Infrared Thermal Image Gender Classifier Based on the Deep ResNet Model

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.

SIFT (scale invariant feature transform) is one of the most robust and the widely used image matching algorithms based on local features. It has been used in the aspect of the cameras target matching. For its high complexity, this paper proposes a target matching method for multiple cameras by combining SIFT and the rotation-invariant LBP (local binary patterns). The object detection is obtained by improved Back-ground subtraction technique, and particle filter is used for region-based object tracking. The descriptor combining SIFT and the rotation-invariant LBP is calculated for every object region. The object matching is obtained by comparing the distance of feature matrix. Experimental results show that the proposed algorithm is more rapid than the standard SIFT algorithm while the performance is favorably compared to the standard SIFT algorithm, and it is robust in various scenes.

Palm tree detection using remote sensing images has received increasing attention in recent years, concerning the issues of sustainability, productivity and profitability. There has been significant progress in the research of using machine learning techniques, especially convolutional neural networks (CNNs) for automatic palm tree detection. However, whether CNNs can actually outperform traditional human-engineered approaches in terms of classification accuracy and detection speed is yet unknown. In the present study, we have compared human-engineered features namely histogram of oriented gradients (HOG), local binary pattern (LBP) and scale-invariant feature transform (SIFT) with features extracted using pre-trained AlexNet model for detecting palm trees in high resolution images obtained via unmanned aerial vehicle (UAV). Support vector machines (SVM) with linear and non-linear kernels were used to classify feature vectors obtained by different feature extractors. Haar-like features used in Viola-Jones framework was also tested in the study. Results showed that features extracted from the fifth convolutional layer of AlexNet achieved the highest accuracy of 96.1% using SVM with RBF kernel as classifier, which surpassed the accuracy obtained by fully-connected CNN, namely 95.6%. The results also suggest that SVM classifier with radial basis function (RBF) kernel using LBP as features is the optimal combination as it achieved comparable accuracy but higher detection speed than CNN approaches.

This paper highlights the ability of residual convolutional neural network (ResNet) at classifying railway shelling defect dataset without the requirement for handcrafted features. A 41-convolutional layers ResNet with the residual learning block is introduced. Bottleneck architecture of three convolutional layers is used in ResNet to decrease the computation cost. VGG convolutional neural network (VGGNet) classifier and Support Vector Machine (SVM) classifiers based on Histogram of Oriented Gradient (HOG), Local Binary Patterns (LBP) and Scale-invariant Feature Transform (SIFT) are compared with ResNet classifier at classifying our dataset. The performance of ResNet presented in this paper achieves 95% TOP-1 accuracy in testing dataset. In comparison, the result of ResNet is better than VGGNet with 92% TOP-1 accuracy and HOG (42.88%), LBP (52.26%), SIFT (60.69%). In the context of designing neural computing models for ResNet analysis, this paper shows that ResNet used in our experiment is able to not only achieve the high accuracy in railway shelling defect testing dataset, but also has a faster testing speed than the other classifiers.

Biometrics is the study of methods of recognizing humans based on their behavioral and physical characteristics or traits. Face recognition is one of the biometric modalities that received a great amount of attention from many researchers during the past few decades because of its potential applications in a variety of security domains. Face recognition however is not only concerned with recognizing human faces, but also with recognizing faces of non-biological entities or avatars. Fortunately, the need for secure and affordable virtual worlds is attracting the attention of many researchers who seek to find fast, automatic and reliable ways to identify virtual worlds’ avatars. In this work, I propose new techniques for recognizing avatar faces, which also can be applied to recognize human faces. Proposed methods are based mainly on a well-known and efficient local texture descriptor, Local Binary Pattern (LBP). I am applying different versions of LBP such as: Hierarchical Multi-scale Local Binary Patterns and Adaptive Local Binary Pattern with Directional Statistical Features in the wavelet space and discuss the effect of this application on the performance of each LBP version. In addition, I use a new version of LBP called Local Difference Pattern (LDP) with other well-known descriptors and classifiers to differentiate between human and avatar face images. The original LBP achieves high recognition rate if the tested images are pure but its performance gets worse if these images are corrupted by noise. To deal with this problem I propose a new definition to the original LBP in which the LBP descriptor will not threshold all the neighborhood pixel based on the central pixel value. A weight for each pixel in the neighborhood will be computed, a new value for each pixel will be calculated and then using simple statistical operations will be used to compute the new threshold, which will change automatically, based on the pixel’s values. This threshold can be applied with the original LBP or any other version of LBP and can be extended to work with Local Ternary Pattern (LTP) or any version of LTP to produce different versions of LTP for recognizing noisy avatar and human faces images.

