klokik

{
  "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"

klokik

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

klokik

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

klokik

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

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

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