Indriyati Atmosukarto | University of Washington (original) (raw)

Papers by Indriyati Atmosukarto

2021 IEEE International Conference on Engineering, Technology & Education (TALE), Dec 5, 2021

This paper outlines the development and the implementation of an online finance-based self-learni... more This paper outlines the development and the implementation of an online finance-based self-learning platform to augment the application of finance concepts by nonbusiness undergraduate students undertaking innovation project-based modules. This online self-learning platform has two key objectives; (i) to empower students in multidisciplinary fields to self-study finance concepts using online learning platforms and to subsequently apply them in project-based modules, (ii) to increase efficiency in teaching finance-based contents with limited resources, especially in this COVID-19 environment with an online mode of learning. This online selflearning platform allows involvement of faculty without any finance background to embed a bite-sized content as supplementary topics for students to self-learn at their own pace. The efficacy of the online self-learning platform was determined through pre and post quizzes to identify student's current and acquired finance knowledge. Quantitative data obtained demonstrate that there is significant improvement in the post-quiz scores; the mean of all the modules improves by 28% for post quiz score, and paired t-test for pre and post quiz scores are significant. These results as well as feedback received from students, demonstrate the effectiveness of the implemented online self-learning platform to augment the application of finance concepts by non-business undergraduate students.

Advances in computer vision and pattern recognition, 2014

Most existing software packages for sports video analysis require manual annotation of important ... more Most existing software packages for sports video analysis require manual annotation of important events in the video. Despite being the most popular sport in the United States, most American football game analysis is still done manually. Line of scrimmage and offensive team formation recognition are two statistics that must be tagged by American Football coaches when watching and evaluating past play video clips, a process which takes many man hours per week. These two statistics are the building blocks for more high-level analysis such as play strategy inference and automatic statistic generation. In this chapter, we propose a novel framework where given an American football play clip, we automatically identify the video frame in which the offensive team lines in formation (formation frame), the line of scrimmage for that play, and the type of player formation the offensive team takes on. The proposed framework achieves 95 % accuracy in detecting the formation frame, 98 % accuracy in detecting the line of scrimmage, and up to 67 % accuracy in classifying the offensive team’s formation. To validate our framework, we compiled a large dataset comprising more than 800 play-clips of standard and high definition resolution from real-world football games. This dataset will be made publicly available for future comparison.

Proceedings of SPIE, Jan 18, 2009

Content-based image retrieval has been applied to many different biomedical applications 1. In al... more Content-based image retrieval has been applied to many different biomedical applications 1. In almost all cases, retrievals involve a single query image of a particular modality and retrieved images are from this same modality. For example, one system may retrieve color images from eye exams, while another retrieves fMRI images of the brain. Yet real patients often have had tests from multiple different modalities, and retrievals based on more than one modality could provide information that single modality searches fail to see. In this paper, we show medical image retrieval for two different single modalities and propose a model for multimodal fusion that will lead to improved capabilities for physicians and biomedical researchers. We describe a graphical user interface for multimodal retrieval that is being tested by real biomedical researchers in several different fields.

This paper demonstrates the usage of security camera footages with deep convolutional neural netw... more This paper demonstrates the usage of security camera footages with deep convolutional neural networks to provide cabin-level crowd density estimates in the video frames. Some applications for this include cabin-level crowd density estimates of incoming trains. With this information, train passengers may choose to board the trains at less crowded cabins, potentially decreasing the dwell time of trains at stations and experiencing a more pleasant commute overall. In a way, the crowd level estimation information will also help to maximize the train and platform capacity. Leveraging on the security camera footages would also serve as a cost-effective solution to the train operator as compared to installing new sensing equipment in the trains. Due to privacy and security concerns of publishing train cabin video frames, this paper will present the experiment results on an indoor pedestrian dataset.

Proceedings of SPIE, Feb 26, 2009

Recent studies have shown an increase in the occurrence of deformational plagiocephaly and brachy... more Recent studies have shown an increase in the occurrence of deformational plagiocephaly and brachycephaly in children. This increase has coincided with the "Back to Sleep" campaign that was introduced to reduce the risk of Sudden Infant Death Syndrome (SIDS). However, there has yet to be an objective quantification of the degree of severity for these two conditions. Most diagnoses are done on subjective factors such as patient history and physician examination. The existence of an objective quantification would help research in areas of diagnosis and intervention measures, as well as provide a tool for finding correlation between the shape severity and cognitive outcome. This paper describes a new shape severity quantification and localization method for deformational plagiocephaly and brachycephaly. Our results show that there is a positive correlation between the new shape severity measure and the scores entered by a human expert.

Proceedings of SPIE, May 19, 2005

Unmanned aerial vehicles with high quality video cameras are able to provide videos from 50,000 f... more Unmanned aerial vehicles with high quality video cameras are able to provide videos from 50,000 feet up that show a surprising amount of detail on the ground. These videos are difficult to analyze, because the airplane moves, the camera zooms in and out and vibrates, and the moving objects of interest can be in the scene, out of the scene, or partly occluded. Recognizing both the moving and static objects is important in order to find events of interest to human analysts. In this paper, we describe our approach to object and event recognition using multiple stages of classification.