Palm print scanning is a widespread method for biometric identity detection which has some advantages over other methods including its simplicity and relatively lower cost. In this study, a novel methods for biometric verification and identification by contactless palm scanning technique is proposed. In the study, Ripplet-I Transform (R-IT) which is a generalized form of Curvelet Transform (CuT), have been used in addition to multi-resolution transforms which were previously used in the literature as palm print verification and identification methods such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Contourlet Transform (CoT). In addition, Principal Component Analysis (PCA) and Local Binary Pattern (LBP) have been utilized to increase the algorithm diversity. In order to investigate the effect of classification methods on the study results and the processing times, Artificial Neural Network (ANN), Euclidean Distance (ED) and Support Vector Machine (SVM) have been used separately for matching in the verification part of study. The performance of Convolutional Neural Network (CNN) as a classifier has also been examined. Verification and identification algorithms proposed in the study have been tested using palm print images of Hong Kong Polytechnic University Contact-free 3D/2D Hand Images Database (Version 1.0). The studies, that were carried out under two main sections yielded interesting results. At the end of the study, AUC (Area Under the ROC Curve) values ranging from 0.550 (Equal Error Rate (EER)= 0.4594) to 0.9875 (EER= 0.0336) were obtained for palm print verification. The highest AUC value without using LBP was obtained as 0.9563 (EER= 0.1096) using R-IT/CuT+DCT+CNN. Study results were showed that CNN is more successful than other classifiers without using LBP. It also has pointed out that the R-IT/CuT provides better results than the DWT and CoT. Using LBP in algorithms has increased success for ED, SVM and ANN. However, it has reduced overall for CNN. The highest AUC value (0.9875 and EER= 0.0336) was provided by the LBP+DWT+ED algorithm for palm print verification. The highest Identification Rate (IR) was achieved by using the LBP+CoT+ED algorithm with 84.444% for for palm print identification.

While biometric recognition typically uses features such as face, fingerprint, and iris to identify individuals, this study focuses on utilising specific characteristics to identify gender. The aim of this article is to propose a procedure for gender recognition under specific conditions. The specific condition addressed is outdoor area monitoring, which presents challenges such as varying lighting conditions and limited camera placement options. To tackle this, a proposed procedure utilises thermal images captured by the drone equipped with a thermal camera. The advantage of thermal images is their independence from ambient light conditions. The captured images are resized and processed using convolutional neural networks (CNNs) (AlexNet, VGG-16, VGG-19) for feature extraction and binary classification. A freely available database of thermal face images is used for training the CNNs, while a own created dataset of thermal images obtained by the drone is used for testing. The findings indicate that the optimised CNNs achieve classification accuracies of 82.4% (VGG-16), 82.9% (AlexNet), and 85.5% (VGG-19). The original contribution of this study lies in demonstrating the suitability of face thermal images obtained through drones for gender recognition purposes.

This study compares the performance of local binary patterns (LBP) and scale invariant feature transform (SIFT) with support vector machines (SVM) in automatic classification of discrete facial expressions. Facial expression recognition is a multiclass classification problem and seven classes; happiness, anger, sadness, disgust, surprise, fear and comtempt are classified. Using SIFT feature vectors and linear SVM, 93.1% mean accuracy is acquired on CK+ database. On the other hand, the performance of LBP-based classifier with linear SVM is reported on SFEW using strictly person independent (SPI) protocol. Seven-class mean accuracy on SFEW is 59.76%. Experiments on both databases showed that LBP features can be used in a fairly descriptive way if a good localization of facial points and partitioning strategy are followed.

Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging tumor segmentation tasks is localization of pancreatic ductal adenocarcinoma (PDAC). Exclusive application of conventional methods does not appear promising. Deep learning approaches has achieved great success in the computer aided diagnosis, especially in biomedical image segmentation. This paper introduces a framework based on convolutional neural network (CNN) for segmentation of PDAC mass and surrounding vessels in CT images by incorporating powerful classic features, as well. First, a 3D-CNN architecture is used to localize the pancreas region from the whole CT volume using 3D Local Binary Pattern (LBP) map of the original image. Segmentation of PDAC mass is subsequently performed using 2D attention U-Net and Texture Attention U-Net (TAU-Net). TAU-Net is introduced by fusion of dense Scale-Invariant Feature Transform (SIFT) and LBP descriptors into the attention U-Net. An ensemble model is then used to cumulate the advantages of both networks using a 3D-CNN. In addition, to reduce the effects of imbalanced data, a multi-objective loss function is proposed as a weighted combination of three classic losses including Generalized Dice Loss (GDL), Weighted Pixel-Wise Cross Entropy loss (WPCE) and boundary loss. Due to insufficient sample size for vessel segmentation, we used the above-mentioned pre-trained networks and fine-tuned them. Experimental results show that the proposed method improves the Dice score for PDAC mass segmentation in portal-venous phase by 7.52% compared to state-of-the-art methods in term of DSC. Besides, three dimensional visualization of the tumor and surrounding vessels can facilitate the evaluation of PDAC treatment response.

