SAM DALEY
INTEGRATING HUMAN AND COMPUTER VISION ARCHITECTURE AS CATALYST 2015 WORKSHOP
Implications for facade design? While the pedestrian will only ever perceive the surfaces of a building normalized to their location in space, they will construct formal abstractions of the building from past experiences. As they move through space their understanding of facade is much deeper than the skin. It includes: vegetation, sidewalk, stairs, ect. The presence of the building acts on the public to shape how the movement, and navigation. Finally, the success of a facade as a navigational device relies on materials, texture, shape, and form. Stripped of any of these layers it does not communicate as successfully.
What does computer vision reveal about how we perceive the built environment? How does human vision model the environment from image? Human vision is biased to our location in the environment. Therefore we only see surfaces that are normalized to the observer. We are able to differentiate these surfaces by understanding value or gradation along a surface seen as a subtle spectrum of light and shadow and contour or edge of a surface seen as a sharp contrast between value. We are able to triangulate the depth of these surfaces through the simultaneous interpretation of two images, one from each eye. If human vision is limited to the surfaces perceived from a fixed location how to we understand form? Walking around an object, we are able to hold a loose collection of images in our minds eye to create an abstraction of form. Each of us have a collection of formal abstractions held in our memory. These forms are based on the past experiences of the built environment. Therefore, while we can only see surfaces normalized to the observer our mind is making inferences using the formal language to complete these facades with formal abstractions. How does computer vision, specifically photogrammetry, model the environment from image? Similar to our walk around an object, the computer can simultaneously analyze a collection of images. The image in our minds eye is simplified through this process, however, computer vision allows for the transcendence of the space-time limitations allowing for accurate modeling rather than abstraction. With this photogrammetric modeling it is important to note that the model accuracy is biased by the method of data collection. What is the difference between a photogrammetric model created by a human and by a drone? Given fluctuation of the elevation, orientation, and trajectory of the camera, human eyes, through space results in a photogrammetric model that biases the visible faces of buildings. Where the ground is excluded because it is not in focus when we orient ourselves. In contrast the drone has set parameters for elevation (15ft), orientation (-30째), and trajectory (10mph). This generates a very detailed and accurate photogrammetric. A further difference is that the focus of this model is of the ground rather than the buildings. What is the difference between human vision and computer vision? While human vision is limited to the perception of surfaces normalized to the observer we are able to supplement the contour, value, and depth with a collection of formal abstractions from all past experiences. In contrast, computer vision is limited to the information that it is given; however, it is not limited by space and time and therefore can perceive hundreds of images simultaneously to generate an accurate holistic understanding of the environment. What are the implications this has on how we construct the build environment? It is given that the built environment acts on the pedestrian to change how we move through space. A building placed in an open square will block paths through the center of the space. However, this study reveals that at the same time this obstacle provides a reference point for gaging distance and functions to guide travelers through space. Therefore, the mass blocks paths while affording reference for navigation. At the scale of the city this results in a network of more or less clearly defined pathways through the constructed environment, within which the built elements provide navigation of these passages. In this complex network form alone would not be sufficient enough to provide reference. Therefore, the differentiation of the facade allows for the construction of mental maps through space.
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Markdown and Terminal Learning Markdown and Terminal was the first step towards better understanding computer vision. It provided another was for understanding the way we organize our digital environment. Furthermore, in conjunction with GitHub it allowed us as a class to quickly share collections of images and photogrammetric models.
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Drone Flight The drone has set parameters for elevation (15ft), orientation (-30째), and trajectory (10mph). This generates a very detailed and accurate photogrammetric. A further difference is that the focus of this model is of the ground rather than the buildings.
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McNamara Alumni Center Texture on the surface of the Photoscan photogrammetry model gives the facade volume. However; when you rotate the model it is clear that not enough photos were used to create a model with depth. Here only six photographs were used to create a quick study. Future modeling would benefit from additional photographs.
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MPLS Recreation and Wellness Center The facade of the Minneapolis Recreation and Wellness Center is composed of both of mass and texture. Given that it is defined by the repetition of primary forms, when the texture is removed in Photoscan it is still easy to recognize the mesh as being the facade of the Recreation Center. When asking my peers to identify the mesh this was easily recognizable.
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MPLS Recreation and Wellness Center Here I began to play with the procedure of collection photos used to generate a photogrammetry model. All the photos were collected from the same location and facing the same direction. Here the subject matter was the throng of people moving through the entrance of the Rec Center. The model generated is a flat surface with ripples starting to form where people cross the frame.
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Weisman Art Museum The Weisman Art Museum provided an interesting case study in how computer vision and photogrammetry read two distinctly different facades of the same building. My hypothesis here was that the dynamic metal panel west facade would be difficult for Photoscan to read because of its formal complexity and reflectivity while the south brick facade would generate a more accurate model. However, What I found was that the complex geometry of the west facade provided a much more clear model in comparison to the south.
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Pillsbury Hall A transversal of Pillsbury Hall’s north entrance, with photographs taken every 5 steps, generated a very accurate photogrammetric model. When the texture is removed it is easy to decipher the accurate capture of textures. Here in the purple mesh one can perceptive the rough sandstone texture of the buildings heavy masonry blocks. The rigorous documentation and even north light resulted in a highly accurate Photoscan model.
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Human | STSS to Rapson The model reveals the elements that influence our navigation of space. It includes; trees, people, flag poles, trash cans, and other landscape elements. These fixtures play a role in how we navigate space and can be considered an extension of the facade of a building as they play an equal role in guiding our navigation through space.
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Human | Coffman to Rapson The model reveals that often we only perceive one face of the buildings that make up the Northrop Mall. However, during this experience we understand their mass. This changes how we imagine the relationship between pedestrian and facade.
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Drone | Circle Similar to the experience we had when a person remained in a fixed location taking pictures outward. The model becomes significantly distorted. Warping the elements in the peripheral of the images.
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Drone | Line The drone has set parameters for elevation (15ft), orientation (-30째), and trajectory (10mph). This generates a very detailed and accurate photogrammetric. A further difference is that the focus of this model is of the ground rather than the buildings.
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