1,720,960 research outputs found
Retaining Image Feature Matching Performance Under Low Light Conditions
No full textPoor image quality in low light images may result in a reduced number of feature matching between images. In this paper, we investigate the performance of feature extraction algorithms in low light environments. To find an optimal setting to retain feature matching performance in low light images, we look into the effect of changing feature acceptance threshold for feature detector and adding pre-processing in the form of Low Light Image Enhancement (LLIE) prior to feature detection. We observe that even in low light images, feature matching using traditional hand-crafted feature detectors still performs reasonably well by lowering the threshold parameter. We also show that applying LLIE algorithms can improve feature matching even further when paired with the right feature extraction algorithm
다중 작업 학습을 및 센서 융합 통한 강인한 전체론적 장면 이해
No full text학위논문(박사) - 한국과학기술원 : 기계공학과, 2022.8,[ix, 131 p.:]Accurate mathematical modeling of the surrounding environment plays a vital role in the safe operation of unmanned systems such as autonomous vehicles, mobile, and aerial robotics. Such modeling of dynamic scenes can be performed using centralized or distributed sensor systems, with the expectation to provide accurate attribute representation in diverse weather conditions. Given these constraints, modern sensor systems leverage LIDAR, RADAR, and Visible spectrum cameras to capture and represent different scene properties. Subsequently, data-driven approaches are utilized to process these raw measurements and extract complex patterns represented as high-level attributes. Such high-level features include object detection, semantic and road marking segmentation,
depth estimation, and multi-object tracking. These attributes are then aggregated to provide a holistic scene understanding based on which path planning and control can be performed to ensure safe operation.
However, current approaches for holistic scene understanding utilize multiple task-specific algorithms, resulting in a computationally expensive solution on account of increased redundant computations. Furthermore, these task-specific algorithms are sensitive to domain gaps arising from varying sensor properties or configurations. As construction of a sensor stack is based on the requirements of the end application. E.g., a mobile robot would require a short-range wide field of view (FoV) surround perception. At the same time, adaptive cruise control within an autonomous vehicle or ADAS would also require long-range forward perception with a narrow FoV. Hence, a well-annotated training dataset is required for each new sensor stack or domain, which is prohibitively expensive.
This dissertation focuses on performing holistic scene understanding for autonomous vehicles using heterogeneous sensor systems where calibration parameters are known. We define holistic scene understanding as estimating attributes such as road markers and unique object instances in a 3D space. Towards this objective, we highlight standard perception systems to either focus on the surround or long-range forward perception using a ring-camera
or stereo-camera apart from sensors such as RADAR and LIDAR. Given the strengths of different sensors, combining signals from these multi-modal sensors is beneficial to provide the necessary robustness for different scenarios. However, due to incompatibility in the signal output, it cannot be directly aggregated. Therefore, we propose a two-stage mechanism to simultaneously solve the issue of multi-modal data fusion while extracting meaningful information. The first mechanism focuses on extracting attributes using cameras into point-cloud space. Following this, we integrate different sensor signals into point-cloud space and perform downstream perception tasks such as 3D Object Detection.
As vision sensors are widely used as primary sensors due to their ability to densely capture scene information, we focus on devising resource friendly algorithms to extract different scene attributes such as scene semantics, road attributes, object detections, etc. To ensure extraction of these attributes without excessive computational overhead, we propose utilizing Multi-Task (MT) Networks. While such an approach is theoretically sound, the practical performance of any data-driven system relies upon the quality of training data, which, while playing a critical role, is usually overlooked. In addition, one caveat of using the multi-task framework is the availability of task-specific ground truth per input. However, current state-of-the-art (SoTA) primarily comprises multiple task-specific datasets focusing on distinct operating conditions and tasks. Furthermore, as each dataset source has a non-identical sensor setup, these cannot be directly used in a multi-task setting. Thus to overcome this critical requirement of well-annotated datasets, we develop domain invariant task-specific networks that can provide high-quality pseudo ground truth labels for training the deep-learning-based multi-task algorithm. Hence we can summarize the contributions of this dissertation as follows,
• We propose a multi-modal multi-task pipeline for performing holistic scene understanding generalizable to
a wide variety of sensor configurations.
• We demonstrate that such a pipeline is computationally efficient and robust to different weather variations
compared to task-specific networks.
• To ensure optimal training without requiring additional annotated labels, we develop different domain in-
variant approaches that can be utilized to provide pseudo ground truth labels.
• To improve the performance of the MT network further, we propose a blind image restoration algorithm to
restore regions within images that are affected by weather variations.
• Finally, we validate the performance and robustness of the proposed framework on publicly available
datasets for downstream 3D perception tasks such as Object Detection.한국과학기술원 :기계공학과
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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