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International Conference on Computational Intelligence and Security

作者： Fanyi Xiao YiXiong Liang Xiaohui Qu Institute of Computer Vision and Virtual Reality Central South University Changsha China Department of Automatics Central South University Changsha China

ISBN: (纸本)9781457720086

Due to its invariance to monotonic grayscale transformation and simple computation, Local Binary pattern (LBP) is broadly used as feature extractor in face recognition tasks in recent years [3]. In previous work, people have proposed methods of using Adaboost to select most representative features in samples. Zhang et al. proposed a method applying Adaboost algorithm to select those most distinctive features from which they extract LBP features. Though LBP features selected by Adaboost represent local textures effectively. Their method, however, neglects exploitation of holistic spatial information in nature of image samples. To solve this problem, we proposed the spatial enhanced multi-level boosing using uniform LBP and multilevel Adaboost algorithm. In this paper, we select most distinctive features which then being concatenated to represent spatial information using multi-level boosting algorithm. Experiments on ORL database yielded an exciting recognition rate of 98.96%.

关键词： Feature extraction Face recognition Histograms Boosting Training Classification algorithms Databases

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Near real-time Fast Bilateral Stereo on the GPU

Near real-time Fast Bilateral Stereo on the GPU

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Stefano Mattoccia Marco Viti Florian Ries DEIS-ARCES University of Bologna Italy DEIS University of Bologna Italy ARCES University of Bologna Italy

State of the art local stereo correspondence algorithms that adapt their supports to image content allow to infer very accurate disparity maps often comparable to algorithms based on global disparity optimization methods. However, despite their effectiveness, accurate local approaches based on this methodology are also computationally expensive and several simplifications aimed at reducing their computational load have been proposed. Unfortunately, compared to the original approaches, the effectiveness of most of these simplified techniques is significantly reduced. In this paper, we consider an efficient and accurate algorithm referred to as Fast Bilateral Stereo (FBS) that enables to efficiently obtain results comparable to state of the art local approaches describing its mapping on GPUs with CUDA. Experimental results on two NVIDIA GPUs show that our CUDA implementation delivers, on standard stereo pairs, accurate and dense disparity maps in near real-time achieving speedup greater than 100X with respect to the equivalent CPU-based implementation.

关键词： Graphics processing unit Kernel Stereo vision Instruction sets Image color analysis Real time systems Throughput

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Classification of lacunarity datasets computed from slice images of three pork ham qualities, using kernel PCA and a multilayer perceptron

Classification of lacunarity datasets computed from slice im...

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6th International CIGR Technical Symposium - Towards a Sustainable Food Chain: Food Process, Bioprocessing and Food Quality Management

作者： Iqbal, Abdullah Valous, Nektarios A. Sun, Da-Wen Biosystems Engineering Agriculture and Food Science Centre FRCFT Group University College Dublin Belfield Dublin 4 Ireland

Textural patterns are often complex, exhibit scale-dependent changes in structure and are difficult to identify and describe. Lacunarity has been proposed as a general method for the analysis of several spatial patterns. Lacunarity data can designate a mathematical index of spatial heterogeneity, therefore the corresponding feature vectors should possess the necessary inter-class statistical properties that would enable them to be used for pattern recognition purposes. The objective of this work is to construct a supervised classification model of binary lacunarity data - computed by Valous et al. (2009) - from pork ham slice (three qualities) surface images, with the aid of kernel principal component analysis (KPCA) and a multilayer perceptron (MLP) neural network, using a portion of informative salient features. According to the principle of parsimony, the smallest possible number of features should be used so as to give an adequate representation of the feature space. Therefore, the dimension of the initial space, comprising of 510 features, was reduced by 90% in order to avoid any noise effects in the subsequent classification. Then, using KPCA, the first nineteen kernel principal components (99.04% of total variance) were extracted from the reduced feature space, and were used as input in the MLP. The correct classification percentages for the training, test and validation sets using the neural classifier were 86.7%, 86.7%, and 85.0%, respectively. The binary lacunarity spatial metric captured relevant information that provided a good level of differentiation among pork ham slice images.

关键词： computer vision

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Spatio-Temporal Context Kernel for Activity recognition

Spatio-Temporal Context Kernel for Activity Recognition

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第一届亚洲模式识别会议

作者： Fei YUAN Veronique Prinet Hichem Sahbi National Laboratory of Pattern Recognition Institute of Automation CAS Beijing P.R.C LTCI CNRS TELECOM ParisTech Paris France

Local space-time features and bag-of-feature (BOF) representation are often used for action recognition in previous approaches. For complicated human activities, however, the limitation of these approaches blows up because of the local properties of features and the lack of context. This paper addresses the problem by exploiting the spatio-temporal context information between *** first define a spatio-temporal context, which combines the scale invariant spatio-temporal neighberhood of local features with the spatio-temporal relationships between them. Then, we introduce a spatio-temporal context kernel (STCK), which not only takes into account the local properties of features but also considers their spatial and temporal context information. STCK has a promising generalization property and can be plugged into SVMs for activities recognition. The experimental results on challenging activity datasets show that, compared to context-free model, the spatio-temporal context kernel improves the recognition performance.

关键词： Context Kernel Support vector machines Humans computer vision pattern recognition Videos

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Recognizing human-vehicle interactions from aerial video without training

Recognizing human-vehicle interactions from aerial video wit...

