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Saliency Detection using Iterative Dynamic Guided Filtering 24

Saliency Detection using Iterative Dynamic Guided Filtering

引用

24th International conference on pattern recognition (ICPR)

作者： Wang, Chen Fan, Yangyu Northwestern Polytech Univ Xian Shaanxi Peoples R China AFEU Xian Shaanxi Peoples R China

ISBN: (纸本)9781538637883

Saliency detection is a basic and complex technology in computer vision, which can guide computer to extract key information from image by simulating human visual habit. When image characteristics are unevenly distributed, the accuracy of saliency detection methods will decrease. Unfortunately, this issue is common in natural images and forms a challenge for contrast based methods. We propose an iterative dynamic guided filtering approach to analyze saliency cues. A new and simple kernel function is designed by combining the information of filtering results and input image, which can ensure a good structure transfer from input image to guidance image. the saliency of image pixel is defined based on a novel contrast model using image boundary and center regions. At last, we highlight the result by an exponential function. Experimental results show that the proposed method is superior to the others in terms of detection accuracy and recall rate.

关键词： saliency detection human visual habit iterative dynamic guided filteing stucture transfer contrast model

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Graph Convolutional Tracking

Graph Convolutional Tracking

引用

IEEE/CVF conference on computer vision and pattern recognition

作者： Junyu Gao Tianzhu Zhang Changsheng Xu National Lab of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) University of Chinese Academy of Sciences (UCAS) Peng Cheng Laboratory ShenZhen China University of Science and Technology of China

Tracking by siamese networks has achieved favorable performance in recent years. However, most of existing siamese methods do not take full advantage of spatial-temporal target appearance modeling under different contextual situations. In fact, the spatial-temporal information can provide diverse features to enhance the target representation, and the context information is important for online adaption of target localization. To comprehensively leverage the spatial-temporal structure of historical target exemplars and get benefit from the context information, in this work, we present a novel Graph Convolutional Tracking (GCT) method for high-performance visual tracking. Specifically, the GCT jointly incorporates two types of Graph Convolutional Networks (GCNs) into a siamese framework for target appearance modeling. Here, we adopt a spatial-temporal GCN to model the structured representation of historical target exemplars. Furthermore, a context GCN is designed to utilize the context of the current frame to learn adaptive features for target localization. Extensive results on 4 challenging benchmarks show that our GCT method performs favorably against state-of-the-art trackers while running around 50 frames per second.

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Perception Enhancement for Bionic vision Preliminary Study on Object Classification with Subretinal Implants 7

Perception Enhancement for Bionic Vision <i>Preliminary Stud...

引用

7th International conference on pattern recognition Applications and Methods (ICPRAM)

作者： Steffen, Johannes Napp, Jonathan Pollmann, Stefan Toennies, Klaus Otto von Guericke Univ Dept Simulat & Graph Magdeburg Germany Otto von Guericke Univ Dept Expt Psychol Magdeburg Germany Otto von Guericke Univ Ctr Brain & Behav Sci Magdeburg Germany

ISBN: (纸本)9789897582769

the restored vision by using subretinal implants of patients suffering from a loss of photoreceptors, e.g., in retinitis pigmentosa and age-related macular degeneration, is, compared to healthy subjects, very limited. therefore, we investigated, whether it is possible to enhance the perception of such patients by transforming the input images in a systematic manner. To this end, we propose a new image transformation network that is capable to learn plausible image transformations in an end-to-end fashion in order to enhance the perception of (virtual) patients with simulated subretinal implants. As a proof of concept, we test our method on an object classification task with three classes. Our results are promising. Compared to a baseline model, the overall object classification accuracy increased significantly from 67.4% to 81.1%. Furthermore, we discuss implications and limitations of our proof of concept and outline aspects of our work that can be improved and need to be subject of further research.

关键词： Bionic vision Retinal Implants Artificial Neural Networks Object Classification

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Sublabel-Accurate Convex Relaxation with Total Generalized Variation Regularization 40th

Sublabel-Accurate Convex Relaxation with Total Generalized V...

