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Mangosteen Quality Grading for Export Markets Using Digital Image Processing Techniques

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Digital Image Processing: Principles and Applications

• First Online: 12 September 2018

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• G. P. Obi Reddy 5  

Part of the book series: Geotechnologies and the Environment ((GEOTECH,volume 21))

2017 Accesses

3 Citations

Digital image processing is an important part in digital analysis of remote sensing data. It allows one to enhance image features of interest while attenuating irrelevant features of a given application and then extract useful information about the scene from the enhanced image. It comprises the four basic steps, which include image correction/restoration, image enhancement, image transformation, and image classification. Image restoration is basically aimed to compensate the data errors, noise, and geometric distortions introduced during the scanning, recording, and playback operations. Image enhancement helps to alter the visual impact that the image has on the interpreter, that improve the information content and information extraction ability by utilizing the decision-making capability of the computer in order to recognize and classify the pixels on the basis of their digital signatures. The information extracted by comparing two or more images of an area that were acquired at different times. Unsupervised classification distinguish the patterns in the reflectance data and groups them into a pre-defined number of classes without any prior knowledge of the image. Whereas, in supervised classification, the user trains the computer, guiding it what type of spectral characteristics to look for and what type of land cover they represent, and guides the image processing software how to classify certain features. Change-detection analysis provides information about the changes in different seasons or dates. In post-classification analysis, image smoothing and accuracy assessment are important to generate the output from digital image processing.

• Digital image processing
• Image enhancement
• Image restoration
• Image classification

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Reddy, G.P.O. (2018). Digital Image Processing: Principles and Applications. In: Reddy, G., Singh, S. (eds) Geospatial Technologies in Land Resources Mapping, Monitoring and Management. Geotechnologies and the Environment, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-319-78711-4_6

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Electrical Engineering and Systems Science > Image and Video Processing

Title: data-efficient unsupervised interpolation without any intermediate frame for 4d medical images.

Abstract: 4D medical images, which represent 3D images with temporal information, are crucial in clinical practice for capturing dynamic changes and monitoring long-term disease progression. However, acquiring 4D medical images poses challenges due to factors such as radiation exposure and imaging duration, necessitating a balance between achieving high temporal resolution and minimizing adverse effects. Given these circumstances, not only is data acquisition challenging, but increasing the frame rate for each dataset also proves difficult. To address this challenge, this paper proposes a simple yet effective Unsupervised Volumetric Interpolation framework, UVI-Net. This framework facilitates temporal interpolation without the need for any intermediate frames, distinguishing it from the majority of other existing unsupervised methods. Experiments on benchmark datasets demonstrate significant improvements across diverse evaluation metrics compared to unsupervised and supervised baselines. Remarkably, our approach achieves this superior performance even when trained with a dataset as small as one, highlighting its exceptional robustness and efficiency in scenarios with sparse supervision. This positions UVI-Net as a compelling alternative for 4D medical imaging, particularly in settings where data availability is limited. The source code is available at this https URL .

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