Search Results - spatial information based detection framework*

VS-BIM: a cognitive map-driven framework enhancing MLLM for automatic safety inspection in construction by Wang, Lei, Liu, Yu, Wang, Cunrui, An, Hongda, Li, Yiting

“…Results demonstrate VS-BIM excels in 3D object detection, achieves near-human spatial reasoning, and surpasses human averages in spatial estimation. …”
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Five-Class SSVEP Response Detection using Common Spatial Pattern (CSP)-SVM Approach by Rashid, Mamunur, Norizam, Sulaiman, Mahfuzah, Mustafa, Bari, Bifta Sama

“…This paper represents the feature extraction and classification frameworks to detect five classes EEG-SSVEP responses. …”
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Deep learning approach for automated geospatial data collection by Al-Azizi, Jalal Ibrahim

“…For some selected classes, a customized data set and a prototype framework "DeepAutoMapping" have been built. "DeepAutoMapping" was developed on the basis of convolutional neural networks inspired by recent rapid advancements in deep learning literature to detect, locate and recognize four main street objects (trees, street light poles, traffic signs, and palms) based on a defined object detection dataset. …”
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Moving objects detection from UAV captured videos using trajectories of matched regional adjacency graphs by Harandi, Bahareh Kalantar Ghorashi

“…Overall, the framework consisting of these two steps is termed as Motion Differences of Matched Region-based Features (MDMRBF). …”
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Testing the minimal bounded space method on vision-based drone navigation / Yap Seng Kuang by Yap , Seng Kuang

“…There is no imaging involved, but the laser sensor does record depth information. The spatial openings are derived by analyzing occlusion information from the environment, which is available from the depth information. …”
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Z-YOLOv8s-based approach for road object recognition in complex traffic scenarios by Zhao, Ruixin, Tang, Sai Hong, Supeni, Eris Elianddy, Abdul Rahim, Sharafiz, Fan, Luxin

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Smile detection using hybrid face representation by Arigbabu, Olasimbo Ayodeji, Mahmood, Saif, Syed Ahmad Abdul Rahman, Sharifah Mumtazah, Arigbabu, Abayomi A.

“…Based on our findings, the proposed framework provides very competitive detection rate to related approaches that have exploited image alignment as an important stage for improving performance of smile detection.…”
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MSMD-YOLO: enhanced printed circuit board defect detection with a multi-scale merging and attention network by Deng, Chan, Abdul Rahman, Ribhan Zafira, Ishak, Asnor Juraiza, Raja Ahmad, Raja Kamil

“…To overcome these limitations, this study introduces MSMD-YOLO, an enhanced detection framework developed as a next-generation extension of YOLOv11 [1] for PCB inspection. …”
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Particle swarm optimization with deep learning for human action recognition by Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., Roy, A.

“…To extract the appearance based and structural information, each frame of the action sequences is evaluated for spatial features. …”
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Passive client-centric rogue access point detection framework for WiFi hotspots by Ahmad, Nazrul Muhaimin