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40th German conference on pattern recognition, GCPR 2018

作者： Strecke, Michael Goldluecke, Bastian University of Konstanz Konstanz Germany

ISBN: (纸本)9783030129385

We propose a novel idea to introduce regularization based on second order total generalized variation (TGV) into optimization frameworks based on functional lifting. the proposed formulation extends a recent sublabel-accurate relaxation for multi-label problems and thus allows for accurate solutions using only a small number of labels, significantly improving over previous approaches towards lifting the total generalized variation. Moreover, even recent sublabel accurate methods exhibit staircasing artifacts when used in conjunction with common first order regularizers such as the total variation (TV). this becomes very obvious for example when computing derivatives of disparity maps computed with these methods to obtain normals, which immediately reveals their local flatness and yields inaccurate normal maps. We show that our approach is effective in reducing these artifacts, obtaining disparity maps with a smooth normal field in a single optimization pass. © 2019, Springer Nature Switzerland AG.

关键词： computer vision

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Weakly- and Semi-supervised Faster R-CNN with Curriculum Learning 24

Weakly- and Semi-supervised Faster R-CNN with Curriculum Lea...

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24th International conference on pattern recognition (ICPR)

作者： Wang, Jiasi Wang, Xinggang Liu, Wenyu Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Hubei Peoples R China

ISBN: (纸本)9781538637883

Object detection is a core problem in computer vision and pattern recognition. In this paper, we study the problem of learning an effective object detector using weaklyannotated images (i.e., only the image level annotation is given) and a small proportion of fully-annotated images (i.e., bounding box level annotation is given) with curriculum learning. Our method is built upon Faster R-CNN. Different from previous weakly-supervised object detectors which rely on hand-craft object proposals, the proposed method learns a region proposal network using weakly-and semi-supervised training data. And the weakly-labeled images are fed into the deep network in a meaningful order which illustrates from easy to gradually more complex examples with curriculum learning. We name the Faster R-CNN trained using Weakly-And Semi-Supervised data with Curriculum Learning as WASSCL R-CNN. the WASSCL RCNN is validated on the PASCAL VOC 2007 benchmark, and obtains 90% of a fully-supervised Faster R-CNN's performance (measured using mAP) with only 15% of fully-supervised annotations together with weak supervision. the results show that the proposed learning framework can significantly reduce the labeling efforts for obtaining reliable object detectors.

关键词： Proposals Detectors Training Feature extraction Object detection Labeling Complexity theory

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Algorithm-hardware Co-design for Deformable Convolution 5

Algorithm-hardware Co-design for Deformable Convolution

引用

5th Workshop on Energy Efficient Machine Learning and Cognitive Computing ((EMC2) / conference on Neural Information Processing Systems (NIPS)

作者： Huang, Qijing Wang, Dequan Gao, Yizhao Cai, Yaohui Dong, Zhen Wu, Bichen Keutzer, Kurt Wawrzynek, John Univ Calif Berkeley Berkeley CA 94720 USA Univ Chinese Acad Sci Beijing Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9781665424189

FPGAs provide a flexible and efficient platform to accelerate rapidly-changing algorithms for computer vision. the majority of existing work focuses on accelerating image classification, while other fundamental vision problems, including object detection and instance segmentation, have not been adequately addressed. Compared with image classification, detection problems are more sensitive to the spatial variance of objects, and therefore, require specialized convolutions to aggregate spatial information. To address this, recent work proposes dynamic deformable convolution to augment regular convolutions. Regular convolutions process a fixed grid of pixels across all the spatial locations in an image, while dynamic deformable convolutions may access arbitrary pixels. the access pattern of deformable convolutions is input-dependent and varies per spatial location. these properties lead to inefficient memory accesses of inputs with existing hardware. In this work, we first investigate the overhead of the deformable convolution on embedded FPGA SoCs, and then show the accuracy-latency tradeoffs for a set of algorithm modifications, including full versus depthwise, fixed-shape, and limited-range. these modifications benefit the efficiency of the embedded accelerator in general. We build an efficient object detection network with modified deformable convolutions and quantize the network using state-of-the-art quantization methods. Experiments show that our co-design optimization for the deformable convolution achieves significant hardware speedup with little accuracy compromised.

关键词： Quantization (signal) Object detection Machine learning Hardware Energy efficiency Real-time systems Object recognition

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CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction 24

CANDID: Robust Change Dynamics and Deterministic Update Poli...

