Image Processing with Python Libraries

Asaad, Renas Rajab; Ali, Rasan Ismael; Ali, Zeravan Arif; Shaaban, Aly H.

doi:10.25007/ajnu.v12n2a1754

Cited by 6 publications

(1 citation statement)

References 7 publications

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“…Initial forays into this domain, primarily focused on rudimentary systems constrained by limited datasets. These systems struggled with accuracy and adaptability, particularly in complex environments [3]. The advent of deep learning, especially convolutional neural networks (CNNs), marked a paradigm shift, significantly enhancing the precision and robustness of gesture recognition systems [4].…”

Section: A Introductionmentioning

confidence: 99%

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Almufti,

Abdulazeez

2024

ijcs

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Gesture-based systems have emerged as a prominent breakthrough in the field of smart access control, effectively integrating security measures with user comfort. This study presents a novel gesture detection framework for smart entry systems that combines the computational capabilities of a Random Forest Classifier with the practicality of Arduino-based hardware. Central to methodology is the utilization of MediaPipe, an advanced computer vision library, to extract hand motion landmarks from live video streams. The selected landmarks function as a comprehensive dataset for training a Random Forest Classifier, which has been specifically chosen due to its high level of accuracy and efficiency in managing intricate classification jobs. The model exhibits outstanding competence in the categorization of gestures in real-time, attaining high levels of accuracy that are crucial for ensuring dependable entrance control. The Arduino microcontroller plays a vital role in the execution of the entry mechanism as it serves as the intermediary between the gesture detection software and the tangible entry control hardware. The incorporation of gesture recognition technology facilitates a cohesive and prompt user experience, wherein identified motions are directly converted into input commands. The system's practical use is demonstrated through a series of detailed tests, which highlight its dependability and efficiency across diverse climatic circumstances. The findings underscore the system's capacity as a flexible and safe solution for contactless access in many environments, including both private homes and highly protected establishments. Furthermore, the study makes a substantial contribution to the larger domain of human-computer interaction by showcasing the practicality of advanced gesture detection systems in many everyday contexts. The suggested framework presents a novel approach to smart entry systems and also paves the way for further investigation in the domains of smart home automation and interactive systems. In these areas, gesture-based interfaces have the potential to deliver user experiences that are both intuitive and efficient.

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Section: A Introductionmentioning

confidence: 99%

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Almufti,

Abdulazeez

2024

ijcs

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Droplet Drift Image Detection Based on Dynamic Small Targets of Drones

Wang,

Tao,

Yang

2024

Lecture Notes on Data Engineering and Communications Technologies

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Attention 3D-CU-Net: Enhancing Kidney Tumor Segmentation Accuracy Through Selective Feature Emphasis

Sitanaboina,

Beeram,

Jonnadula

et al. 2023

IEEE Access

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Kidney tumor segmentation from MR images became a pivotal research area in kidney cancer diagnosis and treatment planning. Accurate and efficient segmentation enables precise tumor localization, treatment planning, and monitoring of disease progression. Former studies have demonstrated the remarkable capability of the U-Net architecture in semantic segmentation of kidney tumors. A recent variant of the U-Net architecture, known as 3D-CU-Net, has been specifically designed with fully-connected dense skip connections to tackle the kidney tumor segmentation challenges related to network depth invariance, segmentation errors, and enforced feature fusion. While the 3D-CU-Net model demonstrated improved effectiveness in kidney tumor segmentation, it still exhibits significant limitations, including challenges in precise localization, fixed feature selection, image diversity, limited contextual information, and computational complexity. To address the limitations of the 3D-CU-Net model, this paper introduces the Attention 3D-CU-Net as a novel variant. The attention-based mechanism is seamlessly integrated with the 3D-CU-Net, prioritizing informative features to enhance segmentation accuracy by concentrating on selective regions. This innovative approach serves to significantly improve the model's performance, particularly in challenging cases. The proposed model is evaluated on the TCGA-KIRC dataset, a widely used benchmark for kidney tumor segmentation. In comparative experiments, we evaluated the results using metrics like IoU, DSC and accuracy. Our Attention 3D-CU-Net model outperforms the baseline 3D-CU-Net and U-Net with notably higher scores: IoU (0.92), DSC (0.94), and accuracy (0.96).

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Image Processing with Python Libraries

Cited by 6 publications

References 7 publications

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Droplet Drift Image Detection Based on Dynamic Small Targets of Drones

Attention 3D-CU-Net: Enhancing Kidney Tumor Segmentation Accuracy Through Selective Feature Emphasis

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