With the rapid rise of AI technologies such as ChatGPT, understanding and integrating AI into K-12 education has become increasingly important. However, teachers often lack the AI literacy necessary to navigate these tools, which can lead to the perpetuation of misconceptions and biases in the classroom. This study seeks to identify K-12 teachers’ self-identified needs regarding AI education and compare them with existing research on professional development (PD) for AI integration. We surveyed 34 K-12 teachers to assess their knowledge of AI, identify areas where they require further support, and evaluate the relevance of current PD offerings. Our findings reveal a significant disconnect between the top-down assumptions of expert-driven PD initiatives and the practical needs articulated by teachers. Key themes emerged, including a diverse range of AI understanding among educators, a strong preference for hands-on, practical training, and a demand for ongoing institutional support. Additionally, teachers expressed a desire for collaborative learning environments to share strategies and experiences related to AI. This study underscores the importance of tailoring PD programs to address the unique contexts and challenges faced by educators, advocating for a more personalized approach that fosters confidence and competence in AI integration. By aligning PD offerings with teachers’ needs, we aim to enhance their ability to effectively utilize AI tools in the classroom, ultimately enriching the educational experience for students.

We present SketchDNN, a generative model for synthesizing CAD sketches that jointly models both continuous parameters and discrete class labels through a unified continuous-discrete diffusion process. Our core innovation is Gaussian-Softmax diffusion, where logits perturbed with Gaussian noise are projected onto the probability simplex via a softmax transformation, facilitating blended class labels for discrete variables. This formulation addresses 2 key challenges, namely, the heterogeneity of primitive parameterizations and the permutation invariance of primitives in CAD sketches. Our approach significantly improves generation quality, reducing Fréchet Inception Distance (FID) from 16.04 to 7.80 and negative log-likelihood (NLL) from 84.8 to 81.33, establishing a new state-of-the-art in CAD sketch generation on the SketchGraphs dataset.

We present WinSyn, a unique dataset and testbed for cre-ating high-quality synthetic data with procedural modeling techniques. The dataset contains high-resolution pho-tographs of windows, selected from locations around the world, with 89,318 individual window crops showcasing diverse geometric and material characteristics. We evaluate a procedural model by training semantic segmentation networks on both synthetic and real images and then comparing their performances on a shared test set of real images. Specifically, we measure the difference in mean Intersection over Union (mIoU) and determine the effective number of real images to match synthetic data's training performance. We design a baseline procedural model as a benchmark and provide 21,290 synthetically generated images. By tuning the procedural model, key factors are identified which significantly influence the model's fidelity in replicating real-world scenarios. Importantly, we highlight the challenge of procedural modeling using current techniques, especially in their ability to replicate the spatial semantics of real-world scenarios. This insight is critical because of the potential of procedural models to bridge to hidden scene aspects such as depth, reflectivity, material properties, and lighting conditions.

We present a system to extract architectural assets from large-scale collections of panoramic imagery. We automatically rectify and crop parts of the panoramic image that contain dominant planes, and then use object detection to extract assets such as façades and windows. We also provide various tools to identify attributes of the assets to determine the asset quality and index the assets for search. In addition, we propose a User Interface (UI) to visualize and query assets. Finally, we present applications for urban modeling and texture synthesis.

Seamlessly blending features from multiple images is extremely challenging because of complex relationships in lighting, geometry, and partial occlusion which cause coupling between different parts of the image. Even though recent work on GANs enables synthesis of realistic hair or faces, it remains difficult to combine them into a single, coherent, and plausible image rather than a disjointed set of image patches. We present a novel solution to image blending, particularly for the problem of hairstyle transfer, based on GAN-inversion. We propose a novel latent space for image blending which is better at preserving detail and encoding spatial information, and propose a new GAN-embedding algorithm which is able to slightly modify images to conform to a common segmentation mask. Our novel representation enables the transfer of the visual properties from multiple reference images including specific details such as moles and wrinkles, and because we do image blending in a latent-space we are able to synthesize images that are coherent. Our approach avoids blending artifacts present in other approaches and finds a globally consistent image. Our results demonstrate a significant improvement over the current state of the art in a user study, with users preferring our blending solution over 95 percent of the time. Source code for the new approach is available at https://zpdesu.github.io/Barbershop.

We present a novel approach for segmenting ribbon-like features, such as sidewalks. Given approximate locations and thicknesses of thin features, this approach can register coarsely digitized footways to high-resolution aerial imagery with high accuracy. The key innovation is a dynamic-programming solution to search for the optimal set of thicknesses and offsets from an initial path. The method provides control over the rate of change of thickness and direction, and it does not rely prior knowledge of the materials in the image. The approach will be helpful as an aid to digitization, and downstream applications such as registration of walking paths, canals, or roadways as well as for damage and change detection.

The creation of high-quality semantically parsed 3D models for dense metropolitan areas is a fundamental urban modeling problem. Although recent advances in acquisition techniques and processing algorithms have resulted in large-scale imagery or 3D polygonal reconstructions, such data-sources are typically noisy, and incomplete, with no semantic structure. In this paper, we present an automatic data fusion technique that produces high-quality structured models of city blocks. From coarse polygonal meshes, street-level imagery, and GIS footprints, we formulate a binary integer program that globally balances sources of error to produce semantically parsed mass models with associated facade elements. We demonstrate our system on four city regions of varying complexity; our examples typically contain densely built urban blocks spanning hundreds of buildings. In our largest example, we produce a structured model of 37 city blocks spanning a total of 1, 011 buildings at a scale and quality previously impossible to achieve automatically.

