Adaptive weighted mean filtering algorithm based on city block distance

2013 ◽  
Vol 33 (11) ◽  
pp. 3197-3200
Author(s):  
Meng CAO ◽  
Youhui ZHANG ◽  
Zhiwei WANG ◽  
Rui DONG ◽  
Yingjuan ZHENG
Keyword(s):  
Weighted Mean ◽  
City Block ◽  
Filtering Algorithm ◽  
City Block Distance ◽  
Mean Filtering

scholarly journals Use of KNN Classifier for Emotion Recognition Based on Distance Measures

2019 ◽  
Vol 9 (1) ◽  
pp. 1294-1298
Keyword(s):  
Gabor Filter ◽  
Texture Features ◽  
Distance Measures ◽  
Still Images ◽  
City Block ◽  
Lighting Conditions ◽  
Knn Classifier ◽  
The Face ◽  
City Block Distance

Human-computer interaction (HCI), in recent times, is gaining a lot of significance. The systems based on HCI have been designed for recognizing different facial expressions. The application areas for face recognition include robotics, safety, and surveillance system. The emotions so captured aid in predicting future actions in addition to providing valuable information. Fear, neutral, sad, surprise, happy are the categories of primary emotions. From the database of still images, certain features can be obtained using Gabor Filter (GF) and Histogram of Oriented Gradient (HOG). These two techniques are being used while extracting features for getting the expressions from the face. This paper focuses on the customized classification of GF and HOG using the KNN classifier.GF provides texture features whereas HOG finds applications for images exhibiting differing lighting conditions. Simplicity and linearity of KNN classifier appeals for its use in the present application. The paper also elaborates various distances used in KNN classifiers like city-block, Euclidean and correlation distance. This paper uses Matlab implementation of GF, HOG and KNN for extracting the required features and classification, respectively. Results exhibit that the accuracy of city- block distance is more .

Risk-Based Selection of Portfolio

2015 ◽  
pp. 744-758
Author(s):  
Soma Panja ◽  
Dilip Roy
Keyword(s):  
Investment Decision ◽  
The Other ◽  
Portfolio Risk ◽  
Heuristic Solution ◽  
Value System ◽  
City Block ◽  
Optimum Decision ◽  
Optimum Solution ◽  
City Block Distance ◽  
Selection Of

This chapter examines the closeness between the optimum portfolio and portfolio selected by an investor who follows a heuristic approach. There may be basically two ways of arriving at an optimum portfolio – one by minimizing the risk and the other by maximizing the return. In this chapter, the authors propose to strike a balance between these two. The optimum portfolio has been obtained through a mathematical programming framework so as to minimize the portfolio risk subject to return constraint expressed in terms of coefficient of optimism (a), where a varies between 0 to 1. Simultaneously, the authors propose to develop four heuristic portfolios for the optimistic and pessimistic investors, risk planners, and random selectors. Given the optimum portfolio and a heuristic portfolio, City Block Distance has been calculated to measure the departure of the heuristic solution from the optimum solution. Based on daily security wise data of ten companies listed in Nifty for the years 2004 to 2008, the authors have obtained that when the value of a lies between 0 to 0.5, the pessimistic investor's decision is mostly closest to the optimum solution, and when the value of a is greater than 0.5, the optimistic investor's decision is mostly near to the optimum decision. Near the point a = 0.5, the random selectors and risk planners' solutions come closer to the optimum decision. This study may help the investors to take heuristic investment decision and, based on his/her value system, reach near to the optimum solution.

scholarly journals Non-local mean filtering algorithm based on deep learning

2018 ◽  
Vol 232 ◽  
pp. 03025
Author(s):  
Baozhong Liu ◽  
Jianbin Liu
Keyword(s):  
Deep Learning ◽  
Noise Reduction ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Denoising Method ◽  
Filtering Algorithm ◽  
Local Mean ◽  
Non Local ◽  
Reduction Effect ◽  
Mean Filtering

Aimed at the problem that the traditional image denoising algorithm is not effective in noise reduction, a new image denoising method is proposed. The method combines deep learning and non-local mean filtering algorithms to denoise the noisy image to obtain better noise reduction effect. By comparing with Gaussian filtering algorithm, median filtering algorithm, bilateral filtering algorithm and early non-local mean filtering algorithm, the noise reduction effect of the new algorithm is better than the traditional method and the peak signal to noise ratio is compared with the early non-local mean algorithm. The performance is better.

A note on city block distance

1998 ◽  
Vol 13 (3) ◽  
pp. 331-334 ◽  
Author(s):  
Ding Ren ◽  
Xu Changqing ◽  
Li Yingzi
Keyword(s):  
City Block ◽  
City Block Distance

scholarly journals IMPLEMENTATION OF MODIFIED MEDIAN FILTERING ALGORITHM FOR SALT & PEPPER NOISE REDUCTION ON IMAGE

2017 ◽  
Author(s):  
Robbi Rahim ◽  
Ali Ikhwan
Keyword(s):  
Image Quality ◽  
Noise Reduction ◽  
Special Technique ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Analog Signals ◽  
Pixel Value ◽  
The Mean ◽  
Spatial Window ◽  
Mean Filtering

Noise is one form of issue in the image, salt & pepper noise is the kind of noise that can be made using a special technique or also due to the conversion from analog signals to digital, the noise can be improved by using algorithms such as the mean filtering, the mid-point filtering and median filtering, median filtering algorithm is widely used for repair image quality, this article will discuss the modification of the median filtering to improve noise in the image by taking the average of neighboring pixels by 2 points from the value of the center clockwise, the value is taken to be processed to retrieve the value of the middle and then the overall result value will be divided to replace the center pixel value 3x3 spatial window.

scholarly journals Implementasi Metode City Block Distance pada Identifikasi Citra Tanda Tangan

2017 ◽  
Vol 5 (2) ◽  
pp. 134
Author(s):  
Markhamah Tri Utami ◽  
Tien Rahayu Tulili ◽  
Anton Topadang
Keyword(s):  
City Block ◽  
City Block Distance
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