[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

During the last years, computer-vision-based diagnosis systems have been used in several hospitals and dermatology clinics, aiming mostly at the early detection of skin cancer, and more specifically, the recognition of malignant melanoma tumour. In this paper, we review the state of the art in such systems by first presenting the installation, the visual features used for skin lesion classification, and the methods for defining them. Then, we describe how to extract these features through digital image processing methods, i.e., segmentation, border detection, and color and texture processing, and we present the most prominent techniques for skin lesion classification. The paper reports the statistics and the results of the most important implementations that exist in the literature, while it compares the performance of several classifiers on the specific skin lesion diagnostic problem and discusses the corresponding findings.

Overview of Advanced Computer Vision Systems for Skin Lesions Characterization

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p = .0012) for our model C=0.75 (95% CI: 0.70 - 0.79) than the human benchmark of C=0.59 (95% CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival.

/pdf/deep-learning-cardiac-motion-analysis-for-human-survival-26yyf0ifvn.pdf

Deep-learning cardiac motion analysis for human survival prediction

This chapter describes digital image compression techniques, and the JPEG image compression standard. Digital image compression techniques are based on algorithms such as the Wavelet transform, Fractal images, Vector Quantisation and Discrete Cosine Transform (DCT). The digital image compression technique developed by the Joint Photographic Experts Group (JPEG) is mainly based on the quantisation of the DCT.

Digital Image Compression Techniques

A new hybrid deep learning model for human action recognition

A novel hybrid bidirectional unidirectional LSTM network for dynamic hand gesture recognition with Leap Motion

A Novel Selective Encryption Scheme for Medical Images Transmission based-on JPEG Compression Algorithm

Human machine interaction becomes one of the most research topics in multimedia processing, traditional techniques for communication are developed in order to tackle technology advances and allow disable person to communicate easily with the machine, and to understand their activity using computer computing. In this paper we are focused on human behavior analysis from video scene and it is worth noticed that many information are hidden behind gesture, sudden motion and walking speed, many research works tried to model and then recognize human behavior through motion analysis. In our work we will explain the human action recognition By K Nearest Neighbors approach.

Human action recognition to human behavior analysis

The recognition of human activities is usually considered to be a simple procedure. Problems occur in complex scenes involving high speeds. Activity prediction using Artificial Intelligence (AI) by numerical analysis has attracted the attention of several researchers. Human activities are an important challenge in various fields. There are many great applications in this area, including smart homes, assistive robotics, human–computer interactions, and improvements in protection in several areas such as security, transport, education, and medicine through the control of falling or aiding in medication consumption for elderly people. The advanced enhancement and success of deep learning techniques in various computer vision applications encourage the use of these methods in video processing. The human presentation is an important challenge in the analysis of human behavior through activity. A person in a video sequence can be described by their motion, skeleton, and/or spatial characteristics. In this paper, we present a novel approach to human activity recognition from videos using the Recurrent Neural Network (RNN) for activity classification and the Convolutional Neural Network (CNN) with a new structure of the human skeleton to carry out feature presentation. The aims of this work are to improve the human presentation through the collection of different features and the exploitation of the new RNN structure for activities. The performance of the proposed approach is evaluated by the RGB-D sensor dataset CAD-60. The experimental results show the performance of the proposed approach through the average error rate obtained (4.5%).

https://www.mdpi.com/1424-8220/20/17/4944/pdf

Med Salim Bouhlel

Papers

A new hybrid deep learning model for human action recognition

A novel hybrid bidirectional unidirectional LSTM network for dynamic hand gesture recognition with Leap Motion

A Novel Selective Encryption Scheme for Medical Images Transmission based-on JPEG Compression Algorithm

Human action recognition to human behavior analysis

Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model.