Methods and systems for learning-based image edge enhancement of sample tube top circles

An image processing and control apparatus, comprising:

• image capture apparatus configured to capture an image of one or more tops of one or more respective sample tubes;
• a robot configured to move the one or more respective sample tubes;
• and
• a system controller comprising a processor and a memory, the system controller configured via programming instructions stored in the memory to process the image of the one or more tops of the one or more respective sample tubes by applying the image to a convolutional neural network to:
• identify and intensify sample tube top edges appearing in the image;
• identify and suppress edge responses from other objects appearing in the image;
• generate an edge map of the image of the one or more tops of the one or more respective sample tubes;
• and
• control the robot to move one or more sample tubes based on the generated edge map;
• wherein:
• the convolutional neural network is a fully convolutional network comprising a plurality of convolution layers;
• the fully convolutional network comprises one or more convolution layers and one or more max-pooling layers followed by one or more fully-connected convolutional layers;
• a first convolution layer includes a kernel size of 5, a stride of 1, and a pad of 0;
• a second convolution layer includes a kernel size of 5, a stride of 1, and a pad of 0;
• a first fully-connected convolution layer includes a kernel size of 5, a stride of 1, and a pad of 0;
• and
• a second fully-connected convolution layer includes a kernel size of 1, a stride of 1, and a pad of 0.

A method of processing an image of sample tube tops and controlling a robot based thereon, comprising:

• receiving an input image of one or more tops of one or more respective sample tubes;
• applying to the input image a fully convolutional network having one or more convolution layers and one or more max-pooling layers;
• identifying and intensifying sample tube top edges appearing in the image in response to the applying;
• generating an edge map in response to the applying and the intensifying;
• determining sample tube categories or characteristics based on the generated edge map;
• and
• controlling a robot based on the determined sample tube categories or characteristics;
• wherein a first convolution layer includes a kernel size of 5, a stride of 1, and a pad of 0.

A method of processing an image of sample tube tops and controlling a robot based thereon, comprising:

• receiving an input image of one or more tops of one or more respective sample tubes;
• applying to the input image a fully convolutional network having one or more convolution layers and one or more max-pooling layers;
• identifying and intensifying sample tube top edges appearing in the image in response to the applying;
• generating an edge map in response to the applying and the intensifying;
• determining sample tube categories or characteristics based on the generated edge map;
• and
• controlling a robot based on the determined sample tube categories or characteristics;
• wherein a first max-pooling layer includes a kernel size of 2, a stride of 2, and a pad of 0.