Springer eBooks, 2013

Craniosynostosis is the premature fusion of the bones of the calvaria resulting in abnormal skull... more Craniosynostosis is the premature fusion of the bones of the calvaria resulting in abnormal skull shapes that can be associated with increased intracranial pressure. While craniosynostoses of multiple different types can be easily diagnosed, quantifying the severity of the abnormality is much more subjective and not a standard part of clinical practice. For this purpose we have developed a severity-based retrieval system that uses a logistic regression approach to quantify the severity of the abnormality of each of three types of craniosynostoses. We compare several different sparse feature selection techniques: L 1 regularized logistic regression, fused lasso, and clustering lasso (cLasso). We evaluate our methodology in three ways: 1) for classification of normal vs. abnormal skulls, 2) for comparing pre-operative to post-operative skulls, and 3) for retrieving skulls in order of abnormality severity as compared with the ordering of a craniofacial expert.

ICC 2022 - IEEE International Conference on Communications, May 16, 2022

Springer eBooks, 2015

Craniofacial researchers have used anthropometric measurements taken directly on the human face f... more Craniofacial researchers have used anthropometric measurements taken directly on the human face for research and medical practice for decades. With the advancements in 3D imaging technologies, computational methods have been developed for the diagnoses of craniofacial syndromes and the analysis of the human face. Using advanced computer vision and image analysis techniques, diagnosis and quantification of craniofacial syndromes can be improved and automated. This paper describes a craniofacial image analysis pipeline and introduces the computational methods developed by the Multimedia Group at the University of Washington including data acquisition and preprocessing, low-and mid-level features, quantification, classification, and content-based retrieval.

Medical Imaging 2007: Visualization and Image-Guided Procedures, Mar 8, 2007

abstract Recent studies have shown that more than 5 million bronchoscopy procedures are performed... more abstract Recent studies have shown that more than 5 million bronchoscopy procedures are performed each year worldwide. The procedure usually involves biopsy of possible cancerous tissues from the lung. Standard bronchoscopes are too large to reach into the peripheral lung, where cancerous nodules are often found. The University of Washington has developed an ultrathin and flexible scanning fiber endoscope that is able to advance into the periphery of the human lungs without sacrificing image quality. To accompany the ...

This paper presents a method for selecting salient 2D views to describe 3D objects for the purpos... more This paper presents a method for selecting salient 2D views to describe 3D objects for the purpose of retrieval. The views are obtained by first identifying salient points via a learning approach that uses shape characteristics of the 3D points (Atmosukarto and Shapiro in International workshop on structural, syntactic, and statistical pattern recognition, 2008; Atmosukarto and Shapiro in ACM multimedia information retrieval, 2008). The salient views are selected by choosing views with multiple salient points on the silhouette of the object. Silhouette-based similarity measures from Chen et al. (Comput Graph Forum 22(3):223-232, 2003) are then used to calculate the similarity between two 3D objects. Retrieval experiments were performed on three datasets: the Heads dataset, the SHREC2008 dataset, and the Princeton dataset. Experimental results show that the retrieval results using the salient views are comparable to the existing light field descriptor method (Chen et al. in Comput Graph Forum 22(3):223-232, 2003), and our method achieves a 15-fold speedup in the feature extraction computation time.

In this paper we describe a new 3D object signature and evaluate its performance for 3D object re... more In this paper we describe a new 3D object signature and evaluate its performance for 3D object retrieval. The signature is based on a learning approach that finds the characteristics of salient points on a 3D object and represents the points in a 2D spatial map based on a longitude-latitude transformation. Experimental results show that the signature is able to achieve good retrieval scores for both pose-normalized and randomly-rotated object queries.

Overcrowding is a common problem faced by train commuters in many countries. While waiting for th... more Overcrowding is a common problem faced by train commuters in many countries. While waiting for the train at the stations, commuters tend to cluster and queue at doors that are closest to escalators and elevators that lead towards the station entrances and exits. This scenario results in trains not being fully utilized in terms of their capacity. As cabins with certain door positions tend to be more crowded than the rest of the cabins. The objective of this paper is to provide a methodology to estimate the crowd density within cabins of incoming trains, while leveraging on the existing train CCTV infrastructures. Providing the train cabin density information to commuters who are waiting for the incoming train allows the commuters to better select which cabin to board based on the provided density information. This will facilitate a better commuting experience without incurring a high cost for the train operator. To achieve this objective, we have adopted the usage of deep convolutional neural networks to analyze the footage from the existing security camera inside the trains and classify the images frames based the crowd level of train cabins. Three different experiments were conducted to train and test different convolutional neural network models. All models are able to make classification with an accuracy rate of over 90%.

