Thank you very much for downloading table of integrals series and products. Maybe you have knowledge that, people have look hundreds times for their chosen books like this table of integrals series and products, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. table of integrals series and products is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the table of integrals series and products is universally compatible with any devices to read.

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

Introduction: Breast cancer is the most common cancer in women. Accurate classification of breast cancer has a key role in medical diagnosis. Hence, researchers seek optimized methods to improve tumor diagnosis. Methods: The current study presents bio-geographical based optimization neural network for classifying data as benign and malignant using principal component analysis in preprocessing stage and updating weights concurrently. The presented algorithm was assessed using the data from Wisconsin databank. Results: Classification accuracy in a normal state, that is, without applying principal component analysis and an optimization algorithm, and applying only neural network at a ratio of %70 to %30 from training and testing set is %97.2. Accuracy reaches %98.5 after applying principal component analysis and decreasing features from nine to eight. Finally, using bio-geographical based optimization algorithm with a 10-fold cross validation, accuracy reaches %100, which is significantly more successful than other similar studies. Conclusion: Applying this algorithm can optimize the performance of the neural network. The optimal performance of this method is revealed by comparing the proposed method with the non-optimized method and the approach which used only PCA and neural network method. The results suggest that the method presented in this paper had a high accuracy in classifying breast cancer data and can be used for its diagnosis.

Using Bio-geographical Algorithm in Optimizing Neural Network for the Diagnosis of Breast Cancer

This study presents a new method for accelerating and optimizing face detection, while preserving a high level of accuracy. This method uses local features that are extracted using block-base discrete cosine transform (DCT). This study uses edge detection method for face recognition with ICA algorithms. The technique used for edge detection is Laplacian of Gaussian (LOG). To find objects, face position and local information the discrete cosine transform is used. Here the main idea is edge detection and finding face position in the picture for DCT processing. Edge detection was applied for accelerating image processing. In this paper we use CohnKanade AU-Coded Facial Expression Database.

/pdf/accelerate-the-face-detection-optimization-with-edge-3wd6gtfnts.pdf

Accelerate the Face Detection Optimization with Edge detection and the Discrete Cosine Transform (DCT)

This paper presents a high accuracy human face recognition system using multi-feature extractors and multi-stages classifiers (MFMC), which are fused together through fuzzy integral. The classifiers used in this paper are Radial Basis Function (RBF) neural network while feature vectors are generated by applying PZM, PCA and DCT to the face images separately. Each of the feature vectors are sent to an RBF neural network classifiers and the output of these classifiers are fused to obtain better recognition rate. Experimental results on the ORL and Yale database yield excellent recognition rate.

Multi-features and Multi-stages RBF Neural Network Classifier with Fuzzy Integral in Human Face Recognition.

. Breast cancer is one of the most common cancers among women. Mammography is currently the most effective method for early detection of breast cancer. In this paper, a method is proposed for detecting masses in mammogram images. First, based on a specific algorithm, image is segmented and a number of the suspicious regions are obtained. Then, many features are extracted from these regions. To reduce the features, a supervised feature selection method is used. In the final step, a cost-sensitive classifier has been used for classification of the samples. This approach was tested on all images having mass from mini-MIAS data set. Based on the classification results, the percentage of true positive detection rate was 91% false-positive detection was 14% and the area under ROC curve was achieved 96%.

An Efficient Method for Detection of Masses in Mammogram Images

iabetes is a disease which is caused by insulin production problems (1). International Diabetes Organization estimates that there are 285 million people worldwide who suffer from diabetes, and this figure is expected to increase to 450 million in next 20 years (2). These patients have high blood sugar which is called Hyperglycemia. This disease is growing rapidly and influences the younger and obese population; it is going D 1Biomedical Engineering Student, Biomedical Engineering Group, Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran. 2Biomedical Engineering Group, Department of Electrical and Computer Engineering, Hakim Sabzevari University of Sabzevar, Sabzevar, Iran.

/pdf/intelligent-and-online-evaluation-of-diabetes-using-wireless-1xzle969m9.pdf

Javad Haddadnia

Papers

Using Bio-geographical Algorithm in Optimizing Neural Network for the Diagnosis of Breast Cancer

Accelerate the Face Detection Optimization with Edge detection and the Discrete Cosine Transform (DCT)

Multi-features and Multi-stages RBF Neural Network Classifier with Fuzzy Integral in Human Face Recognition.

An Efficient Method for Detection of Masses in Mammogram Images

Intelligent and Online Evaluation of Diabetes using Wireless Sensor Networks and Support Vector Machines Algorithm