College has kept me busy but I finally took the time to go through the pivision code on the hansonrobotics github. Correct me if I am wrong, but I saw no integration of visual information being fed into opencog, at least not directly. I don't know what kind of chewing ROS does to the information it gets from pi_vision, but it doesn't seem that is really the design philosophy we are going for based on the CogPrime guidelines: as little hand-holding as possible and let the system form its own rules based on patterned inputs right? Since There seems to be little meaningful integration of pi_vision into opencog and I have a personal dislike for the design philosophy of hansonrobotics (where opencog seems to be just a backend engine for one aspect of functionality rather than the core) I was looking to write a standalone visual processor that hooks straight into a CogPrime build. Obviously python would probably be best suited for this, but what would be the most desired way of getting information into the system? You want me to just use the python api to dump atoms into atomspace? Do they need to be tagged with timestamps/other forms of metadata or are those provided already through other CogPrime systems?
Any guidance is appreciated. I am not a neural networks/AI expert by any means and I'd like to be practically useful now rather than only after I finish reading the Bible that is the Opencog codebase. Noah Bliss On Tuesday, September 20, 2016 at 11:15:49 PM UTC-4, Noah Bliss wrote: > > Afterthought: > > Checked out Kinfu, looks to do something quite similar. I am somewhat > concerned about the resolution currently offered though. I'll see if there > is a way to scale it down to simpler objects for easier atomspace digging > and verification. Otherwise I do understand the draw of Kinfu. Perhaps a > hybrid-type system would be ideal. Off to do more research... > > On Friday, September 16, 2016 at 11:37:31 AM UTC-4, Noah Bliss wrote: >> >> I'm going to be showing a great deal of ignorance in this post, but who >> knows, it might help. >> >> I understand an issue recently discussed with embodiment concerns methods >> for processing visual input. It's well known that at this time sending raw >> video into atomspace is a bad idea and that humans have built in visual >> processors that assist our conscious minds in understanding what our eyes >> see. (Obvious simple example being that the image is preflipped). >> >> I understand opencog has (in some form) a python api which leads me to >> think using the visual processing engine OpenCV may not be a bad idea. It >> has a fantastic python api, allows for exporting specific data from raw >> video such as "33% of the screen is red", or there are 2 lines in the >> field of view." it also has a PHENOMINAL foreground/background separation >> engine that allows only a processing of new or moving objects in the field >> of view. >> >> While a more mature opencog engine may prefer a more "raw" processor, I >> see OpenCV as a great place to start for getting useful information into >> atomspace quickly. >> >> I have yet to start work on this, heck, I have yet to fully learn the >> ropes of the current opencog system, but I wanted to at least drop the info >> here in case anyone else had comments or wanted to get a head-start on me. >> >> Best regards my friends. >> Noah B. >> >> PS: My personal experience with OpenCV was specifically dealing with >> automated turrets. There are great YouTube examples of using OpenCV for >> face-tracking webcams attached to servos, and blob isolating security >> cameras if you wanted specific examples to look up. >> >> -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/opencog. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/ba2a5a62-ac97-4abe-ba60-5b69642ee4f5%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
