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Filter Guided Manifold Optimization in the Autoencoder Latent Space

Filter Guided Manifold Optimization in the Autoencoder Laten...

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Nate Lannan Guoliang Fan School of Electrical and Computer Engineering Oklahoma State University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

An autoencoder is a class of neural network that is trained to output an accurate reproduction of the input while learning key lower dimensional features, other-wise known as a manifold. A lower dimensional representation of the original input, referred to as the latent space, encodes the intrinsic data structure over the manifold. This paper proposes filter-guided manifold optimization in the latent space of a convolutional autoencoder to recover noisy motion data collected by a depth sensor. Autoencoder output is smoothed using four traditional filters and employed as target motion data in an objective function. The difference between the actual output and target is minimized through stochastic gradient descent over the latent space, using manifold optimization to produce the expected smooth output. The advantage of this filter-guided approach over-traditional filtering is that the resultant motion data still adheres to the manifold in the latent space learned by the autoencorder from training on motion data.

关键词： Manifolds Filters

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Variational Learning of Beta-Liouville Hidden Markov Models for Infrared Action recognition

Variational Learning of Beta-Liouville Hidden Markov Models ...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Samr Ali Nizar Bouguila Electrical and Computer Engineering Department Concordia University Concordia Institute for Information Systems Engineering Concordia University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Infrared (IR) images are characterized by a lower sensitivity to lighting conditions than the visible spectrum. This opens the door to relatively untapped research potential of automatic recognition systems that are robust to shadows and variability in illumination levels or appearance. IR action recognition (AR) is one such application. It remains a fairly unexplored domain in IR. As such, in this paper, we propose the use of hidden Markov models (HMM) for IR AR. We also derive the mathematical model for the variational learning of Beta-Liouville (BL) HMMs. Next, we present the results of the proposed model on the Infrared Action recognition (InfAR) dataset. To the best of our knowledge, this is the first application of HMMs to AR in the IR domain, and the first application of the BL HMMs to AR. Experimental results demonstrate promising results using different features extracted from the InfAR dataset.

关键词： Hidden Markov models Argon automated identification system iridium illumination level Infrared imaging Infrared radiation shadows recognition (Psychology) Learning Mathematical Model

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A Customized Camera Imaging Pipeline for Dermatological Imaging

A Customized Camera Imaging Pipeline for Dermatological Imag...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Hakki Can Karaimer Iman Khodadad Farnoud Kazemzadeh Michael S. Brown York University Elucid Labs

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

This paper describes the customization of the camera processing pipeline of a machine vision camera that has been integrated into a hand-held dermatological imaging device. The device uses a combination of visible and non-visible spectral LEDs to allow capture of visible RGB imagery as well as selected non-visible wavelengths. Our customization involves two components. The first component is a color calibration procedure that ensures the captured images are colorimetrically more accurate than those obtained through the machine vision camera's native API. The need for color calibration is a critical component that is often overlooked or poorly understood by computer vision engineers. Our second component is a fast method to integrate the narrow band spectral images (some of which are outside the visible range) into the visible RGB image for enhanced visualization. This component of our pipeline involves evaluating several algorithms capable of multiple image fusion to determine the most suitable one for our application. Quantitative and subject results, including feedback from clinicians, demonstrate the effectiveness of our customization procedure.

关键词： Customization COLOR CORRECTION pipeline images Cameras robot vision Shortcuts computer vision Crucial components red-green-blue colour model image fusion Imaging Feedback

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Human Pose as Calibration pattern;3D Human Pose Estimation with Multiple Unsynchronized and Uncalibrated Cameras 31

Human Pose as Calibration Pattern;3D Human Pose Estimation w...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Takahashi, Kosuke Mikami, Dan Isogawa, Mariko Kimata, Hideaki NIPPON TELEGRAPH & TEL COORPORAT NTT Media Intelligence Labs Tokyo Japan

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This paper proposes a novel algorithm of estimating 3D human pose from multi-view videos captured by unsynchronized and uncalibrated cameras. In a such configuration, the conventional vision-based approaches utilize detected 2D features of common 3D points for synchronization and camera pose estimation, however, they sometimes suffer from difficulties of feature correspondences in case of wide baselines. For such cases, the proposed method focuses on that the projections of human joints can be associated each other robustly even in wide baseline videos and utilizes them as the common reference points. To utilize the projections of joint as the corresponding points, they should be detected in the images, however, these 2D joint sometimes include detection errors which make the estimation unstable. For dealing with such errors, the proposed method introduces two ideas. The first idea is to relax the reprojection errors for avoiding optimizing to noised observations. The second idea is to introduce an geometric constraint on the prior knowledge that the reference points consists of human joints. We demonstrate the performance of the proposed algorithm of synchronization and pose estimation with qualitative and quantitative evaluations using synthesized and real data.

