A Novel Food Image Segmentation Based on Homogeneity Test of K-Means Clustering

Abdulateef, Salwa Khalid; Ahmed, Saadaldeen Rashid; Salman, Mohanad Dawood

doi:10.1088/1757-899x/928/3/032059

Cited by 34 publications

(3 citation statements)

References 15 publications

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“…Some scholars have elaborated on how to protect and inherit national costumes from many aspects. While encouraging all ethnic groups to establish selfconfidence and belief in protecting national costume culture, they actively encourage individuals and local people to collect and protect national costumes [7,8]. Other scholars proposed that national costumes should be displayed and promoted through different channels, and the contributions made by excellent promoters of national costumes should be rewarded [9].…”

Section: Current Situation and Development Of Digital And Image Segme...mentioning

confidence: 99%

Application of Spatial Neighborhood Fuzzy c-Means Algorithm in 3D Image Segmentation of National Clothing

Hai

2022

Wireless Communications and Mobile Computing

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National costume contains rich patterns and patterns with national characteristics, which is an important part of the study of national costume culture. Neither the traditional protection of national costumes nor the early digital protection can clearly and effectively preserve the details of national costumes, which is not conducive to the development and research of national costume culture. The development of image processing technology provides new technical support for the protection and inheritance of national costumes. This paper proposes the application of spatial neighborhood fuzzy c-means algorithm in 3D image segmentation of national clothing. Based on the traditional fuzzy c-means algorithm, combined with spatial information and gray information, a 3D image segmentation model based on spatial neighborhood fuzzy c-means is constructed. The experimental results show that the 3D image segmentation model based on spatial neighborhood fuzzy c-means has faster convergence speed and better algorithm performance than the improved fuzzy c-means algorithm and has better image segmentation effect in different noise levels. In the 3D image segmentation of national costumes, more detailed information can be retained on the basis of maintaining high accuracy and effect.

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Section: Current Situation and Development Of Digital And Image Segme...mentioning

confidence: 99%

Application of Spatial Neighborhood Fuzzy c-Means Algorithm in 3D Image Segmentation of National Clothing

Hai

2022

Wireless Communications and Mobile Computing

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“…Data clustering is a crucial topic in machine learning. It can be used for a variety of purposes, one of which being image segmentation [16]. SVM, K-Nearest Neighbors (KNN), Decision tree, Logistic Regression, and Artificial Neural Network back propagation are a few of the many categorization models available.…”

Section: Figure 3 Direct Dnn Approachmentioning

confidence: 99%

Speaker Identification Model Based on Deep Nural Netwoks

Ahmed

Abbood²,

Farhan

et al. 2022

ijcsm

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This study aims is to establish a small system of text-independent recognition of speakers for a relatively small group of speakers at a sound stage. The fascinating justification for the International Space Station (ISS) to detect if the astronauts are speaking at a specific time has influenced the difficulty. In this work, we employed Machine Learning Applications. Accordingly, we used the Direct Deep Neural Network (DNN)-based approach, in which the posterior opportunities of the output layer are utilized to determine the speaker’s presence. In line with the small footprint design objective, a simple DNN model with only sufficient hidden units or sufficient hidden units per layer was designed, thereby reducing the cost of parameters through intentional preparation to avoid the normal overfitting problem and optimize the algorithmic aspects, such as context-based training, activation functions, validation, and learning rate. Two commercially available databases, namely, TIMIT clean speech and HTIMIT multihandset communication database and TIMIT noise-added data framework, were tested for this reference model that we developed using four sound categories at three distinct signal-to-noise ratios. Briefly, we used a dynamic pruning method in which the conditions of all layers are simultaneously pruned, and the pruning mechanism is reassigned. The usefulness of this approach was evaluated on all the above contact databases

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“…The training process begins with the raw input images and ends with detection maps. Path marking with RANSAC can also be explored when utilizing a detection network model as the basis [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

The Use of DCNN for Road Path Detection and Segmentation

2022

ijcsm

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In this study, various organizations that have participated in several road path-detecting experiments are analyzed. However, the majority of techniques rely on attributes or form models built by humans to identify sections of the path. In this paper, a suggestion was made regarding a road path recognition structure that is dependent on a deep convolutional neural network. A tiny neural network has been developed to perform feature extraction to a massive collection of photographs to extract the suitable path feature. The parameters obtained from the model of the route classification network are utilized in the process of establishing the parameters of the layers that constitute the path detection network. The deep convolutional path discovery network’s production is pixel-based and focuses on the identification of path types and positions. To train it, a detection failure job is provided. Failure in path classification and regression are the two components that make up a planned detection failure function. Instead of laborious postprocessing, a straightforward solution to the problem of route marking can be found using observed path pixels in conjunction with a consensus of random examples. According to the findings of the experiments, the classification precision of the network for classifying every kind is higher than 98.3%. The simulation that was trained using the suggested detection failure function is capable of achieving an accuracy of detection that is 85.5% over a total of 30 distinct scenarios on the road.

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A Novel Food Image Segmentation Based on Homogeneity Test of K-Means Clustering

Cited by 34 publications

References 15 publications

Application of Spatial Neighborhood Fuzzy c-Means Algorithm in 3D Image Segmentation of National Clothing

Application of Spatial Neighborhood Fuzzy c-Means Algorithm in 3D Image Segmentation of National Clothing

Speaker Identification Model Based on Deep Nural Netwoks

The Use of DCNN for Road Path Detection and Segmentation

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