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International IEEE Conference on Intelligent Transportation Systems

作者： Arthur Daniel Costea Andreea Valeria Vesa Sergiu Nedevschi Image Processing and Pattern Recognition Research Center Computer Science Department Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781467365970

In this paper we present a fast and robust solution for pedestrian detection that can run in real time conditions even on mobile devices with limited computational power. An optimization of the channel features based multiscale detection schemes is proposed by using 8 detection models for each half octave scales. The image features have to be computed only once each half octave and there is no need for feature approximation. We use multiscale square features for training the multiresolution pedestrian classifiers. The proposed solution achieves state of art detection results on Caltech pedestrian benchmark at over 100 FPS using a CPU implementation, being the fastest detection approach on the benchmark. The solution is fast enough to perform under real time conditions on mobile platforms, yet preserving its robustness. The full detection process can run at over 20 FPS on a quad-core ARM CPU based smartphone or tablet, being a suitable solution for limited computational power mobile devices or embedded platforms.

关键词： pedestrian detection image features solution image feature Mobile devices Pedestrians Solutions Fasting Robust solution FES gene Smartphone Solution computational power

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A comparative study of different feature mapping methods for image annotation

A comparative study of different feature mapping methods for...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Yiren Wang Hassan Dawood Qian Yin Ping Guo the Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China the Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Computer Science Department Beijing Normal University Beijing China

Automatic image annotation and tagging is necessary for indexing and searching of images using querying a text. It is widely used in search engines like Google, Yahoo, Baidu, etc. Fast image Tagging (FastTag) algorithm is proposed to accelerate image annotation process, while keeping the precision of automatic image annotation results. Feature mapping is used to map image features vectors onto higher dimensional feature space. Feature mapping methods plays an important role in automatic image annotation. In this paper, we have compared 6 kernels, among which four kernels are used in homogeneous feature mapping and two kernels are used in discriminative tree based feature mapping, to investigate which feature mapping performs better for automatic image annotation. The performance of these methods has been analyzed by conducting intensive experiments on three different datasets as used by FastTag algorithm in their experiments. We have found that the homogeneous feature mapping with χ 2 kernel is more suitable when used in FastTag algorithm in terms of precision, recall, FI score and N+ measures, and with a relatively acceptable performance.

关键词： Phase change materials Search engines Kernel Indexing Histograms

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Theory of ϕ-Jensen variance and its applications in higher education

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Journal of Inequalities and Applications 2015年第1期2015卷 1-40页

作者： Wen, JiaJin Huang, Yi Cheng, Sui Sun Institute of Mathematical Inequalities and Applications College of Mathematics and Computer Science Chengdu University Chengdu 610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Higher Education Institutions of Sichuan Province College of Mathematics and Computer Science Chengdu University Chengdu 610106 China Department of Mathematics Tsing Hua University Hsinchu 30043 Taiwan

This paper introduces the theory of ϕ-Jensen variance. Our main motivation is to extend the connotation of the analysis of variance and facilitate its applications in probability, statistics and higher education. To this end, we first introduce the relevant concepts and properties of the interval function. Next, we study several characteristics of the log-concave function and prove an interesting quasi-log concavity conjecture. Next, we introduce the theory of ϕ-Jensen variance and study the monotonicity of the interval function JVar_ϕ φ(X_[a,b]) by means of the log concavity. Finally, we demonstrate the applications of our results in higher education, show that the hierarchical teaching model is ‘normally’ better than the traditional teaching model under the appropriate hypotheses, and study the monotonicity of the interval function Var A (X_[a,b]). © 2015, Wen et al.

关键词： hierarchical teaching model interval function k-normal distribution truncated random variable ϕ-Jensen variance

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An image fusion method of multi-scale image based on HMM and genetic algorithms

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Journal of Computational Information Systems 2015年第12期11卷 4367-4374页

作者： Jiang, Zetao Wu, Donghui The Institute of Computer Science and Engineering Guilin University of Electronic Technology Guilin China The Key Lab of Image Processing and Pattern Recognition of Jiangxi Province Nanchang Hangkong University Nanchang China

At present, in the field of pixel-level image fusion, researchers tend to treat each pixel independently, which destroys the relationship between the images to be fused. In view of this defects, this paper aims to propose a method for multi-scale image fusion based on Genetic Algorithm (GA) under the guidance of Hidden Markov Model (HMM). This fusion method is set on the following principles. First of all, the wavelet transformation (WT) of the two images to be fused will be conducted. And then establish HMM for the fusion coefficients from high to low in descending hierarchical order. Finally, guided by HMM, the author applies GA to searching hierarchically for the optimal fusion coefficient. With this method, the best fusion effect will be achieved. This method take good advantage of the correlation information between the images to be fused so as to improve the fidelity and accuracy of the fusion. Given this, the author makes an empirical analysis on this fusion method, which proves to be feasible and effective. ©, 2015, Binary Information Press. All right reserved.

