Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

Gururaj, C.; Jayadevappa, D.; Tunga, Satish

doi:10.1007/s40031-018-0319-7

Cited by 9 publications

(3 citation statements)

References 11 publications

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“…Object extraction is used to identify and interpret meaningful operations in images [23]. Refers to the segmentation of objects of interest and background from a single image or a series of images to extract the characteristics of different images [24]. In other words, the target is extracted from the scene [25][26].…”

Section: A Image Target Extraction In Machine Visionmentioning

confidence: 99%

Retracted: Martial Arts Routine Difficulty Action Technology VR Image Target Real-Time Extraction Simulation

Sun

Shi-hong

et al. 2020

IEEE Access

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With the gradual increase in the difficulty of competitive martial arts, athletes must complete fine, stable, high-quality and difficult movements in order to achieve excellent performance. The real-time extraction of martial arts movements is a topic that many martial arts enthusiasts care about. This research mainly discusses the real-time extraction and simulation of VR image targets of martial arts routine difficulty action technology. Considering the complex characteristics of martial arts movements, this article will analyze the preprocessing content of existing images. This includes image enhancement and image filtering, and uses median filtering methods to enhance the characteristics of the collected images. In this way, the visual effect of the original image can be improved, and the processed image will contribute to the subsequent segmentation. A new image segmentation method is proposed for the color model of the image. According to the H component of the HSV model representing the characteristics of chromaticity, the color image is transformed into the HSV model, and the H component is extracted. The histogram concept applies to H components. Based on the histogram of the H component, the segmentation threshold is determined, and the cropping target in the image is detected. Because the model space is very sensitive to color, VR technology is used to automatically determine the segmentation target. Combined with the above division methods, the automatic extraction of objects in the image is completed. The method of using VR technology for image extraction processing has high precision, and the error value is 3.92%<5%. The research results show that the VR segmentation results are good, suitable for image segmentation in complex backgrounds and automatic image extraction in complex backgrounds.

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Section: A Image Target Extraction In Machine Visionmentioning

confidence: 99%

Retracted: Martial Arts Routine Difficulty Action Technology VR Image Target Real-Time Extraction Simulation

Sun

Shi-hong

et al. 2020

IEEE Access

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“…Pre-processing involves many steps, including conversion of RGB image into a grayscale image [9], calculating the threshold for the image, converting the grayscaled image to a binarized image, noise removal or removal of the lower pixels, determining the brightness of the image and adjusting the brightness to required range [10] and many more. These pre-processing steps act as a major role in optical character recognition.…”

Section: Pre-processingmentioning

confidence: 99%

Automatic number plate recognition

Nayak¹

2020

IJATCSE

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We live in a world where; a new technology is being invented every day. In earlier days, books were the major source of information. Optical Character Recognition (OCR) plays an important role to archive the information obtained. The need for reformation of vehicle between reality and the information system has led to the need of OCR system in vehicle's name plate recognition. OCR converts written information to a machine-readable format. In this format information becomes accessible at any time or place. The automatic number-plate recognition (ANPR) use OCR technology where the writings on the number-plate are extracted from the scanned image. This method helps to retrieve many other details about the vehicle using the extracted characters.The implementation of the system uses coloured number plate image. It includes majorly two stages-The first stage is to convert the image of the number plate into text and the second stage is to process the text obtained from the number plate that is the recognition. Most of the OCR systems follow the same procedure which comprises of image acquisition, pre-processing, character segmentation and character recognition. The main advantage of using OCR systems is high speed, decreased error and less effort.

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“…Computer vision is the use of computers to carry out a series of analysis and processing on images, so that the computer perceives important features in the image, and rewards and punishments according to the loss function, and finally obtains a visual model that can perceive important image features [2] . In order to accomplish this task, how to understand the characteristics of the image is the most critical part of it.…”

Section: Introductionmentioning

confidence: 99%

Research on image features extraction based on graph convolutional network (Withdrawal Notice)

Zheng

2023

Third International Conference on Signal Image Processing and Communication (ICSIPC 2023)

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With the continuous development and progress of information technology and image sensors, the amount of data contained in digital images is increased. Therefore, the image feature extraction and downstream applications have begun to receive widespread attention by computer vision field. However, the majority of existing contributions are concentrated on the utilization of machine learning models and ignore the graph convolution theory can also be utilized to extract the small differences features from input images. In this paper, we utilize the graph convolution model to dispose the issue that small differences between classes make sample differentiation difficult in the process of feature extraction with optimization loss functions in the feature space. From our experimental results, we can conclude that our proposed model can achieve the image feature extraction and validate the identification accuracy reaching to approximately 95% for image classification task with reasonable system costs.

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Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

Cited by 9 publications

References 11 publications

Retracted: Martial Arts Routine Difficulty Action Technology VR Image Target Real-Time Extraction Simulation

Retracted: Martial Arts Routine Difficulty Action Technology VR Image Target Real-Time Extraction Simulation

Automatic number plate recognition

Research on image features extraction based on graph convolutional network (Withdrawal Notice)

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