Relevant bibliographies by topics / Small target motion detector

Academic literature on the topic 'Small target motion detector'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Small target motion detector"

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Guo, Baicheng, Li Miao, and Shilin Zhou. "Small target detection based on point cloud feature learning." Journal of Physics: Conference Series 2284, no. 1 (June 1, 2022): 012025. http://dx.doi.org/10.1088/1742-6596/2284/1/012025.

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Abstract Small target detection is an important means of target detection, the photoelectric detector of which mainly uses infrared and visible light band. In many issues, the targets imaging in optical detector can be seen as small targets. Taking the time domain of the single frame detection result of small target motion as the third-dimensional information input, the small target detection can be transformed into the problem of point cloud target detection. The purpose of this paper is to implement a small target detection neural network based on point cloud feature learning.
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Uzair, Muhammad, Russell S. A. Brinkworth, and Anthony Finn. "Detecting Small Size and Minimal Thermal Signature Targets in Infrared Imagery Using Biologically Inspired Vision." Sensors 21, no. 5 (March 5, 2021): 1812. http://dx.doi.org/10.3390/s21051812.

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Thermal infrared imaging provides an effective sensing modality for detecting small moving objects at long range. Typical challenges that limit the efficiency and robustness of the detection performance include sensor noise, minimal target contrast and cluttered backgrounds. These issues become more challenging when the targets are of small physical size and present minimal thermal signatures. In this paper, we experimentally show that a four-stage biologically inspired vision (BIV) model of the flying insect visual system have an excellent ability to overcome these challenges simultaneously. The early two stages of the model suppress spatio-temporal clutter and enhance spatial target contrast while compressing the signal in a computationally manageable bandwidth. The later two stages provide target motion enhancement and sub-pixel motion detection capabilities. To show the superiority of the BIV target detector over existing traditional detection methods, we perform extensive experiments and performance comparisons using high bit-depth, real-world infrared image sequences of small size and minimal thermal signature targets at long ranges. Our results show that the BIV target detector significantly outperformed 10 conventional spatial-only and spatiotemporal methods for infrared small target detection. The BIV target detector resulted in over 25 dB improvement in the median signal-to-clutter-ratio over the raw input and achieved 43% better detection rate than the best performing existing method.
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Wiederman, Steven D., and David C. O’Carroll. "Biologically Inspired Feature Detection Using Cascaded Correlations of off and on Channels." Journal of Artificial Intelligence and Soft Computing Research 3, no. 1 (January 1, 2013): 5–14. http://dx.doi.org/10.2478/jaiscr-2014-0001.

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Abstract Flying insects are valuable animal models for elucidating computational processes underlying visual motion detection. For example, optical flow analysis by wide-field motion processing neurons in the insect visual system has been investigated from both behavioral and physiological perspectives [1]. This has resulted in useful computational models with diverse applications [2,3]. In addition, some insects must also extract the movement of their prey or conspecifics from their environment. Such insects have the ability to detect and interact with small moving targets, even amidst a swarm of others [4,5]. We use electrophysiological techniques to record from small target motion detector (STMD) neurons in the insect brain that are likely to subserve these behaviors. Inspired by such recordings, we previously proposed an ‘elementary’ small target motion detector (ESTMD) model that accounts for the spatial and temporal tuning of such neurons and even their ability to discriminate targets against cluttered surrounds [6-8]. However, other properties such as direction selectivity [9] and response facilitation for objects moving on extended trajectories [10] are not accounted for by this model. We therefore propose here two model variants that cascade an ESTMD model with a traditional motion detection model algorithm, the Hassenstein Reichardt ‘elementary motion detector’ (EMD) [11]. We show that these elaborations maintain the principal attributes of ESTMDs (i.e. spatiotemporal tuning and background clutter rejection) while also capturing the direction selectivity observed in some STMD neurons. By encapsulating the properties of biological STMD neurons we aim to develop computational models that can simulate the remarkable capabilities of insects in target discrimination and pursuit for applications in robotics and artificial vision systems.
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Li, Zhaoxu, Qiang Ling, Jing Wu, Zhengyan Wang, and Zaiping Lin. "A Constrained Sparse-Representation-Based Spatio-Temporal Anomaly Detector for Moving Targets in Hyperspectral Imagery Sequences." Remote Sensing 12, no. 17 (August 27, 2020): 2783. http://dx.doi.org/10.3390/rs12172783.

