A survey on fall detection: Principles and approaches

Pattern recognition

"Graefe's Archive" is a distinguished international journal that presents original clinical reports and clinically relevant experimental studies. Founded in 1854 by Albrecht von Graefe to serve as a source of useful clinical information and a stimulus for discussion, the journal has published articles by leading ophthalmologists and vision research scientists for more than a century. With peer review by an international Editorial Board and prompt English-language publication, "Graefe's Archive" provides rapid dissemination of clinical and clinically related experimental information.

Graefe's archive for clinical and experimental ophthalmology

Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm.

WiFall: Device-Free Fall Detection by Wireless Networks

Traditional and recent approaches in background modeling for foreground detection: An overview

In different methods of Biometrics, recognition by iris images in recent years has been taken into consideration by researchers as one of the common methods of identification like passwords, credit cards or keys Iris recognition a new biometric technology has great advantages such as variability, stability and security In this paper we propose a new feature extraction method for iris recognition based on contourlet transform Contourlet transform captures the intrinsic geometrical structures of iris image It decomposes the iris image into a set of directional sub-bands with texture details captured in different orientations at various scales so for reducing the feature vector dimensions we use the method for extract only significant bit and information from normalized iris images In this method we ignore fragile bits At last the feature vector is approximated by non linear approximation coefficient Experimental results show that the proposed method reduces processing time and increase the classification accuracy and outperforms the wavelet based method

A new method for iris recognition based on contourlet transform and non linear approximation coefficients

Retinal vessel tortuosity has shown to be significantly associated with cardiovascular diseases such as hypertension and diabetes. Despite importance of this field a few techniques have been proposed yet. All previous methods rely on a vessel extraction phase, which its accuracy affects final output and also time consuming. Nobility of presented algorithm is to introduce a method for evaluating retinal vessel tortuosity without any explicit vessel detection. We use the Circular Hough Transform (CHT) based on gradient field of the retinal image. Each vessel curve is detected as a semi-circle by Hough transform and tortuosity of the curve is determined with the help of accumulated value of circle center and its radius. As there are no any specific database for tortuosity evaluation, the algorithm was tasted on database consisting of 40 images, mixture of DRIVE database and images from Khatam-Al-Anbia Hospital consisting of 40 retinal images, of which 20 were tortuous and 20 were non-tortuous. The proposed algorithm can achieve classification rate of 92% along with less computation time in compare of previous methods.

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Retinal vessel tortuosity evaluation via Circular Hough Transform

Vessel detection is an important task for diagnosis of vascular diseases in clinical images. Many diseases such as diabetic retinopathy and hypertension can be detected by retinal vessel map or scanning conjunctival vessels. There are a lot of techniques for vessel extraction from retinal images but most of them have failed to face with some patterns like hemorrhages and micro aneurysms. In this paper we develop an algorithm based on Non-subsampled Contourlet Transform (NSCT) and morphological operations. By combining of information from two scales of contourlet and gray scale image, vessel map is extracted. Optic disc border is eliminated by Non-subsampled Contourlet directional information. In addition, circular shapes such as micro aneurysms are removed using morphological operations. We examine our algorithm on retinal images of DRIVE database and conjuntival images of Khatam database. Experimental results show significant improvements in achieving high accuracy and decreasing False Positive Rate (FPR) of vessel detection on both databases.

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A novel method for vessel detection using Contourlet Transform

Camera parameters estimation is an important issue in machine vision. This paper proposes a new method to find translation and rotation matrix of camera in sport scene on the basis of vanishing points. Vanishing point (VP) of parallel lines is the image of the point at infinity, which corresponds to the projection of the intersection of parallel lines at infinity. According to projective geometry constraint, camera rotation of the projection matrix is computed directly by two vanishing points and pan and tilt of camera extracted from this matrix. Mathematical proof and Computer simulation are carried out to validate our novel method.

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Camera pose estimation in soccer scenes based on vanishing points

The main goal of medical imaging applications is to diagnose some diseases, try to prevent the progression of them, and actually cure the patients. The number of people that suffer from diabetes is growing very fast these recent years in many countries and it is needed to diagnose this disease in the beginning to prevent the subsequent side effects like blindness and so on. One of the first ways to detect this disease is analysis of vessels in some parts of the eye such as retina and conjunctiva. Some studies have been done on effects of vessel changes of conjunctiva in diabetes diagnosis and it is proved that conjunctival vessel extraction and analysis is a good way for this purpose. In this paper, we proposed a method to detect and extract the vessels of conjunctiva automatically. It is the first stage of the process of diabetes diagnosis. We first extract some textural features from each pixel of the conjunctiva image using LBP and then classify each pixel to vessels or nonvessels according to the features vector based on a supervised classifier, ANFIS. We tested the proposed algorithm on 40 conjunctival images to show the performance and efficiency of our method.

/pdf/vessel-extraction-of-conjunctival-images-using-lbps-and-3hbmd0h2tu.pdf

Hamid Reza Pourreza

Papers

A new method for iris recognition based on contourlet transform and non linear approximation coefficients

Retinal vessel tortuosity evaluation via Circular Hough Transform

A novel method for vessel detection using Contourlet Transform

Camera pose estimation in soccer scenes based on vanishing points

Vessel Extraction of Conjunctival Images Using LBPs and ANFIS