Segmentation of River Scenes Based on Water Surface Reflection Mechanism

In computer vision, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT) are two widely used local descriptors. In this paper, we propose to combine them effectively for scene categorization. First, LBP and SIFT features are regularly extracted from training images for constructing a LBP feature codebook and a SIFT feature codebook. Then, a two-dimensional table is created by combining the obtained codebooks. For creating a representation for an image, LBP and SIFT features extracted from the same positions of the image are encoded together based on sparse coding by using the two-dimensional table. After processing all features in the input image, we adopt spatial max pooling to determine its representation. Obtained image representations are forwarded to a Support Vector Machine classifier for categorization. In addition, in order to improve the scene categorization performance further, we propose a method to select correlated visual words from large codebooks for constructing the two-dimensional table. Finally, for evaluating the proposed method, extensive experiments are implemented on datasets Scene Categories 8, Scene Categories 15 and MIT 67 Indoor Scene. It is demonstrated that the proposed method is effective for scene categorization.

With huge and ever-growing products in the factory, image retrieval can help the worker retrieve the same, or similar, existing products rapidly and accurately to guide production. In this paper, an effective method based on Fourier transform and local binary pattern is proposed to improve the retrieval efficiency of wool fabric. After capturing the fabric image, histogram equalization was implemented on the value of the Hue, Saturation, Value (HSV) mode to enhance the contrast. Subsequently, Fourier transform together with local binary pattern operator were performed to obtain the frequency spectrum and the local binary pattern, respectively. Each frequency spectrum was divided into 22 rings with the same width, and the standard deviation of the frequencies in each ring was calculated as a Fourier feature. Distinct output values of each local binary pattern were counted and normalized as local binary pattern features. Finally, Euclidean distance was adopted to measure the similarity based on the Fourier feature and local binary pattern feature. Twenty thousand wool fabric images were captured to demonstrate the efficacy of the proposed method. Experimental results indicate that the framework is effective and superior for image retrieval of wool fabric, providing referential assistance for the worker in the factory and improving retrieval efficiency.

A face occlusion detection scheme which is based on both skin color ratio (SCR) and Local Binary Pattern (LBP) feature, is proposed.The proposed method mainly consists of four steps: foreground extraction, head detection, feature extraction, and occlusion detection.First, foreground is extracted by codebook background subtraction algorithm.Then, the head region is located using HOG head detector.After that, the skin-color ratio and LBP feature are extracted.Finally, SVM is trained based on LBP feature.The recognition result of SVM and the result of skin-color ratio feature are merged by weighted voting strategy, and then occluded faces are classified as three categories: concealed, partially concealed, and visible.Experimental results show that the proposed detection system can achieve desirable results in intelligent video surveillance systems.

Local binary features for texture classification: Taxonomy and experimental study

HighlightsAbnormal grain temperature changes were detected by calculating the similarity of HSV features in cloud maps.The F-measures were higher for the improved method than for methods based on HSV and LBP feature similarity.The improved method can detect abnormal heating of grain due to mold activity or spoilage and the fluctuation in grain temperature caused by aeration.The temperature field of the grain bulk in adjacent time periods has high similarity during normal storage.Abstract. Analyses of grain temperature data are time-consuming and labor-intensive, and thorough analyses are difficult to perform. This article proposes an improved method based on the hue, saturation, and value (HSV) color feature similarity of temperature field cloud maps to detect abnormal changes in grain temperature. Historical grain temperature data are preprocessed to generate temperature field cloud maps. The improved method based on HSV feature similarity is used to calculate the similarity of temperature field cloud maps for two successive days during normal storage, and a similarity threshold is set. Five types of grain bulk temperature anomalies are then simulated. Additionally, a comparative experiment is carried out that considers traditional methods based on HSV feature similarity and local binary pattern (LBP) feature similarity. The results show that the average recall rates of the F-measures of the improved method, the traditional method based on HSV feature similarity, and the method based on LBP feature similarity are 96.2%, 89.3%, and 95.4%, respectively, and the processing speeds are 340, 300, and 690 ms per group, respectively. Finally, an abnormal grain temperature experiment is carried out. The experimental results show that the improved method can detect sudden changes in the temperature field due to mold activity or spoilage and the fluctuations in grain temperature caused by aeration. Keywords: Grain storage, HSV feature, LBP feature, Similarity, Temperature.

