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Publication Metadata only Coarse-to-fine combinatorial matching for dense isometric shape correspondence(Wiley, 2011) N/A; Department of Computer Engineering; Sahillioğlu, Yusuf; Yemez, Yücel; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; 215195; 107907We present a dense correspondence method for isometric shapes, which is accurate yet computationally efficient. We minimize the isometric distortion directly in the 3D Euclidean space, i.e., in the domain where isometry is originally defined, by using a coarse-to-fine sampling and combinatorial matching algorithm. Our method does not require any initialization and aims to find an accurate solution in the minimum-distortion sense for perfectly isometric shapes. We demonstrate the performance of our method on various isometric (or nearly isometric) pairs of shapes.Publication Metadata only Coarse-to-fine isometric shape correspondence by tracking symmetric flips(Wiley, 2013) N/A; Department of Computer Engineering; Sahillioğlu, Yusuf; Yemez, Yücel; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; 215195; 107907We address the symmetric flip problem that is inherent to multi-resolution isometric shape matching algorithms. To this effect, we extend our previous work which handles the dense isometric correspondence problem in the original 3D Euclidean space via coarse-to-fine combinatorial matching. The key idea is based on keeping track of all optimal solutions, which may be more than one due to symmetry especially at coarse levels, throughout denser levels of the shape matching process. We compare the resulting dense correspondence algorithm with state-of-the-art techniques over several 3D shape benchmark datasets. The experiments show that our method, which is fast and scalable, is performance-wise better than or on a par with the best performant algorithms existing in the literature for isometric (or nearly isometric) shape correspondence. Our key idea of tracking symmetric flips can be considered as a meta-approach that can be applied to other multi-resolution shape matching algorithms, as we also demonstrate by experiments.Publication Open Access Deep generation of 3D articulated models and animations from 2D stick figures(Elsevier, 2022) Akman, Alican; Sahillioğlu, Yusuf; Department of Computer Engineering; Sezgin, Tevfik Metin; Faculty Member; Department of Computer Engineering; College of Engineering; 18632Generating 3D models from 2D images or sketches is a widely studied important problem in computer graphics. We describe the first method to generate a 3D human model from a single sketched stick figure. In contrast to the existing human modeling techniques, our method does not require a statistical body shape model. We exploit Variational Autoencoders to develop a novel framework capable of transitioning from a simple 2D stick figure sketch, to a corresponding 3D human model. Our network learns the mapping between the input sketch and the output 3D model. Furthermore, our model learns the embedding space around these models. We demonstrate that our network can generate not only 3D models, but also 3D animations through interpolation and extrapolation in the learned embedding space. In addition to 3D human models, we produce 3D horse models in order to show the generalization ability of our framework. Extensive experiments show that our model learns to generate compatible 3D models and animations with 2D sketches.Publication Metadata only Foreword to the special section on expressive 2015(Pergamon-Elsevier Science Ltd, 2016) N/A; Department of Computer Engineering; Sezgin, Tevfik Metin; Faculty Member; Department of Computer Engineering; College of Engineering; 18632N/APublication Open Access From noon to sunset: interactive rendering, relighting, and recolouring of landscape photographs by modifying solar position(Wiley, 2021) Türe, Murat; Çıklabakkal, Mustafa Ege; Erdem, Erkut; Satılmış, Pınar; Akyüz, Ahmet Oğuz; Department of Computer Engineering; Erdem, Aykut; Faculty Member; Department of Computer Engineering; College of Engineering; 20331Image editing is a commonly studied problem in computer graphics. Despite the presence of many advanced editing tools, there is no satisfactory solution to controllably update the position of the sun using a single image. This problem is made complicated by the presence of clouds, complex landscapes, and the atmospheric effects that must be accounted for. In this paper, we tackle this problem starting with only a single photograph. With the user clicking on the initial position of the sun, our algorithm performs several estimation and segmentation processes for finding the horizon, scene depth, clouds, and the sky line. After this initial process, the user can make both fine- and large-scale changes on the position of the sun: it can be set beneath the mountains or moved behind the clouds practically turning a midday photograph into a sunset (or vice versa). We leverage a precomputed atmospheric scattering algorithm to make all of these changes not only realistic but also in real-time. We demonstrate our results using both clear and cloudy skies, showing how to add, remove, and relight clouds, all the while allowing for advanced effects such as scattering, shadows, light shafts, and lens flares.Publication Metadata only Leveraging semantic saliency maps for query-specific video summarization(Springer, 2022) Cizmeciler, Kemal; Erdem, Erkut; Department of Computer Engineering; Erdem, Aykut; Faculty Member; Department of Computer Engineering; College of Engineering; 20331The immense amount of videos being uploaded to video sharing platforms makes it impossible for a person to watch all the videos understand what happens in them. Hence, machine learning techniques are now deployed to index videos by