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At present, small dim moving target detection in hyperspectral imagery sequences is mainly based on anomaly detection (AD). However, most conventional detection algorithms only utilize the spatial spectral information and rarely employ the temporal spectral information. Besides, multiple targets in complex motion situations, such as multiple targets at different velocities and dense targets on the same trajectory, are still challenges for moving target detection. To address these problems, we propose a novel constrained sparse representation-based spatio-temporal anomaly detection algorithm that extends AD from the spatial domain to the spatio-temporal domain. Our algorithm includes a spatial detector and a temporal detector, which play different roles in moving target detection. The former can suppress moving background regions, and the latter can suppress non-homogeneous background and stationary objects. Two temporal background purification procedures maintain the effectiveness of the temporal detector for multiple targets in complex motion situations. Moreover, the smoothing and fusion of the spatial and temporal detection maps can adequately suppress background clutter and false alarms on the maps. Experiments conducted on a real dataset and a synthetic dataset show that the proposed algorithm can accurately detect multiple targets with different velocities and dense targets with the same trajectory and outperforms other state-of-the-art algorithms in high-noise scenarios.
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Nordström, Karin, Douglas M. Bolzon, and David C. O'Carroll. "Spatial facilitation by a high-performance dragonfly target-detecting neuron." Biology Letters 7, no. 4 (January 26, 2011): 588–92. http://dx.doi.org/10.1098/rsbl.2010.1152.

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Many animals visualize and track small moving targets at long distances—be they prey, approaching predators or conspecifics. Insects are an excellent model system for investigating the neural mechanisms that have evolved for this challenging task. Specialized small target motion detector (STMD) neurons in the optic lobes of the insect brain respond strongly even when the target size is below the resolution limit of the eye. Many STMDs also respond robustly to small targets against complex stationary or moving backgrounds. We hypothesized that this requires a complex mechanism to avoid breakthrough responses by background features, and yet to adequately amplify the weak signal of tiny targets. We compared responses of dragonfly STMD neurons to small targets that begin moving within the receptive field with responses to targets that approach the same location along longer trajectories. We find that responses along longer trajectories are strongly facilitated by a mechanism that builds up slowly over several hundred milliseconds. This allows the neurons to give sustained responses to continuous target motion, thus providing a possible explanation for their extraordinary sensitivity.
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Niven, Jeremy E. "Visual Motion: Homing in on Small Target Detectors." Current Biology 16, no. 8 (April 2006): R292—R294. http://dx.doi.org/10.1016/j.cub.2006.03.044.

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Li, Biao, Xu Zhiyong, Jianlin Zhang, Xiangru Wang, and Xiangsuo Fan. "Dim-Small Target Detection Based on Adaptive Pipeline Filtering." Mathematical Problems in Engineering 2020 (May 30, 2020): 1–15. http://dx.doi.org/10.1155/2020/8234349.

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In order to improve the robustness of the pipeline target detection algorithm against strong noises and occlusion, this paper presents an adaptive pipeline filtering algorithm (APFA). In APFA, the velocity and the center of the target are firstly predicted based on the smooth motion trajectory after background suppression. Then, time-domain energy enhancement of targets is adopted to improve the obscure target detection, and adaptively updating the center and radius of the pipeline filter are carried out for targets’ motion variation. Experiments on five different typical scenes show that APFA can improve the robustness of the pipeline filter against strong noises and even when targets are temporarily obscured partially or completely. Simultaneously, APFA can significantly improve the energy and signal-to-noise ratio of targets, and as a result, the target detection rate is significantly promoted on all experiments.
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O'Carroll, David C., and Steven D. Wiederman. "Contrast sensitivity and the detection of moving patterns and features." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1636 (February 19, 2014): 20130043. http://dx.doi.org/10.1098/rstb.2013.0043.

