(Re)Imagining Relationality: Brad Isaacs and The Map of the Empire

The exhibition, The Map of the Empire (30 March – 6 May, 2016), featured photography, video, and installation works by Toronto-based artist, Brad Isaacs (Mohawk | mixed heritage). The majority of the artworks within the exhibition were produced from the Canadian Museum of Nature’s research and collections facility (Gatineau, Québec). The Canadian Museum of Nature (CMN), is the national natural history museum of (what is now called) Canada, with its galleries located in Ottawa, Ontario. The exhibition was the first to open at the Centre for Indigenous Research Creation at Queen’s University under the supervision of Dr. Dylan Robinson. Through the installment of The Map of the Empire, Isaacs effectively claimed space on campus grounds – within the geopolitical space of Katarokwi | Kingston – and pushed back against settler colonial imaginings of natural history. The Map of the Empire explored the capacity of Brad’s artistic practice in challenging the general belief under which natural history museums operate: that the experience of collecting/witnessing/interacting with a deceased and curated more-than-human animal will increase conservation awareness and facilitate human care towards nature. The exhibition also featured original poetry by Cecily Nicholson, author of Triage (2011) and From the Poplars (2014), as a response to Brad’s artwork. I locate the work of The Map of the Empire within the broader context of curatorship as a political practice engaging with conceptual and actualized forms of slow violence, both inside of and beyond the museum space. By unmapping the structures of slow, showcased and archived violence within the natural history museum, we can begin to radically transform and reimagine our connections with more-than-humans and encourage these relations to be reciprocal rather than hyper-curated or preserved.

Automatic Identification of Large Fragments in a Pile of Broken Rock Using a Time-of-Flight Camera

This paper presents a solution to part of the problem of making robotic or semi-robotic digging equipment less dependant on human supervision. A method is described for identifying rocks of a certain size that may affect digging efficiency or require special handling. The process involves three main steps. First, by using range and intensity data from a time-of-flight (TOF) camera, a feature descriptor is used to rank points and separate regions surrounding high scoring points. This allows a wide range of rocks to be recognized because features can represent a whole or just part of a rock. Second, these points are filtered to extract only points thought to belong to the large object. Finally, a check is carried out to verify that the resultant point cloud actually represents a rock. Results are presented from field testing on piles of fragmented rock. Note to Practitioners—This paper presents an algorithm to identify large boulders in a pile of broken rock as a step towards an autonomous mining dig planner. In mining, piles of broken rock can contain large fragments that may need to be specially handled. To assess rock piles for excavation, we make use of a TOF camera that does not rely on external lighting to generate a point cloud of the rock pile. We then segment large boulders from its surface by using a novel feature descriptor and distinguish between real and false boulder candidates. Preliminary field experiments show promising results with the algorithm performing nearly as well as human test subjects.