Sparse Radial Sampling LBP for Writer Identification

In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features.Comment: Submitted to the 13th International Conference on Document Analysis and Recognition (ICDAR 2015

Deteksi Efusi Pleura Pada Citra Thorax Mengunakan Jaringan Syaraf Tiruan Propagasi Balik Melalui Ekstraksi Ciri Biner

The research about detection pleural effusion of the thoracic using neural network back propagation by binary feature extraction has been done. A common cause of pleural effusion disease is cancer. It is estimated that pleural effusion Malignant affects 150,000 people every year in the United States. The normal pleural space only has a few milliliters of liquid that helps lubricate of the lungs during breathing. Pleural effusion (large amounts of liquid in the pleural space) can lead to a partial or complete compression of the lung. The difficulty to distinguish excess accumulation of fluid in the pleural cavity should be minimized by radiologist. This research contributes interpretation pleural effusion in the thoracic and reduces doubts of doctor in the treatment of patients. The purpose of this research is to develop algorithms to identify pleural effusion using artificial neural networks back propagation by binary feature extraction the thoracic. Binary feature extraction is obtained from the level set segmentation. The process of image enhancement by histogram equalization and contrast enhancement should be performed before the level set segmentation process. Binary feature extraction patterns were training on ANN was taken from 5% until 25% of costophrenic angle in the thoracic. Neural network can recognize the characteristic patterns of the binary feature 15% are well trained. Validation ANN pattern training by up to 100%, while process of testing the ANN is able to identify 14 data from 15 test data to test validation value reaches 93.33% on the condition of setting 2 hidden layers, each of hidden layer contain 10 neurons

Classification of EMI discharge sources using time–frequency features and multi-class support vector machine

This paper introduces the first application of feature extraction and machine learning to Electromagnetic Interference (EMI) signals for discharge sources classification in high voltage power generating plants. This work presents an investigation on signals that represent different discharge sources, which are measured using EMI techniques from operating electrical machines within power plant. The analysis involves Time-Frequency image calculation of EMI signals using General Linear Chirplet Analysis (GLCT) which reveals both time and frequency varying characteristics. Histograms of uniform Local Binary Patterns (LBP) are implemented as a feature reduction and extraction technique for the classification of discharge sources using Multi-Class Support Vector Machine (MCSVM). The novelty that this paper introduces is the combination of GLCT and LBP applications to develop a new feature extraction algorithm applied to EMI signals classification. The proposed algorithm is demonstrated to be successful with excellent classification accuracy being achieved. For the first time, this work transfers expert's knowledge on EMI faults to an intelligent system which could potentially be exploited to develop an automatic condition monitoring system

Surface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patterns

Developments in defect descriptors and computer vision-based algorithms for automatic optical inspection (AOI) allows for further development in image-based measurements. Defect classification is a vital part of an optical-imaging-based surface quality measuring instrument. The high-speed production rhythm of hot continuous rolling requires an ultra-rapid response to every component as well as algorithms in AOI instrument. In this paper, a simple, fast, yet robust texture descriptor, namely selectively dominant local binary patterns (SDLBPs), is proposed for defect classification. First, an intelligent searching algorithm with a quantitative thresholding mechanism is built to excavate the dominant non-uniform patterns (DNUPs). Second, two convertible schemes of pattern code mapping are developed for binary encoding of all uniform patterns and DNUPs. Third, feature extraction is carried out under SDLBP framework. Finally, an adaptive region weighting method is built for further strengthening the original nearest neighbor classifier in the feature matching stage. The extensive experiments carried out on an open texture database (Outex) and an actual surface defect database (Dragon) indicates that our proposed SDLBP yields promising performance on both classification accuracy and time efficiencyPeer reviewe

Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted ncomponent of this work in other works.Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intraclass variation, ambiguous interclass distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional local binary patterns variants, descriptive information hidden in nonuniform patterns is innovatively excavated for the better defect representation. This paper focuses on the following aspects. First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest-neighbor classifier. The classification accuracy and time efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database [Northeastern University (NEU)]. The experimental results promise that the proposed method can be widely applied in online automatic optical inspection instruments for hot-rolled strip steel.Peer reviewe

Graph Cut Based Local Binary Patterns for Content Based Image Retrieval

In this paper, a new algorithm which is based on the graph cut theory and local binary patterns (LBP) for content based image retrieval (CBIR) is proposed. In graph cut theory, each node is compared with the all other nodes for edge map generation. The same concept is utilized at LBP calculation which is generating nine LBP patterns from a given 3—3 pattern. Finally, nine LBP histograms are calculated which are used as a feature vector for image retrieval. Two experiments have been carried out for proving the worth of our algorithm. It is further mentioned that the database considered for experiments are Brodatz database (DB1), and MIT VisTex database (DB2). The results after being investigated shows a significant improvement in terms of their evaluation measures as compared to LBP and other existing transform domain techniques. Keywords: Feature Extraction; Local Binary Patterns; Image Retrieva

A Fast Object Recognition Using Edge Texture Analysis for Image Retrieval

A Robust Object Recognition for Content Based Image Retrieval (CBIR) based on Discriminative Robust Local Binary Pattern (DRLBP) and Local Ternary Pattern (LTP) analysis. The Robust Object Recognition using edge and texture feature extraction. The extension of Local Binary Pattern (LBP) is called DRLBP. The category recognition system will be developed for application to image retrieval. The category recognition is to classify an object into one of several predefined categories. LBP is defined as an ordered set of binary comparisons of pixel intensities between the center pixel and its eight surrounding pixels .DRLBP features identifying the contrast information of image patterns. The proposed features preserve the contrast information of image patterns. The DRLBP discriminates an object like the object surface texture and the object shape formed by its boundary

Counting the learnable functions of structured data

Cover's function counting theorem is a milestone in the theory of artificial neural networks. It provides an answer to the fundamental question of determining how many binary assignments (dichotomies) of $p$ points in $n$ dimensions can be linearly realized. Regrettably, it has proved hard to extend the same approach to more advanced problems than the classification of points. In particular, an emerging necessity is to find methods to deal with structured data, and specifically with non-pointlike patterns. A prominent case is that of invariant recognition, whereby identification of a stimulus is insensitive to irrelevant transformations on the inputs (such as rotations or changes in perspective in an image). An object is therefore represented by an extended perceptual manifold, consisting of inputs that are classified similarly. Here, we develop a function counting theory for structured data of this kind, by extending Cover's combinatorial technique, and we derive analytical expressions for the average number of dichotomies of generically correlated sets of patterns. As an application, we obtain a closed formula for the capacity of a binary classifier trained to distinguish general polytopes of any dimension. These results may help extend our theoretical understanding of generalization, feature extraction, and invariant object recognition by neural networks

Handwritten digit classification

Pattern recognition is one of the major challenges in statistics framework. Its goal is the feature extraction to classify the patterns into categories. A well-known example in this field is the handwritten digit recognition where digits have to be assigned into one of the 10 classes using some classification method. Our purpose is to present alternative classification methods based on statistical techniques. We show a comparison between a multivariate and a probabilistic approach, concluding that both methods provide similar results in terms of test-error rate. Experiments are performed on the known MNIST and USPS databases in binary-level image. Then, as an additional contribution we introduce a novel method to binarize images, based on statistical concepts associated to the written trace of the digitDigit, Classification, Images