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Theories based on optimal sampling by the retina have been widely applied to visual ecology at the level of the optics of the eye, supported by visual behaviour. This leads to speculation about the additional processing that must lie in between—in the brain itself. But fewer studies have adopted a quantitative approach to evaluating the detectability of specific features in these neural pathways. We briefly review this approach with a focus on contrast sensitivity of two parallel pathways for motion processing in insects, one used for analysis of wide-field optic flow, the other for detection of small features. We further use a combination of optical modelling of image blur and physiological recording from both photoreceptors and higher-order small target motion detector neurons sensitive to small targets to show that such neurons operate right at the limits imposed by the optics of the eye and the noise level of single photoreceptors. Despite this, and the limitation of only being able to use information from adjacent receptors to detect target motion, they achieve a contrast sensitivity that rivals that of wide-field motion sensitive pathways in either insects or vertebrates—among the highest in absolute terms seen in any animal.
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Li, Jing, Yanran Dai, Congcong Li, Junqi Shu, Dongdong Li, Tao Yang, and Zhaoyang Lu. "Visual Detail Augmented Mapping for Small Aerial Target Detection." Remote Sensing 11, no. 1 (December 21, 2018): 14. http://dx.doi.org/10.3390/rs11010014.

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Moving target detection plays a primary and pivotal role in avionics visual analysis, which aims to completely and accurately detect moving objects from complex backgrounds. However, due to the relatively small sizes of targets in aerial video, many deep networks that achieve success in normal size object detection are usually accompanied by a high rate of false alarms and missed detections. To address this problem, we propose a novel visual detail augmented mapping approach for small aerial target detection. Concretely, we first present a multi-cue foreground segmentation algorithm including motion and grayscale information to extract potential regions. Then, based on the visual detail augmented mapping approach, the regions that might contain moving targets are magnified to multi-resolution to obtain detailed target information and rearranged into new foreground space for visual enhancement. Thus, original small targets are mapped to a more efficient foreground augmented map which is favorable for accurate detection. Finally, driven by the success of deep detection network, small moving targets can be well detected from aerial video. Experiments extensively demonstrate that the proposed method achieves success in small aerial target detection without changing the structure of the deep network. In addition, compared with the-state-of-art object detection algorithms, it performs favorably with high efficiency and robustness.
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Yano, Taihei, Naoteru Gouda, Yukiyasu Kobayashi, Takuji Tsujimoto, Yoshito Niwa, and Yoshiyuki Yamada. "The scientific goal of the Japanese small astrometric satellite, Small-JASMINE." Proceedings of the International Astronomical Union 8, S289 (August 2012): 433–36. http://dx.doi.org/10.1017/s1743921312021898.

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AbstractSmall-JASMINE is a small Japanese astrometric satellite, developed mainly at the National Astronomical Observatory of Japan. The target launch date of Small-JASMINE is around 2017. The satellite will be equipped with a telescope with an aperture size of 30 cm and a focal length of approximately 3.9 m. The operational wavelength will be centered on the infrared Hw band, between 1.1 and 1.7 μm, using a HgCdTe detector with 4k × 4k pixels. This will enable us to observe the central regions of our Galaxy and clarify the dynamical structure of the bulge region. A restricted region of the Galactic bulge will be observed using a frame-linking method, which is different from the approach taken by both Hipparcos and Gaia, both developed at ESA. The target accuracy of the annual parallax and proper motion is approximately 10 μas and 10 μas yr−1, respectively, in the central region of the survey area of 0.3 × 0.3 deg2. The target accuracy of the annual parallax, ~ 50 μas, and that of the proper motion, ~ 50 μas yr−1, will be obtained within a region of 2 × 2 deg2. The observing region covers a field of approximately 3 × 3 deg2. The mission is required to continue for around three years to obtain reliable measurements. In the winter season, the angular distance between the Sun and the Galactic bulge region is small. Accordingly, we may have the chance to observe different regions which contain scientifically interesting targets, such as Cygnus X-1. If we are successful in observing the object over the course of a few weeks, the orbital elements of the star accompanying Cygnus X-1 can be resolved by Small-JASMINE.
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Dissertations / Theses on the topic "Small target motion detector"

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Bolzon, Douglas. "Small moving bars elicit local receptive field orientation preference in hoverfly (Eristalis tenax) small target motion detector neurons /." Title page and summary only, 2005. http://web4.library.adelaide.edu.au/theses/09SB/09sbb6949.pdf.

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Barnett, Paul. "Retinotopically arranged small target motion detectors in the lobula of hoverflies, Eristalis tenax /." Title page and summary only, 2005. http://web4.library.adelaide.edu.au/theses/09SB/09sbb2618.pdf.

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Johansen, David Linn. "Video Stabilization and Target Localization Using Feature Tracking with Video from Small UAVs." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1522.pdf.

