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A Digital Image Processing Pipeline for Modelling of Realistic Noise in Synthetic Images

A Digital Image Processing Pipeline for Modelling of Realist...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Oleksandra Bielova Ronny Hansch Andreas Ley Olaf Hellwich Technische Universitat Berlin

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

The evaluation of computer vision methods on synthetic images offers control over scene, object, and camera properties. The disadvantage is that synthetic data usually lack many of the effects of real cameras that pose the actual challenge to the methods under investigation. Among those, noise is one of the effects more difficult to simulate as it changes the signal at an early stage and is strongly influenced by the camera's internal processing chain. The resulting noise is highly complex, intensity dependent, as well as spatially and spectrally correlated. We propose to transform synthetic images into the raw format of digital cameras, alter them with a physically motivated noise model, and then apply a processing chain that resembles a digital camera. Experiments show that the resulting noise exhibits a strong similarity to noise in real digital images, which further decreases the gap between synthesized images and real photographs.

关键词： Noise images Digital cameras COMPOSITE IMAGES Modeling Digital images Cameras Noise models Image processing SYNTHETICS computer vision pipeline Real number

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Learning Fashion By Simulated Human Supervision 31

Learning Fashion By Simulated Human Supervision

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

作者： Alshan, Eli Alpert, Sharon Neuberger, Assaf Bubis, Nathaniel Oks, Eduard Amazon Lab126 Sunnyvale CA 94089 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We consider the task of predicting subjective fashion traits from images using neural networks. Specifically, we are interested in training a network for ranking outfits according to how well they fit the user. In order to capture the variability induced by human subjective considerations, each training example is annotated by a panel of fashion experts. Similarly to previous works on subjective data, the panel votes are converted to a classification or regression problem and the corresponding network is trained and evaluated using standard objective metrics. The question is which objective metric, if any, is most suitable to measure the performance of a network trained for subjective tasks? In this paper, we conducted human approval tests for outfit ranking networks trained using various objective metrics. We show that these metrics do not adequately estimate the human approval of subjective tasks. Instead, we introduce a supervising network that unlike objective metrics, is designed to capture the variability induced by human subjectivity. We use it to supervise our outfit ranking network and we demonstrate empirically, that training our outfit ranking network with the suggested supervising network achieves greater approval ratings from human subjects.

关键词： Task analysis Training Measurement Level set Prediction algorithms computer vision Standards

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Learning Network Architectures of Deep CNNs under Resource Constraints 31

Learning Network Architectures of Deep CNNs under Resource C...

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

作者： Chan, Michael Scarafoni, Daniel Duarte, Ronald Thornton, Jason Skelly, Luke MIT Lincoln Lab 244 Wood St Lexington MA 02421 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Recent works in deep learning have been driven broadly by the desire to attain high accuracy on certain challenge problems. The network architecture and other hyper-parameters of many published models are typically chosen by trial-and-error experiments with little considerations paid to resource constraints at deployment time. We propose a fully automated model learning approach that (1) treats architecture selection as part of the learning process, (2) uses a blend of broad-based random sampling and adaptive iterative refinement to explore the solution space, (3) performs optimization subject to given memory and computational constraints imposed by target deployment scenarios, and (4) is scalable and can use only a practically small number of GPUs for training. We present results that show graceful model degradation under strict resource constraints for object classification problems using CIFAR-10 in our experiments. We also discuss future work in further extending the approach.

关键词： computer architecture Computational modeling Optimization Adaptation models Network architecture Linear programming Training

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KCNN: Extremely-Efficient Hardware Keypoint Detection with a Compact Convolutional Neural Network 31

KCNN: Extremely-Efficient Hardware Keypoint Detection with a...

