kor01

kinmematics frame of each link is infered rather than defined in urdf
each joint position and orientation is defined relative to it's parents kinematics frame
the parent kinematics frame is defined as the frame of it's parent joint
the kinematics frame of root link is eye(4)

	DBSCAN: Density-Based Spatial Clustering of Application with Noise

	Assummes: known distance boundary but not number of clusters

	Also named as: Euclidean Clustering

	two hyper parameters:
	maximum distance within cluster
	minimum number of points in a cluster

	import matplotlib.pyplot as plt
	import matplotlib.image as mpimg
	import numpy as np

	# Read in the image
	image = mpimg.imread('test.jpg')

	# Grab the x and y size and make a copy of the image
	ysize = image.shape[0]
	xsize = image.shape[1]

	in conventional notations:

	1. layer input = the tensor before activation

	2. layer output = the tensor after activation

	3. next layer input = this layer output transformed by the edges

	4. delta_layer = dLoss / dlayer_input

	depth = number of channel
	path / kernel = [depth, convolutional sliding window weight]

	sliding window weight = [height, width, depth], where depth equals to previous image channels

	feature map_i = output[i, :, :] the ith layer of the output hyper image after convolution
	stride = number of pixels when shifting the kernel window
	valid_padding = stop slide at the edge (without padding)
	same_padding = pad with zeros to make output image the same as input

	import numpy as np
	import cv2
	import matplotlib.pyplot as plt
	import matplotlib.image as mpimg

	image = mpimg.imread('bbox-example-image.jpg')

	# Define a function that takes an image, a list of bounding boxes,
	# and optional color tuple and line thickness as inputs
	# then draws boxes in that color on the output


	purpose:

	to give each cluster in segmentation output a label

	s.t. the grounding geometric specification of semantic command can be extracted

	hold on
	plot(x(1, :), x(2,:))