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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

robust face detection in complex background is a challenging task. The complexity in such detection systems stems from the variances in image background, view, illumination, articulation, and facial expression. This article is allocated to introducing a new algorithm for face detection. Fuzzy skin color detection and template matching composed our method instrument. At this paper non-skin parts in the pictures omitted firstly and then result passed to template matching section. Easy implementation and high accuracy detection be feature of our work in compare with other works and indicate the superiority of our work.

Face detection base on fuzzy skin region segmentation

Removing noise from the color images is a very active research scope in image processing. In this paper, a new fuzzy based image filtering algorithm is proposed for reducing and removing impulse noise in color images. For dealing with the Impulse noise, an algorithm is developed to search for a set of uncorrupted pixels in the neighborhood of the pixel of interest and to compute the median of this set. A modified fuzzy filter consisting of two sub filters with novel membership functions is proposed to cancel out the impulse noise. The first sub filter detects the noisy pixel by utilizing three fuzzy membership functions, defined for this purpose. The corrupted pixels are then corrected using the median of the noise free pixels. The second sub filter makes use of the relation between different color components of a pixel to remove the residual noise in the color image. Simulation results shows that the proposed fuzzy filter effectively removes the additive noise by preserving fine details in the image.

Design of a new filtering for the noise removing in images by fuzzy logic

Personal identification based on vein pattern is one of the latest biometric approaches that have ever attracted lots of attentions. The method of personal identification suggested in this study utilises the individual's dorsal hand vein pattern. However, hand spin and relocation in different trials of image acquisition is a limiting factor in application of this approach. We introduce a new procedure for automatic selection of region of interest (ROI) designated 'floating ROI' in which adjusting the lengths and angles of sides in the ROI quadrant, the imaging process stays resistant against any hand relocation. Moreover, a new method for the vein pattern extraction called 'square thresholding' is introduced that greatly improves the extraction of vein-patterns. For this, the average of grey level of the pixels in a 5 × 5 neighbourhood is compared with 9 × 9 neighbourhood for any pixel. To verify validity of the proposed methods, 1,200 images taken from 100 individuals is used. As a result, an identification rate with the accuracy of 96.41% is obtained.

A new method for automatic extraction of region of interest from infrared images of dorsal hand vein pattern based on floating selection model

An accurate intelligent breast cancer diagnosis system

Background: Accuracy in feature extraction is an important factor in image classification and retrieval In this paper, a breast tissue density classification and image retrieval model is introduced for breast cancer detection based on thermographic images The new method of thermographic image analysis for automated detection of high tumor risk areas, based on two-directional two-dimensional principal component analysis technique for feature extraction, and a support vector machine for thermographic image retrievalwas tested on 400 images The sensitivity and specificity of the model are 100% and 98%, respectively

Javad Haddadnia

Papers

Face detection base on fuzzy skin region segmentation

Design of a new filtering for the noise removing in images by fuzzy logic

A new method for automatic extraction of region of interest from infrared images of dorsal hand vein pattern based on floating selection model

An accurate intelligent breast cancer diagnosis system

A novel model for smart breast cancer detection in thermogram images.