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mxnet

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

netscope

Neural network visualizer

neural_artistic_style

Neural Artistic Style in Python

notebooks

Google Cloud Datalab samples and documentation

object-proposals

Object-Proposal Evaluation Protocol is 'Gameable'

paper_reading

paper reading group

pg2dm-python

pragmatic-scala

Pragmatic Scala 中文版——《Scala实用指南》代码清单（包含 SBT 版本（切到sbt分支））

pure-bash-bible

📖 A collection of pure bash alternatives to external processes.

py-faster-rcnn

Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version

pylearn2

A Machine Learning library based on Theano

pysaliencymap

python implementation of Itti's saliency map

python_for_visres

Intro to Python for Vision Research

rcnn

R-CNN: Regions with Convolutional Neural Network Features

redash

Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.

region-proposal-network

region proposal network

rgbd

Code for CVPR13 paper - Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images

rgbd-object-propsal

rgbd_detection

RGB-D detection pipeline with object proposals by EdgeBoxes and object classification by Multimodal CNN

rgbdmaze

robotics-course-project

Haze can cause poor visibility and loss of contrast in images and videos. In this article, we study the dehazing problem which can improve visibility and thus help in many computer vision applications. An extensive comparison of state of the art single image dehazing methods is done. One simple contrast enhancement method is used for dehazing. Structure- texture decomposition has been used in conjunction with this enhancement method to improve its performance in presence of synthetic noise. Methods which use a haze formation model and attempt at solving an ill-posed problem using computer vision priors are also investigated. The two priors studied are dark channel prior and the non-local prior. Both qualitative and quantitative comparisons for atmospheric and underwater images on all three methods provide a conclusive idea of which dehazing method performs better. All this knowledge has been extended to video dehazing. A video dehazing method which uses the spatial and temporal information in a video is studied in depth. An improved version of video dehazing is proposed in this article, which uses the spatial-temporal information fusion framework but does not suffer from some of its limitations. The new video dehazing method is shown to produce better results on test videos

saliency

Saliency

saliency-2016-cvpr

Shallow and Deep Convolutional Networks for Saliency Prediction

salientobjectdetection

salienttoolbox

显著性特征提取

salientwatersheds

Algorithm that generates accurate superpixel segmentation for noisy images (particularly useful for biological images such as electron microscopic images etc.)

scenenetv1.0

Still a work in progress and adding code..

scrap-cvpr2016

sds_eccv2014

selective_search_3d