Proc. SPIE. 11050, International Forum on Medical Imaging in Asia 2019
KEYWORDS: Logic, Image processing, Field programmable gate arrays, Gaussian filters, Digital imaging, Systems engineering, Color vision, Digital electronic circuits, Digital electronics, RGB color model
In this study, we designed a digital hardware circuit for a field-programmable gate array (FPGA) to provide an effective contrast improvement algorithm for dichromats. The proposed method employs the Craik-O ’Brien (C-O) effect. The C-O effect is an optical illusion effect in which subjective contrast is created from contour information. In the proposed method, the contrast modification is only conducted around the contours of objects to apply the C-O effect for dichromats. To extract the contour information of objects, a T-model filter which only requires a one-line buffer is introduced. The proposed method can realize the C-O effect without using dividers and multipliers. Therefore, it is relatively simple to implement in the FPGA. Through experiments with software and logic simulation, the effectiveness and validity of the proposed method were evaluated.
In this paper, we propose a new color quantization method that can preserve infrequent salient colors of an original image. The infrequent salient colors mean that they are not dominant globally, but are dominant locally and are important to keep the impression of the original image. In the proposed method, color quantization is realized by k-means clustering and an input dataset for the clustering are adaptively and repeatedly modified based on local quantization errors to preserve the infrequent salient colors. The proposed method is implemented as an Android application to verify the feasibility of the use of the proposed method on mobile devices.
In this paper, we propose a land-cover classification method based on a modified hierarchical k-nearest neighbor (MHkNN) algorithm to achieve a high classification accuracy. The proposed method introduces a reliability measure for each training sample, which is defined as confidence in the sample belonging to each of the considered classes. The method performs the majority voting considering not only the number of the training samples, but also their reliabilities. The classification performance of the proposed method is compared to that of the conventional land-cover classification methods. The effectiveness of the proposed method is verified by applying it to real remote sensing images.