Fast marching method and modified features fusion in enhanced dynamic hand gesture segmentation and detection method under complicated background

Recent development in the field of human–computer interaction has led renewed interest in dynamic hand gesture segmentation based on gesture recognition system. Despite its long clinical success, dynamic hand gesture segmentation using webcam vision becomes technically challenging and suffers the problem of non-accurate and poor hand gesture segmentation where the hand region is not integral due to complicated environment, partial occlusion and light effects. Therefore, for segmenting complete hand gesture region and improving the segmentation accuracy, this study proposes a combination of four modified visual features segmentation procedures, which are skin, motion, skin moving as well as contour features and fast marching method. Quantitative measurement was performed for evaluating hand gesture segmentation algorithm. Besides, qualitative measurement was done to conduct a comparison based on segmentation accuracy with previous studies. Consequently, the experiment results showed a great enhancement in hand area segmentation with a high accuracy rate of 98%

Automated Facial Features Points Localization for Age Estimation Based on Ideal Frontal Symmetry and Proportion of the Face

Age Estimation Components, i.e. the extraction and localization of facial features landmark points, which represent ageing pattern are very important steps in the age estimation process. A good age estimation technique will contain enough variation of landmark points and consider all important points to express the full complexity of the problem. Major progress has been achieved recently in the area of facial landmark localization and extraction method for age estimation. Moreover, measuring facial features landmark points for age classification algorithm has become an interesting subject when dealing with automatic localization process. However, the difficulties to measure the points automatically divert to wrong result if the method unable to locate exact facial features landmark points properly at critical area with complex appearance. Such as measuring points at upper region of face when dealing with individuals with no hair or hair that covered part of the forehead. Therefore, we address this issue by proposing a new method to automatically localize optimal facial landmark points from an input of face image based on Ideal Frontal Symmetry and Proportion of the face. The performance of the proposed algorithm is evaluated with baseline localization using qualitative evaluations. The proposed method achieved a satisfying outcome, which is an average of 80% detection rate for every detected landmark points. The advantage of this method is to accurately identify the points with automatic processing. Each of the point’s position localization process was learned independently so that it is suitable to be implemented in real-time face tracking application

An image-based children age range verification and classification based on facial features angle distribution and face shape elliptical ratio

Verifying children are much easier than verifying adults, based on physical and body appearances. However it would be rather difficult to verify children’s age referring only to their face properties. Therefore, this research presents an image-based method to classify children from adult and to verify children’s age range. The method consists of two main stages; the process to distinguish children from adult based on input facial image and the process to verify children age range. The classification and verification algorithm was based on face shape elliptical ratio and facial features angle distribution. The angle that forms on human face images has been calculated based on selected facial features landmark points. The method was tested on FG-NET aging database. The classification of children from adults and the verification of children age range are implemented using SVM and Multi-SVM classification process. The results show an accuracy of classifying children from adults which are 92% more accurate than previous works

A study of physiological signals-based emotion recognition systems.

The use of physiological signals is relatively recent development in human emotion recognition. Interest in this field has been motivated by the unbiased nature of such signals, which are generated autonomously from the central nervous system. Generally, these signals can be collected from the cardiovascular system, respiratory system, electrodermal activities, muscular system and brain activities. This paper presents an overview of emotion recognition using physiological signals. The main components of a physiological signals-based emotion recognition system are explained, including discussion regarding the concepts and problems about the various stages involved in its framework

A liver level set (LLS) algorithm for extracting liver's volume containing disconnected regions automatically

In this paper a specified method is presented to facilitate segmentation of liver volume from CT images that contain disconnected regions automatically. The disconnected region appears because the physic of the liver containing multi-lobe structure, thus different lobe make different region in a single slice image. Most of the available liver segmentation algorithms that are based on gray level operation such as thresholding and active contour fail to extract the liver volume from these images automatically. Thus the core of the algorithm is a level set function that has the availability to manage separating and joining liver boundary routinely. The liver level set (LLS) is separated into two stages which a pre-processing stage and a level set with a hybrid energy minimization algorithm. The current slice is initialized by previous segmented liver boundary allowing changes in liver boundary topological changes to be inherited. The result show a respective segmentation with average 85% DCS when comparing with manual segmentation

A fast and accurate method for automatic coronary arterial tree extraction in angiograms

