In this paper Cubic Bezier curve-based image retrieval system is proposed. This system evaluates similarity of each image in its database to a query image in terms of shape characteristics. Then, returns those images within a desired range of similarity. The proposed system determines nonlinear relationship between image's features for more accurate similarity comparison between query image and existing database images. Among existing approaches to shape feature analysis, statistical approach to extract shape features is adopted here. It works in form of control points of spline curves from both given query image and images of available database. These control points are further used to find out the Fourier Descriptors. Control points and Fourier descriptors are used for image retrieval in proposed system. With the vast number of images available on-line, quality CBIR systems are critical. The performance and results obtained by proposed system are compared to other CBIR systems. Comparison reveals proposed system performance is state of the art. In many parameters it outperforms other CBIR systems.
In this paper Cubic Bezier curve-based leaf classification system using CapsNet is proposed. This system extracts image features in terms of shape characteristics of query leaf image then the CapsNet is trained to classify the input leaf image to a particular class within a desired range of similarity. The proposed system determines nonlinear relationship between leaf image's features for more accurate similarity comparison between query leaf image and existing class images. Among existing approaches to shape feature analysis, statistical approach to extract shape features is adopted here. It works in form of control points of Bezier curves from both given query leaf image and leaf images of available database. These control points are further used to find out the Fourier Descriptors. control points and Fourier descriptors are further used to train the CapsNet for image classification in proposed system. Nowadays, multiple leaf images are generated and stored online hence, classical classification systems are crucial. The proposed system is compared with other classification systems in terms of performance and results. Comparison reveals proposed system performance is state of the art. In many parameters it outperforms other classification systems.
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This paper presents an image feature representation using geometrical feature descriptor for image recognition. Geometrical feature representation is primarily used in shape descriptors feature representation. The shape feature representation using Bezier curve representation and control point selection is defined. Curve geometry is used in feature representation defining the curvature details of an image. A new linear Bezier curve control point feature is proposed to improve the control point detection for feature representation. The accuracy of control point extraction is made by a linear threshold approach using Bezier curve. The performance w. r. t. retrieval accuracy and feature overhead outlines the significance of the proposed approach in geometrical feature representation and image retrieval.
In accordance with the inability of various hair artefacts subjected to dermoscopic medical images, undergoing illumination challenges that include chest-Xray featuring conditions of imaging acquisi-tion situations built with clinical segmentation. The study proposed a novel deep-convolutional neural network (CNN)-integrated methodology for applying medical image segmentation upon chest-Xray and dermoscopic clinical images. The study develops a novel technique of segmenting medical images merged with CNNs with an architectural comparison that incorporates neural networks of U-net and fully convolutional networks (FCN) schemas with loss functions associated with Jaccard distance and Binary-cross entropy under optimised stochastic gradient descent + Nesterov practices. Digital image over clinical approach significantly built the diagnosis and determination of the best treatment for a patient’s condition. Even though medical digital images are subjected to varied components clarified with the effect of noise, quality, disturbance, and precision depending on the enhanced version of images segmented with the optimised process. Ultimately, the threshold technique has been employed for the output reached under the pre- and post-processing stages to contrast the image technically being developed. The data source applied is well-known in PH2 Database for Melanoma lesion segmentation and chest X-ray images since it has variations in hair artefacts and illumination. Experiment outcomes outperform other U-net and FCN architectures of CNNs. The predictions produced from the model on test images were post-processed using the threshold technique to remove the blurry boundaries around the predicted lesions. Experimental results proved that the present model has better efficiency than the existing one, such as U-net and FCN, based on the image segmented in terms of sensitivity = 0.9913, accuracy = 0.9883, and dice coefficient = 0.0246.
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