local binary pattern

Local binary pattern is one of the most popular descriptor that widely used in feature extraction of texture images. Deep convolutional neural network is also one of the best classification methodthat provides very high accuracy. In this research, by combining the features that produced by these two methods, a structure for noisy texture classification is proposed, which provides a very high classification rate. This method is based on two extracted features. The first part uses completed local binary pattern features and in the second part the features of texture images are extracted by using the DenseNet-121 convolution deep neural network. Another motivation of this research related to feature reduction, which significantly reduces the dimensions of extracted features. It employs a shallow convolution neural network to convert the extracted features into lower number of new features. The accuracy of the proposed method has been evaluated on noisy Outex, CUReT and UIUC datasets. The classification accuracy of the proposed method for different level of noise has increased significantly compared to many advanced methods and has improved between 3 and 25%.

Local Binary Pattern (LBP) is one of popular texture images descriptors. It is used to extraction features of texture images for classification.  However, in this paper a new version of LBP is proposed that is not used for this purpose.  The proposed descriptor is used for image retrieval. In other words it can be compare to Scale Invariant Features Transform (SIFT). In term of speed, the proposed method outperforms the SIFT. In addition, in this paper it is used for Infrared images retrieval. These images are low quality and low contrast and if they are used without any pre processing and enhancement, SIFT descriptor versions cannot extract good features of them. The proposed method can provide better result with higher speed. The proposed method have been compared to center symmetric LBP (CS-LBP) and SIFT by using both infrared images and standard image data-sets.

In today’s modern medicine, with the spreading use of radiological imaging devices in medical centers, the need to accurate, reliable, and portable medi cal image analysis and understanding systems has been increasing constantly. Since usually the images are not accompanied by the required clinical anno tation, automatic tagging and captioning systems are among the most desired applications. This research proposes an automatic structured radiology report generation system that is based on annotation methods. Extracting useful and descriptive image features to model conceptual contents of the images is one of the main challenges in this regard. Considering the ability of deep neural networks in soliciting informative and effective features as well as lower reso urce requirements, MobileNets are employed as the main building block of the proposed system. Furthermore due to the lack of large labeled medical data for training the network and risk of over-fitting, a joint descriptor is induced from the deep features and local bina ry patterns. Experimental results confirm the efficiency of the proposed hybrid approach with accuracy 91.4%, as compared to the end-to-end deep networks and classic annotation methods.

The content based image retrieval searches digital images in a large image database and uses visual content of images instead of metadata. This approach has many usages in security and authentication for example scanning the iris, fingerprint or finger knuckle print. This paper contributes a new method for personal authentication using finger knuckle print based on a new local binary pattern and image segmentation. The capabilities of quantum science lead to take its advantage in different areas of image processing. The main idea is inspired by the theory of quantum estimation and is applied to the feature extraction phase, in addition, the quantum circuit of the proposed feature is also designed. In order to measure the efficiency and accuracy of the proposed method, the EER (Equal Error Rate) is calculated. After implementing the proposed algorithm on the POLYU dataset, which contains 7920 images, the EER = 0.67 and accuracy =99% are obtained which indicate that the new method has more efficiency and accuracy than the similar approaches.