We investigated the feasibility of using multiresolution Local Binary Pattern (LBP) texture analysis to reduce falsepositive (FP) detection in a computerized mass detection framework. A new and novel approach for extracting LBP features is devised to differentiate masses and normal breast tissue on mammograms. In particular, to characterize the LBP texture patterns of the boundaries of masses, as well as to preserve the spatial structure pattern of the masses, two individual LBP texture patterns are then extracted from the core region and the ribbon region of pixels of the respective ROI regions, respectively. These two texture patterns are combined to produce the so-called multiresolution LBP feature of a given ROI. The proposed LBP texture analysis of the information in mass core region and its margin has clearly proven to be significant and is not sensitive to the precise location of the boundaries of masses. In this study, 89 mammograms were collected from the public MAIS database (DB). To perform a more realistic assessment of FP reduction process, the LBP texture analysis was applied directly to a total of 1,693 regions of interest (ROIs) automatically segmented by computer algorithm. Support Vector Machine (SVM) was applied for the classification of mass ROIs from ROIs containing normal tissue. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the classification accuracy and its improvement using multiresolution LBP features. With multiresolution LBP features, the classifier achieved an average area under the ROC curve, , z A of 0.956 during testing. In addition, the proposed LBP features outperform other state-of-the-arts features designed for false positive reduction.

Face recognition is efficiently used in computer vision, pattern recognition, biometric, law enforcement, surveillance, criminal investigations, and missing person detection. However, face recognition is still a challenging task under variant illuminations, pose, expression, occlusion, and background. In this paper, we proposed a cascading of histogram of oriented gradients (HOG) and local binary pattern (LBP) feature extraction method on ORL face dataset, which can improve recognition rates while also addressing pose, scale, expression, and variant illumination issues. The facial recognition network was trained using various classifiers such as with KNN, SVM, and random forest (RF). The results have been shown that the accuracy rate of the combined LBP and HOG feature extraction method is better than individual LBP or HOG features for ORL database face recognition.

Histopathological analysis is important for detection of the breast cancer (BC). Computer-aided diagnosis and detection systems are developed to assist the radiologist in the diagnosis process and to relieve the patient from unnecessary pain. In this study, a computer-aided diagnosis system for the early detection of benign and malignant breast cancer is proposed. The proposed system consists of feature extraction, feature ensemble, and classification stages. Various preprocessing steps such as grayscale conversion, noise filtering, and image resizing are employed on the input histopathological images. The local texture descriptors namely Local Binary Pattern (LBP), Frequency Decoded LBP (FDLBP), Binary Gabor Pattern (BGP), Local Phase Quantization (LPQ), Binarized Statistical Image Features (BSIF), CENsus TRansform hISTogram (CENTRIST), and Pyramid Histogram of Oriented Gradients (PHOG) are employed for feature extraction from the histopathologic images. The obtained features are then concatenated for the construction of the ensemble of the features. Three classifiers namely Support Vector Machines (SVM), K-nearest neighbor (KNN), and Neural Networks (NN) are used in the detection of the BC and the classification accuracy score is used for performance evaluation. A dataset called BreaKHis is used in the studies. There are 9109 microscopic pictures in BreaKHis, with 2480 benign samples and 5429 malignant samples. During the collection of the data, 82 patients' breast tumor tissues were envisioned using various magnification factors such as 40X, 100X, 200X, and 400X. The accuracy score is used to assess the acquired findings. The results show that the proposed method has the potential to use accurate BC detection.

Facial emotion recognition using modified HOG and LBP features with deep stacked autoencoders

In this paper, an algorithm is proposed for Trilingual Script Identification System in block wise for camera captured images. The Local Binary Pattern (LBP) features are used for Kannada, Hindi and English images for testing the performance of a proposed algorithm, a dataset of 6000 neat block images are considered. For each script a total of 2000 images are used for the proposed method. The segmentation technique is used to segment the document image in blocks. Block of sizes 128×128, 256×256, 512×512 and 1024×1024 for Kannada, Hindi and English have been considered. The LBP features are extracted in 8 neighbors, there by generating 59 features and submitted to KNN and SVM classifiers to classify the underlying image. The identification accuracy for KNN and SVM classifiers are respectively 96.60% and 98.00% for block size 128×128, 98.71% and 98.07% for block size 256×256, 99.70% and 98.00% for block size 512×512 and further 94.90% and 99.01% for block size 1024×1024 respectively. The optimal accuracy is 99.01% for SVM classifier for block size 1024×1024. The proposed method is independent of thinning.

Local Binary Patterns (LBP) is an effective texture description operator and the histogram that it generates has been proved to be a very useful texture feature to adapt to rotation and illumination. Using the LBP features as feature vectors in adaBoost classifier for target identification has become a trend. But LBP is bound by the scale transformation, so it is not widely used in adaBoost face detector. This paper proposes a scale transform formula for Local Binary Patterns. Based on this formula, LBP features extracted from single fixed size templates can be trained to identify any size of faces. This paper also proposes a method to obtain particular detecting sub-areas called binary ring-shaped sub-windows, which can keep the LBP features rotation invariant. Experimental results show that the method we proposed here is feasible in face detecting.

