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Table Of Integrals Series And Products

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Surveillance videos are able to capture a variety of realistic anomalies. In this paper, we propose to learn anomalies by exploiting both normal and anomalous videos. To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i.e. the training labels (anomalous or normal) are at video-level instead of clip-level. In our approach, we consider normal and anomalous videos as bags and video segments as instances in multiple instance learning (MIL), and automatically learn a deep anomaly ranking model that predicts high anomaly scores for anomalous video segments. Furthermore, we introduce sparsity and temporal smoothness constraints in the ranking loss function to better localize anomaly during training. We also introduce a new large-scale first of its kind dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies such as fighting, road accident, burglary, robbery, etc. as well as normal activities. This dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities. Our experimental results show that our MIL method for anomaly detection achieves significant improvement on anomaly detection performance as compared to the state-of-the-art approaches. We provide the results of several recent deep learning baselines on anomalous activity recognition. The low recognition performance of these baselines reveals that our dataset is very challenging and opens more opportunities for future work. The dataset is available at: http://crcv.ucf.edu/projects/real-world/

/pdf/real-world-anomaly-detection-in-surveillance-videos-4jb8p2a0v1.pdf

Real-World Anomaly Detection in Surveillance Videos

Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.

https://www.koreascience.or.kr:443/article/JAKO200920237949770.pdf

A Survey of Face Recognition Techniques

Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations. In this paper, we propose a novel end-to-end cascaded network of CNNs to jointly learn crowd count classification and density map estimation. Classifying crowd count into various groups is tantamount to coarsely estimating the total count in the image thereby incorporating a high-level prior into the density estimation network. This enables the layers in the network to learn globally relevant discriminative features which aid in estimating highly refined density maps with lower count error. The joint training is performed in an end-to-end fashion. Extensive experiments on highly challenging publicly available datasets show that the proposed method achieves lower count error and better quality density maps as compared to the recent state-of-the-art methods.

CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting

Display OmittedThe block diagram of the proposed system. Occasionally certain areas in the retina can be questionable for physicians which can lead to wrong interpretations for patients.A method is proposed that introduces a higher ability of segmentation by employing Skeletonization and a threshold selection based on Fuzzy Entropy.By extracting indices of the human retina properly, physicians will be able to estimate pathological injuries with a higher confidence.The proposed approach is fast and outperforms over other previously competitive techniques.The proposed approach consists of two stages. First of all, the retinal vessels was preprocessed by the HSV space and Wiener Filter. Then, the segmentation level is implemented by using Adaptive Filter that employs optimum threshold based on Fuzzy Entropy and Skeleton algorithm. The analysis of retina blood vessels in clinics indices is one of the most efficient methods employed for diagnosing diseases such as diabetes, hypertension and arthrosclerosis. In this paper, an efficient algorithm is proposed that introduces a higher ability of segmentation by employing Skeletonization and a threshold selection based on Fuzzy Entropy. In the first step, the blurring noises caused by hand shakings during ophthalmoscopy and color photography imageries are removed by a designed Wieners filter. Then, in the second step, a basic extraction of the blood vessels from the retina based on an adaptive filtering is obtained. At the last step of the proposed method, an optimal threshold for discriminating main vessels of the retina from other parts of the tissue is achieved by employing fuzzy entropy. Finally, an assessment procedure based on four different measurement techniques in the terms of retinal fundus colors is established and applied to DRIVE and STARE database images. Due to the evaluation comparative results, the proposed extraction of retina blood vessels enables specialists to determine the progression stage of potential diseases, more accurate and in real-time mode.

Optimized clinical segmentation of retinal blood vessels by using combination of adaptive filtering, fuzzy entropy and skeletonization

This paper introduces a new method for the recognition of human faces in 2-dimensional digital images using a new localization of facial information and Pseudo Zernike Moment Invariants (PZMI) as features and a radial basis function (RBF) neural network as the classifier. In this paper the effect of two parameters in recognition rate improvement are studied. These include the order of the PZMI as well as facial candidate ratio (FCR) of images. The tests are carried out on the Olivetti Research Laboratory (ORL) database and a comparative study with two of the existing techniques are included to show the effectiveness of the proposed technique.

An efficient method for recognition of human faces using higher orders Pseudo Zernike Moment Invariant

The aim of this paper is to detect the electrical equipment faults by making use of the moment method and statistical features of thermo images. Using the support vector machine (SVM) as a classifier and Zernike moment as image feature, a method for the intelligent detection of electrical equipment faults based on thermography has been introduced in this paper. By attention to the commonly occurring faults in the substations of distribution networks, two major faults occurring in ground substations low pressure panels that are related to the fuses have been chosen. The simulation results have been applied to the completely practical databases of real images of the distribution networks of North West of Tehran

Intelligent fault detection of electrical equipment in ground substations using thermo vision technique

Introduces a method for the recognition of human faces in 2-dimensional digital images using a new hybrid learning algorithm (HLA) for radial basis function (RBF) neural network as classifier and pseudo Zernike moment invariant (PZMI) as face feature. Also we evaluate the effect of orders of the PZMI on recognition rate, in the proposed technique. Simulation has been carried out on the face database of Olivetti Research Laboratory (ORL) and recognition rate of 98.7% is obtained using this proposed technique.

A hybrid learning RBF neural network for human face recognition with pseudo Zernike moment invariant

Introduction This study is an effort to diagnose breast cancer by processing the quantitative and qualitative information obtained from medical infrared imaging. The medical infrared imaging is free from any harmful radiation and it is one of the best advantages of the proposed method. By analyzing this information, the best diagnostic parameters among the available parameters are selected and its sensitivity and precision in cancer diagnosis is improved by utilizing genetic algorithm and artificial neural network. Materials and Methods In this research, the necessary information is obtained from thermal imaging of 200 people, and 8 diagnostic parameters are extracted from these images by the research team. Then these 8 parameters are used as input of our proposed combinatorial model which is formed using artificial neural network and genetic algorithm. Results Our results have revealed that comparison of the breast areas; thermal pattern and kurtosis are the most important parameters in breast cancer diagnosis from proposed medical infrared imaging. The proposed combinatorial model with a 50% sensitivity, 75% specificity and, 70% accuracy shows good precision in cancer diagnosis. Conclusion The main goal of this article is to describe the capability of infrared imaging in preliminary diagnosis of breast cancer. This method is beneficial to patients with and without symptoms. The results indicate that the proposed combinatorial model produces optimum and efficacious parameters in comparison to other parameters and can improve the capability and power of globalizing the artificial neural network. This will help physicians in more accurate diagnosis of this type of cancer.

http://ijmp.mums.ac.ir/article_470_506d6c77e0ebff4fd5ece319576d59a3.pdf

Javad Haddadnia

Papers

Optimized clinical segmentation of retinal blood vessels by using combination of adaptive filtering, fuzzy entropy and skeletonization

An efficient method for recognition of human faces using higher orders Pseudo Zernike Moment Invariant

Intelligent fault detection of electrical equipment in ground substations using thermo vision technique

A hybrid learning RBF neural network for human face recognition with pseudo Zernike moment invariant

Diagnosis of Breast Cancer using a Combination of Genetic Algorithm and Artificial Neural Network in Medical Infrared Thermal Imaging