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Fish Detection from Low Visibility Underwater Videos 24

Fish Detection from Low Visibility Underwater Videos

引用

24th International conference on pattern recognition (ICPR)

作者： Shevchenko, Violetta Eerola, Tuomas Kaarna, Arto Lappeenranta Univ Technol Sch Engn Sci Machine Vis & Pattern Recognit Lab Lappeenranta Finland

ISBN: (纸本)9781538637883

Counting and tracking fish populations is important for conservation purposes as well as for the fishing industry. Various non-invasive automatic fish counters exist based on such principles as resistivity, light beams and sonar. However, such methods typically cannot make distinction between fish and other passing objects, and moreover, cannot recognize different species. computer vision techniques provide an attractive alternative for building a more robust and versatile fish counting systems. In this paper we present the fish detection framework for noisy videos captured in water with low visibility. For this purpose, we compare three background subtraction methods for the task. Moreover, we propose necessary post-processing steps and heuristics to detect the fish and separate them from other moving objects. the results showed that by choosing an appropriate background subtraction method, it is possible to achieve a satisfying detection accuracy of 80% and 60% for two challenging datasets. the proposed method will form a basis for the future development of fish species identification methods.

关键词： Videos Task analysis Fish Lighting Adaptation models Optical distortion Adaptive optics

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Facial Expression recognition for Different Pose Faces Based on Special Landmark Detection 24

Facial Expression Recognition for Different Pose Faces Based...

引用

24th International conference on pattern recognition (ICPR)

作者： Wu, Wenqi Yin, Yingjie Wang, Yingying Wang, Xingang Xu, De Chinese Acad Sci Inst Automat Res Ctr Precis Sensing & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 101408 Peoples R China

ISBN: (纸本)9781538637883

Facial expression recognition is a challenging task in computer vision field using only single facial image. As we know, human faces are convex spheres. the self-occlusion phenomenon generated from face pose will seriously affect the accuracy of expression recognition. In order to solve this problem, we propose a novel facial expression recognition method for different pose faces based on special landmark detection (FER-MPI-SFL). Our method is based on two shared networks. the outputs of the first Network are 29 special landmarks and 1 face box, which are the inputs of the second network and used to estimate face pose. the methods of RoIAlign and feature map concatenation are introduced in the second network to recognize the facial expression. the weight allocation of feature maps concatenation is guided by the result of pose estimation. In addition, an improved center loss is proposed to make the distances between the features of different expressions larger and easier to be classified in the feature space. As a result, superior performance to other state-of-the-art methods is achieved in facial expression databases CK+, MMI, Oulu-CASIA VIS and a new created database CASIA-MFE which contains more faces with different poses.

关键词： Facial Expression recognition Deep Learning Deep Convolutional Neural Network Landmark Detection

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Spindle-Net: CNNs for Monocular Depth Inference with Dilation Kernel Method 24

Spindle-Net: CNNs for Monocular Depth Inference with Dilatio...

引用

24th International conference on pattern recognition (ICPR)

作者： He, Lei Yu, Miao Wang, Guanghui Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Henan Peoples R China Univ Kansas Dept Elect Engn & Comp Sci Lawrence KS 66045 USA

ISBN: (纸本)9781538637883

Learning depth from a single image is an important issue in computer vision. To solve this problem, encoder-decoder architect is usually employed as a powerful architecture to learn the dense corresponding function. In this work, we propose a symmetrical Spindle network of the encoder-decoder to learn the fine-grained depth. Unlike traditional convolution neural network, we first boost up the feature maps from low-dimension space to a high-dimension space, then extract the features for monocular depth learning. In order to overcome limitation of the computer memory, a single image super-resolution technique is proposed to replace the boosting process by fusing local cues in edge direction. Given the super-resolution images, the monocular depth learning needs more global information than most architectures for pixel-wise predictions. To address this issue, dilation kernel method is proposed to enlarge the receptive field in each layer. For the task of the super-resolution, the proposed method achieves better performance than the state-of-the-art methods. Extensive experiments on the monocular depth inference demonstrate that the Spindle network could achieve comparable performance on the NYU and Make3D datasets, compared with the state-of-the-art algorithms. the proposed method reveals a new perspective to learn the depth from a single image, which shows a promising generality to other pixel-wise prediction problems.