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Wiederman, Steven. "A neurobiological and computational analysis of target discrimination in visual clutter by the insect visual system." 2009. http://hdl.handle.net/2440/52596.

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Some insects have the capability to detect and track small moving objects, often against cluttered moving backgrounds. Determining how this task is performed is an intriguing challenge, both from a physiological and computational perspective. Previous research has characterized higher-order neurons within the fly brain known as 'small target motion detectors‘ (STMD) that respond selectively to targets, even within complex moving surrounds. Interestingly, these cells still respond robustly when the velocity of the target is matched to the velocity of the background (i.e. with no relative motion cues). We performed intracellular recordings from intermediate-order neurons in the fly visual system (the medulla). These full-wave rectifying, transient cells (RTC) reveal independent adaptation to luminance changes of opposite signs (suggesting separate 'on‘ and 'off‘ channels) and fast adaptive temporal mechanisms (as seen in some previously described cell types). We show, via electrophysiological experiments, that the RTC is temporally responsive to rapidly changing stimuli and is well suited to serving an important function in a proposed target-detecting pathway. To model this target discrimination, we use high dynamic range (HDR) natural images to represent 'real-world‘ luminance values that serve as inputs to a biomimetic representation of photoreceptor processing. Adaptive spatiotemporal high-pass filtering (1st-order interneurons) shapes the transient 'edge-like‘ responses, useful for feature discrimination. Following this, a model for the RTC implements a nonlinear facilitation between the rapidly adapting, and independent polarity contrast channels, each with centre-surround antagonism. The recombination of the channels results in increased discrimination of small targets, of approximately the size of a single pixel, without the need for relative motion cues. This method of feature discrimination contrasts with traditional target and background motion-field computations. We show that our RTC-based target detection model is well matched to properties described for the higher-order STMD neurons, such as contrast sensitivity, height tuning and velocity tuning. The model output shows that the spatiotemporal profile of small targets is sufficiently rare within natural scene imagery to allow our highly nonlinear 'matched filter‘ to successfully detect many targets from the background. The model produces robust target discrimination across a biologically plausible range of target sizes and a range of velocities. We show that the model for small target motion detection is highly correlated to the velocity of the stimulus but not other background statistics, such as local brightness or local contrast, which normally influence target detection tasks. From an engineering perspective, we examine model elaborations for improved target discrimination via inhibitory interactions from correlation-type motion detectors, using a form of antagonism between our feature correlator and the more typical motion correlator. We also observe that a changing optimal threshold is highly correlated to the value of observer ego-motion. We present an elaborated target detection model that allows for implementation of a static optimal threshold, by scaling the target discrimination mechanism with a model-derived velocity estimation of ego-motion. Finally, we investigate the physiological relevance of this target discrimination model. We show that via very subtle image manipulation of the visual stimulus, our model accurately predicts dramatic changes in observed electrophysiological responses from STMD neurons.
http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1368818
Thesis (Ph.D.) - University of Adelaide, School of Molecular and Biomedical Science, 2009
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Book chapters on the topic "Small target motion detector"

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Wang, Hongxin, Jigen Peng, and Shigang Yue. "A Feedback Neural Network for Small Target Motion Detection in Cluttered Backgrounds." In Artificial Neural Networks and Machine Learning – ICANN 2018, 728–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01424-7_71.

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Wang, Jingyi, Niandong Jiao, Yongliang Yang, Steve Tung, and Lianqing Liu. "3D Motion Control and Target Manipulation of Small Magnetic Robot." In Intelligent Robotics and Applications, 110–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65289-4_11.

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Zhou, Qian, Fuxin Sun, and Junyou Zhang. "Research on Multi-Target Detection and Tracking Algorithm Based on Improved YOLOv5." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde221115.

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A detection and tracking algorithm based on improved YOLOv5 is proposed for the poor recognition and tracking of obscured targets and small-sized targets. The K-means ++ algorithm is used to cluster to obtain new anchor values; the CIOU-NMS is introduced to improve the missed detection problem when the target is obscured; the CBAM is proposed to be embedded into the Backbone and Neck part to improve the feature extraction capability of the algorithm for small targets. DeepSORT is chosen as the multi-target tracker to plot the motion trajectory of the target in real time. The experimental results show that the improved algorithm has a 2.1% improvement in detection accuracy and a detection speed of 32.32/s, satisfying real-time efficient detection with better tracking.
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Wong, Agnes. "The Smooth Pursuit System." In Eye Movement Disorders. Oxford University Press, 2008. http://dx.doi.org/10.1093/oso/9780195324266.003.0011.

