Search Results (12)

Generally, traumatic and aneurysmal brain injuries cause intracranial hemorrhages, which is a severe disease that results in death, if it is not treated and diagnosed properly at the early stage. Compared to other imaging techniques, Computed Tomography (CT) images are extensively utilized by clinicians for locating and identifying intracranial hemorrhage regions. However, it is a time-consuming and complex task, which majorly depends on professional clinicians. To highlight this problem, a novel model is developed for the automatic detection of intracranial hemorrhages. After collecting the 3D CT scans from the Radiological Society of North America (RSNA) 2019 brain CT hemorrhage database, the image segmentation is carried out using Fuzzy C Means (FCM) clustering algorithm. Then, the hybrid feature extraction is accomplished on the segmented regions utilizing the Histogram of Oriented Gradients (HoG), Local Ternary Pattern (LTP), and Local Binary Pattern (LBP) to extract discriminative features. Furthermore, the Cuckoo Search Optimization (CSO) algorithm and the Optimized Gated Recurrent Unit (OGRU) classifier are integrated for feature selection and sub-type classification of intracranial hemorrhages. In the resulting segment, the proposed ORGU-CSO model obtained 99.36% of classification accuracy, which is higher related to other considered classifiers. Full article

Numerous researchers have used machine vision in recent years to identify and categorize clouds according to their volume, shape, thickness, height, and coverage. Due to the significant variations in illumination, climate, and distortion that frequently characterize cloud images as a type of naturally striated structure, the Local Binary Patterns (LBP) descriptor and its variants have been proposed as feature extraction methods for characterizing natural texture images. Rotation invariance, low processing complexity, and resistance to monotonous brightness variations are characteristics of LBP. The disadvantage of LBP is that it produces binary data that are extremely noise-sensitive and it struggles on regions of the image that are “flat” because it depends on intensity differences. This paper considers the Local Ternary Patterns (LTP) feature to overcome the drawbacks of the LBP feature. We also propose the fusion of color characteristics, LBP features, and LTP features for the classification of cloud/sky images. Morover, this study proposes to apply the Intra-Class Similarity (ICS) technique, a histogram selection approach, with the goal of minimizing the number of histograms for characterizing images. The proposed approach achieves better performance of recognition with less features in use by fusing LBP and LTP features and using the ICS technique to choose potential histograms. Full article

The effortless detection of salient objects by humans has been the subject of research in several fields, including computer vision, as it has many applications. However, salient object detection remains a challenge for many computer models dealing with color and textured images. Most of them process color and texture separately and therefore implicitly consider them as independent features which is not the case in reality. Herein, we propose a novel and efficient strategy, through a simple model, almost without internal parameters, which generates a robust saliency map for a natural image. This strategy consists of integrating color information into local textural patterns to characterize a color micro-texture. It is the simple, yet powerful LTP (Local Ternary Patterns) texture descriptor applied to opposing color pairs of a color space that allows us to achieve this end. Each color micro-texture is represented by a vector whose components are from a superpixel obtained by the SLICO (Simple Linear Iterative Clustering with zero parameter) algorithm, which is simple, fast and exhibits state-of-the-art boundary adherence. The degree of dissimilarity between each pair of color micro-textures is computed by the FastMap method, a fast version of MDS (Multi-dimensional Scaling) that considers the color micro-textures’ non-linearity while preserving their distances. These degrees of dissimilarity give us an intermediate saliency map for each RGB (Red–Green–Blue), HSL (Hue–Saturation–Luminance), LUV (L for luminance, U and V represent chromaticity values) and CMY (Cyan–Magenta–Yellow) color space. The final saliency map is their combination to take advantage of the strength of each of them. The MAE (Mean Absolute Error), MSE (Mean Squared Error) and F${}_{\beta }$ measures of our saliency maps, on the five most used datasets show that our model outperformed several state-of-the-art models. Being simple and efficient, our model could be combined with classic models using color contrast for a better performance. Full article