In this paper, we propose a robust framework for building extraction in visible band images. We first get an initial classification of the pixels based on an unsupervised presegmentation. Then, we develop a novel conditional random field (CRF) formulation to achieve accurate rooftops extraction, which incorporates pixel-level information and segment-level information for the identification of rooftops. Comparing with the commonly used CRF model, a higher order potential defined on segment is added in our model, by exploiting region consistency and shape feature at segment level. Our experiments show that the proposed higher order CRF model outperforms the state-of-the-art methods both at pixel and object levels on rooftops with complex structures and sizes in challenging environments.

An alternative to the traditional classroom structure that has seen increased use in higher education is the flipped classroom. Flipping the classroom switches when assignments (e.g. homework) and knowledge transfer (e.g. lecture) occur. Flipped classrooms are getting popular in secondary and post-secondary teaching institutions as evidenced by the marked increase in the study, use, and application of the flipped pedagogy as it applies to learning and retention. The majority of the courses that have undergone this change use applied learning strategies and include a significant “learning-by-doing” component. The research in this area is skewed towards such courses and in general there are many considerations that educators ought to account for if they were to move to this form of teaching. Introductory courses in computer programming can appear to have all the elements needed to move to a flipped environment; however, initial observations from our research identify possible pitfalls with the assumption. In this work in progress the authors discuss early results and observations of implementing a flipped classroom to teach an introductory programming course (CS1) to engineering, engineering technology, and software engineering undergraduates.

Heat map based data visualization and mining is an emerging area in game engine design and architecture. Employed by many state of the art game engines and popular commercial games, this technology helps populate and collate player activity and behavior to better inform the system for further action. Simulation and serious games can tremendously benefit by applying heat map based visualization for the purposes of analyzing and tracking player behavior. Heat maps are time varying texture maps that represent a chosen activity over a certain grid at any particular interval of elapsed time. In this paper results of applying a real-time heat map data capture and generation tool on two military simulations: 1) Ground-based combat scenario and 2) Unmanned Aerial Vehicle sensor operator scenario is presented. The research showcases several real-time visualization techniques developed into the simulation with the main goal of understanding participant behavior. Novice and expert data is populated as part of the experiment to validate the effectiveness of our methods.

Abstract We present an image processing method that converts a raster image to a simplical two‐complex which has only a small number of vertices (base mesh) plus a parametrization that maps each pixel in the original image to a combination of the barycentric coordinates of the triangle it is finally mapped into. Such a conversion of a raster image into a base mesh plus parametrization can be useful for many applications such as segmentation, image retargeting, multi‐resolution editing with arbitrary topologies, edge preserving smoothing, compression, etc. The goal of the algorithm is to produce a base mesh such that it has a small colour distortion as well as high shape fairness, and a parametrization that is globally continuous visually and numerically. Inspired by multi‐resolution adaptive parametrization of surfaces and quadric error metric, the algorithm converts pixels in the image to a dense triangle mesh and performs error‐bounded simplification jointly considering geometry and colour. The eliminated vertices are projected to an existing face. The implementation is iterative and stops when it reaches a prescribed error threshold. The algorithm is feature‐sensitive, i.e. salient feature edges in the images are preserved where possible and it takes colour into account thereby producing a better quality triangulation.

The HSV color space is an intuitive way to reason about color, but the nonlinear relationship to RGB coordinates complicates histogram analysis of colors in HSV. We present novel Interval-HSV formulas to identify a range in HSV for each RGB interval. We show the usefulness by introducing a parameter-free and completely automatic technique to extract both colored and black ink annotations from faded backgrounds such as digitized aerial photographs, maps, or printed-text documents. We discuss the characteristics of ink mixing in the HSV color space and discover a single feature, the upper limit of the saturation-interval, to extract ink even when it is achromatic. We form robust Interval-HV histograms in order to identify the number and colors of inks in the image.

In this paper, an automatic road tracking method is presented for detecting roads from satellite images. This method is based on shape classification of a local homogeneous region around a pixel. The local homogeneous region is enclosed by a polygon, called the pixel footprint. We introduce a spoke wheel operator to obtain the pixel footprint and propose a Fourier-based approach to classify footprints for automatic seeding and growing of the road tracker. We experimentally demonstrate that our proposed road tracker can extract the centerlines of roads with sharp turns and intersections effectively, and has relatively small amount of leakage.

In this paper, a new two-step approach (detecting and pruning) for automatic extraction of road networks from aerial images is presented. The road detection step is based on shape classification of a local homogeneous region around a pixel. The local homogeneous region is enclosed by a polygon, called the footprint of the pixel. This step involves detecting road footprints, tracking roads, and growing a road tree. We use a spoke wheel operator to obtain the road footprint. We propose an automatic road seeding method based on rectangular approximations to road footprints and a toe-finding algorithm to classify footprints for growing a road tree. The road tree pruning step makes use of a Bayes decision model based on the area-to-perimeter ratio (the A/P ratio) of the footprint to prune the paths that leak into the surroundings. We introduce a lognormal distribution to characterize the conditional probability of A/P ratios of the footprints in the road tree and present an automatic method to estimate the parameters that are related to the Bayes decision model. Results are presented for various aerial images. Evaluation of the extracted road networks using representative aerial images shows that the completeness of our road tracker ranges from 84% to 94%, correctness is above 81%, and quality is from 82% to 92%.