, in this Congestion and Throughput Optimization Protocol (CTOP) work, we present a model which optimizes the RTSP, RTP, and RTCP protocol to improve the throughput and QoS. The CTOP model outperforms the Big Packet Protocol model in terms of average throughput, multimedia loss, delay, and energy consumption for both less and high-traffic scenarios. For less-level of traffic and high level of traffic, the CTOP model achieves a better average throughput, and average multimedia delay, reducing the average multimedia loss and average energy consumption in comparison to the existing BBP model. These results highlight the improved performance and efficiency of the CTOP model compared to the BBP model.</p>

The Problem of Reconstructing the Profile of the Sea Surface from the Video Image of Laser Beams

The Problem of Reconstructing the Profile of the Sea Surface from the Video Image of Laser Beams

DSWIN: Automated hunger detection model based on hand-crafted decomposed shifted windows architecture using EEG signals

Hunger is a physiological state that arises from complex interactions of multiple factors, including higher brain center control. We purposed to develop an accurate and efficient machine-learning model for the automated detection of hunger using EEG signals. We prospectively acquired 14-channel EEG (sampling frequency 128 Hz) from 43 and 48 fasted and post-prandial healthy subjects (hungry vs. control groups, respectively) using the EMOTIV EPOC+ mobile brain cap system. To augment the hunger response, fasted subjects were also shown video images of food during EEG recording. The EEG signals were divided into 15-second segments. 877 and 852 participants/subjects were in the hungry and control groups. We created a novel handcrafted architecture—decomposed shifted window (DSWIN)—that combined swin patch division with tunable Q-factor wavelet transform-based signal decomposition for multilevel feature extraction of EEG signals. Textural and statistical features were extracted from the multiple patches and decomposed signals using a novel penta pattern-based extractor and statistical moments, respectively, and then merged. Iterative neighborhood component analysis (INCA) and iterative ReliefF (IRF) were applied. Twenty-eight selected feature vectors were generated, which were then fed to a shallow k-nearest neighbors (kNN) classifier to calculate channel-wise prediction vectors. From the 28 channel-wise prediction vectors, another 26 modes of function-based voted results were calculated using iterative hard majority voting, and the best overall model result was selected using a greedy algorithm. Our model attained 99.54% and 82.71% binary classification accuracies of hungry status vs. control using 10-fold and leave-one-subject-out cross-validations, respectively.

Survey of Techniques and Applications in Object Recognition

Abstract: Object detection, which is closely related to video analysis and image comprehension, has received attention in years from researchers. Traditional approaches to object detection rely on crafted features and shallow trainable architectures. However, these methods face challenges. Often reach a plateau, in performance improvement. To overcome these limitations complex ensembles are often created by combining low-level image features with high-level context from object detectors and scene classifiers. The emergence of learning has introduced powerful tools that can learn semantics high level and deeper features. These tools address the limitations of architectures by introducing network structures training strategies and optimization functions. This paper provides a review of learning-based frameworks for object detection. The review starts with an overview of the history of learning with a particular emphasis on the Convolutional Neural Network (CNN) as an exemplary tool. It then delves into generic object detection architectures exploring modifications and effective strategies for improving detection performance. Recognizing the characteristics of detection tasks, the paper also briefly surveys several tasks such, as salient object detection, face detection, crowd analysis, and pedestrian detection.

Capillarity interaction of NiTi melt with crucible oxides

The interaction of nitinol melt with Al2O3, ZrO2, BeO and Y2O3 oxides used as crucible materials has been studied. Capillary interaction has been monitored directly using the method of sessile drop with video and thermal imaging in 10-3torr vacuum at 1350 °C. Nitinol melt drops have been formed in a high-density graphite dispenseror directly on the substrate surface. The contact angleson the surface of polished oxides have been obtained. The contact angles established within 30 sec increase in the sequence Al2O3, ZrO2, BeO and Y2O3 from 30° to 70°. The corrosion resistance of the oxides has been studied for cross-sections using scanning electron microscopy with energy dispersion analysis. The phase composition at the ceramic/melt boundary has been studied using transmission electron microscopy. Thermodynamic analysis of melt chemical interaction with oxides has been conducted within equilibrium concentration calculation.

Tip shape effects on the axial-flow-induced vibration of a cantilever rod

The influence of tip shape on flow-induced vibration of a cantilever rod subjected to axial water flow is experimentally investigated, through optical tracking of the rod movement and mapping of the instantaneous flow field around the rod tip. The experimental setup consists of a vertical cantilever rod housed within a tube. The rod tip shapes considered in this study include a blunt tip and cones with height-to-diameter ratios of 0.5, 1, and 2. The experiments were conducted across a Reynolds number range between 20k to 100k in both clamped-free and free-clamped configurations, representing opposite flow directions. The rod tip motion was captured using fast video image tracking, whereas the flow field near the rod tip was obtained using particle image velocimetry (PIV). The rod vibration dynamics exhibited a primarily fuzzy period-1 behavior, characterized by a periodic motion with a chaotic component. Flutter-like oscillation and buckling were also observed at higher Reynolds numbers, depending on the flow direction. The mechanisms of fluid-structure interaction involved turbulent buffeting and movement-induced excitations. Unsteady flow separation around the rod tip was identified as a further contributing mechanism to flow excitation. In the clamped-free configuration, unsteady flow separation was more pronounced for the cone tips due to the increased rod surface area in the wake region, leading to larger vibration amplitude. In the free-clamped configuration, flow separation effects were more prominent for the blunt tip, as the streamlined cone shapes were less prone to flow separations. Overall, the rod with a blunt tip resulted in smaller displacement in the clamped-free configuration, while the rods with cone tips led to smaller displacement in the free-clamped configuration.