关键词： Convolution Image resolution Kernel Feature extraction Neural networks computer architecture Task analysis

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Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch 24

Learning Cross-Modal Deep Embeddings for Multi-Object Image ...

引用

24th International conference on pattern recognition (ICPR)

作者： Dey, Sounak Dutta, Anjan Ghosh, Suman K. Valveny, Ernest Llados, Josep Pal, Umapada Autonomous Univ Barcelona Comp Vis Ctr Dept Comp Sci Barcelona Spain Indian Stat Inst CVPR Unit Kolkata India

ISBN: (纸本)9781538637883

In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is used to selectively focus the attention on the different objects of the image, allowing for retrieval with multiple objects in the query. Experiments show that the proposed method performs the best in both single and multiple object image retrieval in standard datasets.

关键词： Image retrieval Image representation Visualization Feature extraction Image edge detection computer vision Standards

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Automatic Facial Attractiveness Prediction by Deep Multi-Task Learning 24

Automatic Facial Attractiveness Prediction by Deep Multi-Tas...

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24th International conference on pattern recognition (ICPR)

作者： Gao, Lian Li, Weixin Huang, Zehua Huang, Di Wang, Yunhong Beihang Univ Sch Comp Sci & Engn IRIP Lab Beijing 100191 Peoples R China Beihang Univ Adv Innovat Ctr Big Data & Brain Comp Beijing 100191 Peoples R China TuSimple San Diego CA USA

ISBN: (纸本)9781538637883

Facial Attractiveness Prediction (FAP) is a useful yet challenging problem in the domain of computer vision. In this paper, we propose a deep learning based approach. Different from the existing deep methods, the proposed one models both the texture and shape clues within a multi-task learning framework consisting of attractiveness score prediction and fiducial landmark localization, thus highlighting both of their roles in assessing attractiveness of faces. Considering that the training data are not extensive, a lightweight CNN is designed to jointly learn the facial representation, landmark location, and facial attractiveness score. the proposed method is evaluated on the SCUT-FBP database, and a prediction correlation 0.92, is delivered, which shows the effectiveness of our method. Furthermore, two additional experiments in terms of comparison between facial images before and after make-up or beautification are conducted. the results also prove the advantage of the proposed method.

关键词： facial attractiveness prediction facial beauty analysis deep learning multi-task learning landmark detection

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A Fast Local Analysis by thresholding applied to image matching 24

A Fast Local Analysis by Thresholding applied to image match...

引用

24th International conference on pattern recognition (ICPR)

作者： Faula, Yannick Bres, Stephane Eglin, Veronique Univ Lyon INSA Lyon LIRIS UMR5205 F-69621 Lyon France

ISBN: (纸本)9781538637883

Key structures extraction and matching are key steps in computer vision. Many fields of application need large image acquisition and fast extraction of fine structures. In this study, we focus on situations where existing local feature extractors give not enough satisfying results concerning both accuracy and time processing. Among good illustrations, we can quote short-line extraction in local weakly-contrasted images. We propose a new Fast Local Analysis by threSHolding (FLASH) designed to process large images under hard time constraints. We use "micro-line" points as key feature. these are used for shape reconstruction (like lines) and local signature design. We apply FLASH on the field of concrete infrastructure monitoring where robots and UAVs are more and more used for automated defect detection (like cracks). For large concrete surfaces, there are several hard constraints such as the computational time and the reliability. Results show us that the computations are faster than several existing algorithms in image matching and FLASH has invariance to rotation, partial occlusion, and scale range from 0.7 to 1.4 without scale-space exploration.

关键词： Image matching

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Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements 24

Fast Motion Deblurring for Feature Detection and Matching Us...

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24th International conference on pattern recognition (ICPR)

作者： Mustaniemi, Janne Kannala, Juho Sarkka, Simo Matas, Jiri Heikkila, Janne Univ Oulu Ctr Machine Vis & Signal Anal Oulu Finland Aalto Univ Helsinki Finland Czech Tech Univ Dept Cybernet Ctr Machine Percept Prague Czech Republic

ISBN: (纸本)9781538637883

Many computer vision and image processing applications rely on local features. It is well-known that motion blur decreases the performance of traditional feature detectors and descriptors. We propose an inertial-based deblurring method for improving the robustness of existing feature detectors and descriptors against the motion blur. Unlike most deblurring algorithms, the method can handle spatially-variant blur and rolling shutter distortion. Furthermore, it is capable of running in real-time contrary to state-of-the-art algorithms. the limitations of inertial-based blur estimation are taken into account by validating the blur estimates using image data. the evaluation shows that when the method is used with traditional feature detector and descriptor, it increases the number of detected keypoints, provides higher repeatability and improves the localization accuracy. We also demonstrate that such features will lead to more accurate and complete reconstructions when used in the application of 3D visual reconstruction.