“…The proliferation of Wi-Fi hotspots in public places provides seamless Internet connectivity anywhere at any time to the wireless clients.Although many hotspots are often unprotected,unmanaged and unencrypted,this does not prevent the clients from actively connecting to the network.The underlying problem is that the network Access Point (AP) is always trusted.The adversary can impersonate a legitimate AP by setting up a rogue AP to commit espionage and to launch evil-twin attack,session hijacking,and eavesdropping.To aggravate the threats, existing detection solutions are ill-equipped to safeguard the client against rogue AP.Infrastructure- centric solutions are heavily relied on the deployment of sensors or centralized server for rogue AP detection, which are limited,expensive and rarely to be implemented in hotspots.Even though client-centric solutions offer threat-aware protection for the client,but the dependency of the existing solutions on the spoofable contextual network information and the necessity to be associated with the network makes those solutions are not viable for the hotspot’s client.Hence,this work proposes a framework of passive client-centric rogue AP detection for hotspots.Unlike existing solutions,the key idea is to piggyback AP-specific and network-specific information in IEEE 802.11 beacon frame that enables the client to perform the detection without authentication and association to any AP.Based on the spatial fingerprints included in the broadcasted information from the APs in the vicinity of the client,this work discloses a novel concept that enables the rogue AP detection via the client’s ability to self-colocalize and self-validate its own position in the hotspot.The legitimacy of the APs in the hotspot,in this view,lies in the fact that the correct matching between the Received Signal Strength Indicator (RSSI) measurements at the client and pre-recorded fingerprints is attainable when the beacons are transmitted only from the legitimate APs.Hence,any anomalousness in AP’s beacon frame or any attempt to replay the legitimate AP’s beacon frame from different location can be detected and classified as rogue AP threats.Through experiments in real environment,the results demonstrate that with proper algorithm selection and parameters tuning,the rogue AP detection framework can achieve over 90% detection accuracy in classifying the absence and presence of rogue AP threats in the hotspot.…”
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Large-scale detection, mapping, and initial health assessment of date palm trees using multiplatform remotely-sensed data and deep learning techniques by Gibril, Mohamed Barakat Abdelfatah

“…The proposed framework also exhibits great generalizability in detecting and mapping individual date palm trees from different UAV images with diverse spatial resolutions. …”
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Efficient NASNetMobile-enhanced Vision Transformer for weakly supervised video anomaly detection by Arif Mohamad, Muhammad Luqman, Abd Rahman, Mohd Amiruddin, Mohd Shah, Nurisya, Kumar Sangaiah, Arun

“…This research presents NASNetMobile–EViT, a lightweight framework that addresses both the computational burden and the contextual information deficiency that hinder weakly supervised anomaly detection. …”
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Development of geospatial model for tuberculosis prediction in Gombak, Selangor, Malaysia by Mohidem, Nur Adibah

“…This could be attributed to the overcrowding of inmates in the Sungai Buloh prison located there. The GWR model based on the environmental factor (GWR2) was the best model to determine the spatial distribution of TB cases based on the highest values of R2 i.e. 0.98 and local R2 > 0.70, which consisted of 2006 cases of TB. …”
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Coherent crowd analysis with visual attributes / Nurul Japar by Nurul , Japar

“…Unlike existing studies that focus on temporal information, the proposed framework detects the collective behavior by computing attributes similarity on individuals’ heads visual attributes. …”
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Image steganography: comparative analysis of their techniques, complexity and enhancements by Abd Aziz, Ahmad Zulfakar, Mohd Sultan, Muhammad Fitri, Mohamad Zulkufli, Nurul Liyana

“…This paper focuses on the three steganography methods in the spatial domain: Least Significant Bit (LSB), Pixel-Value Difference (PVD) and Edge-based Data Embedding (EBE) methods. …”
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Leveraging sMRI, Self-Attention Mechanisms, and Evolving Spiking Neural Networks for Enhanced Suicide Ideation Detection in Depressed Young Adults by Corrine, Francis, Abdulrazak Yahya, Saleh

“…Furthermore, the study incorporates a user-centric evaluation framework that enables mental health professionals and service users to assess the model's detections and rationale, facilitating informed decision-making processes. …”
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Computed tomography and echocardiography image fusion technique for cardiac images by Kalahroodi, Samaneh Mazaheri

“…The goal of this thesis is integrating detected features, segmentation result information, and intensity information from two mentioned images, into a non-rigid registration framework, and achieve a high quality spatial mapping. …”
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Enhanced algorithms for three-dimensional object interpreter by Haron, Habibollah

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Steel strips surface classification and defect detection based on multiple integrated features and classification schemes by Ashour, Mohammed W. M.

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Wifi-based location-independent human activity recognition and localization using deep learning by Abuhoureyah, Fahd Saad Amed

“…Precise activity recognition and incorporation of through-wall sensing capabilities are achieved within the deep learning framework. Second, Multi-head Attention Mechanism Networks capture intricate patterns in Channel State Information (CSI) data, enhancing recognition accuracy for human activities detected through WiFi signals. …”
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