The defocus or motion effect in images is one of the main reasons for the blurry regions in digital images. It can affect the image artifacts up to some extent. However, there is a need for automatic defocus segmentation to separate blurred and sharp regions to extract the information about defocus-blur objects in some specific areas, for example, scene enhancement and object detection or recognition in defocus-blur images. The existence of defocus-blur segmentation algorithms is less prominent in noise and also costly for designing metric maps of local clarity. In this research, the authors propose a novel and robust defocus-blur segmentation scheme consisting of a Local Ternary Pattern (LTP) measured alongside Pulse Coupled Neural Network (PCNN) technique. The proposed scheme segments the blur region from blurred fragments in the image scene to resolve the limitations mentioned above of the existing defocus segmentation methods. It is noticed that the extracted fusion of upper and lower patterns of proposed sharpness-measure yields more noticeable results in terms of regions and edges compared to referenced algorithms. Besides, the suggested parameters in the proposed descriptor can be flexible to modify for performing numerous settings. To test the proposed scheme’s effectiveness, it is experimentally compared with eight referenced techniques along with a defocus-blur dataset of 1000 semi blurred images of numerous categories. The model adopted various evaluation metrics comprised of Precision, recall, and F1-Score, which improved the efficiency and accuracy of the proposed scheme. Moreover, the proposed scheme used some other flavors of evaluation parameters, e.g., Accuracy, Matthews Correlation-Coefficient (MCC), Dice-Similarity-Coefficient (DSC), and Specificity for ensuring provable evaluation results. Furthermore, the fuzzy-logic-based ranking approach of Evaluation Based on Distance from Average Solution (EDAS) module is also observed in the promising integrity analysis of the defocus blur segmentation and also in minimizing the time complexity. Full article

This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this work utilizes the local intensity of pixels in all eight directions of its neighborhood. The local octa-directional pattern results in two patterns, i.e., magnitude and directional, and each is quantized into a 40-bin histogram. A joint histogram is created by concatenating directional and magnitude histograms. To measure similarities between images, the Manhattan distance is used. Moreover, to maintain the computational cost, PCA is applied, which reduces the dimensionality. The proposed methodology is tested on a subset of a Multi-PIE face dataset. The dataset contains almost 800,000 images of over 300 people. These images carries different poses and have a wide range of facial expressions. Results were compared with state-of-the-art local patterns, namely, the local tri-directional pattern (LTriDP), local tetra directional pattern (LTetDP), and local ternary pattern (LTP). The results of the proposed model supersede the work of previously defined work in terms of precision, accuracy, and recall. Full article

Congenital heart disease (CHD) is a heart disorder associated with the devastating indications that result in increased mortality, increased morbidity, increased healthcare expenditure, and decreased quality of life. Ventricular Septal Defects (VSDs) and Arterial Septal Defects (ASDs) are the most common types of CHD. CHDs can be controlled before reaching a serious phase with an early diagnosis. The phonocardiogram (PCG) or heart sound auscultation is a simple and non-invasive technique that may reveal obvious variations of different CHDs. Diagnosis based on heart sounds is difficult and requires a high level of medical training and skills due to human hearing limitations and the non-stationary nature of PCGs. An automated computer-aided system may boost the diagnostic objectivity and consistency of PCG signals in the detection of CHDs. The objective of this research was to assess the effects of various pattern recognition modalities for the design of an automated system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series. The proposed model in this study adopts three-stage processing: pre-processing, feature extraction, and classification. Empirical mode decomposition (EMD) was used to denoise the raw PCG signals acquired from subjects. One-dimensional local ternary patterns (1D-LTPs) and Mel-frequency cepstral coefficients (MFCCs) were extracted from the denoised PCG signal for precise representation of data from different classes. In the final stage, the fused feature vector of 1D-LTPs and MFCCs was fed to the support vector machine (SVM) classifier using 10-fold cross-validation. The PCG signals were acquired from the subjects admitted to local hospitals and classified by applying various experiments. The proposed methodology achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects. The proposed model can be put into practice and serve as a second opinion for cardiologists by providing more objective and faster interpretations of PCG signals. Full article

Classification of complex acoustic scenes under real time scenarios is an active domain which has engaged several researchers lately form the machine learning community. A variety of techniques have been proposed for acoustic patterns or scene classification including natural soundscapes such as rain/thunder, and urban soundscapes such as restaurants/streets, etc. In this work, we present a framework for automatic acoustic classification for behavioral robotics. Motivated by several texture classification algorithms used in computer vision, a modified feature descriptor for sound is proposed which incorporates a combination of 1-D local ternary patterns (1D-LTP) and baseline method Mel-frequency cepstral coefficients (MFCC). The extracted feature vector is later classified using a multi-class support vector machine (SVM), which is selected as a base classifier. The proposed method is validated on two standard benchmark datasets i.e., DCASE and RWCP and achieves accuracies of $97.38\%$ and $94.10\%$ , respectively. A comparative analysis demonstrates that the proposed scheme performs exceptionally well compared to other feature descriptors. Full article

