Reflectance Hashing for Material Recognition

We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of the material but is challenging to measure and use for recognizing materials due to its high-dimensionality. In this work, one-shot reflectance is captured using a unique optical camera measuring {\it reflectance disks} where the pixel coordinates correspond to surface viewing angles. The reflectance has class-specific stucture and angular gradients computed in this reflectance space reveal the material class. These reflectance disks encode discriminative information for efficient and accurate material recognition. We introduce a framework called reflectance hashing that models the reflectance disks with dictionary learning and binary hashing. We demonstrate the effectiveness of reflectance hashing for material recognition with a number of real-world materials

Students on the Spectrum

changed many aspects of our lives this year. Things we’ve taken for granted in the past are now different, and we’re being forced to become comfortable with ways of doing things that are unfamiliar, and often initially uncomfortable. Last week, I had an issue with my Verizon bill and had to call customer service. I understood that because of COVID-19, customer service representatives were working from home, and wait times would be considerably longer. The wait was long. It was almost an hour long, whereas in the past connecting to a representative might have taken 10 minutes. I felt impatient; past the half hour mark, I even felt a bit agitated. But then she answered—a human being on the other end of the line, apologetic and ready to help. My issue was resolved quickly and professionally, and not for a moment could I behave in a way that was impatient, upset, or worse yet, unkind, to this person who was doing her best in a circumstance she couldn’t control. It is critical that we recognize and appropriately respond to circumstances that are beyond anyone’s control. This encounter reminded me of the importance of teaching and practicing tolerance. Teaching tolerance is something I try to do in the college classroom, and exercising tolerance is something I’ve found to be a necessity when dealing with students with diverse needs

Learning a Pedestrian Social Behavior Dictionary

Understanding pedestrian behavior patterns is a key component to building autonomous agents that can navigate among humans. We seek a learned dictionary of pedestrian behavior to obtain a semantic description of pedestrian trajectories. Supervised methods for dictionary learning are impractical since pedestrian behaviors may be unknown a priori and the process of manually generating behavior labels is prohibitively time consuming. We instead utilize a novel, unsupervised framework to create a taxonomy of pedestrian behavior observed in a specific space. First, we learn a trajectory latent space that enables unsupervised clustering to create an interpretable pedestrian behavior dictionary. We show the utility of this dictionary for building pedestrian behavior maps to visualize space usage patterns and for computing the distributions of behaviors. We demonstrate a simple but effective trajectory prediction by conditioning on these behavior labels. While many trajectory analysis methods rely on RNNs or transformers, we develop a lightweight, low-parameter approach and show results comparable to SOTA on the ETH and UCY datasets