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General moving objects recognition method based on graph embedding dimension reduction algorithm 被引量:1
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作者 Yi ZHANG Jie YANG Kun LIU 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2009年第7期976-984,共9页
Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents... Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents a general moving objects recognition method using global features of targets. Targets are extracted with an adaptive Gaussian mixture model and their silhouette images are captured and unified. A new objects silhouette database is built to provide abundant samples to train the subspace feature. This database is more convincing than the previous ones. A more effective dimension reduction method based on graph embedding is used to obtain the projection eigenvector. In our experiments, we show the effective performance of our method in addressing the moving objects recognition problem and its superiority compared with the previous methods. 展开更多
关键词 Moving objects recognition Adaptive Gaussian mixture model Principal component analysis Linear discriminant analysis Marginal Fisher analysis
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Intelligent Recognition Using Ultralight Multifunctional Nano‑Layered Carbon Aerogel Sensors with Human‑Like Tactile Perception 被引量:4
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作者 Huiqi Zhao Yizheng Zhang +8 位作者 Lei Han Weiqi Qian Jiabin Wang Heting Wu Jingchen Li Yuan Dai Zhengyou Zhang Chris RBowen Ya Yang 《Nano-Micro Letters》 SCIE EI CAS CSCD 2024年第1期172-186,共15页
Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this uniq... Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence. 展开更多
关键词 Multifunctional sensor Tactile perception Multimodal machine learning algorithms Universal tactile system Intelligent object recognition
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Real-Time Recognition and Location of Indoor Objects
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作者 Jinxing Niu Qingsheng Hu +2 位作者 Yi Niu Tao Zhang Sunil Kumar Jha 《Computers, Materials & Continua》 SCIE EI 2021年第8期2221-2229,共9页
Object recognition and location has always been one of the research hotspots in machine vision.It is of great value and significance to the development and application of current service robots,industrial automation,u... Object recognition and location has always been one of the research hotspots in machine vision.It is of great value and significance to the development and application of current service robots,industrial automation,unmanned driving and other fields.In order to realize the real-time recognition and location of indoor scene objects,this article proposes an improved YOLOv3 neural network model,which combines densely connected networks and residual networks to construct a new YOLOv3 backbone network,which is applied to the detection and recognition of objects in indoor scenes.In this article,RealSense D415 RGB-D camera is used to obtain the RGB map and depth map,the actual distance value is calculated after each pixel in the scene image is mapped to the real scene.Experiment results proved that the detection and recognition accuracy and real-time performance by the new network are obviously improved compared with the previous YOLOV3 neural network model in the same scene.More objects can be detected after the improvement of network which cannot be detected with the YOLOv3 network before the improvement.The running time of objects detection and recognition is reduced to less than half of the original.This improved network has a certain reference value for practical engineering application. 展开更多
关键词 Object recognition improved YOLOv3 network RGB-D camera object location
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Promotion of structural plasticity in area V2 of visual cortex prevents against object recognition memory deficits in aging and Alzheimer's disease rodents
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作者 Irene Navarro-Lobato Mariam Masmudi-Martín +8 位作者 Manuel F.López-Aranda Juan F.López-Téllez Gloria Delgado Pablo Granados-Durán Celia Gaona-Romero Marta Carretero-Rey Sinforiano Posadas María E.Quiros-Ortega Zafar U.Khan 《Neural Regeneration Research》 SCIE CAS CSCD 2024年第8期1835-1841,共7页
Memory deficit,which is often associated with aging and many psychiatric,neurological,and neurodegenerative diseases,has been a challenging issue for treatment.Up till now,all potential drug candidates have failed to ... Memory deficit,which is often associated with aging and many psychiatric,neurological,and neurodegenerative diseases,has been a challenging issue for treatment.Up till now,all potential drug candidates have failed to produce satisfa ctory effects.Therefore,in the search for a solution,we found that a treatment with the gene corresponding to the RGS14414protein in visual area V2,a brain area connected with brain circuits of the ventral stream and the medial temporal lobe,which is crucial for object recognition memory(ORM),can induce enhancement of ORM.In this study,we demonstrated that the same treatment with RGS14414in visual area V2,which is relatively unaffected in neurodegenerative diseases such as Alzheimer s disease,produced longlasting enhancement of ORM in young animals and prevent ORM deficits in rodent models of aging and Alzheimer’s disease.Furthermore,we found that the prevention of memory deficits was mediated through the upregulation of neuronal arbo rization and spine density,as well as an increase in brain-derived neurotrophic factor(BDNF).A knockdown of BDNF gene in RGS14414-treated aging rats and Alzheimer s disease model mice caused complete loss in the upregulation of neuronal structural plasticity and in the prevention of ORM deficits.These findings suggest that BDNF-mediated neuronal structural plasticity in area V2 is crucial in the prevention of memory deficits in RGS14414-treated rodent models of aging and Alzheimer’s disease.Therefore,our findings of RGS14414gene-mediated activation of neuronal circuits in visual area V2 have therapeutic relevance in the treatment of memory deficits. 展开更多
