There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

Deep learning models for plant disease detection and diagnosis

Recognizing the quality of fingerprints in advance can be beneficial for improving the performance of fingerprint recognition systems. The representative features to assess the quality of fingerprint images from different types of capture sensors are known to vary. In this paper, an effective quality estimation system that can be adapted for different types of capture sensors is designed by modifying and combining a set of features including orientation certainty, local orientation quality and consistency. The proposed system extracts basic features, and generates next level features which are applicable for various types of capture sensors. The system then uses the Support Vector Machine (SVM) classifier to determine whether or not an image should be accepted as input to the recognition system. The experimental results show that the proposed method can perform better than previous methods in terms of accuracy. In the meanwhile, the proposed method has an ability to eliminate residue images from the optical and capacitive sensors, and the coarse images from thermal sensors.

https://www.mdpi.com/1424-8220/10/9/7896/pdf

Effective Fingerprint Quality Estimation for Diverse Capture Sensors

Human face gender recognition requires fast image processing with high accuracy. Existing face gender recognition methods used traditional local features and machine learning methods have shortcomings of low accuracy or slow speed. In this paper, a new framework for face gender recognition to reach fast face gender recognition is proposed, which is based on Local Ternary Pattern (LTP) and Extreme Learning Machine (ELM). LTP is a generalization of Local Binary Pattern (LBP) that is in the presence of monotonic illumination variations on a face image, and has high discriminative power for texture classification. It is also more discriminate and less sensitive to noise in uniform regions. On the other hand, ELM is a new learning algorithm for generalizing single hidden layer feed forward networks without tuning parameters. The main advantages of ELM are the less stringent optimization constraints, faster operations, easy implementation, and usually improved generalization performance. The experimental results on public databases show that, in comparisons with existing algorithms, the proposed method has higher precision and better generalization performance at extremely fast learning speed.

Fast Face Gender Recognition by Using Local Ternary Pattern and Extreme Learning Machine

JPEG2000 is the latest still-image coding standard. It was designed to overcome the limitation of the original JPEG standard and provide high quality images at low bit rates. The JPEG2000 algorithm is fundamentally based on the discrete wavelet transform (DWT) and embedded block coding with optimal truncation (EBCOT). Both algorithms are computation- and memory- intensive. JPEG2000 uses an adapted version of Q-coder, called MQ-coder. Pervious researches mainly focused on speeding up the DWT and BPC other than renormalization part of MQ-coder. Renormalization procedure in MQ-coder contributes to the computation in relatively high percentage. In this paper, we propose an enhanced renormalization algorithm for arithmetic coding in EBCOT tier-1 coding procedure. By analyzing and simplifying the existing algorithm, the proposed algorithm can reduce the computation with a significant percentage. Experimental results show that the computation is reduced approximately 25% comparing to the renormalization procedure of the regular arithmetic coding.

Enhanced Renormalization Algorithm in MQ-Coder of JPEG2000

Automatic License Plate Detection (ALPD) is a key technology for a efficient traffic control. It is used to improve work efficiency in many applications such as toll payment systems and parking and traffic management. Until recently, the hand-crafted features made for image processing are used to detect license plates in most studies. It has the advantage in speed. but can degrade the detection rate with respect to various environmental changes. In this paper, we propose a way to utilize a Faster Region based Convolutional Neural Networks (Faster R-CNN) and a Conventional Convolutional Neural Networks (CNN), which improves the computational speed and is robust against changed environments. The module based on Faster R-CNN is used to detect license plate candidate regions from images and is followed by the module based on CNN to remove False Positives from the candidates. As a result, we achieved a detection rate of 99.94% from images captured under various environments. In addition, the average operating speed is 80ms/image. We implemented a fast and robust Real-Time License Plate Detection System.

https://www.koreascience.or.kr:443/article/JAKO201608965832459.pdf

Real-Time License Plate Detection Based on Faster R-CNN

The fingerprint quality can be used as a good predictor for fingerprint recognition performance. Knowing the fingerprint quality in advance is useful to improve the performance of fingerprint recognition system. In this paper, we propose an effective quality estimation system with 4 rules which are applied in two-step. Each rule consists of a Back-Propagation Neural Networks (BPNN) classifier based on Optimized Orientation Certainty Level (OOCL) features extracted locally from fingerprint images. Experimental results show that the proposed two-step OOCL method can estimate fingerprint quality more effectively.

Dong Sun Park

Papers

Effective Fingerprint Quality Estimation for Diverse Capture Sensors

Fast Face Gender Recognition by Using Local Ternary Pattern and Extreme Learning Machine

Enhanced Renormalization Algorithm in MQ-Coder of JPEG2000

Real-Time License Plate Detection Based on Faster R-CNN

Rule-Based Fingerprint Quality Estimation System Using the Optimal Orientation Certainty Level Approach