One Convolutional Layer Model For Parking Occupancy Detection

Goumiri, Soumia; Benboudjema, Dalila; Pieczynski, Wojciech

doi:10.1109/isc253183.2021.9562946

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Segmentation involves dividing a sequence of observations into segments where every segment corresponds to a distinct hidden state. In segmentation, the HMRFM is utilized to effectively identify and isolate segments in sequential data which makes it easier to analyze the accurate data from the input sequence [26]. The complexity is to recognize the process of implicit parameters from the observable parameter and then utilize these parameters for further process.…”

Section: Segmentationmentioning

confidence: 99%

Hidden Markov Random Field Model Based VGG-16 for Segmentation and Classification of Head and Neck Cancer

2024

IJIES

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The head and neck squamous cell carcinoma (HNSCC) is a group of malignant tumors that typically originates in squamous cells lining mucous membranes of head and neck regions. This paper proposed a hidden markov random field model (HMRFM) and VGG-16 for effective segmentation and classification of head and neck cancer. The preprocessing is done by using the min-max normalization method which improves the model performance and feeds into the segmentation process. In segmentation, the HMRFM is utilized to effectively identify and isolate segments in sequential data which makes it easier to analyze the accurate data from input sequence. The VGG-16 model is fine-tuned by certain convolutional layers which is utilized due to less image dataset and overcome the overfitting issues. This method is evaluated on the HNSCC-3DCT-RT dataset and attains better results with regards to AUC, accuracy, sensitivity, specificity, and F1-score values about 95.2%, 98.7%, 91.4%, 97.5% and 91.5% correspondingly. The obtained result shows that the HMRFM with VGG-16 is better compared to other techniques like Deep CNN with data augmentation, 3D-unet convolutional neural network (CNN), and voted ensemble machine learning (VEML).

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Section: Segmentationmentioning

confidence: 99%

Hidden Markov Random Field Model Based VGG-16 for Segmentation and Classification of Head and Neck Cancer

2024

IJIES

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“…It is composed of three convolutional layers and each of them is followed by a Max-pooling layer. The first convolutional layer takes the parameters: 16 for filters, (11,11) for kernel size, and 4 for the value of stride. The second convolutional layer takes the parameters: 20 for filters, (5,5) for kernel size, and 1 for the value of stride.…”

Section: Malexnet Descriptionmentioning

confidence: 99%

“…CNN is widely used for image and video classification in several fields such as medical applications, transportation systems, agriculture, manufacturing, etc. Some examples are the diagnosis of breast cancer using mammogram images [1], the annotation of breast cancer images [2], brain tumor segmentation [3], COVID-19 diagnosis using X-Ray images [4], Alzheimer's disease detection [5], patterns of cystic fibrosis [6], pedestrians' detection [7], moving object detection [8], deep fakes in videos [9], face recognition [10], parking occupancy detection [11][12][13], and recognition of fire base on video [14]. CNN is also used for scene classification using a deep attention CNN [15], semantic correspondence [16,17], and select of interest [18].…”

Section: Introductionmentioning

confidence: 99%

A new hybrid model of convolutional neural networks and hidden Markov chains for image classification

Goumiri

Benboudjema

Pieczynski

2023

Neural Comput & Applic

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Convolutional neural networks (CNNs) have lately proven to be extremely effective in image recognition. Besides CNN, hidden Markov chains (HMCs) are probabilistic models widely used in image processing. This paper presents a new hybrid model composed of both CNNs and HMCs. The CNN model is used for feature extraction and dimensionality reduction and the HMC model for classification. In the new model, named CNN-HMC, convolutional and pooling layers of the CNN model are applied to extract features maps. Also a Peano scan is applied to obtain several HMCs. Expectation–Maximization (EM) algorithm is used to estimate HMC’s parameters and to make the Bayesian Maximum Posterior Mode (MPM) classification method used unsupervised. The objective is to enhance the performances of the CNN models for the image classification task. To evaluate the performance of our proposal, it is compared to six models in two series of experiments. In the first series, we consider two CNN-HMC and compare them to two CNNs, 4Conv and Mini AlexNet, respectively. The results show that CNN-HMC model outperforms the classical CNN model, and significantly improves the accuracy of the Mini AlexNet. In the second series, it is compared to four models CNN-SVMs, CNN-LSTMs, CNN-RFs, and CNN-gcForests, which only differ from CNN-HMC by the second classification step. Based on five datasets and four metrics recall, precision, F1-score, and accuracy, results of these comparisons show again the interest of the proposed CNN-HMC. In particular, with a CNN model of 71% of accuracy, the CNN-HMC gives an accuracy ranging between 81.63% and 92.5%.

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Deep learning-based parking occupancy detection framework using ResNet and VGG-16

Thakur,

Bhattacharjee,

Jain

et al. 2023

Multimed Tools Appl

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One Convolutional Layer Model For Parking Occupancy Detection

Cited by 4 publications

References 8 publications

Hidden Markov Random Field Model Based VGG-16 for Segmentation and Classification of Head and Neck Cancer

Hidden Markov Random Field Model Based VGG-16 for Segmentation and Classification of Head and Neck Cancer

A new hybrid model of convolutional neural networks and hidden Markov chains for image classification

Deep learning-based parking occupancy detection framework using ResNet and VGG-16

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