关键词： Genetic algorithms

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Learning distance transform for boundary detection and deformable segmentation in CT prostate images

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8679卷 93-100页

作者： Gao, Yaozong Wang, Li Shao, Yeqin Shen, Dinggang Department of Radiology and BRIC University of North Carolina at Chapel Hill United States Department of Computer Science University of North Carolina at Chapel Hill United States Institution of Image Processing & Pattern Recognition Shanghai Jiao Tong University China

Segmenting the prostate from CT images is a critical step in the radiotherapy planning for prostate cancer. The segmentation accuracy could largely affect the efficacy of radiation treatment. However, due to the touching boundaries with the bladder and the rectum, the prostate boundary is often ambiguous and hard to recognize, which leads to inconsistent manual delineations across different clinicians. In this paper, we propose a learning-based approach for boundary detection and deformable segmentation of the prostate. Our proposed method aims to learn a boundary distance transform, which maps an intensity image into a boundary distance map. To enforce the spatial consistency on the learned distance transform, we combine our approach with the auto-context model for teratively refining the estimated distance map. After the refinement, the prostate boundaries can be readily detected by finding the valley in the distance map. In addition, the estimated distance map can also be used as a new external force for guiding the deformable segmentation. Specifically, to automatically segment the prostate, we integrate the estimated boundary distance map into a level set formulation. Experimental results on 73 CT planning images show that the proposed distance transform is more effective than the traditional classification-based method for driving the deformable segmentation. Also, our method can achieve more consistentsegmentations than human raters, and more accurate results than the existing methods under comparison. © Springer International Publishing Switzerland 2014.

关键词： computerized tomography

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Pedestrian Detection in Infrared images Using HOG,LBP,Gradient Magnitude and Intensity Feature Channels

Pedestrian Detection in Infrared Images Using HOG,LBP,Gradie...

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17th International IEEE Conference on Intelligent Transportation Systems & The Asia-Pacific Council on Systems Engineering Conference 2014

作者： Raluca Brehar Sergiu Nedevschi the Image Processing and Pattern Recognition Group Computer Science DepartmentTechnical University of Cluj-Napoca

Recent work in monocular pedestrian detection is trying to improve the execution time while keeping the accuracy as high as possible.A popular and successful approach for monocular intensity pedestrian detection is based on the approximation（instead of computation） of image features for multiple scales based on the features computed on set of predefined *** port this idea to the infrared *** contributions reside in the combination of four channel features,namely infrared,histogram of gradient orientations,normalized gradient magnitude and local binary patterns with the objective of detecting pedestrians for night vision applications dealing with far infrared *** scale feature computation is done by feature *** contribution is the study of different formulations for Local Binary patterns like uniform patterns and rotation invariant patterns and their effect on detection *** detection speed is also boosted by the aid of a fast morphological based region of interest *** vary the number of approximated scales per octave and study the impact on execution time and accuracy.A reasonable result hits a speed of 18 fps with a log average miss rate of 39%.

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Word Channel Based Multiscale Pedestrian Detection Without image Resizing and Using Only One Classifier

Word Channel Based Multiscale Pedestrian Detection Without I...

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IEEE Conference on computer Vision and pattern recognition

作者： Arthur Daniel Costea Sergiu Nedevschi Image Processing and Pattern Recognition Group Computer Science Department Technical University of Cluj-Napoca

ISBN: (纸本)9781479951192

Most pedestrian detection approaches that achieve high accuracy and precision rate and that can be used for real-time applications are based on histograms of gradient orientations. Usually multiscale detection is attained by resizing the image several times and by recomputing the image features or using multiple classifiers for different scales. In this paper we present a pedestrian detection approach that uses the same classifier for all pedestrian scales based on image features computed for a single scale. We go beyond the low level pixel-wise gradient orientation bins and use higher level visual words organized into Word Channels. Boosting is used to learn classification features from the integral Word Channels. The proposed approach is evaluated on multiple datasets and achieves outstanding results on the INRIA and Caltech-USA benchmarks. By using a GPU implementation we achieve a classification rate of over 10 million bounding boxes per second and a 16 FPS rate for multiscale detection in a 640×480 image.

