All in Conference Talk

Following the Master’s Hands: Capturing Piano Performances for Mixed Reality Piano Learning Applications

Following the Master’s Hands: Capturing Piano Performances for Mixed Reality Piano Learning Applications

Cagri Zaman
Augmented Reality, Conference Talk, Research

ACM Human Factors in Computing System (CHI) 2023.

Piano learning applications in Mixed Reality (MR) are a promising substitute for physical instruction when a piano teacher is absent. Existing piano learning applications that use visual indicators to highlight the notes to be played on the keyboard or employ video projections of a pianist provide minimal guidance on how the learner should execute hand movements to develop their technique in performance and prevent injuries. To address this gap, we developed an immersive first-person piano learning experience that uses a library of targeted visualizations of the teacher’s hands and 3D traces of hand movements in MR.

An inquiry into active spatial language using virtual reality

An inquiry into active spatial language using virtual reality

Cagri Zaman
Publication, Conference Talk

International Conference on Spatial Cognition 2018.

In this research we aim to understand the interactions between visual space perception and language in various environmental conditions and tasks. In particular, we investigate spatial concepts in language that are used during active explorations and characterize them in relation to corresponding visuospatial context. We introduce an immersive virtual reality system that allows conducting spatial exploration tasks and recording perceptual and language data. Using this system, we conduct an experiment with 16 subjects, and generate a novel dataset, which includes audio recordings and time coded transcriptions, visual modalities such as RGB and depth, and camera extrinsic for a 10-minute exploration per subject.

Machine Vision for Urban Morphology

Machine Vision for Urban Morphology

Cagri Zaman
Publication, Conference Talk

ISUF International Conference, 2015

Proliferation of data centric methods in mapping practices brings about the question of whether they can integrate the urban morphology and its implications on spatial data analysis. While quantitative data are processed within geographic information systems (GIS) framework through an extensive set of spatial data extraction and processing tools, qualitative assessment of city form is mostly a manual task that requires careful examination of formal and material features. In this research, we introduce a novel computational method for the analysis of the morphological features of cities ranging from macro scale street networks to building stock patterns. We use image processing and pattern matching techniques that are often used in computer vision algorithms for the assessment of morphological features that of street networks, parcel and building stock. Through a comparative analysis of four neighborhoods in Istanbul, we show that there is a strong parallelism between socio- economic development and urban form of Istanbul.