Red blood cells estimation using Hough transform technique

The number of red blood cells contributes more to clinical diagnosis with respect to blood diseases. The aim of this research is to produce a computer vision system that can detect and estimate the number of red blood cells in the blood sample image. Morphological is a very powerful tool in image processing, and it is been used to segment and extract the red blood cells from the background and other cells. The algorithm used features such as shape of red blood cells for counting process, and Hough transform is introduced in this process. The result presented here is based on images with normal blood cells. The tested data consists of 10 samples and produced the accurate estimation rate closest to 96% from manual counting

Abnormal behavior detection using sparse representations through sequential generalization of k-means

The potential capability to automatically detect and classify human behavior as either normal or abnormal events is an important aspect in intelligent monitoring/surveillance systems. This study presents a new high-performance framework for detecting behavioral abnormalities in video streams by utilizing only the patterns for normal behaviors. In this paper, we used a hybrid descriptor, called a foreground optical flow energy (FGOFE), which makes use of two effective motion techniques in order to extract the most descriptive spatiotemporal features in video sequences. The FGOFE descriptor can effectively capture both weak and sudden incidents in a scene. The sequential generalization of k-means (SGK) algorithm was applied in this study to generate the dictionary set that can sparsely represent each signal; in addition, the orthogonal matching pursuit algorithm was utilized to recover high-dimensional sparse features when referring to a few numbers of noisy linear measurements. Using the SGK allows gaining a less complex and quicker implementation compared to other dictionary learning methods. We conducted comprehensive experiments to analyze and evaluate the ability of our framework in detecting abnormalities using several public benchmarks, which contain different abnormal samples and various contextual compositions. The experimental results show that the proposed framework achieved high detection accuracy (up to 95.33%) and low frame processing time (31 ms on average) compared to the relevant related work

Measurement of the area and diameter of human pupil using Matlab

This paper presents the simple guide of measuring the area and diameter of human pupil using the MATLAB. The pupil measurement and recognition system is very useful in biometric field because the measurement of pupil is unique and is different for each person. The image of eye use in this work is an image that has been downloaded from CASIA Iris Image Database. The image is converted into the binary image and estimated the threshold value of the dark region. After several steps, the result of diameter and area of pupil have been successful calculated in the pixel unit. The measurement is converted in millimeter (mm) unit, and the result is displayed using Graphical User Interface (GUI). It shows that from five samples, different people have different area and diameter of the pupil, and this is the reason why it could be use in biometric field like fingerprint recognition

Determination of epoch length and regression model for 15-second segment of SEMG signal used in joint analysis of electromyography spectrum and amplitude

Regression model is one of the techniques employed in Joint Analysis of Electromyography Spectrum and Amplitude (JASA) to investigate the behaviour of muscle fatigue indices. However, the analysis of the electromyography signal is influenced by the epoch length and regression model used. To meaningfully describe the behaviour of fatigue indices, this study was conducted to determine the appropriate epoch length and regression model for 15-second segment of electromyography signal. Ten subjects participated in this study. With their right forearm and upper arm formed an angle of 90 degree, the subjects were asked to hold a 2-kg dumbbell and stayed in that position for 2 minutes. Surface electromyography (sEMG) was used to record the signal from the biceps brachii muscle. Two fatigue indices were extracted: Root Mean Square (RMS) and Mean Frequency (MNF). The 120-second sEMG signal from each subject was then sliced into 8 segments (15 seconds each). In each segment, the effect of different epoch lengths (1second, 3-second, and 5-second) was studied. Standard Error Estimate (SEE) was used to decide the suitable epoch length. The 3-second and 5-second epoch lengths were found to fit the regression model better (smaller SEE value). When 3-second and 5-second epoch lengths were applied in different regression models (linear and polynomial), polynomial regression was found to better estimate the behaviour of the fatigue indices (higher correlation coefficient). This study concludes that 3-second and 5second epoch length can fit the polynomial regression well. However, fatigue behaviour (pattern of changes in fatigue indices) for every 15-second segment of sEMG signal is better described by JASA using polynomial regression with 3-second epoch length

Applications of cascade-forward neural networks for nasal, lateral and trill arabic phonemes

