High-Precision Localization Using Ground Texture

Location-aware applications play an increasingly critical role in everyday life. However, satellite-based localization (e.g., GPS) has limited accuracy and can be unusable in dense urban areas and indoors. We introduce an image-based global localization system that is accurate to a few millimeters and performs reliable localization both indoors and outside. The key idea is to capture and index distinctive local keypoints in ground textures. This is based on the observation that ground textures including wood, carpet, tile, concrete, and asphalt may look random and homogeneous, but all contain cracks, scratches, or unique arrangements of fibers. These imperfections are persistent, and can serve as local features. Our system incorporates a downward-facing camera to capture the fine texture of the ground, together with an image processing pipeline that locates the captured texture patch in a compact database constructed offline. We demonstrate the capability of our system to robustly, accurately, and quickly locate test images on various types of outdoor and indoor ground surfaces

Low Carbon Energy Policy Research

AbstractCase study of Korea, Low carbon energy efficiency labeling schemes (Energy Efficiency Label and Standard Program, High efficiency Appliance Certification Program, e-Standby Program) play a key role in carrying out the energy efficiency improvement policy in the appliances and equipment sector in Korea. Korea operates these Programs in an effort to improve energy efficiency in appliances and equipments. Mandatory energy efficiency standard which bans production and sales of low energy efficiency products which fall below the minimum energy performance standard. Ministry of Knowledge of Economy (MKE) and Korea Energy Management Corporation (KEMCO) is the key organizations in implementing energy efficiency standards and labeling. National energy efficiency efforts can be realized through energy efficiency improvements with the successful implementation of an energy efficient appliances dissemination policy and the phase out of low efficiency appliances. Through the implementation of the Energy Efficiency Label and Standard Program (1992), High-efficiency Appliance Certification Program (1996) and e-Standby Program (1999), significant energy efficiency improvements have been achieved, and 1.37 billion USD worth of energy savings

3D ShapeNets: A Deep Representation for Volumetric Shapes

3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representations automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet -- a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.Comment: to be appeared in CVPR 201

Case report: Giant cystic ileal gastrointestinal stromal tumor with an atypical intratumoral abscess

BackgroundGastrointestinal stromal tumors (GISTs) are typically solid, sometimes with small cystic areas, but rarely manifest as predominantly cystic neoplasms. In addition, cystic intestinal GISTs with intratumoral abscess formation are rare.Case presentationWe present the case of a 49-year-old male patient with a history of frequent and urgent urination for 2 weeks. Radiologic studies revealed a large cystic mass in the lower abdomen. The patient underwent abdominal laparotomy, which revealed a large cystic mass arising from the distal ileum invading the sigmoid mesocolon and apex vesicae. Partial resection of the ileum along with the tumor and the adjacent bladder was performed. Macroscopic examination revealed that the cystic mass contained a large amount of foul-smelling pus and a tumor-bowel fistula. The final pathology revealed an abdominal stromal tumor. Postoperative recovery was uneventful, and adjuvant imatinib mesylate 400 mg was administered daily. No tumor recurrence or metastasis was observed during the 9-month follow-up period.ConclusionFingings of a cystic tumor in the abdomen should raise concern for cystic GISTs. This case report reviews a rare presentation of an ileal giant cystic GIST with atypical intratumoral abscess formation. Complete surgical resection and adjuvant imatinib is still the mainstay treatment for GISTs

Identity-Aware Hand Mesh Estimation and Personalization from RGB Images

Reconstructing 3D hand meshes from monocular RGB images has attracted increasing amount of attention due to its enormous potential applications in the field of AR/VR. Most state-of-the-art methods attempt to tackle this task in an anonymous manner. Specifically, the identity of the subject is ignored even though it is practically available in real applications where the user is unchanged in a continuous recording session. In this paper, we propose an identity-aware hand mesh estimation model, which can incorporate the identity information represented by the intrinsic shape parameters of the subject. We demonstrate the importance of the identity information by comparing the proposed identity-aware model to a baseline which treats subject anonymously. Furthermore, to handle the use case where the test subject is unseen, we propose a novel personalization pipeline to calibrate the intrinsic shape parameters using only a few unlabeled RGB images of the subject. Experiments on two large scale public datasets validate the state-of-the-art performance of our proposed method.Comment: ECCV 2022. Github https://github.com/deyingk/PersonalizedHandMeshEstimatio

ASSVd infection inhibits the vegetative growth of apple trees by affecting leaf metabolism

Apple scar skin viroid (ASSVd) can infect apple trees and cause scar skin symptoms. However, the associated physiological mechanisms are unclear in young saplings. In this study, ASSVd-infected and control ‘Odysso’ and ‘Tonami’ apple saplings were examined to clarify the effects of ASSVd on apple tree growth and physiological characteristics as well as the leaf metabolome. The results indicated that leaf ASSVd contents increased significantly after grafting and remained high in the second year. Leaf size, tree height, stem diameter, branch length, and leaf photosynthetic efficiency decreased significantly in viroid-infected saplings. In response to the ASSVd infection, the chlorophyll a and b contents decreased significantly in ‘Odysso’, but were unchanged in ‘Tonami’. Moreover, the N, P, K, Fe, Mn, and Ca contents decreased significantly in the leaves of viroid-infected ‘Odysso’ or ‘Tonami’. Similarly, the CAT and POD contents decreased significantly in the viroid-infected saplings, but the SOD content increased in the viroid-infected ‘Tonami’ saplings. A total of 15 and 40 differentially abundant metabolites were respectively identified in the metabolome analyses of ‘Odysso’ and ‘Tonami’ leaves. Specifically, in the viroid-infected ‘Odysso’ and ‘Tonami’ samples, the L-2-aminobutyric acid, 6″-O-malonyldaidzin, and D-xylose contents increased, while the coumarin content decreased. These metabolites are related to the biosynthesis of isoflavonoids and phenylpropanoids as well as the metabolism of carbohydrates and amino acids. These results imply that ASSVd affects apple sapling growth by affecting physiological characteristics and metabolism of apple leaves. The study data may be useful for future investigations on the physiological mechanisms underlying apple tree responses to ASSVd

