Review of Various Techniques for Medical Image Compression

Kaur, Harpreet; Kaur, Rupinder; Kumar, Navdeep

doi:10.5120/ijca2015905282

Cited by 9 publications

(9 citation statements)

References 17 publications

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“…The segmentation process is done using several techniques in literature. The most traditional segmentation schemes are histogram techniques and Otsu's and Kapur's thresholding techniques [43]. The segmentation techniques based on Otsu's and Kapur's thresholding has been considered as the better techniques.…”

Section: Literature Surveymentioning

confidence: 99%

Improved Segmentation With Optimization Based Multilevel Thresholding and K-Means Clustering for Plant Disease Identification

David

Gomathi²

2023

Preprint

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Plant disease identification is an important application for plant protection in agriculture production. The early detection of crop disease helps to reduce the effect of disease in cultivation. The detection of disease should be done precisely. Hence the hyperspectral sensors are extensively used in plant disease detection. Artificial intelligence and machine learning-based techniques have been presented in many works for plant disease detection. Deep learning is the latest method used in image processing and pattern recognition with improved accuracy. For plant disease detection, accurate classification of disease can be obtained with the utilization of deep learning techniques. In this paper, adaptive extreme learning machine (AELT) is presented for classifying the disease. Before the classification process, the segmentation and feature extraction process is performed to improve the disease detection accuracy. Multilevel thresholding-based K-means clustering with probability-induced butterfly optimization algorithm is presented for segmentation. The entropy-based features are extracted from plant images. The features are applied to the AELT classifier. The results are evaluated with the standard dataset and compared with the state of art techniques.

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Section: Literature Surveymentioning

confidence: 99%

Improved Segmentation With Optimization Based Multilevel Thresholding and K-Means Clustering for Plant Disease Identification

David

Gomathi²

2023

Preprint

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“…Communication between vehicles and other objects, known as V2X, is used throughout these exchanges. Communications between vehicles ("V2V") and between vehicles and infrastructure ("V2I") are enabled, as is interaction between cars and nearby pedestrians, cyclists, and charging stations [13]. The linkages between the various components, such as the Raspberry-Pi 3b, camera, rain sensor, buzzer, GPS-GSM, phone, limit switch, blink sensor, alcohol sensor, and LCD, are shown in this block diagram.…”

Section: Literaturereviewmentioning

confidence: 99%

Internet of Things-Based Car Wiper and Accident Location Notification

Singh Rajawat,

Singh Rao,

Sisodia

2023

IJSMEM

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To reduce the number of car crashes that result in deaths or severe property damage was the primary goal of accident prevention programmes. Recognising Danger and Taking Precautions This strategy reduces the amount of time it takes for emergency services to reach a dangerous situation, saving lives. This apparatus can detect fog and clear it from the screen, allowing for safer navigation. If the car detects that you have been drinking, it will not start for you. A buzzer will ring if a blink sensor detects the driver is closing their eyes while driving. If the motorist ignores the warning, the car’s ignition will be turned off. The windscreen wipers will activate automatically if the rain sensor detects rain. When an accident happens, a limit switch is triggered, which shuts off the system and notifies the parents by the Twilio account they provided before the accident.

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“…However, the current lane-detection network based on deep learning still faces many challenges, mainly including the following: (1) Owing to road structures like guardrails and pavement markings that bear a resemblance to lanes, the detection model may occasionally misinterpret lanes as pavement markings, guardrails, and the like. (2) As lane detection necessitates repetitive execution, the importance of a rapid lane-detection strategy with parallel processing becomes evident. In traditional methods, parallel processing of fast lane detection has been performed very well, and models based on deep learning can learn from the ideas in traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Lane Detection Based on Adaptive Cross-Scale Region of Interest Fusion

Deng,

Liu,

Jiang

et al. 2023

Electronics

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Lane detection, a crucial component of autonomous driving systems, is in charge of precise lane location to ensure that cars navigate lanes appropriately. However, in challenging conditions like shadows and extreme lighting, lanes may become obstructed or blurred, posing a significant challenge to the lane-detection task as the model struggles to extract sufficient visual information from the image. The current anchor-based lane-detection network detects lanes in complex scenes by mapping anchors to images to extract features and calculating the relationship between each anchor and other anchors for feature fusion. However, it is insufficient for anchors to extract subtle features from images, and there is no guarantee that the information carried by each anchor is valid. Therefore, this study proposes the adaptive cross-scale ROI fusion network (ACSNet) to fully extract the features in the image so that the anchor carries more useful information. ACSNet selects important anchors in an adaptive manner and fuses these important anchors with the original anchors across scales. Through this feature extraction method, the features of different field-of-view ranges under complex road surfaces can be learned, and diversified features can be integrated to ensure that lanes can be well detected under complex road surfaces such as shadows and extreme lighting. Furthermore, due to the slender structure of lane lines, there are relatively few useful features in the images. Therefore, this study also proposes a Three-dimensional Coordinate Attention Mechanism (TDCA) to enhance image features. The Three-dimensional Coordinate Attention Mechanism extensively explores relationships among features in the row, column, and spatial dimensions. It calculates feature weights for each of these dimensions and ultimately performs element-wise multiplication with the entire feature map. Experimental results demonstrate that our network achieves excellent performance on the existing public datasets, CULane and Tusimple.

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Review of Various Techniques for Medical Image Compression

Cited by 9 publications

References 17 publications

Improved Segmentation With Optimization Based Multilevel Thresholding and K-Means Clustering for Plant Disease Identification

Improved Segmentation With Optimization Based Multilevel Thresholding and K-Means Clustering for Plant Disease Identification

Internet of Things-Based Car Wiper and Accident Location Notification

Lane Detection Based on Adaptive Cross-Scale Region of Interest Fusion

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