关键词 behavioral performance brain-derived neurotrophic factor cognitive dysfunction episodic memory memory circuit activation memory deficits memory enhancement object recognition memory prevention of memory loss regulator of G protein signaling
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Multimodal fusion recognition for digital twin
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作者 Tianzhe Zhou Xuguang Zhang +1 位作者 Bing Kang Mingkai Chen 《Digital Communications and Networks》 SCIE CSCD 2024年第2期337-346,共10页
The digital twin is the concept of transcending reality,which is the reverse feedback from the real physical space to the virtual digital space.People hold great prospects for this emerging technology.In order to real... The digital twin is the concept of transcending reality,which is the reverse feedback from the real physical space to the virtual digital space.People hold great prospects for this emerging technology.In order to realize the upgrading of the digital twin industrial chain,it is urgent to introduce more modalities,such as vision,haptics,hearing and smell,into the virtual digital space,which assists physical entities and virtual objects in creating a closer connection.Therefore,perceptual understanding and object recognition have become an urgent hot topic in the digital twin.Existing surface material classification schemes often achieve recognition through machine learning or deep learning in a single modality,ignoring the complementarity between multiple modalities.In order to overcome this dilemma,we propose a multimodal fusion network in our article that combines two modalities,visual and haptic,for surface material recognition.On the one hand,the network makes full use of the potential correlations between multiple modalities to deeply mine the modal semantics and complete the data mapping.On the other hand,the network is extensible and can be used as a universal architecture to include more modalities.Experiments show that the constructed multimodal fusion network can achieve 99.42%classification accuracy while reducing complexity. 展开更多
关键词 Digital twin Multimodal fusion Object recognition Deep learning Transfer learning
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The Fusion of Temporal Sequence with Scene Priori Information in Deep Learning Object Recognition
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作者 Yongkang Cao Fengjun Liu +2 位作者 Xian Wang Wenyun Wang Zhaoxin Peng 《Open Journal of Applied Sciences》 2024年第9期2610-2627,共18页
For some important object recognition applications such as intelligent robots and unmanned driving, images are collected on a consecutive basis and associated among themselves, besides, the scenes have steady prior fe... For some important object recognition applications such as intelligent robots and unmanned driving, images are collected on a consecutive basis and associated among themselves, besides, the scenes have steady prior features. Yet existing technologies do not take full advantage of this information. In order to take object recognition further than existing algorithms in the above application, an object recognition method that fuses temporal sequence with scene priori information is proposed. This method first employs YOLOv3 as the basic algorithm to recognize objects in single-frame images, then the DeepSort algorithm to establish association among potential objects recognized in images of different moments, and finally the confidence fusion method and temporal boundary processing method designed herein to fuse, at the decision level, temporal sequence information with scene priori information. Experiments using public datasets and self-built industrial scene datasets show that due to the expansion of information sources, the quality of single-frame images has less impact on the recognition results, whereby the object recognition is greatly improved. It is presented herein as a widely applicable framework for the fusion of information under multiple classes. All the object recognition algorithms that output object class, location information and recognition confidence at the same time can be integrated into this information fusion framework to improve performance. 展开更多
关键词 Computer Vison Object recognition Deep Learning Consecutive Scene Information Fusion
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YOLOv8 for Fire and Smoke Recognition Algorithm Integrated with the Convolutional Block Attention Module
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作者 Zhangchi Liu Risheng Zhang +1 位作者 Hao Zhong Yingjie Sun 《Open Journal of Applied Sciences》 2024年第1期159-170,共12页
