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View cancer_projectror.json
{
"embeddings": [
{
"tensorName": "DIAG",
"tensorShape": [
1434,
15
],
"tensorPath": "https://gist.githubusercontent.com/klokik/d5ef9b8346802b39d551b244c2caf75a/raw/00b13bb72bab361ad2586cc242fd5872a5102bde/cancer_data.tsv",
"metadataPath": "https://gist.githubusercontent.com/klokik/ac646d087fba8a73e35a070bf3e4f700/raw/08cef0c631b29ab9d0cbb6f978f81b80c85c7fc5/cancer_meta.tsv"
View cancer_meta.tsv
We can make this file beautiful and searchable if this error is corrected: It looks like row 10 should actually have 16 columns, instead of 5. in line 9.
tag #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12 #13 #14 #15
benign 7.59600 .261931 1.75000 4.35000 2.91416 .206897 .793103 .760425E-01 .103684E-01 .169831E-02 1.16416 29 6 23 2
benign 12.9290 .430967 2.40000 8.70000 4.15919 .166667 .833333 .560245 .448266E-01 .103678E-01 1.75919 30 5 25 6
benign 8.90000 .296667 1.90000 4.90000 3.06529 .466667 .533333 .805306E-01 .936040E-02 .147558E-02 1.16529 30 14 16 2
benign 7.09500 .253393 2.25000 5.95000 3.21655 .250000 .750000 .992727E-01 .145101E-01 .180742E-02 .966546 28 7 21 1
benign 9.75900 .232357 1.16667 2.30000 2.15219 .952381E-01 .904762 .525070E-01 .551160E-02 .925402E-03 .985528 42 4 38 1
benign 8.00100 .228600 .416667 .583333 1.07053 .000000 1.00000 .382414E-01 .492015E-02 .933085E-03 .653863 35 0 35 2
benign 7.47800 .186950 .833333 .900000 1.29447 .000000 1.00000 .497692E-01 .682607E-02 .849804E-03 .461133 40 0 40 2
benign 6.40500 .188382 .800000 .960000 1.37735 .000000 1.00000 .458545E-01 .737612E-02 .124744E-02 .577350 34 0 34 2
benign 8.58100 .252382 2.54167 7
View cancer_data.tsv
We can make this file beautiful and searchable if this error is corrected: It looks like row 10 should actually have 15 columns, instead of 5. in line 9.
7.59600 .261931 1.75000 4.35000 2.91416 .206897 .793103 .760425E-01 .103684E-01 .169831E-02 1.16416 29 6 23 2
12.9290 .430967 2.40000 8.70000 4.15919 .166667 .833333 .560245 .448266E-01 .103678E-01 1.75919 30 5 25 6
8.90000 .296667 1.90000 4.90000 3.06529 .466667 .533333 .805306E-01 .936040E-02 .147558E-02 1.16529 30 14 16 2
7.09500 .253393 2.25000 5.95000 3.21655 .250000 .750000 .992727E-01 .145101E-01 .180742E-02 .966546 28 7 21 1
9.75900 .232357 1.16667 2.30000 2.15219 .952381E-01 .904762 .525070E-01 .551160E-02 .925402E-03 .985528 42 4 38 1
8.00100 .228600 .416667 .583333 1.07053 .000000 1.00000 .382414E-01 .492015E-02 .933085E-03 .653863 35 0 35 2
7.47800 .186950 .833333 .900000 1.29447 .000000 1.00000 .497692E-01 .682607E-02 .849804E-03 .461133 40 0 40 2
6.40500 .188382 .800000 .960000 1.37735 .000000 1.00000 .458545E-01 .737612E-02 .124744E-02 .577350 34 0 34 2
8.58100 .252382 2.54167 7.20833 3.42529 .264706 .735294 .107714 .129330E-01 .115439E-02 .883627 34 9 25 3
8.19300 .227583 .880000 1.20000 1.545
View [GSoC 2017] Final evaluation report.md

Multi-modal cluttered scene analysis in knowledge intensive scenarios

Detection of transparent objects in real world RGB-D data, and estimation of it's pose

Introduction

The project's algorithmical part is mainly based on the following paper: Recognition and Pose Estimation of Rigid Transparent Objects with a Kinect Sensor

My original proposal contained one more segmentation algorithm, but during the bonding period we decided to focus mostly on this one, thus the second part was somewhat excluded from my original plan.

New Annotators

TransparentSegmentationAnnotator - Consumes an RGB-D data from Kinect or Xtion sensor (possibly Intel RealSence, but not tested) and outputs segments, requires a PlaneAnnotator to be runned first;

@klokik
klokik / README Milestone 2.md
Last active Jul 27, 2017
Milestone 2 readme
View README Milestone 2.md

README Milestone 2

Input data

Robosherlock pipeline consumes data from .bag file you uploaded on Trello. So rosbag play --loop it before running demos.

What was implemented

Mapping the 2d transformation obtained from procrustes analisys to 3d space is performed by solving a Perspective-n-Point problem for the CAD model's vertices projected onto the plane (and transformed on it) and CAD model's vertices. This process is performed for each pose hypothesis of each segment. Then, for each pose estimation a shape's silhouette is extracted and chamfer distance to segmented contour is calculated. Distances are used to build a ranking table for each segment. A whole table is rejected if all it's hypotheses have too large distance. A few closest hypotheses are left for further processing. I've posted a [video](https://www.youtube.com/watch?v=k-Y5j

@klokik
klokik / computability.md
Last active Apr 11, 2016
computability
View computability.md

An algorithm is a procedure that you can write as a C function or program, or any other language or more formally construct some variant of turing machine. An algorithm states explicitly how the data will be manipulated.

#Algorithm Efficiency Some algorithms are more efficient than others. We would prefer to chose an efficient algorithm, so it would be nice to have metrics for comparing algorithm efficiency. The complexity of an algorithm is a function describing the efficiency of the algorithm in terms of the amount of data the algorithm must process. Usually there are natural units for the domain and range of this function. There are two main complexity measures of the efficiency of an algorithm:

  • Time complexity is a function describing the amount of time an algorithm takes in terms of the amount of input to the algorithm. "Time" can mean the number of memory accesses performed, the number of comparisons between integers, the number of times some inner loop is executed, or some other natural unit rela