In the field of speech recognition using Artificial Neural Network (ANN) system, a lot of research has been done and ongoing research is looking for better algorithm to improve the existing recognition methods. In this paper, we monitored and analyzed the performance of multi-layer feed-forward with back-propagation (MLFFBP) and cascade-forward (CF) networks on our phoneme recognition system of Standard Arabic (SA). This study focused on Malaysian children as test subjects. It is focused on four chosen phonemes from SA, which composed of nasal, lateral and trill behaviors, i.e. tabulated at four different articulation places. The highest training recognition rate for multi-layer and cascade-layer network are 98.8 % and 95.2 % respectively, while the highest testing recognition rate achieved for both networks is 92.9 % for all four phonemes under study

Online video-based abnormal detection using highly motion techniques and statistical measures

At the essence of video surveillance, there are abnormal detection approaches, which have been proven to be substantially effective in detecting abnormal incidents without prior knowledge about these incidents. Based on the state-of-the-art research, it is evident that there is a trade-off between frame processing time and detection accuracy in abnormal detection approaches. Therefore, the primary challenge is to balance this trade-off suitably by utilizing few, but very descriptive features to fulfill online performance while maintaining a high accuracy rate. In this study, we propose a new framework, which achieves the balancing between detection accuracy and video processing time by employing two efficient motion techniques, specifically, foreground and optical flow energy. Moreover, we use different statistical analysis measures of motion features to get robust inference method to distinguish abnormal behavior incident from normal ones. The performance of this framework has been extensively evaluated in terms of the detection accuracy, the area under the curve (AUC) and frame processing time. Simulation results and comparisons with ten relevant online and non-online frameworks demonstrate that our framework efficiently achieves superior performance to those frameworks, in which it presents high values for he accuracy while attaining simultaneously low values for the processing time

Left hand and right hand throwing mechanism patterns classification

This study investigates and acts as a trial clinical outcome for human hand motion and behaviour analysis. It was analysed and accessed the quality of human motion that can be used to differentiate the left and right hand throwing action patterns and also the effect of throwing distance to shoulder pain. It aims to establish how widespread the quality of life effects of human motion especially hands movement. Gyroscope, accelerometer and compass sensors were used to measure the hand movement for a throwing process. 2D and 3D scatter plotting were proposed to represent data in graphical form. An experiment was set up in a laboratory environment with conjunction of analysing human motion. The instruments demonstrate 2D and 3D scatter plot enable distinguish left and right hand throwing action patterns significantly. Distribution of gyroscope data shows that a throwing mechanism for a greater distance may bring greater probability of shoulder injur

Model sensor device for 3D object reconstruction

In this paper, the use of infrared sensor installed in the model sensor device is presented. Infrared sensor measured distance of the model surface and the data are used to reconstruct the 3D image. The implementation of five sensors helps in reducing time for data collection, and blind spots can be minimized. The scanning device consists of IR sensor array is placed in a black box with the object in the middle. The scanning process required the object to turn 360° in clockwise in x-y plane, and the resolution for z-axis is 2 mm in order to obtain data for the image reconstruction. Four different lower limb prosthetic models with different shapes were used as the object in the scanning experiments. The device scan object diameter every 2 mm in thickness, 100 mm in height, and total time require to collect data for each layer is 60 seconds. The reconstructed object accuracy is above 90 % based on the comparison between a solid and printed model dimension. The accuracy for each model, and all results and graphs are shown in the paper

The used of infrared sensor for 3D image reconstruction

In this paper, new development of a sensor rig device with infrared sensors is used to reconstruct the 3D image of an object’s surfaces. Five sensors have been used to acquire data that measure distance from the object and sensors. A stepper motor controls the object rotation with 2 degrees per turn. A few objects such as tulip, curvilinear, flower and star shape have been selected for data collection, and the results proved that this sensor rig device is capable of reconstruct a 3D image of an object’s surface. A prosthetic model with distinct size in diameter was tested to prove that this device is capable of measuring distance with different size. From the results obtained, this sensor rig device shows a capability to reconstruct 3D image of an object surfaces with simple post processing technique. A prosthetic object was tested and results show that the accuracy was around 75%

Ovary ultrasound image edge detection analysis: a tutorial using MATLAB

Ultrasound imaging has been used over several years and has an excellent safety record. The problem of edge detection is fundamental to many image processing systems. Besides that, the existing of the sparkle noise creates difficulties in a diagnosis image captured by the ultrasound modality. The purpose of this study is to overcome the boundary problem by using several methods in image processing. The methods include image segmentation, morphological technique and image filtering. The ovary image captured from the ultrasound modality is used in this study. The results show that, by combining the region of interest method with threshold and morphological method, the edges and the border of the ovary can be detected