Volatile metabolome and floral transcriptome analyses reveal the volatile components of strongly fragrant progeny of Malus × robusta

Floral fragrance is an important trait that contributes to the ornamental properties and pollination of crabapple. However, research on the physiological and molecular biology of the floral volatile compounds of crabapple is rarely reported. In this study, metabolomic and transcriptomic analyses of the floral volatile compounds of standard Malus robusta flowers (Mr), and progeny with strongly and weakly fragrant flowers (SF and WF, respectively), were conducted. Fifty-six floral volatile compounds were detected in the plant materials, mainly comprising phenylpropane/benzene ring-type compounds, fatty acid derivatives, and terpene compounds. The volatile contents were significantly increased before the early flowering stage (ES), and the contents of SF flowers were twice those of WF and Mr flowers. Odor activity values were determined for known fragrant volatiles and 10–11 key fragrant volatiles were identified at the ES. The predominant fragrant volatiles were methyl benzoate, linalool, leaf acetate, and methyl anthranilate. In the petals, stamens, pistil, and calyx of SF flowers, 26 volatiles were detected at the ES, among which phenylpropane/benzene ring-type compounds were the main components accounting for more than 75% of the total volatile content. Functional analysis of transcriptome data revealed that the phenylpropanoid biosynthesis pathway was significantly enriched in SF flowers. By conducting combined analyses between volatiles and differentially expressed genes, transcripts of six floral scent-related genes were identified and were associated with the contents of the key fragrant volatiles, and other 23 genes were potentially correlated with the key volatile compounds. The results reveal possible mechanisms for the emission of strong fragrance by SF flowers, and provide a foundation for improvement of the floral fragrance and development of new crabapple cultivars

Learning and Deploying Local Features

Many computer vision applications including image matching, image-based reconstruction and localization rely on extracting and matching robust local features. A typical local feature pipeline first detects repeatable keypoints in the image (i.e., keypoint detector), and then computes a short vector to uniquely describe each keypoint (i.e., feature descriptor). Both the keypoint detector and the feature descriptor are conventionally hand-crafted based on what is intuitive to the designer. For example, corners or blobs are popular choices of keypoints, and the image gradient is a useful clue for descriptors. However, it is often difficult to define these principles to accommodate various applications. In this thesis, we study data-driven approaches which can more easily tailor the local feature pipeline for target applications. We start with a mobile robotics application that leverages local features extracted from ground texture images to achieve high-precision global localization. The second part of the thesis addresses the problem that existing keypoint detectors that are optimized for natural images suffer from sub-optimal performance on texture images. We therefore learn a keypoint detector specifically for each type of texture using a deep neural network. Our detector automatically learns to identify keypoints that are distinctive in the target texture rather than relying on a set of pre-defined rules. Finally, we focus on a non-parametric approach for learning feature descriptors. Many well-performing local feature descriptors are trained using a triplet loss that includes a tunable margin, which limits its ability to generalize to other types of data and problems. We propose to replace the hard margin with a soft margin that self-tunes as learning progresses. To summarize, we first demonstrate through a novel visual-based localization system where a customized local feature pipeline is critical. Then, we tackle both the keypoint detector and the feature descriptor with generalizable data-driven approaches

Learning and Deploying Local Features

Many computer vision applications including image matching, image-based reconstruction and localization rely on extracting and matching robust local features. A typical local feature pipeline first detects repeatable keypoints in the image (i.e., keypoint detector), and then computes a short vector to uniquely describe each keypoint (i.e., feature descriptor). Both the keypoint detector and the feature descriptor are conventionally hand-crafted based on what is intuitive to the designer. For example, corners or blobs are popular choices of keypoints, and the image gradient is a useful clue for descriptors. However, it is often difficult to define these principles to accommodate various applications. In this thesis, we study data-driven approaches which can more easily tailor the local feature pipeline for target applications. We start with a mobile robotics application that leverages local features extracted from ground texture images to achieve high-precision global localization. The second part of the thesis addresses the problem that existing keypoint detectors that are optimized for natural images suffer from sub-optimal performance on texture images. We therefore learn a keypoint detector specifically for each type of texture using a deep neural network. Our detector automatically learns to identify keypoints that are distinctive in the target texture rather than relying on a set of pre-defined rules. Finally, we focus on a non-parametric approach for learning feature descriptors. Many well-performing local feature descriptors are trained using a triplet loss that includes a tunable margin, which limits its ability to generalize to other types of data and problems. We propose to replace the hard margin with a soft margin that self-tunes as learning progresses. To summarize, we first demonstrate through a novel visual-based localization system where a customized local feature pipeline is critical. Then, we tackle both the keypoint detector and the feature descriptor with generalizable data-driven approaches