关键词： Cameras Two dimensional displays Three-dimensional displays Pose estimation Videos Calibration

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SCAN: Spatial Color Attention Networks for Real Single Image Super-Resolution

SCAN: Spatial Color Attention Networks for Real Single Image...

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Xuan Xu Xin Li Lane Department of Computer Science and Electrical Engineering West Virginia University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Conceptually similar to adaptation in model-based approaches, attention has received increasing more attention in deep learning recently. As a tool to reallocate limited computational resources based on the importance of informative components, attention mechanism has found successful applications in both high-level and low-level vision tasks which includes channel attention, spatial attention, non-local attention and etc. However, to the best of our knowledge, attention mechanism has not been studied for the R,G,B channels of color images in the open literature. In this paper, we propose a spatial color attention networks (SCAN) designed to jointly exploit the spatial and spectral dependency within color images. More specifically, we present a spatial color attention module that calibrates important color information for individual color components from output feature maps of residual groups. When compared against previous state-of-the-art method Residual Channel Attention Networks (RCAN), SCAN has achieved superior performance in terms of both subjective and objective qualities on the dataset provided by NTIRE2019 real single image super-resolution challenge.

关键词： color image super-resolution low-level vision Color chromatic information red-green-blue colour model

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MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning

MultiNet++: Multi-Stream Feature Aggregation and Geometric L...

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Sumanth Chennupati Ganesh Sistu Senthil Yogamani Samir A Rawashdeh Valco North America Valeo Vision Systems University of Michigan-Dearborn

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Multi-task learning is commonly used in autonomous driving for solving various visual perception tasks. It offers significant benefits in terms of both performance and computational complexity. Current work on multi-task learning networks focus on processing a single input image and there is no known implementation of multi-task learning handling a sequence of images. In this work, we propose a multi-stream multi-task network to take advantage of using feature representations from preceding frames in a video sequence for joint learning of segmentation, depth, and motion. The weights of the current and previous encoder are shared so that features computed in the previous frame can be leveraged without additional computation. In addition, we propose to use the geometric mean of task losses as a better alternative to the weighted average of task losses. The proposed loss function facilitates better handling of the difference in convergence rates of different tasks. Experimental results on KITTI, Cityscapes and SYNTHIA datasets demonstrate that the proposed strategies outperform various existing multi-task learning solutions.

关键词： Learning PARALLEL PROCESSING (computerS) Video sequences image sequences Weighted Average urban landscape Geometric mean Frame

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Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds

Complexer-YOLO: Real-Time 3D Object Detection and Tracking o...

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Martin Simon Karl Amende Andrea Kraus Jens Honer Timo Samann Hauke Kaulbersch Stefan Milz Horst Michael Gross Valeo Schalter und Sensoren GmbH Ilmenau University of Technology

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Accurate detection of 3D objects is a fundamental problem in computer vision and has an enormous impact on autonomous cars, augmented/virtual reality and many applications in robotics. In this work we present a novel fusion of neural network based state-of-the-art 3D detector and visual semantic segmentation in the context of autonomous driving. Additionally, we introduce Scale-Rotation-Translation score (SRTs), a fast and highly pa-rameterizable evaluation metric for comparison of object detections, which speeds up our inference time up to 20% and halves training time. On top, we apply state-of-the-art online multi target feature tracking on the object measurements to further increase accuracy and robustness utilizing temporal information. Our experiments on KITTI show that we achieve same results as state-of-the-art in all related categories, while maintaining the performance and accuracy trade-off and still run in real-time. Furthermore, our model is the first one that fuses visual semantic with 3D object detection.