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

作者： Di Febbo, Paolo Dal Mutto, Carlo Tieu, Kinh Mattoccia, Stefano Aquifi Inc Palo Alto CA 94303 USA Univ Bologna Bologna Italy

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Keypoint detection algorithms are typically based on handcrafted combinations of derivative operations implemented with standard image filtering approaches. The early layers of Convolutional Neural Networks (CNNs) for image classification, whose implementation is nowadays often available within optimized hardware units, are characterized by a similar architecture. Therefore, the exploration of CNNs for keypoint detection is a promising avenue to obtain a low-latency implementation, also enabling to effectively move the computational cost of the detection to dedicated Neural Network processing units. This paper proposes a methodology for effective keypoint detection by means of an efficient CNN characterized by a compact three-layer architecture. A novel training procedure is proposed for learning values of the network parameters which allow for an approximation of the response of handcrafted detectors, showing that the proposed architecture is able to obtain results comparable with the state of the art. The capability of emulating different detectors allows to deploy a variety of algorithms to dedicated hardware by simply retraining the network. A sensor-based FPGA implementation of the introduced CNN architecture is presented, allowing latency smaller than 1[ms].

关键词： Detectors Training computer architecture Hardware Field programmable gate arrays Convolution Complexity theory

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Dual Graphical Models for Relational Modeling of Indoor Object Categories

Dual Graphical Models for Relational Modeling of Indoor Obje...

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Lin Guo Guoliang Fan Weihua Sheng School of Electrical and Computer Engineering Oklahoma State University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

There are three levels for indoor scene understanding, pixel level labeling, object level recognition and scene level holistic understanding. The three levels provide complementary bottom-up scene representation. Traditional research often addresses these three tasks separately where the three levels of semantic data are seldom jointly considered. We propose a new method to bridge the three semantic levels by using dual graphical models for relational modeling of object categories in indoor scenes. The vertical placement model captures top-down object configuration by which the visible pixels of some accessory objects could be used to infer the presence of a supportive object underneath. The horizontal placement model reveals how multiple object categories are related to each other on the ground in different indoor scenes. The experimental results show improvements on the bounding box accuracy using both vertical and horizontal placement models from pixel level labeling.

关键词： Graphical models pixel three-level vertical placement target configurations Dual INDOORS

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QuadricSLAM: Dual Quadrics as SLAM Landmarks 31

QuadricSLAM: Dual Quadrics as SLAM Landmarks

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

作者： Nicholson, Lachlan Milford, Michael Sunderhauf, Niko Queensland Univ Technol ARC Ctr Excellence Robot Vis Brisbane Qld Australia

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Research in Simultaneous Localization And Mapping (SLAM) is increasingly moving towards richer world representations involving objects and high level features that enable a semantic model of the world for robots, potentially leading to a more meaningful set of robot-world interactions. Many of these advances are grounded in state-of-the-art computer vision techniques primarily developed in the context of image-based benchmark datasets, leaving several challenges to be addressed in adapting them for use in robotics. In this paper, we derive a SLAM formulation that uses dual quadrics as 3D landmark representations, exploiting their ability to compactly represent the size, position and orientation of an object, and show how 2D bounding boxes (such as those typically obtained from visual object detection systems) can directly constrain the quadric parameters via a novel geometric error formulation. We develop a sensor model for deep-learned object detectors that addresses the challenge of partial object detections often encountered in robotics applications, and demonstrate how to jointly estimate the camera pose and constrained dual quadric parameters in factor graph based SLAM with a general perspective camera.

关键词： Simultaneous localization and mapping Cameras Object detection Trajectory Robot vision systems Three-dimensional displays

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Comparing the Effects of Annotation Type on Machine Learning Detection Performance

Comparing the Effects of Annotation Type on Machine Learning...

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： James F. Mullen Franklin R. Tanner Phil A. Sallee Raytheon Corporation

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

The most prominent machine learning (ML) methods in use today are supervised, meaning they require ground-truth labeling of the data on which they are trained. Annotating data is arduous and expensive. Additionally, data sets for image object detection may be annotated by drawing polygons, drawing bounding boxes, or providing single points on targets. Selection of annotation technique is a tradeoff between time to annotate and accuracy of the annotation. When annotating a dataset for machine object recognition algorithms, researchers may not know the most advantageous method of annotation for their experiments. This paper evaluates the performance tradeoffs of three alternative methods of annotating imagery for use in ML. A neural network was trained using the different types of annotations and compares the detection accuracy of and differences between the resultant models. In addition to the accuracy, cost is analyzed for each of the models and respective datasets.