关键词： Feature extraction

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A Novel Model for Multi-label Image Annotation 24

A Novel Model for Multi-label Image Annotation

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24th International conference on pattern recognition (ICPR)

作者： Wu, Xinjian Zhang, Li Li, Fanzhang Wang, Bangjun Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Soochow Univ Joint Int Res Lab Machine Learning & Neuromorph C Prov Key Lab Comp Informat Proc Technol Suzhou Peoples R China

ISBN: (纸本)9781538637883

Multi-label image annotation is one of the most important open problems in machine learning and computer vision. In this paper, we propose a novel model for image annotation. Unlike existing works that usually use conventional visual features to annotate images, this paper adopts features based on convolutional neural network (CNN), which have shown potential to achieve outstanding performance. In particular, we use CNN to extract image features with higher semantic meaning and apply them to the image annotation method - Tag Propagation (TagProp). Experimental results on four challenging datasets indicate that our model makes a marked improvement as compared to the current state-of-the-art.

关键词： image annotation convolutional neural network Tag Propagation Multi-label learning

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A Framework Design of National Healthy Diet Monitoring System 7th

A Framework Design of National Healthy Diet Monitoring Syste...

引用

7th International conference for Smart Health, ICSH 2019

作者： Chen, Lei Ma, Xiao-Qian Shang, Wei Beijing Wuzi University No. 321 FuHe Street TongZhou District Beijing101149 China Academy of Mathematics and Systems Science Chinese Academy of Sciences No. 55 Zhongguancun East Rd Beijing100190 China

ISBN: (纸本)9783030344818

Proper diet is one of the most important prerequisites for people’s health. Resident nutrition and diet building are crucial to the success of national health program of China in its new stage of development. Although there has been numerous research on dietary and its relation of health, there is a lack of overall vision of the current dietary situations in China. In this paper, a national health diet monitoring system is proposed based on the national nutrition survey and the Internet nutrition diet related data, using the text knowledge discovery method. the purpose of the proposed framework is to find out the current situation of diet and nutrition of national residents, monitor the dietary purchase, dietary habits and changes of residents, and provide decision-making support for the formulation of relevant policies and the dietary guidance and improvement of residents. © 2019, Springer Nature Switzerland AG.

关键词： Nutrition

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Facial Expression recognition by Multi-Scale CNN with Regularized Center Loss 24

Facial Expression Recognition by Multi-Scale CNN with Regula...

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24th International conference on pattern recognition (ICPR)

作者： Li, Zhenghao Wu, Song Xiao, Guoqiang Southwest Univ Coll Comp & Informat Sci Chongqing Peoples R China

ISBN: (纸本)9781538637883

Facial Expression recognition (FER) has attracted considerable attention due to its potential applications in computer vision. Recently, convolutional neural network (CNN) has shown excellent performance on FER. However, most established deeper, wider and more complex network structures trained by small facial expression training dataset have a risk of overfitting. Moreover, most existing CNN models utilize the softmax loss as a supervision signal to penalize the deviation of classification, which enhances inter-class separation, yet intra-class compactness is not taken into consideration. In this paper, we propose a novel multi-scale CNN integrated with an attention-based learning layer (AMSCNN) for robust facial expression recognition. the attention-based learning layer is designed to automatically learn the importance of different receptive fields in the face during training. Moreover, the multi-scale CNN is optimized by the proposed Regularized Center Loss (RCL). Regularized center loss learns a center for deep features of each class and penalizes the distance between deep features and corresponding center, aiming to strengthen the discriminability of different facial expression. Extensive experiments conducted on two popular human FER benchmarks (CK+ and Oulu-CASIA dataset) demonstrated the effective of our proposed AMSCNN, and it obtained competitive results compared to the state-of-the-art.

关键词： facial expression recognition multi-scale CNN regularized center loss

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