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Smooth pursuit consists of conjugate eye movements that allow both eyes to smoothly track a slowly moving object so that its image is kept on the foveae. For example, smooth pursuit eye movements are used when you track a child on a swing. Only animals with foveae make smooth pursuit eye movements. Rabbits, for instance, do not have foveae, and their eyes cannot track a small moving target. However, if a rabbit is placed inside a rotating drum painted on the inside with stripes so that the rabbit sees the entire visual field rotating en bloc, it will track the stripes optokinetically. Humans have both smooth pursuit and optokinetic eye movements, but pursuit predominates. When you track a small, moving object against a detailed stationary background, such as a bird flying against a background of leaves, the optokinetic system will try to hold your gaze on the stationary background, but it is overridden by pursuit. Pursuit works well at speeds up to about 70°/sec, but top athletes may generate pursuit as fast as 130°/sec. Pursuit responds slowly to unexpected changes—it takes about 100 msec to track a target that starts to move suddenly, and this is why we need the faster acting vestibulo-ocular reflex (VOR) to stabilize our eyes when our heads move. However, pursuit can detect patterns of motion and respond to predictable target motion in much less than 100 msec. Pursuit cannot be generated voluntarily without a suitable target. If you try to pursue an imaginary target moving across your visual field, you will make a series of saccades instead of pursuit. However, the target that evokes pursuit does not have to be visual; it may be auditory (e.g., a moving, beeping pager), proprioceptive (e.g., tracking your outstretched finger in the dark), tactile (e.g., an ant crawling on your arm in the dark), or cognitive (e.g., tracking a stroboscopic motion in which a series of light flashes in sequence, even though no actual motion occurs.
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Su, Dan, Qiong-lan Na, Hui-min He, and Yi-xi Yang. "Detection Method of Computer Room Personnel Based on Improved DERT." In Proceedings of CECNet 2021. IOS Press, 2021. http://dx.doi.org/10.3233/faia210461.

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Recently developed methods such as DETR [1] apply Transformer [2] structure to target detection. The performance of using Transformers for target detection (DETR) is similar to that of two-stage target detector. First of all, this paper attempts to apply Transformer to computer room personnel detection. The contributions of the improved DETR include: 1) in order to improve the poor performance of small target detection. Embed Depthwise Convolution in the encoder. When the coding feature is reconstructed, the channel information is retained. 2) in order to solve the problem of slow convergence in DETR training. This paper improves the cross-attention in DECODE and adds the spatial query module. It can accelerate the convergence of DETR. The convergence speed of the improved method is six times faster than that of the original DETR, and the mAP0.5 is improved by 3.1%.
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Franks, Kevin, Fiona McDonald, and Gerard G. Hanna. "Radiotherapy for thoracic tumours." In External Beam Therapy, 115–44. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198786757.003.0006.

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Chapter 6 discusses radiotherapy for thoracic tumours and includes discussion on lung cancer. The chapter covers assessment of patients with lung cancer for radical radiotherapy, patient positioning for radical radiotherapy for lung cancer, tumour motion, target delineation, organs at risk, implementation on the treatment machine, treatment verification , radical treatment of non-small cell lung cancer, radical treatment for small cell lung cancer, and palliative thoracic treatment for lung cancer. It also covers mesothelioma (including current indications for radiotherapy, patient positioning, target delineation, planning, and dose prescription) and thymic tumours (including current indications for radiotherapy, radical treatment, and palliative radiotherapy).
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Chen, Gang, and Frank L. Lewis. "Cooperative Control of Unknown Networked Lagrange Systems using Higher Order Neural Networks." In Artificial Higher Order Neural Networks for Modeling and Simulation, 214–36. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2175-6.ch010.

