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Medical image retrieval is of great significance for forming correct medical judgments during the diagnosis and treatment process. In response to the fact that there are rich types of features in medical images, this paper constructs a multi-feature fusion model and applies it to medical image retrieval. This multi-feature fusion model utilizes three features. Color information is characterized using three different moments, texture information is obtained using the LBP (Local Binary Pattern) operator, and shape information is described using Hu moments. After collecting three features of medical images, they are fused into a similarity measure model through a self-set weight structure for comparison in medical image retrieval. Experiments show that our method has satisfactory image retrieval result for medical images such as endoscopic images and computed tomography (CT) images. Despite the continuous improvement of recall indicators, the proposed method still has high precision.

In this study, a bio-inspired approach for extracting efficient features prior to the recognition of scenes is proposed. It is highly inspired from the model of the mammals visual system. The retina contains many levels of neurons (bipolar, amacrine, horizontal and ganglion cells) accurately organized from cones and rods to the optic nerve up till the lateral geniculate nucleus (LGN) which is the main thalamic relay for inputs to the visual cortex. This structure probably eases other brain areas tasks in preprocessing the visual information. This paper is focusing on the study of these specific structures, relying on a bottom up approach to propose a comprehensive mathematical model of the low level image processing performed within the eye. The presented system takes into account the foveolar structure of the retina to produce a low-resolution representation of observed images by decomposing them into a local summation of elementary gaussian color histograms. This representation corresponds to the LGN biological organization. It has been thought that due to short timings, some very quick localization tasks involving particularly fast information processing pathways cannot be provided by the classical ones passing through higher level cortical areas. This work proposes a model of retinal coding and LGN-visual representation that we show provides reliable and sufficient early features for scenes recognition and localization. Experiments on real scenes using the developed model are presented showing the efficiency of the approach on localization.

Restoration work of archaeological artifacts broken into pieces is similar to putting together a jigsaw puzzle. The purpose of this study is to construct an intelligent computer assistance system to conveniently restore archaeological discoveries from some fragments. AReal-Coded Genetic Algorithm (RCGA) was applicable for solving the positioning problem of a three-dimensional (3D) restoration. The fitness function value for RCGA was calculated from image similarity between the target and correct patterns in plane images at multiple camera angles. Image features of a 3D object were obtained by the ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), and Accelerated KAZE (AKAZE) techniques; they were considered as a part of the fitness function value. Simulation study revealed that the RCGA approach was capable of automatically and efficiently adjusting the positions of 3D fragments, especially in the AKAZE technique. A user interface with the functions of design drawing was also created to assist in repair work. The interactive assistance interface for 3D restoration based on RCGA and followed by the hill-climbing algorithm would be applied to practical applications for digital archives of artifacts.

In this paper, we present a scheme of novel color image retrieval based on regional features. First, an RGB-color image is transformed into HSI color model, and then the region segmentation process is executed. The spatial features within each region are used to represent the whole color image. To illustrate the effectiveness of our proposed scheme, two experimental databases are used to compare the performances of our scheme with other related methods in terms of the accuracy. According to the experimental results, we find that the proposed scheme can effectively retrieve more similar images than other ones.