关键词： Visualization Boosting Training Feature extraction Graphics processing units Semantics image color analysis

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Exponential image Enhancement in Daytime Fog Conditions

Exponential Image Enhancement in Daytime Fog Conditions

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17th International IEEE Conference on Intelligent Transportation Systems & The Asia-Pacific Council on Systems Engineering Conference 2014

作者： Mihai Negru Sergiu Nedevschi Radu Ioan Peter Image Processing and Pattern Recognition Group Computer Science DepartmentTechnical University of Cluj-Napoca head of the Image Processing and Pattern Recognition Group Computer Science DepartmentTechnical University of Cluj-Napoca Mathematics Department Technical University of Cluj-Napoca

The images captured in fog conditions have degraded contrast,that makes current image processing applications sensitive and error *** propose in this paper an efficient single image enhancement algorithm suitable for daytime fog conditions and based on an original mathematical model,for computing the atmospheric veil,that takes into account the variation in fog density to the *** model is inspired by the functions that appear in partition of unity in the differential geometry *** observing images captured in fog conditions,usually the fog has a very low density in front of the camera and this density has a non-linear increase with the distance,such that objects are no longer visible at greater *** using our mathematical model we are able to obtain superior reconstructions of the original fog-free image,when comparing to traditional *** advantage of our method is the ability to adapt the model in accordance to the density of the fog.A quantitative and qualitative evaluation is performed on both synthetic and real camera *** evaluation proves that our mathematical model is more suitable for image enhancement in both homogeneous and heterogeneous fog *** algorithm is able to perform image enhancement in real time for both color and gray scale images.

关键词： image Exponential image Enhancement in Daytime Fog Conditions

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Moving rigid objects segmentation in 3D dynamic traffic scenes using a stereovision system

Moving rigid objects segmentation in 3D dynamic traffic scen...

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IEEE International Conference on Intelligent computer Communication and processing (ICCP)

作者： Catalin Golban Sergiu Nedevschi Computer Science Department Technical University Image Processing and Pattern Recognition Group Cluj-Napoca Romania

This paper proposes a novel method for detecting the moving vehicles in dynamic urban traffic scenes using a stereo camera. Relying on the fact that a set of feature points on a rigid 3D scene object are staying in a rigid 3D configuration, we propose to compute the relative motion between the camera and a moving object with an algorithm that follows from the visual odometry based motion estimation methods. Subtracting the camera motion we obtain the absolute object motion. Additionally we create a compact representation of the scene using superpixels computed from intensity and depth information. A graph-like structure is built, having superpixels as nodes and indicating neighboring relationships between adjacent superpixels. Objects are segmented using a fast region growing algorithm that considers as seeds the features used to compute the object motion.

关键词： Three-dimensional displays Visualization computer vision Cameras Feature extraction Stereo vision image motion analysis

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Multi-Class Segmentation for Traffic Scenarios at Over 50 FPS

Multi-Class Segmentation for Traffic Scenarios at Over 50 FP...

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IEEE Intelligent Vehicles Symposium

作者： Arthur D. Costea Sergiu Nedevschi Image Processing and Pattern Recognition Group Computer Science Department Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781479936397

Multi-class segmentation assigns a class label to each pixel in an image. It represents a significant task for the semantic understanding of images and has received plentiful attention over the last years. The current state of art is dominated by conditional random field based approaches, defined over pixels or image segments. However, high accuracy segmentation comes at a high computational cost. The best performing methods can barely run at few frames per second and are far from real-time applications. Our goal is to bridge the gap between current state of the art segmentation approaches and real-time applications. In this paper we propose an efficient approach for individual pixel classification. Multiple local descriptors are computed densely and then quantized using visual codebooks. Joint boosting is used to classify each pixel based on the quantized local descriptors. We show that using careful design choices and GPU optimization we can achieve sate of the art segmentation results at over 50 FPS. We also propose a Conditional Random Field (CRF) model defined over superpixels that uses the proposed pixel classifier for the estimation of unary potentials. The CRF based multi-class segmentation can run at over 30 FPS. The proposed approach is validated on the MSRC21 and CamVid multi-class segmentation benchmarks, the former one consisting of urban traffic sequences.

关键词： choices GPU CRF

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