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24th International conference on pattern recognition (ICPR)

作者： Mandal, Murari Saxena, Prafulla Vipparthi, Santosh Kumar Murala, Subrahmanyam MNIT Jaipur Dept Comp Sci & Engn Jaipur Rajasthan India IIT Ropar Dept Elect Engn Comp Vis & Pattern Recognit Lab Rupnagar Punjab India

ISBN: (纸本)9781538637883

Background subtraction in video provides the preliminary information essential for many computer vision applications. In this paper, we propose a sequence of approaches named CANDID to solve the change detection problem in challenging video scenarios. the CANDID adaptively initializes the pixel-level distance threshold and update rate. these parameters are updated by computing the change dynamics at a location. Further, the background model is maintained by formulating a deterministic update policy. the performance of the proposed method is evaluated over various challenging scenarios such as dynamic background and extreme weather conditions. the qualitative and quantitative measures of the proposed method outperform the existing state-of-the-art approaches.

关键词： background subtraction deterministic motion detection adaptive threshold background modelling

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Gender recognition from face images using trainable shape and color features 24

Gender recognition from face images using trainable shape an...

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24th International conference on pattern recognition (ICPR)

作者： Azzopardi, George Foggia, Pasquale Greco, Antonio Saggese, Alessia Vento, Mario Univ Groningen Johann Bernoulli Inst Math & CompSc Fac Sci & Engn Groningen Netherlands Univ Salerno Dept Comp Engn & Elect Engn & Appl Math Fac Engn Salerno Italy

ISBN: (纸本)9781538637883

Gender recognition from face images is an important application and it is still an open computer vision problem, even though it is something trivial from the human visual system. Variations in pose, lighting, and expression are few of the problems that make such an application challenging for a computer system. Neurophysiological studies demonstrate that the human brain is able to distinguish men and women also in absence of external cues, by analyzing the shape of specific parts of the face. In this paper, we describe an automatic procedure that combines trainable shape and color features for gender classification. In particular the proposed method fuses edge-based and color-blob-based features by means of trainable COSFIRE filters. the former types of feature are able to extract information about the shape of a face whereas the latter extract information about shades of colors in different parts of the face. We use these two sets of features to create a stacked classification SVM model and demonstrate its effectiveness on the GENDER-COLOR-FERET dataset, where we achieve an accuracy of 96.4%.

关键词： Face Face recognition Image color analysis Feature extraction Shape Image edge detection Dogs

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Towards Unconstrained Pointing Problem of Visual Question Answering: A Retrieval-based Method 24

Towards Unconstrained Pointing Problem of Visual Question An...

引用

24th International conference on pattern recognition (ICPR)

作者： Cheng, Wenlong Huang, Yan Wang, Liang Univ Chinese Acad Sci Chinese Acad Sci Inst Automat Natl Lab Pattern RecognitCRIPAC Beijing 100190 Peoples R China

ISBN: (纸本)9781538637883

the pointing problem of visual question answering (VQA) is that given an image and a question which asks for the location of the interested object, find a region that answers the question. It is an important research problem in VQA tasks and has many potential applications in our daily life. Most of the existing work on this task can only solve it in the form of multiple choices, i.e., given candidate answers in advance, and then selecting a correct one. In this paper, we propose a retrieval model, which can not only deal with the multiple-choices task, but also provide a feasible solution for the no-candidate-answer task. the principle of our method is to pull the question and correct answer close, and push the question and incorrect answer away in a common feature space. To our best knowledge, we are the first to use retrieval method to solve the unconstrained (no-candidate-answer) pointing problem of VQA. Furthermore, our proposed method outperforms the state-of-the-art methods on the Visual7W [1] dataset in terms of the pointing problem of VQA.

关键词： computer vision

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A new bag of visual words encoding method for human action recognition 24

A new bag of visual words encoding method for human action r...

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24th International conference on pattern recognition (ICPR)

作者： Cortes, Xavier Conte, Donatello Cardot, Hubert Univ Tours LiFAT EA 6300 64 Ave Jean Portalis F-37000 Tours France

ISBN: (纸本)9781538637883

Human action recognition in videos is one of the key problems in computer vision. Inspired by image classification models, techniques based on bags of visual words have become one of the most effective approaches to solve this problem. the most usual way to engage an interest point in a bag of words is by means of the closest word found in a previously trained codebook. However, the quality of representation decreases when interest points have different visual words at similar distances or when we map noisy interest points. the aim of this paper is to present a new encoding procedure to engage the interest points in a bag of visual words improving the quality of the representation. the encoding that we propose tries to map only the relevant interest points detected in the scene. We experimentally show that using the new encoding method we can significantly improve the classification ratio.

关键词： Visualization Encoding Videos Vector quantization Training Histograms Classification algorithms

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