A contemporary approach for object recognition based on spatial layout and low level features’ integration

Shaikh, Riaz Ahmed; Memon, Imran; Hussain, Rafaqat; Maitlo, Abdullah; Shaikh, Hidayatullah

doi:10.1007/s11042-018-6796-5

Cited by 11 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous biomedical image segmentation methods based on thresholding, region growing, statistical pattern recognition, fuzzy modeling, and Markov random field techniques have been explored in recent literature ( 36 – 38 ). While some methods require user interaction and are not fully automated, many automatic segmentation approaches rely on hand-designed features ( 39 , 40 ). These techniques may produce erroneous results when dealing with small lesions or lesions in close proximity to normal tissue.…”

Section: Automatic Lesion Segmentationmentioning

confidence: 99%

Patient-specific modeling for guided rehabilitation of stroke patients: the BrainX3 use-case

Sharma,

Páscoa dos Santos,

Verschure

2023

Front. Neurol.

View full text Add to dashboard Cite

BrainX3 is an interactive neuroinformatics platform that has been thoughtfully designed to support neuroscientists and clinicians with the visualization, analysis, and simulation of human neuroimaging, electrophysiological data, and brain models. The platform is intended to facilitate research and clinical use cases, with a focus on personalized medicine diagnostics, prognostics, and intervention decisions. BrainX3 is designed to provide an intuitive user experience and is equipped to handle different data types and 3D visualizations. To enhance patient-based analysis, and in keeping with the principles of personalized medicine, we propose a framework that can assist clinicians in identifying lesions and making patient-specific intervention decisions. To this end, we are developing an AI-based model for lesion identification, along with a mapping of tract information. By leveraging the patient's lesion information, we can gain valuable insights into the structural damage caused by the lesion. Furthermore, constraining whole-brain models with patient-specific disconnection masks can allow for the detection of mesoscale excitatory-inhibitory imbalances that cause disruptions in macroscale network properties. Finally, such information has the potential to guide neuromodulation approaches, assisting in the choice of candidate targets for stimulation techniques such as Transcranial Ultrasound Stimulation (TUS), which modulate E-I balance, potentiating cortical reorganization and the restoration of the dynamics and functionality disrupted due to the lesion.

show abstract

Section: Automatic Lesion Segmentationmentioning

confidence: 99%

Patient-specific modeling for guided rehabilitation of stroke patients: the BrainX3 use-case

Sharma,

Páscoa dos Santos,

Verschure

2023

Front. Neurol.

View full text Add to dashboard Cite

show abstract

“…The IoT system change the Agriculture system the farmers got real-time monitor the plants and observed the temperature and water level in the field through the deployment of technologies the IoT system addressed the problems related to Plants. Shaikh et al [32] presents the contemporary approach for object recognition by the fusion of low-level features and spatial layout. Lowlevel features are very important for contentbased image retrieval, further Hussain et al, [33] discussed an innovative idea for the segmentation of connected characters in the text-based CAPTCHAs for character recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning-Based Rice Leaf Diseases Detection Using Yolov5

Jhatial¹,

Shaikh²,

Shaikh³

et al. 2022

SJCMS

View full text Add to dashboard Cite

The Rice crop in Agriculture field is playing an important role in economy of Pakistan and fulfilling the needs of living hood of human beings. The rice leaf faces several diseases like Bacterial Bligh, Brown Spot, Blast and Tungro. This research attempts to create a simple and best model for Rice leaf disease detection using deep learning model Yolov5. The model has been upgraded to v5 which is the latest version of Yolo. The performance and accuracy of object detection using Yolov5 is better than Yolov3 and Yolov4 models. This model is able to differentiate and successfully detect the rice leaf diseases. The Rice leaf images Dataset is downloaded from Kaggle website, the dataset contains 400 images of leaf infected by disease. This paper uses Google colab platform to train, validate and test the model for Rice Leaf disease detection. All necessary steps to be implemented, the rice leaf disease are detected and fully described. The developed model utilize epochs: 100. The experimental results show that the deep learning model created with 100 epochs has shown the best performance with precision, recall, and mAP value of 1.00, 0.94, and 0.62, respectively.

show abstract

“…Some recommendations were offered as guide for designing Arabic robust CAPTCHAs. Shaikh et al [15] presents the contemporary approach for object recognition by the fusion of low level features and spatial layout. Low level features are very important for content based image retrieval, further Hussain et al, [16] discussed innovative idea for the segmentation of connected characters in the text based CAPTCHAs for character recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Development of Sindhi Text Based CAPTCHAs for Regional Websites

Kehar¹,

Arain²,

Shaikh

et al. 2021

SJET

Self Cite

View full text Add to dashboard Cite

Bots are created to use the resources maliciously on World Wide Web. The misuse of the resources could be prevented by employing CAPTCHAs. Several types of CAPTCHAs are being used against the bots (robot) attacks but text-based CAPTCHA type is the most popular being very secured and easy to use. Latin language based text CAPTCHAs can be found ubiquitously on Internet but English text based CAPTCHAs are already decoded by many researchers. Thus, a novel Sindhi language based text CAPTCHA was proposed for regional websites where Arabic style script was utilized. This scheme offered two fold benefits: first, the proposed scheme could easily be understood by averagely literate person; second, this scheme paved a way for Arabic style OCR developers to understand Sindhi language specific features and facilitate Sindhi text recognition in future. A survey was also conducted to analyze the usability and strength of proposed CAPTCHA.

show abstract

A contemporary approach for object recognition based on spatial layout and low level features’ integration

Cited by 11 publications

References 18 publications

Patient-specific modeling for guided rehabilitation of stroke patients: the BrainX3 use-case

Patient-specific modeling for guided rehabilitation of stroke patients: the BrainX3 use-case

Deep Learning-Based Rice Leaf Diseases Detection Using Yolov5

Design and Development of Sindhi Text Based CAPTCHAs for Regional Websites

Contact Info

Product

Resources

About