The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Con... The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Convolutional Block Attention Module (CBAM) has been developed. This algorithm initially employs the latest YOLOv8 for object recognition. Subsequently, the integration of CBAM enhances its feature extraction capabilities. Finally, the WIoU function is used to optimize the network’s bounding box loss, facilitating rapid convergence. Experimental validation using a smoke and fire dataset demonstrated that the proposed algorithm achieved a 2.3% increase in smoke and fire detection accuracy, surpassing other state-of-the-art methods. 展开更多
关键词 Object recognition CBAM WioU State-of-the-Art Methods
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Bioinspired Passive Tactile Sensors Enabled by Reversible Polarization of Conjugated Polymers
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作者 Feng He Sitong Chen +3 位作者 Ruili Zhou Hanyu Diao Yangyang Han Xiaodong Wu 《Nano-Micro Letters》 SCIE EI CAS 2025年第1期361-377,共17页
Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced robots.Compared to active sensing devices,passive piezoelectric and triboelectric tactile sensors c... Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced robots.Compared to active sensing devices,passive piezoelectric and triboelectric tactile sensors consume less power,but lack the capability to resolve static stimuli.Here,we address this issue by utilizing the unique polarization chemistry of conjugated polymers for the first time and propose a new type of bioinspired,passive,and bio-friendly tactile sensors for resolving both static and dynamic stimuli.Specifically,to emulate the polarization process of natural sensory cells,conjugated polymers(including poly(3,4-ethylenedioxythiophen e):poly(styrenesulfonate),polyaniline,or polypyrrole)are controllably polarized into two opposite states to create artificial potential differences.The controllable and reversible polarization process of the conjugated polymers is fully in situ characterized.Then,a micro-structured ionic electrolyte is employed to imitate the natural ion channels and to encode external touch stimulations into the variation in potential difference outputs.Compared with the currently existing tactile sensing devices,the developed tactile sensors feature distinct characteristics including fully organic composition,high sensitivity(up to 773 mV N^(−1)),ultralow power consumption(nW),as well as superior bio-friendliness.As demonstrations,both single point tactile perception(surface texture perception and material property perception)and two-dimensional tactile recognitions(shape or profile perception)with high accuracy are successfully realized using self-defined machine learning algorithms.This tactile sensing concept innovation based on the polarization chemistry of conjugated polymers opens up a new path to create robotic tactile sensors and prosthetic electronic skins. 展开更多
关键词 Passive tactile sensors Reversible polarization of conjugated polymers Tactile perception Machine learning algorithm Object recognition
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Online object detection and recognition using motion information and local feature co-occurrence
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作者 张索非 Filliat David 吴镇扬 《Journal of Southeast University(English Edition)》 EI CAS 2012年第4期404-409,共6页
An object learning and recognition system is implemented for humanoid robots to discover and memorize objects only by simple interactions with non-expert users. When the object is presented, the system makes use of th... An object learning and recognition system is implemented for humanoid robots to discover and memorize objects only by simple interactions with non-expert users. When the object is presented, the system makes use of the motion information over consecutive frames to extract object features and implements machine learning based on the bag of visual words approach. Instead of using a local feature descriptor only, the proposed system uses the co-occurring local features in order to increase feature discriminative power for both object model learning and inference stages. For different objects with different textures, a hybrid sampling strategy is considered. This hybrid approach minimizes the consumption of computation resources and helps achieving good performances demonstrated on a set of a dozen different daily objects. 展开更多
关键词 object recognition online learning motion information computer vision
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Redundant discrete wavelet transforms based moving object recognition and tracking 被引量:3
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作者 Gao Tao Liu Zhengguang Zhang Jun 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2009年第5期1115-1123,共9页
A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transf... A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transform (RDWT) based moving object recognition algorithm is put forward, which directly detects moving objects in the redundant discrete wavelet transform domain. An improved adaptive mean-shift algorithm is used to track the moving object in the follow up frames. Experimental results show that the algorithm can effectively extract the moving object, even though the object is similar to the background, and the results are better than the traditional frame-subtraction method. The object tracking is accurate without the impact of changes in the size of the object. Therefore the algorithm has a certain practical value and prospect. 展开更多
关键词 traffic monitoring moving object recognition moving object tracking redundant discrete wavelet.