关键词： Object detection images computer vision Semantics tracking time information robotics Clouds

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DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images 31

DeepGlobe 2018: A Challenge to Parse the Earth through Satel...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Demir, Ilke Koperski, Krzysztof Lindenbaum, David Pang, Guan Huang, Jing Bast, Saikat Hughes, Forest Tuia, Devis Raskar, Ramesh Facebook Menlo Pk CA 94025 USA DigitalGlobe Westminster CO USA CosmiQ Works Burnaby BC Canada Wageningen Univ Wageningen Netherlands MIT Media Lab Cambridge MA 02139 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite images (Figure 1). Similar to other challenges in computer vision domain such as DAVIS[21] and COCO[33], DeepGlobe proposes three datasets and corresponding evaluation methodologies, coherently bundled in three competitions with a dedicated workshop co-located with CVPR 2018. We observed that satellite imagery is a rich and structured source of information, yet it is less investigated than everyday images by computer vision researchers. However, bridging modern computer vision with remote sensing data analysis could have critical impact to the way we understand our environment and lead to major breakthroughs in global urban planning or climate change research. Keeping such bridging objective in mind, DeepGlobe aims to bring together researchers from different domains to raise awareness of remote sensing in the computer vision community and vice-versa. We aim to improve and evaluate state-of-the-art satellite image understanding approaches, which can hopefully serve as reference benchmarks for future research in the same topic. In this paper, we analyze characteristics of each dataset, define the evaluation criteria of the competitions, and provide baselines for each task.

关键词： Satellites Roads Buildings Task analysis Urban areas computer vision Image segmentation

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Pseudo-labels for Supervised Learning on Dynamic vision Sensor Data, Applied to Object Detection under Ego-motion 31

Pseudo-labels for Supervised Learning on Dynamic Vision Sens...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chen, Nicholas F. Y. DSO Natl Labs 12 Sci Pk Dr Singapore 118225 Singapore

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In recent years, dynamic vision sensors (DVS), also known as event-based cameras or neuromorphic sensors, have seen increased use due to various advantages over conventional frame-based cameras. Using principles inspired by the retina, its high temporal resolution overcomes motion blurring, its high dynamic range overcomes extreme illumination conditions and its low power consumption makes it ideal for embedded systems on platforms such as drones and self-driving cars. However, event-based data sets are scarce and labels are even rarer for tasks such as object detection. We transferred discriminative knowledge from a state-of-the-art frame-based convolutional neural network (CNN) to the event-based modality via intermediate pseudo-labels, which are used as targets for supervised learning. We show, for the first time, event-based car detection under ego-motion in a real environment at 100 frames per second with a test average precision of 40.3% relative to our annotated ground truth. The event-based car detector handles motion blur and poor illumination conditions despite not explicitly trained to do so, and even complements frame-based CNN detectors, suggesting that it has learnt generalized visual representations.

关键词： vision sensors Cameras Vehicle dynamics Object detection Voltage control Supervised learning Automobiles

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Live Demonstration: Digit recognition On Pixel Processor Arrays

Live Demonstration: Digit Recognition On Pixel Processor Arr...

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Laurie Bose Jianing Chen Stephen J. Carey Piotr Dudek Walterio Mayol-Cuevas Faculty of Engineering Computer Science University of Bristol School of Electrical and Electronic Engineering The University of Manchester

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

In this demo, we will showcase recent work on implementing convolutional neural networks directly on pixel processor arrays (PPA). As CNNs demonstrate enhanced performance across tasks from classification to image synthesis, it becomes essential to find the most adequate ways to realize them, especially for embedded, real-time and reactive tasks in areas across computer vision and Robotics. The PPA concept is one architecture that pairs sensing and massively parallel processing at the focal plane level and allow mid to high level tasks to be run wholly embedded within them. They allow operation at high framerates and low energy consumption (≤ 2W), and without the need for external signal interpretation or processing. In this demo we will showcase our recent work on the implementation of CNNs on the SCAMP5 architecture as a step towards true end-to-end operation on flexibly programmable PPA hardware. In particular, we will showcase handwritten number classification from image capture to classification wholly embedded on the PPA.

关键词： Religious Missions Image synthesis image capture Taxonomy images computer vision Digit embedding Surgery massively parallel processing work implement low energy consumption Digital external signals

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