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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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Interclass Visual Similarity Based Visual Vocabulary Learning

Interclass Visual Similarity Based Visual Vocabulary Learnin...

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第一届亚洲模式识别会议

作者： Guangming Chang Chunfen Yuan Weiming Hu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beij National Laboratory of Pattern RecognitionInstitute of Automation Chinese Academy of SciencesBeijin

Visual vocabulary is now widely used in many video analysis tasks, such as event detection, video retrieval and video classification. In most approaches the vocabularies are solely based on statistics of visual features and generated by clustering. Little attention has been paid to the interclass similarity among different events or actions. In this paper, we present a novel approach to mine the interclass visual similarity statistically and then use it to supervise the generation of visual vocabulary. We construct a measurement of interclass similarity, embed the similarity to the Euclidean distance and use the refined distance to generate visual vocabulary iteratively. The experiments in Weizmann and KTH datasets show that our approach outperforms the traditional vocabulary based approach by about 5％.

关键词： Vocabulary Visualization Clustering algorithms pattern recognition Conferences Vectors 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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Learning Local Binary patterns with Enhanced Boosting for Face recognition

Learning Local Binary Patterns with Enhanced Boosting for Fa...

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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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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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心理认知计算的研究现状及发展趋势

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模式识别与人工智能 2011年第2期24卷 215-225页

作者：王志良郑思仪王先梅王巍北京科技大学信息工程学院北京100083 武警北京指挥学院北京100012

心理认知计算是智能化人机交互技术的一个重要组成部分,近年来得到广泛的关注.文中综述国内外心理认知计算的研究进展.首先介绍心理认知计算的相关概念,详细叙述心理状态理解的研究内容和机制.其次总结心理认知的神经生物学研究成果,对... 详细信息

心理认知计算是智能化人机交互技术的一个重要组成部分,近年来得到广泛的关注.文中综述国内外心理认知计算的研究进展.首先介绍心理认知计算的相关概念,详细叙述心理状态理解的研究内容和机制.其次总结心理认知的神经生物学研究成果,对心理情感状态和心理认知状态的研究现状进行了比较,从模式提取和模型建立两方面对心理认知在人机交互中的发展趋势作了分析,提出多模态信息融合的多层级心理状态视觉认知计算模型框架.最后讨论研究心理认知计算的重要意义和当前存在的主要困难.

关键词：心理状态理解认知计算视觉人机交互

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Classification of facial expressions by sparse coding

Classification of facial expressions by sparse coding

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IEEE Signal Processing and Communications Applications (SIU)

作者： Nesli Erdoğmuş Jean-Luc Dugelay Multimedia Communications Department Eurecom Institute Sophia-Antipolis France

Expression variations in facial images is one of the most crucial and difficult problems in face-based computer vision applications. Although numerous systems have been proposed for robustness against facial expressions, so far it still persists to be an open *** that the knowledge on the type of the expression in a facial image would greatly facilitate the solution of this issue, in this paper we present an analysis for facial expressions classification in 2D frontal views. With the motivation of the success that sparse coding achieved in face recognition, similar principals are applied for to both original and dimension-reduced (via PCA) images and the resulting codes are classified based on two different approaches: minimum residual error and maximum interclass summation of the coefficients. Extensive tests are conducted on Bosphorus database, in which different expressions are available for 105 persons.

关键词： Conferences Face recognition Face Signal processing pattern analysis Principal component analysis

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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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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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