This work tackles a recent challenge in digital image processing: how to identify the steganographic images from a steganographer, who is unknown among multiple innocent actors. The method does not need a large number of samples to train classification model, and thus it is significantly different from the traditional steganalysis. The proposed scheme consists of textural features and clustering ensembles. Local ternary patterns (LTP) are employed to design low-dimensional textural features which are considered to be more sensitive to steganographic changes in texture regions of image. Furthermore, we use the extracted low-dimensional textural features to train a number of hierarchical clustering results, which are integrated as an ensemble based on the majority voting strategy. Finally, the ensemble is used to make optimal decision for suspected image. Extensive experiments show that the proposed scheme is effective and efficient and outperforms the state-of-the-art steganalysis methods with an average gain from $4\%$ to $6\%$ . Full article

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases. Full article

Cross-domain ground-based cloud classification is a challenging issue as the appearance of cloud images from different cloud databases possesses extreme variations. Two fundamental problems which are essential for cross-domain ground-based cloud classification are feature representation and similarity measurement. In this paper, we propose an effective feature representation called transfer of local features (TLF), and measurement method called discriminative metric learning (DML). The TLF is a generalized representation framework that can integrate various kinds of local features, e.g., local binary patterns (LBP), local ternary patterns (LTP) and completed LBP (CLBP). In order to handle domain shift, such as variations of illumination, image resolution, capturing location, occlusion and so on, the TLF mines the maximum response in regions to make a stable representation for domain variations. We also propose to learn a discriminant metric, simultaneously. We make use of sample pairs and the relationship among cloud classes to learn the distance metric. Furthermore, in order to improve the practicability of the proposed method, we replace the original cloud images with the convolutional activation maps which are then applied to TLF and DML. The proposed method has been validated on three cloud databases which are collected in China alone, provided by Chinese Academy of Meteorological Sciences (CAMS), Meteorological Observation Centre (MOC), and Institute of Atmospheric Physics (IAP). The classification accuracies outperform the state-of-the-art methods. Full article

Face spoofing is considered to be one of the prominent threats to face recognition systems. However, in order to improve the security measures of such biometric systems against deliberate spoof attacks, liveness detection has received significant recent attention from researchers. For this purpose, analysis of facial skin texture properties becomes more popular because of its limited resource requirement and lower processing cost. The traditional method of skin analysis for liveness detection was to use Local Binary Pattern (LBP) and its variants. LBP descriptors are effective, but they may exhibit certain limitations in near uniform patterns. Thus, in this paper, we demonstrate the effectiveness of Local Ternary Pattern (LTP) as an alternative to LBP. In addition, we adopted Dynamic Local Ternary Pattern (DLTP), which eliminates the manual threshold setting in LTP by using Weber’s law. The proposed method was tested rigorously on four facial spoof databases: three are public domain databases and the other is the Universiti Putra Malaysia (UPM) face spoof database, which was compiled through this study. The results obtained from the proposed DLTP texture descriptor attained optimum accuracy and clearly outperformed the reported LBP and LTP texture descriptors. Full article

This paper presents an improved local ternary pattern (LTP) for automatic target recognition (ATR) in infrared imagery. Firstly, a robust LTP (RLTP) scheme is proposed to overcome the limitation of the original LTP for achieving the invariance with respect to the illumination transformation. Then, a soft concave-convex partition (SCCP) is introduced to add some flexibility to the original concave-convex partition (CCP) scheme. Referring to the orthogonal combination of local binary patterns (OC_LBP), the orthogonal combination of LTP (OC_LTP) is adopted to reduce the dimensionality of the LTP histogram. Further, a novel operator, called the soft concave-convex orthogonal combination of robust LTP (SCC_OC_RLTP), is proposed by combing RLTP, SCCP and OC_LTP. Finally, the new operator is used for ATR along with a blocking schedule to improve its discriminability and a feature selection technique to enhance its efficiency. Experimental results on infrared imagery show that the proposed features can achieve competitive ATR results compared with the state-of-the-art methods. Full article