I BIBI's starred repositories
python_for_microscopists
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
export_fig
A MATLAB toolbox for exporting publication quality figures
SalBenchmark
Salient Object Detection: A Benchmark
MatlabToolbox
General purpose Matlab toolbox
bigdata-ucsd
Coursera Big Data - UC San Diego
extended-berkeley-segmentation-benchmark
Extended version of the Berkeley Segmentation Benchmark [1] used for evaluation in [2].
MATLAB-Tutorials
Image Processing MATLAB Codes, Simulink, GUI, and Standalone Applications
SkeletalSimilarityMetric
A MATLAB implementation of the skeletal similarity metric for quality evaluation of vessel segmentation.
an-improved-NLM-image-denoising-algorithm-based-on-edge-detection
Aiming at the removal of gaussian noise, we systematically analyze the shortage of non-local means image denonising algorithm (NLM), finding it is easy to lose structure information when dealing with the image containing complex edges and textures by NLM algorithm. In order to solve this problem, a non-local means image denoising based on edge detection is proposed in this thesis. The innovation of the proposed algorithm is mainly manifested in the following : (1) An improved Sobel operator with eight directions is proposed to extract a more accurate edge image; (2) To make the neighborhoods with similar structure obtain more weight, not only the Euclidean distance but also the edge image are considered when the similarity of neighborhoods is measured. Many experiments demonstrate that in both subjective and objective evaluation principles the performance of the improved algorithm has a good effect, and the visual effect of the denoised image is good.
Normalized-cut-segmentaion
Normalized cut segmentation was introduced by Jianbo shi and Jitendra Malik. They published a paper “Normalized Cuts and Image Segmentation” in 2000 (Journal IEEE Transaction on pattern Analysis and machine Intelligence volume 22 Issue 8, August 2000) Here they have proposed a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, their approach aims at extracting the global impression of an image. This algorithm treat image segmentation as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups. And also they have proposed an efficient computational technique based on a generalized eigenvalue problem which can be used to optimize this criterion. This project is a Matlab implementation of that segmentation method.
Face_Recognition-High_Performance
High Performance Face Recognition