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Optimizing Deep Learning Parameters Using Genetic Algorithm for Object Recognition and Robot Grasping 被引量:2
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作者 Delowar Hossain Genci Capi Mitsuru Jindai 《Journal of Electronic Science and Technology》 CAS CSCD 2018年第1期11-15,共5页
The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We... The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks. 展开更多
关键词 Deep learning(DL) deep belief neural network(DBNN) genetic algorithm(GA) object recognition robot grasping
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Adaptive key SURF feature extraction and application in unmanned vehicle dynamic object recognition 被引量:1
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作者 杜明芳 王军政 +2 位作者 李静 李楠 李多扬 《Journal of Beijing Institute of Technology》 EI CAS 2015年第1期83-90,共8页
A new method based on adaptive Hessian matrix threshold of finding key SRUF ( speeded up robust features) features is proposed and is applied to an unmanned vehicle for its dynamic object recognition and guided navi... A new method based on adaptive Hessian matrix threshold of finding key SRUF ( speeded up robust features) features is proposed and is applied to an unmanned vehicle for its dynamic object recognition and guided navigation. First, the object recognition algorithm based on SURF feature matching for unmanned vehicle guided navigation is introduced. Then, the standard local invariant feature extraction algorithm SRUF is analyzed, the Hessian Metrix is especially discussed, and a method of adaptive Hessian threshold is proposed which is based on correct matching point pairs threshold feedback under a close loop frame. At last, different dynamic object recognition experi- ments under different weather light conditions are discussed. The experimental result shows that the key SURF feature abstract algorithm and the dynamic object recognition method can be used for un- manned vehicle systems. 展开更多
关键词 dynamic object recognition key SURF feature feature matching adaptive Hessianthreshold unmanned vehicle
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Human-Object Interaction Recognition Based on Modeling Context 被引量:1
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作者 Shuyang Li Wei Liang Qun Zhang 《Journal of Beijing Institute of Technology》 EI CAS 2017年第2期215-222,共8页
This paper proposes a method to recognize human-object interactions by modeling context between human actions and interacted objects.Human-object interaction recognition is a challenging task due to severe occlusion b... This paper proposes a method to recognize human-object interactions by modeling context between human actions and interacted objects.Human-object interaction recognition is a challenging task due to severe occlusion between human and objects during the interacting process.Since that human actions and interacted objects provide strong context information,i.e.some actions are usually related to some specific objects,the accuracy of recognition is significantly improved for both of them.Through the proposed method,both global and local temporal features from skeleton sequences are extracted to model human actions.In the meantime,kernel features are utilized to describe interacted objects.Finally,all possible solutions from actions and objects are optimized by modeling the context between them.The results of experiments demonstrate the effectiveness of our method. 展开更多
关键词 human-object interaction action recognition object recognition modeling context
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Exploring Local Regularities for 3D Object Recognition
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作者 TIAN Huaiwen QIN Shengfeng 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2016年第6期1104-1113,共10页
In order to find better simplicity measurements for 3D object recognition, a new set of local regularities is developed and tested in a stepwise 3D reconstruction method, including localized minimizing standard deviat... In order to find better simplicity measurements for 3D object recognition, a new set of local regularities is developed and tested in a stepwise 3D reconstruction method, including localized minimizing standard deviation of angles(L-MSDA), localized minimizing standard deviation of segment magnitudes(L-MSDSM), localized minimum standard deviation of areas of child faces (L-MSDAF), localized minimum sum of segment magnitudes of common edges (L-MSSM), and localized minimum sum of areas of child face (L-MSAF). Based on their effectiveness measurements in terms of form and size distortions, it is found that when two local regularities: L-MSDA and L-MSDSM are combined together, they can produce better performance. In addition, the best weightings for them to work together are identified as 10% for L-MSDSM and 90% for L-MSDA. The test results show that the combined usage of L-MSDA and L-MSDSM with identified weightings has a potential to be applied in other optimization based 3D recognition methods to improve their efficacy and robustness. 展开更多
关键词 stepwise 3D reconstruction localized regularities 3D object recognition polyhedral objects line drawing