关键词： ANNOTATIONS Dataset tradeoffs bounding boxes Artificial Intelligence Machine Learning alternative methods computer Programming Languages drawing Drawing

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Incorporating Touch Biometrics to Mobile One-Time Passwords: Exploration of Digits 31

Incorporating Touch Biometrics to Mobile One-Time Passwords:...

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

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Fierrez, Julian Ortega-Garcia, Javier Univ Autonoma Madrid Biometr & Data Pattern Analyt BiDA Lab E-28049 Madrid Spain

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This work evaluates the advantages and potential of incorporating touch biometrics to mobile one-time passwords (OTP). The new e-BioDigit database, which comprises on-line handwritten numerical digits from 0 to 9, has been acquired using the finger touch as input to a mobile device. This database is used in the experiments reported in this work and it is publicly available to the research community. An analysis of the OTP scenario using handwritten digits is carried out regarding which are the most discriminative handwritten digits and how robust the system is when increasing the number of them in the user password. Additionally, the best features for each handwritten numerical digit are studied in order to enhance our proposed biometric system. Our proposed approach achieves remarkable results with EERs ca. 5.0% when using skilled forgeries, outperforming other traditional biometric verification traits such as the handwritten signature or graphical passwords on similar mobile scenarios.

关键词： Biometrics (access control) Databases Password Mobile handsets Feature extraction Protocols

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DIY Human Action dataset Generation 31

DIY Human Action dataset Generation

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

作者： Khodabandeh, Mehran Joze, Hamid Reza Vaezi Zharkov, Ilya Pradeep, Vivek Simon Fraser Univ Burnaby BC Canada Microsoft Redmond WA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The recent successes in applying deep learning techniques to solve standard computer vision problems has aspired researchers to propose new computer vision problems in different domains. As previously established in the field, training data itself plays a significant role in the machine learning process, especially deep learning approaches which are data hungry. In order to solve each new problem and get a decent performance, a large amount of data needs to be captured which may in many cases pose logistical difficulties. Therefore, the ability to generate de novo data or expand an existing dataset, however small, in order to satisfy data requirement of current networks may be invaluable. Herein, we introduce a novel way to partition an action video clip into action, subject and context. Each part is manipulated separately and reassembled with our proposed video generation technique. Furthermore, our novel human skeleton trajectory generation along with our proposed video generation technique, enables us to generate unlimited action recognition training data. These techniques enables us to generate video action clips from an small set without costly and time-consuming data acquisition. Lastly, we prove through extensive set of experiments on two small human action recognition datasets, that this new data generation technique can improve the performance of current action recognition neural nets.

关键词： Skeleton Gallium nitride Task analysis Trajectory Generators computer vision Training

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Cosmetic Features Extraction by a Single Image Makeup Decomposition 31

Cosmetic Features Extraction by a Single Image Makeup Decomp...

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

作者： Yamagishi, Kanami Yamamoto, Shintaro Kato, Takuya Morishima, Shigeo Waseda Univ Tokyo Japan Waseda Res Inst Sci & Engn Tokyo Japan

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In recent years, a large number of makeup images have been shared on social media. Most of these images lack information about the cosmetics used, such as color, glitter or etc., while they are difficult to infer due to the diversity of skin color or lighting conditions. In this paper, our goal is to estimate cosmetic features only from a single makeup image. Previous work has measured the material parameters of cosmetic products from pairs of images showing the face with and without makeup, but such comparison images are not always available. Furthermore, this method cannot represent local effects such as pearl or glitter since they adapted physically-based reflectance models. We propose a novel image-based method to extract cosmetic features considering both color and local effects by decomposing the target image into makeup and skin color using Difference of Gaussian (DoG). Our method can be applied for single, standalone makeup images, and considers both local effects and color. In addition, our method is robust to the skin color difference due to the decomposition separating makeup from skin. The experimental results demonstrate that our method is more robust to skin color difference and captures characteristics of each cosmetic product.

关键词： Image color analysis Feature extraction Dogs Skin Lighting computer vision pattern recognition

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