Skythianos/BLIINDER

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MATLAB
Created over 3 years ago
Updated over 3 years ago

Skythianos/BLIINDER

Skythianos

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GSF-IQA

No-Reference Image Quality Assessment with Global Statistical Features

CNN-LSTM

FRIQA-ActMapFeat

Full-reference image quality assessment based on convolutional activation maps.

SPF-IQA

No-reference image quality assessment based on the fusion of statistical and perceptual features

Manipulating-objects-using-keypoints

Finding objects in an arbitrary environment is one of the unsolved problems about robots operating in such environments, e.g. households. In this project a robotics application is presented. The software controlls a robotic arm, and estimates the spatial position and orientation of an object for which it has been trained previously. The estimation is done using images retrieved from a camera mounted on the end effector of the robot. The software uses PnP algorithm which estimates the spatial pose from object points with known 3D coordinates and the corresponding image points. The image points are found via SURF keypoint detector. During training the algorithm, 3D reconstruction is done via multi-view triangulation using multiple images taken from known positions.

Stock-Market-Time-Series-Data-Mining-Using-Deep-Learning-Algorithms

In this project we try to predict future price from historical prices with data mining and time series analysis methods. Then based on these retrieved informationa a portfolio is assembled which provides the maximal expected profit. Deep neural networks is used for prediction.

Multi-Pooled-Inception-Features-for-No-Reference-Image-Quality-Assessment

Multi-pooled Inception Features for No-reference image quality assessment

NoReference-Image-Quality-Assessment-for-JPEG2000

This is a MATLAB implementation of Sazzad et al.'s paper "No reference image quality assessment for JPEG2000 based on spatial features".

DF-CNN-IQA

No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion

Jupyter Notebook

CNN-SVR

SSIM-CNN

No-Reference-Video-Quality-Assessment-Based-on-the-Temporal-Pooling-of-Deep-Features

MSDF-IQA

No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features

Arteries-based-Traffic-Control-Methods

In this thesis there will be introduced two main traffic control methods, both were designed for networks with arteries. The permeability of these networks is bigger, and also the capability of travelling faster motivates the drivers to use the arteries thanks to the correct traffic light phases. Therefore fewer roads are used intensively and needs to be maintained more often. The first observed algorithm was designed for really specific conditions; therefore the usage of this method is limited. Due to this property in these conditions this method is more efficient, than the others were designed for more general situations. On the oth- er hand the second observed algorithm can be used in general situations without any serious restriction. The first and maybe the most important restriction of the first method is that the traf- fic can flow only in one direction in one artery. In the case of two way traffic, there is a chance, that the side streets will be totally blocked. The main purpose of the method is to serve the arterial traffic in the most efficient way, but it is not acceptable, that side streets do not get green light at all. Other typical property of the algorithm is it is based on prediction, so the algorithm tries to figure out how will look like the traffic in the future based on the traffic right now and some statistics information which is the result of previous measurements. The lights will be controlled based on these predic- tions. The further we want to optimize, the greater will be the inaccuracy. The other method was designed for two way arteries, and the prediction gets less at- tention, because in this case the phases of the lights are determined based on the network, not the traffic. Basically here we need to figure out one cycle of the phases and it will be used for the whole simulation. Therefore this method is less efficient, but here we have the opportunity to use this algorithm for the case, when the network contains intersecting arteries, or a whole arterial loop.

IIRFilter-and-FIRFilter-in-MATLAB-mex

IIRFilter and FIRFilter implemented in MATLAB mex files.

SGL-IQA

Pretrained-CNNs-for-full-reference-image-quality-assessment

Autocorrelation-with-Wiener-Khinchin-theorem

Autocorrelation can be computed direct from the definition but is order n*n, while there are several algorithms of order n*log(n) for example the Wiener Khinchin Theorem.

Benford-IQA

Analysis of Benford’s Law for No-Reference Quality Assessment of Natural, Screen-Content, and Synthetic Images

Color-reduction-using-kMeans

Color Reduction and Quantization using k-Means Clustering

Color-reduction-using-FCM

Color Reduction and Quantization using Fuzzy C-means Clustering

Player-tracking-for-football

Player tracking part of a football analysis software. The player tracking problem is based on the combination of detection and tracking, including preprocessing of the video sequences coming from a fixed camera layout.

Visualization-of-machine-learning-algorithms

This thesis demonstrates common data mining algorithms, and widely used visualization techniques, and methods to apply them together, in order to get a feedback about the created models behavior, and this way it improve the efficiency and the speed of data analysis. Furthermore, the paper demonstrate the application of these methods on two different data sets.

FLG-IQA

No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features

Path-tracing-in-OpenCL

It is significantly easier and more cost effective to render photorealistic images of 3D models created by 3D modeler/designer programs (architectural, automotive etc.), than building them up, just to see how they would look in real life.

Optimisation-using-AI-techniques

This project introduces a few AI based algorithms which were created in the last two decades. These algorithms solve the optimization problem where they maximize or minimize their cost function for global optimum. All methods are looking for the global optimum, thus the function includes valleys then the methods will not return with local optimum. This project shows some swarm intelligence methods. These methods refer to the symptom/creature where their names come from. For example, the Bee algorithm imitates the pathfinding for nectar.

SG-ESSIM

Saliency guided local full-reference image quality assessment