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Role of Cannabinoid CB1 Receptor in Object Recognition Memory Impairment in Chronically Rapid Eye Movement Sleep-deprived Rats
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作者 Kaveh Shahveisi Seyedeh Marziyeh Hadi +1 位作者 Hamed Ghazvini Mehdi Khodamoradi 《Chinese Medical Sciences Journal》 CAS CSCD 2023年第1期29-37,共9页
Objective We aimed to investigate whether antagonism of the cannabinoid CB1 receptor(CB1R)could affect novel object recognition(NOR)memory in chronically rapid eye movement sleep-deprived(RSD)rats.Methods The animals ... Objective We aimed to investigate whether antagonism of the cannabinoid CB1 receptor(CB1R)could affect novel object recognition(NOR)memory in chronically rapid eye movement sleep-deprived(RSD)rats.Methods The animals were examined for recognition memory following a 7-day chronic partial RSD paradigm using the multiple platform technique.The CB1R antagonist rimonabant(1 or 3 mg/kg,i.p.)was administered either at one hour prior to the sample phase for acquisition,or immediately after the sample phase for consolidation,or at one hour before the test phase for retrieval of NOR memory.For the reconsolidation task,rimonabant was administered immediately after the second sample phase.Results The RSD episode impaired acquisition,consolidation,and retrieval,but it did not affect the reconsolidation of NOR memory.Rimonabant administration did not affect acquisition,consolidation,and reconsolidation;however,it attenuated impairment of the retrieval of NOR memory induced by chronic RSD.Conclusions These findings,along with our previous report,would seem to suggest that RSD may affect different phases of recognition memory based on its duration.Importantly,it seems that the CB1R may,at least in part,be involved in the adverse effects of chronic RSD on the retrieval,but not in the acquisition,consolidation,and reconsolidation,of NOR memory. 展开更多
关键词 REM sleep deprivation novel object recognition memory cannabinoid CB1 receptor RIMONABANT
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Object Recognition Algorithm Based on an Improved Convolutional Neural Network
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作者 Zheyi Fan Yu Song Wei Li 《Journal of Beijing Institute of Technology》 EI CAS 2020年第2期139-145,共7页
In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted... In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted from the original image.Then,candidate object windows are input into the improved CNN model to obtain deep features.Finally,the deep features are input into the Softmax and the confidence scores of classes are obtained.The candidate object window with the highest confidence score is selected as the object recognition result.Based on AlexNet,Inception V1 is introduced into the improved CNN and the fully connected layer is replaced by the average pooling layer,which widens the network and deepens the network at the same time.Experimental results show that the improved object recognition algorithm can obtain better recognition results in multiple natural scene images,and has a higher degree of accuracy than the classical algorithms in the field of object recognition. 展开更多
关键词 object recognition selective search algorithm improved convolutional neural network(CNN)
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Melatonin Enhances Object Recognition Memory through Melatonin MT1 and MT2 Receptor-Mediated and Non-Receptor-Mediated Mechanisms in Male Mice
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作者 Masahiro Sano Hikaru Iwashita +1 位作者 Atsuhiko Hattori Atsuhiko Chiba 《Journal of Behavioral and Brain Science》 CAS 2022年第12期640-657,共18页
Melatonin (MEL) has been reported to have acute enhancing effects on some aspects of cognition. Recently, we revealed that N1-acetyl-5-methoxyquinuramine (AMK), a brain metabolite of MEL, is much more potent than MEL ... Melatonin (MEL) has been reported to have acute enhancing effects on some aspects of cognition. Recently, we revealed that N1-acetyl-5-methoxyquinuramine (AMK), a brain metabolite of MEL, is much more potent than MEL in converting short-term memory (STM) to long-term memory (LTM) with a single administration immediately after the acquisition trial of the novel object recognition (NOR) task. These data suggest that the memory-enhancing effects of MEL may be mediated by mechanisms independent of the activation of MEL MT1 and MT2 receptors. In the present study, we examined the contribution of MT1 and MT2 receptor-mediated and non-receptor-mediated mechanisms to the acute memory-enhancing effects of MEL using NOR task. Mice were administered with either MEL, AMK, or a highly selective MT1/MT2 receptor agonist ramelteon (RAM) immediately after the acquisition trial and the effects of varying doses of these drugs on both STM and LTM performance were compared. We found that both AMK and RAM were more potent than MEL in both facilitating STM and promoting LTM formation. We also found that pretreatment with luzindole, a MT1/MT2 receptor antagonist, markedly suppressed only the effects of RAM. These results suggest that acutely administered MEL enhances NOR memory through both MT1 and MT2 receptor-mediated and non-receptor-mediated mechanisms. 展开更多