recognizing key objects, actions and scenes or places. Summarization is another alternative as it offers to extract only important parts while covering the gist of the video content. Ideally, the user may prefer to analyze a certain action or scene by searching a query term within the video. Current summarization methods generally do not take queries into account or require exhaustive data labeling. In this work, we present a weakly supervised query-focused video summarization method. Our proposed approach makes use of semantic attributes as an indicator of query relevance and semantic attention maps to locate related regions in the frames and utilizes both within a submodular maximization framework. We conducted experiments on the recently introduced RAD dataset and obtained highly competitive results. Moreover, to better evaluate the performance of our approach on longer videos, we collected a new dataset, which consists of 10 videos from YouTube and annotated with shot-level multiple attributes. Our dataset enables much diverse set of queries that can be used to summarize a video from different perspectives with more degrees of freedom.Publication Open Access Location pairs: a test coverage metric for shared-memory concurrent programs(Springer, 2012) Muslu, Kıvanç; Department of Computer Engineering; Keremoğlu, M. Erkan; Taşıran, Serdar; Faculty Member; Department of Computer Engineering; College of EngineeringWe present a coverage metric targeted at shared-memory concurrent programs: the Location Pairs (LP) coverage metric. The goals of this metric are (i) to measure how thoroughly a program has been tested from a concurrency standpoint, i.e., whether enough qualitatively different thread interleavings have been explored, and (ii) to guide testing towards unexplored concurrency scenarios. This metric was inspired by an access pattern known to lead to high-level concurrency errors in industrial software and in the literature. We built a monitoring tool to measure LP coverage of test programs. We used the LP metric for interactive debugging, and compared LP coverage with other concurrency coverage metrics on Java benchmarks. We demonstrated that LP coverage corresponds better to concurrency errors, is a better measure of how well a program is exercised concurrency-wise by a test set, reaches saturation later than other coverage metrics, and is viable and useful as an interactive testing and debugging tool.Publication Metadata only Multimodal person recognition for human-vehicle interaction(IEEE Computer Society, 2006) Ercil, A; Erdogan, H; Abut, H; Department of Computer Engineering; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Erzin, Engin; Yemez, Yücel; Tekalp, Ahmet Murat; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; College of Engineering; College of Engineering; College of Engineering; 34503; 107907; 26207Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies.Publication Open Access Multiple shape correspondence by dynamic programming(Wiley, 2014) Sahillioğlu, Y.; Department of Computer Engineering; Yemez, Yücel; Faculty Member; Department of Computer Engineering; College of EngineeringWe present a multiple shape correspondence method based on dynamic programming, that computes consistent bijective maps between all shape pairs in a given collection of initially unmatched shapes. As a fundamental distinction from previous work, our method aims to explicitly minimize the overall distortion, i.e., the average isometric distortion of the resulting maps over all shape pairs. We cast the problem as optimal path finding on a graph structure where vertices are maps between shape extremities. We exploit as much context information as possible using a dynamic programming based algorithm to approximate the optimal solution. Our method generates coarse multiple correspondences between shape extremities, as well as denser correspondences as by-product. We assess the performance on various mesh sequences of (nearly) isometric shapes. Our experiments show that, for isometric shape collections with non-uniform triangulation and noise, our method can compute relatively dense correspondences reasonably fast and outperform state of the art in terms of accuracy.Publication Metadata only NOVA: rendering virtual worlds with humans for computer vision tasks(Wiley, 2021) Kerim, Abdulrahman; Aslan, Cem; Çelikcan, Ufuk; Erdem, Erkut; Department of Computer Engineering; Erdem, Aykut; Faculty Member; Department of Computer Engineering; College of Engineering; 20331Today, the cutting edge of computer vision research greatly depends on the availability of large datasets, which are critical for effectively training and testing new methods. Manually annotating visual data, however, is not only a labor-intensive process but also prone to errors. In this study, we present NOVA, a versatile framework to create realistic-looking 3D rendered worlds containing procedurally generated humans with rich pixel-level ground truth annotations. NOVA can simulate various environmental factors such as weather conditions or different times of day, and bring an exceptionally diverse set of humans to life, each having a distinct body shape, gender and age. To demonstrate NOVA's capabilities, we generate two synthetic datasets for person tracking. The first one includes 108 sequences, each with different levels of difficulty like tracking in crowded scenes or at nighttime and aims for testing the limits of current state-of-the-art trackers. A second dataset of 97 sequences with normal weather conditions is used to show how our synthetic sequences can be utilized to train and boost the performance of deep-learning based trackers. Our results indicate that the synthetic data generated by NOVA represents a good proxy of the real-world and can be exploited for computer vision tasks.