In the field of quantitative microscopy, textural information plays a significant role very often in tissue characterization and diagnosis, in addition to morphology and intensity. The objective of this work is to improve the classification accuracy based on textural features for the development of a computer assisted screening of oral sub-mucous fibrosis (OSF). In fact, the approach introduced is used to grade the histopathological tissue sections into normal, OSF without dysplasia (OSFWD) and OSF with dysplasia (OSFD), which would help the oral onco-pathologists to screen the subjects rapidly. The main objective of this work is to evaluate the use of Higher Order Spectra (HOS) features and Local Binary Pattern (LBP) features extracted from the epithelial layer in classifying normal, OSFWD and OSFD. For this purpose, we extracted twenty three HOS features and nine LBP features and fed them to a Support Vector Machine (SVM) for automated diagnosis. One hundred and fifty eight images (90 normal, 42 OSFWD and 26 OSFD images) were used for analysis. LBP features provide a good sensitivity of 82.85% and specificity of 87.84%, and the HOS features provide higher values of sensitivity (94.07%) and specificity (93.33%) using SVM classifier. The proposed system, can be used as an adjunct tool by the onco-pathologists to cross-check their diagnosis.

A new approach for inner-knuckle-print (IKP) recognition is proposed. The approach is based on the local binary pattern (LBP) features. In our algorithm, straight line neighbourhood is used to calculate the LBP features, so that more distinctive IKP features can be obtained. Moreover, as the LBP feature for each IKP sample, 59 binary images are extracted, and then matched by using a cross-correlation-based algorithm, which is developed to calculate the similarity between the IKP samples. The experiments on a finger image database which includes 2,000 images from 100 different individuals show the good performance of the proposed approach.KeywordsBiometricsPersonal authenticationLocal binary pattern

Colon cancer is cancer that is present on the inner side of colon walls or the rectum walls in the large intestine. Most of these types of cancer begin as abnormal growth of tissue called as polyp. Colonography uses low dose radiation Computed tomography (CT) scanning to obtain an interior view of the colon making use of special x-ray machine to view the large intestine for cancer and abnormal growths known as polyps. Radiologists examine these images to find polyp like structure using computer tools. As CT Colonography image contain noise such as lungs, small intestine, instruments during image capturing; segmenting colon from noise is the key task. Polyp occurrence can be detected mainly using shape feature; eliminating shapes similar to polyp is challenging. Hence, to tackle above issues, Image processing techniques are used by applying deep learning algorithm - Convolution Neural Network (CNN) and the results are compared with classical machine learning algorithm. In proposed method, each image is pre-processed to filter air filled dark region that includes colon, lungs etc. Next, each pre-processed CT Image separated into fixed number of blocks. Using pre-trained CNN, each block of ROI is classified as Type 1 (Usually Ascending and descending colon), Type 2 (Usually Traversal and sigmoidal colon) and Type3 (Noise such as lungs, instruments) to segment colon blocks by eliminating noise. Classified Blocks is further diagnosed for polyp like structure using pre-trained CNN by classifying each colon block as normal or abnormal. The experiment is setup with classical machine learning algorithms - Random Forest (RF) and k-nearest neighbor (KNN) by extracting texture feature - Local binary pattern (LBP) and shape feature - Histogram oriented gradient (HOG) for comparison. The experiment results showed the accuracy of proposed method for colon segmentation using CNN (87%) outperforms RF (85%) and KNN (83%). In, addition, the polyp detection accuracy of CNN (88%) is better than Random forest (85%) and KNN (80%). Hence, in the proposed method, there is significant accuracy improvement using deep learning algorithm compared to classical machine learning algorithms. It also provide baseline for automated colon cancer diagnosis using Deep learning algorithms for further research.

This paper presents a novel face recognition method by using the new image representations. While the commonly used gray-scale image is derived from the linear combination of R, G, and B color component images, the new image representations are derived from the Principal Component Analysis (PCA) tranform upon the hybrid configurations of different color component images. Compared to the correlated color space RGB, the correlations in other configurations of color components (such as RCrQ, YIQ, YCbQ, and so on) are reduced and hence the diversities among their misclassification outputs are enhanced. The new image representations, which inherit advantages from all the individual color components, are thus more invariable than the gray-scale image to the image variations for the face recognition task. Furthermore, we propose to encode the facial information from the new image representations by using an effective Local Binary Pattern (LBP) feature extraction method, which extracts and fuses the multi-resolution LBP features. Finally, the resulting LBP features undergo the Fisher discriminant analysis for face recognition. The most challenging Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 shows the proposed method, which achieves the face verification rate of 83.41% at the false accept rate of 0.1%, performs better than some recent face recognition methods.