The Journal of Pediatrics, Apr 1, 2012

Objectives-To assess three-dimensional (3D) changes in head shape ininfancy and at age 18 months ... more Objectives-To assess three-dimensional (3D) changes in head shape ininfancy and at age 18 months in children with and without plagiocephaly or brachycephaly. Study design-Using a longitudinal design, we evaluated head shape using 3D surface imaging. We compared the head shapes of children with (1) diagnosed deformational plagiocephaly or brachycephaly (cases; n=233); (2) unaffected controls, with no evidence of dysmorphology (n=167); and (3) affected controls, who despite having no previous diagnosis demonstrated skull dysmorphology on 3D surface imaging (n=70). Results-Cases had greater skull flattening and asymmetry than unaffected controls at both time points, as did controls with skull dysmorphology. In all groups, head shapes became less flat and more symmetric over time. Among cases, symmetry improved slightly more for those who received orthotic treatment. Conclusions-Although head shape improves over time for children with deformational plagiocephaly or brachycephaly, skull dysmorphology persists relative to unaffected controls. Further research is needed to clarify the extent to which thesedifferences are detectable to clinicians and lay observers.

The Cleft Palate-Craniofacial Journal, Oct 14, 2009

Objective: We developed and tested three dimensional (3-D) indices for quantifying severity of de... more Objective: We developed and tested three dimensional (3-D) indices for quantifying severity of deformational plagiocephaly (DP). Design: We evaluated the extent to which infants with and without DP (as determined by clinic referral and two experts' ratings) could be correctly classified. Participants: Infants ages 4-11 months, including 154 with diagnosed DP and 100 infants without a history of DP or other craniofacial condition. After excluding participants with discrepant expert ratings, data from 90 infants with DP and 50 infants without DP were retained. Measurements: Two-dimensional histograms of surface normal vector angles were extracted from 3-D mesh data and used to compute the severity scores below. Outcome measures: Left Posterior Flattening Score (LPFS), Right Posterior Flattening Score (RPFS), Asymmetry Score (AS), Absolute Asymmetry Score (AAS) and an approximation of a previously described 2-D measure, the Oblique Cranial Length Ratio (aOCLR). Twodimensional histograms localized the posterior flatness for each participant. Analysis: We fit receiver operating characteristic curves and calculated the area under the curves (AUC) to evaluate the relative accuracy of DP classification using the above measures. Results: The AUC statistics were: AAS=91%; LPFS=97%, RPFS=91%; AS=99%, and aOCLR=79%. Conclusion: Novel 3-D-based plagiocephaly posterior severity scores provided better sensitivity and specificity in the discrimination of plagiocephalic and typical head shapes than the 2-D measurements provided by a close approximation of OCLR. These indices will allow for more precise quantification of the DP phenotype in future studies on the prevalence of this condition, which may lead to improved clinical care.

We address the problem of modeling and classifying American Football offense teams' plays in vide... more We address the problem of modeling and classifying American Football offense teams' plays in video, a challenging example of group activity analysis. Automatic play classification will allow coaches to infer patterns and tendencies of opponents more efficiently, resulting in better strategy planning in a game. We define a football play as a unique combination of player trajectories. To this end, we develop a framework that uses player trajectories as inputs to MedLDA, a supervised topic model. The joint maximization of both likelihood and inter-class margins of MedLDA in learning the topics allows us to learn semantically meaningful play type templates, as well as, classify different play types with 70% average accuracy. Furthermore, this method is extended to analyze individual player roles in classifying each play type. We validate our method on a large dataset comprising 271 play clips from real-world football games, which will be made publicly available for future comparisons.

In this paper, we develop a novel framework for action recognition in videos. The framework is ba... more In this paper, we develop a novel framework for action recognition in videos. The framework is based on automatically learning the discriminative trajectory groups that are relevant to an action. Different from previous approaches, our method does not require complex computation for graph matching or complex latent models to localize the parts. We model a video as a structured bag of trajectory groups with latent class variables. We model action recognition problem in a weakly supervised setting and learn discriminative trajectory groups by employing multiple instance learning (MIL) based Support Vector Machine (SVM) using pre-computed kernels. The kernels depend on the spatio-temporal relationship between the extracted trajectory groups and their associated features. We demonstrate both quantitatively and qualitatively that the classification performance of our proposed method is superior to baselines and several state-of-the-art approaches on three challenging standard benchmark datasets.

CCF Transactions on Pervasive Computing and Interaction, May 2, 2023

Three-dimensional objects are now commonly used in a large number of applications including games... more Three-dimensional objects are now commonly used in a large number of applications including games, mechanical engineering, archaeology, culture, and even medicine. As a result, researchers have started to investigate the use of 3D shape descriptors that aim to encapsulate the ...

Springer eBooks, 2009

Craniofacial disorders are one of the most common category of birth defects worldwide, and are an... more Craniofacial disorders are one of the most common category of birth defects worldwide, and are an important topic of biomedical research. In order to better understand these disorders and correlate them with genetic patterns and life outcomes, researchers need to quantify the craniofacial anatomy. In this paper we introduce several different craniofacial descriptors that are being used in research studies for two craniofacial disorders: the 22q11.2 deletion syndrome (a genetic disorder) and plagiocephaly/brachycephaly, disorders caused by pressure on the head. Experimental results show that our descriptors show promise for quantifying craniofacial shape.