Coronary arterial tree extraction in angiograms is an essential component of each cardiac image processing system. Once physicians decide to check up coronary arteries from x-ray angiograms, extraction must be done precisely, fast, automatically and including whole arterial tree to help diagnosis or treatment during the cardiac surgical operation. This application is very helpful for the surgeon on deciding the target vessels prior to coronary artery bypass graft surgery. Some techniques and algorithms are proposed for extracting coronary arteries in angiograms. However, most of them suffer from some disadvantages such as time complexity, low accuracy, extracting only parts of main arteries instead of the full coronary arterial tree, need manual segmentation, appearance of artifacts and so forth. This study presents a new method for extracting whole coronary arterial tree in angiography images using Starlet wavelet transform. To this end, firstly we remove noise from raw angiograms and then sharpen the coronary arteries. Then coronary arterial tree is extracted by applying a modified Starlet wavelet transform and afterwards the residual noises and artifacts are cleaned. For evaluation, we measure proposed method performance on our created data set from 4932 Left Coronary Artery (LCA) and Right Coronary Artery (RCA) angiograms and compared with some state-of-the-art approaches. The proposed method shows much higher accuracy 96% for LCA and 97% for RCA, higher sensitivity 86% for LCA and 89% for RCA, higher specificity 98% for LCA and 99% for RCA and also higher precision 87% for LCA and 93% for RCA angiograms

SwinUNeLCsT: Global–local spatial representation learning with hybrid CNN–transformer for efficient tuberculosis lung cavity weakly supervised semantic segmentation

Radiological diagnosis of lung cavities (LCs) is the key to identifying tuberculosis (TB). Conventional deep learning methods rely on a large amount of accurate pixel-level data to segment LCs. This process is timeconsuming and laborious, especially for those subtle LCs. To address such challenges, firstly, we introduce a novel 3D TB LCs imaging convolutional neural network (CNN)-transformer hybrid model (SwinUNeLCsT). The core idea of SwinUNeLCsT is to combine local details and global dependencies for TB CT scan image feature representation to effectively improve the recognition ability of LCs. Secondly, to reduce the dependence on accurate pixel-level annotations, we design an end-to-end LCs weakly supervised semantic segmentation (WSSS) framework. Through this framework, radiologists need only to classify the number and the approximate location (e.g., left lung, right lung, or both) of LCs in the CT scan to achieve efficient segmentation of the LCs. This process eliminates the need for meticulously drawing boundaries, greatly reducing the cost of annotation. Extensive experimental results show that SwinUNeLCsT outperforms currently popular medical 3D segmentation methods in the supervised semantic segmentation paradigm. Meanwhile, our WSSS framework based on SwinUNeLCsT also performs best among the existing state-of-the-art medical 3D WSSS methods

Removing shadow for hand segmentation based on background subtraction

Hand segmentation is an important stage for accurate hand detection and background subtraction is one of the best solutions to detect the hand motion accurately, however the shadow is the critical problem in this technique which is not easy to separate the hand region from the shadow area. Removing shadow using an automatic threshold will be a good solution to detect the hand region where the variety of skin color and lighting condition affect the hand segmentation. The proposed approach involves three stages: First, we convert RGB color model to YUV space to get the benefit of separation the luminance channel (Y) from the chrominance channels (U, V) to reduce the effect of shadow, reflections and, etc. In the second stage, we applied background subtraction technique to the V channel to remove the unwanted background noise and to get the hand and shadow pixels. Finally, we used shareholding technique by considering a mean value of the pixels of foreground image (the hand and shadow pixels) as automatic threshold value and other tow static thresholds to distinguish the hand region from shadow pixels. After background subtraction, we used the famous morphology techniques (Erosion and Dilation) to enhance the accuracy of hand detection. We measure the accuracy for the results by compare the detect hand pixels to the actual hand pixels quantitatively. From the results, we noticed that our proposed approach is accurate and suitable for real time application systems

Filling sharp features on corner of triangular mesh by using Enhanced Advancing Front Mesh (EAFM) method

Repairing an incomplete polygon mesh constitutes a primary difficulty in 3D model construction, especially in the computer graphics area. The objective of hole-filling methods is to keep surfaces smoothly and continually filled at hole boundaries while conforming with the shapes. The Advancing Front Mesh (AFM) method was normally used to fill simple holes. However, there has not been much implementation of AFM in handling sharp features. In this paper, we use an AFM method to fill a holes on sharp features. The Enhanced Advancing Front Mesh (EAFM) method was introduced when there was a conflict during triangle creation. The results of the study show that the presented method can effectively improve the AFM method, while preserving the geometric features and details of the original mesh