关键词 MELATONIN N1-Acetyl-5-Methoxykynuramine Ramelteon Novel Object recognition Memory Melatonin Receptors
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Objects Description and Extraction by the Use of Straight Line Segments in Digital Images
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作者 Vladimir Volkov Rudolf Germer +1 位作者 Alexandr Oneshko Denis Oralov 《Computer Technology and Application》 2011年第12期939-947,共9页
An advanced edge-based method of feature detection and extraction is developed for object description in digital images. It is useful for the comparison of different images of the same scene in aerial imagery, for des... An advanced edge-based method of feature detection and extraction is developed for object description in digital images. It is useful for the comparison of different images of the same scene in aerial imagery, for describing and recognizing categories, for automatic building extraction and for finding the mutual regions in image matching. The method includes directional filtering and searching for straight edge segments in every direction and scale, taking into account edge gradient signs. Line segments are ordered with respect to their orientation and average gradients in the region in question. These segments are used for the construction of an object descriptor. A hierarchical set of feature descriptors is developed, taking into consideration the proposed straight line segment detector. Comparative performance is evaluated on the noisy model and in real aerial and satellite imagery. 展开更多
关键词 Object recognition local descriptors affine and scale invariance edge-based feature detector feature-based imagematching building extraction.
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An object detection approach with residual feature fusion and second-order term attention mechanism
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作者 Cuijin Li Zhong Qu Shengye Wang 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第2期411-424,共14页
Automatically detecting and locating remote occlusion small objects from the images of complex traffic environments is a valuable and challenging research.Since the boundary box location is not sufficiently accurate a... Automatically detecting and locating remote occlusion small objects from the images of complex traffic environments is a valuable and challenging research.Since the boundary box location is not sufficiently accurate and it is difficult to distinguish overlapping and occluded objects,the authors propose a network model with a second-order term attention mechanism and occlusion loss.First,the backbone network is built on CSPDarkNet53.Then a method is designed for the feature extraction network based on an item-wise attention mechanism,which uses the filtered weighted feature vector to replace the original residual fusion and adds a second-order term to reduce the information loss in the process of fusion and accelerate the convergence of the model.Finally,an objected occlusion regression loss function is studied to reduce the problems of missed detections caused by dense objects.Sufficient experimental results demonstrate that the authors’method achieved state-of-the-art performance without reducing the detection speed.The mAP@.5 of the method is 85.8%on the Foggy_cityscapes dataset and the mAP@.5 of the method is 97.8%on the KITTI dataset. 展开更多
关键词 artificial intelligence computer vision image processing machine learning neural network object recognition
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Enhanced Object Detection and Classification via Multi-Method Fusion
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作者 Muhammad Waqas Ahmed Nouf Abdullah Almujally +2 位作者 Abdulwahab Alazeb Asaad Algarni Jeongmin Park 《Computers, Materials & Continua》 SCIE EI 2024年第5期3315-3331,共17页
Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occ... Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled data.To address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection accuracy.The proposed approach involves the integration ofmultiple methods in a complementary way.The process commences with the application of Gaussian filters tomitigate the impact of noise interference.These images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent regions.The Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented images.For precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms areemployed.Genetic Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved performance.Our method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize parameters.This minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall efficacy.The proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,respectively.Furthermore,detection accuracies of 87.2%and 86.6%have been attained.Although ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex backgrounds.Despite these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system. 展开更多
关键词 BRIEF features saliency map fuzzy c-means object detection object recognition
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