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Jong Taek Lee Chia-Chih Chen J. K. Aggarwal Computer & Vision Research Center Department of ECE University of Texas Austin USA

We propose a novel framework to recognize human-vehicle interactions from aerial video. In this scenario, the object resolution is low, the visual cues are vague, and the detection and tracking of objects are less reliable us a consequence. Any methods that require, the accurate tracking of objects or the exact matching of event definition are better avoided. To address these issues, we present a temporal logic based approach which does not require training from event examples. At the low-level, we employ dynamic programming to perform fast model fitting between the tracked vehicle and the rendered 3-D vehicle models. At the semantic-level, given the localized event region of interest (ROI), we verify the time series of human-vehicle relationships with the pre-specified event definitions in a piecewise fashion. With special interest in recognizing a person getting into and out of a vehicle, we have tested our method on a subset of the VIRAT Aerial Video dataset [ ] and achieved superior results. Our framework can be easily extended to recognize other types of human-vehicle interactions.

关键词： Vehicles Humans Solid modeling Training Support vector machines Estimation Dynamic programming

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A robust structure extraction method based on image edge

A robust structure extraction method based on image edge

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Congress on Image and Signal Processing, CISP

作者： Guang Yang Lei Hu Qichao Lu Feng Gao Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University 100191 China

In this paper, we propose a novel approach with desirable universality and expansibility for structural feature extraction. Structural feature is a high-level 2D feature that delivers information including image components and spatial relations of them. In the proposed approach, we first trace edges to line segments, fit line segments to longer lines and extract parallel pairs. Then, we extract contours of image components by grouping lines and parallel pairs. Lastly, we reveal the salient spatial relations of components by theorems and judging rules. The theorems, which are concluded from the step of components extraction, facilitate the following analytical procedure. Experimental results tested on 1960 real images demonstrate that our approach can extract integrated contours of image components and analyze the spatial relations between them in desirable time consuming, and the approach is suitable for many application fields.

关键词： Feature extraction Image edge detection Image segmentation Complexity theory computer vision pattern recognition Target recognition

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computer vision based date fruit grading system: Design and implementation

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JOURNAL OF KING SAUD UNIVERSITY-computer AND INFORMATION SCIENCES 2011年第1期23卷 29-36页

作者： Al Ohali, Yousef King Saud Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh Saudi Arabia

The Kingdom of Saudi Arabia is the world's largest producer of date fruit. It produces almost 400 date varieties in bulk. During the harvesting season the date grading and sorting pose problems for date growers. Since it is a labor intensive and time consuming process, it delays the post harvesting operations which costs them dearly. The date grading and sorting is a repetitive process. In practice, it is carried out by humans manually through visual inspection. The manual inspection poses further problems in maintaining consistency in grading and uniformity in sorting. To speed up the process as well as maintain the consistency and uniformity we have designed and implemented a prototypical computer vision based date grading and sorting system. We have defined a set of external quality features. The system uses RGB images of the date fruits. From these images, it automatically extracts the aforementioned external date quality features. Based on the extracted features it classifies dates into three quality categories (grades 1, 2 and 3) defined by experts. We have studied the performance of a back propagation neural network classifier and tested the accuracy of the system on preselected date samples. The test results show that the system can sort 80% dates accurately. (C) 2010 King Saud University. Production and hosting by Elsevier B.V. All rights reserved.

关键词： Fruit classification pattern recognition Image processing Neural networks Automation in agriculture

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Histogram correlation based classifier fusion for object tracking

Histogram correlation based classifier fusion for object tra...

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IEEE Signal Processing and Communications Applications (SIU)

作者： İbrahim Saygın Topkaya Hakan Erdoğan Sabancı Üniversitesi Turkey

Mean shift is a popular method used in object tracking. The method, which relies on shifting the search area to the weight center of a generated “weight image” to track objects between consecutive frames, acquired a classifier based framework by using classifiers to generate the weight image. In this work, using multiple classifiers to generate the weight image and calculating contributions of the independent classifiers dynamically by using correlations between histograms of their weight images and histogram of a defined ideal weight image are presented.

关键词： Histograms pattern recognition Signal processing Conferences Computational modeling computer vision Adaptation model

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Scene classification using saliency based segmentation

Scene classification using saliency based segmentation

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IEEE Signal Processing and Communications Applications (SIU)

作者： Cemalettin Çiftçi Emrah Ergül Nafiz Arıca Bilgisayar Mühendisligi Bölümü Deniz Harp Okulu Turkey

For a better expression of images, we propose a Bag of Words approach which is position aware and uses saliency based segmentation to build vocabularies of their own segment. We also apply pLSA algorithm to find hidden topics in each scene image. The experiments show that the separate representation of the regions found by a rough segmentation increase the classification performance.

关键词： computer vision Conferences Signal processing pattern recognition Probabilistic logic Semantics Image segmentation

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Edge Saliency Map Detection with Texture Suppression

Edge Saliency Map Detection with Texture Suppression

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International Conference on Image and Graphics (ICIG)

作者： Huanhuan Ao Nenghai Yu

Edge is a basic feature in the field of computing vision. So to find edge saliency map is an indispensable operation for many applications on image processing. In this paper we present a fast algorithm to find edge saliency map for a natural image. The approach integrates three basic edge features: edge gradient value, edge segment length and edge density, and it works well to detect salient region boundaries and to suppress ill edges from texture. An edge saliency map can be used to image segmentation and boundaries detection. Experimental results demonstrate that our algorithm outperforms other edge saliency detection methods. Finally, our algorithm is applied on salient objects segmentation, compared with several state of-the-art salient region detection methods and the results show our work is valuable.

关键词： Image edge detection Image segmentation Visualization Feature extraction computer vision Object segmentation pattern recognition

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