This paper presents a novel method for stereo matching, We introduce the extension version of the well-known local binary pattern (LBP) feature called local ternary pattern (LTP) serving as s distinctive local descriptor of local image feature point region detected by the scale invariant feature transformation (SIFT) detector. In our experiments, our descriptor combined with the advantage of SIFT and rotation invariant uniform LTP performs favourably compared to SIFT and LBP, and computes more efficient than SIFT.

A case study on computer-aided diagnosis of nonerosive reflux disease using deep learning techniques

Any human–computer interaction application needs to be able to recognize gestures. Hand gesture detection systems that recognize gestures in real time can improve human–computer interaction by making it more intuitive and natural. Colour gloves and skin colour detection are two prominent hand segmentation and detection technologies, but each has its own set of benefits and drawbacks. For physically challenged people, gesture identification is a crucial technique of sharing information. Support vector machine algorithm with principal component analysis, hidden Markov model, superposed network with multiple restricted Boltzmann machines, growing neural gas algorithm, convolutional neural network, double channel convolutional neural network, artificial neural network, and linear support vector machine with gesture dataset matches the interaction of gestures for various postures in real time. Although this method can recognize a huge number of gestures, it does have certain downsides, such as missing movements due to the accuracy of the categorization algorithms. Furthermore, matching the vast dataset takes a longer time. The main contribution of this work lies in a conceptual framework based on the findings of a systematic literature review that provides fruitful implications based on recent research findings and insights that can be used to direct and initiate future research initiatives in the field of hand gesture recognition and artificial intelligence. As a result, a novel study based on a hybrid recurrent neural network (RNN) with chaos game optimization may reduce classification mistakes, increase stability, maximize resilience, and efficiently use the hand gestures recognition system.

In a near of wide spread technological change that has givena positive impact to the society andhelpedinbuildingauser-friendly environment, object detection framework, an importantpart of Computer Vision (CV) plays a vital role. Starting fromasimpleautomaticattendancesystemforstudentsusingfacedetection, recognizing the presence of tumors in medical images,helping with automatic surveillance of cctv cameras to identifypeoplewhobreakstrafficrulescausingroadaccidentstobeingthecentral mechanism behind self-driving cars, object detection haswide range of applications and assist building an easy to cope withsmart environments. This in turn urges the need to evaluate theperformanceofthetechniquesbehindtheseframeworks.Thecentralideabehindthemodern-dayobjectdetectionandclassification is Convolutional Neural Network (CNN) which triesto mimic the occipital lobe, the visual cortex of the human brain.CNNhaswiderangeofvariationsandhascomethroughalongwaystarting from basic CV techniques like Scale Invariant FeatureTransform(SIFT),HistogramsofOrientedGradientsn(HOG)tillRegionbasedCNN’s(R-CNN).Theperformanceofeachandeverymethodthathas led throughthe evolutionofobjectdetectionmethods, its advantages and the disadvantages which has pavedway for the innovation of next technique has been discussed andrepresentedindetail.

Sign language is used as a primary means of communication by millions of people who suffer from hearing problems. The unhearing people used visual language to interact with each other, Represented in sign language. There are features that the hearing impaired use to understand each other, which are difficult for normal people to understand. Therefore, deaf people will struggle to interact with society. This research aims to introduce a system for recognizing hand gestures in Arabic Sign Language (ArSL) through training the Convolutional Neural Network (CNN) on the images of ArSL gestures launched by the University of Prince Mohammad Bin Fahd, Saudi Arabia. A Scale Invariant Feature Transform (SIFT) algorithm is used for creating the feature vectors that contain shape, finger position, size, center points of palm, and hand margin by extracting the Important features for images of ArSL and transforming them to points of the vector. The accuracy of the proposed system is 97% using the SIFT with CNN, and equal to 94.8% nearly without SIFT. Finally, the proposed system was tried and tested on a group of persons and its effectiveness was proven after considering their observations.

One of the most representative manners which human shows his inner feelings is through facial expressions. Automatic recognition of these gestures is of great importance in human computer interaction (HCI) applications. In order to automatically recognize facial expressions, local binary patterns (LBPs), which are powerful and computationally simple descriptors, have been extensively utilized for feature extraction from facial images. To improve image description accuracy, we have employed a new version of LBP, namely high order joint derivative local binary pattern (HJDLBP) in this paper which reveals spatial relations between patterns in different resolutions. Furthermore, a rotation invariant radial partitioning paradigm is utilized to localize LBP histogram calculation regions. Using radial partitioning instead of blocking eliminates head rotation effects in real world applications and improves classification accuracy. A bunch of support vector machines are trained by feature vectors to classify different facial expressions. To evaluate the proposed method we have used known and publicly available JAFFE dataset for experiments and compared the obtained results with results from state of the art approaches which use original LBP and rectangular partitioning. The comparison results show the effectiveness of the new descriptor in combination with radial partitioning for automatic facial expression categorization.