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This chapter investigates the cooperative control problem for a group of Lagrange systems with a target system to be tracked. The development is suitable for the case that the desired trajectory of the target node is only available to a portion of the networked systems. All the networked systems can have different dynamics. The dynamics of the networked systems, as well as the target system, are all assumed unknown. A higher-order neural network is used at each node to approximate the distributed unknown dynamics. A distributed adaptive neural network control protocol is proposed so that the networked systems synchronize to the motion of the target node. The theoretical analysis shows that the synchronization error can be made arbitrarily small by appropriately tuning the design parameters.
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Sorlin, Pierre. "André Bazin, or the Ambiguity of Reality." In The Major Realist Film Theorists. Edinburgh University Press, 2016. http://dx.doi.org/10.3366/edinburgh/9781474402217.003.0007.

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André Bazin, a teacher and a film critic, was intent on making his students and readers realize that the cinema offered them a unique tool to discover the world. After his premature death at the age of 50, his friends collected some of his articles, republishing them in a variety of formats. However, the variable nature of this series of montages sometimes provoked misinterpretations. For example, a sentence on the “irresistible realism” of film was considered a proof that, for him, cinematic images copied reality. However, this chapter will argue that Bazin’s conception of both film and reality was far more elaborate and sophisticated than that. Bazin argued that there are so many things around us that we cannot see them all, we thus only ever know a small portion of the surrounding reality. Human beings have long drawn portraits and landscapes in order to observe at leisure what interests them. Unlike drawings, biased by the artist’s feelings, photography is “objective” since it is merely the effect of a chemical reaction and, beside its target, for instance a person, it registers, unwillingly, aspects of the surroundings such as they are. Film is as unbiased as photography and in addition gives faithful motion reproduction. While watching a long sequence taken in distant shot we may become aware of people, actions, situations appearing in the background and that we wouldn’t have noticed otherwise. Thanks to its realism a film can help us to gain a less narrow vision of reality.
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Conference papers on the topic "Small target motion detector"

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Dunbier, James R., Steven D. Wiederman, Patrick A. Shoemaker, and David C. O'Carroll. "Modelling the temporal response properties of an insect small target motion detector." In 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2011. http://dx.doi.org/10.1109/issnip.2011.6146600.

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Wang, Hongxin, Jigen Peng, and Shigang Yue. "Bio-inspired small target motion detector with a new lateral inhibition mechanism." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727824.

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Billah, Md Arif, and Imraan A. Faruque. "Modeling Small-Target Motion Detector Neurons as Switched Systems with Dwell Time Constraints." In 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867750.

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Qiu-ying, Yang, and Gao Xing-yuan. "Tracking on Motion of Small Target Based on Edge Detection." In 2009 WRI World Congress on Computer Science and Information Engineering. IEEE, 2009. http://dx.doi.org/10.1109/csie.2009.778.

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Wu, Dongmei, Li Zhang, and Lihua Lin. "Based on the Moving Average and Target Motion Information for Detection of Weak Small Target." In 2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). IEEE, 2018. http://dx.doi.org/10.1109/icitbs.2018.00167.

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Wang, Hongxin, Jigen Peng, Qinbing Fu, Huatian Wang, and Shigang Yue. "Visual Cue Integration for Small Target Motion Detection in Natural Cluttered Backgrounds." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8851913.

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Jinqiu Sun, Yanning Zhang, Lei Jiang, and Yu Wang. "Small and dim target detection based on motion integration in visual attention model." In 2008 9th International Conference on Signal Processing (ICSP 2008). IEEE, 2008. http://dx.doi.org/10.1109/icosp.2008.4697323.

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Zhang, WenWen, and ZhiChao Lian. "Infrared Dim-small Object Detection Algorithm Based on Saliency Map Combined with Target Motion Feature." In 2020 IEEE International Conference on Progress in Informatics and Computing (PIC). IEEE, 2020. http://dx.doi.org/10.1109/pic50277.2020.9350820.

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Billah, Md Arif, and Imraan Faruque. "The Multi-Agent Group Motions Generated by Models of Insect Small Target Detector Neurons and Feedback." In AIAA SCITECH 2022 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2022. http://dx.doi.org/10.2514/6.2022-0962.

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Wang, Xiaoyang, Zhenming Peng, and Ping Zhang. "Boolean map saliency combined with motion feature used for dim and small target detection in infrared video sequences." In International Symposium on Optoelectronic Technology and Application 2016. SPIE, 2016. http://dx.doi.org/10.1117/12.2245655.

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Reports on the topic "Small target motion detector"

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Shoemaker, Patrick, and David O'Carroll. Insect Small-Target Motion Detection for Seeker Applications. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada418203.

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