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作者： Shafiee, M.J. Wong, A. Siva, P. Fieguth, P. Vision and Image Processing Lab System Design Engineering Dept. University of Waterloo Canada

ISBN: (纸本)9781479957514

Conditional random fields (CRFs) are one of the most powerful frameworks in image modeling. However practical CRFs typically have edges only between nearby nodes;using more interactions and expressive relations among nodes make these methods impractical for large-scale applications, due to the high computational complexity. Recent work has shown that fully connected CRFs can be tractable by defining specific potential functions. In this paper, we present a novel framework to tackle the computational complexity of a fully connected graph without requiring specific potential functions. Instead, inspired by random graph theory and sampling methods, we propose a new clique structure called stochastic cliques. The stochastically fully connected CRF (SFCRF) is a marriage between random graphs and random fields, benefiting from the advantages of fully connected graphs while maintaining computational tractability. The effectiveness of SFCRF was examined by binary image labeling of highly noisy images. The results show that the proposed framework outperforms an adjacency CRF and a CRF with a large neighborhood size. © 2014 IEEE.

关键词： Computational complexity

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DeWAFF: A novel image abstraction approach to improve the performance of a cell tracking system

DeWAFF: A novel image abstraction approach to improve the pe...

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International Work Conference on Bio-inspired Intelligence (IWOBI)

作者： S. Calderón A. Sáenz R. Mora F. Siles I. Orozco M.E. Buemi Department of Electrical Engineering School of Engineering Cancer-Lab Universidad de Costa Rica San José Costa Rica Image Processing and Computer Vision Group Universidad de Buenos Aires Buenos Aires Argentina

This paper presents a new image abstraction approach, aiming to improve typical image related pattern recognition tasks such as segmentation, tracking, and classification. The proposed image abstraction framework performs image denoising and homogeneous region simplification, along with border and region enhancement. The proposed framework consists in a novel generalized approach of common weighted averaging denoising algorithms mixed with Unsharp Masking (USM) border enhancement techniques, to avoid typical USM artifacts as ringing. Results of the different configurations within the image abstraction framework for a cell tracking application are presented.

关键词： image edge detection Noise reduction Histograms AWGN Pattern recognition image segmentation

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Development of young oil palm tree recognition using Haar- based rectangular windows

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IOP Conference Series: Earth and Environmental Science 2016年第1期37卷

作者： S Daliman S A R Abu-Bakar S H Md Nor Azam Computer Vision Video and Image Processing (CvviP) Research Lab Department of Electronics and Computer Engineering Faculty of Electrical Engineering Universiti Teknologi Malaysia 81310 Skudai Johor MALAYSIA. Sime Darby Research Sdn. Bhd. Jalan Pulau Carey 42960 Pulau Carey Selangor MALAYSIA.

This paper presents development of Haar-based rectangular windows for recognition of young oil palm tree based on WorldView-2 imagery data. Haar-based rectangular windows or also known as Haar-like rectangular features have been popular in face recognition as used in Viola-Jones object detection framework. Similar to face recognition, the oil palm tree recognition would also need a suitable Haar-based rectangular windows that best suit to the characteristics of oil palm tree. A set of seven Haar-based rectangular windows have been designed to better match specifically the young oil palm tree as the crown size is much smaller compared to the matured ones. Determination of features for oil palm tree is an essential task to ensure a high successful rate of correct oil palm tree detection. Furthermore, features that reflects the identification of oil palm tree indicate distinctiveness between an oil palm tree and other objects in the image such as buildings, roads and drainage. These features will be trained using support vector machine (SVM) to model the oil palm tree for classifying the testing set and subimages of WorldView-2 imagery data. The resulting classification of young oil palm tree with sensitivity of 98.58% and accuracy of 92.73% shows a promising result that it can be used for intention of developing automatic young oil palm tree counting.

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A low resolution multi-camera system for person tracking

A low resolution multi-camera system for person tracking

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作者： Eldib, Mohamed Bo, Nyan Bo Deboeverie, Francis Nino, Jorge Guan, Junzhi Van De Velde, Samuel Steendam, Heidi Aghajan, Hamid Philips, Wilfried Department of Telecommunications and Information Processing Ghent University-iMinds Belgium Image Processing and Interpretation-Vision Systems United States Ambient Intelligence Research Lab Stanford University United States

ISBN: (纸本)9781479957514

The current multi-camera systems have not studied the problem of person tracking under low resolution constraints. In this paper, we propose a low resolution sensor network for person tracking. The network is composed of cameras with a resolution of 30×30 pixels. The multi-camera system is used to evaluate probability occupancy mapping and maximum likelihood trackers against ground truth collected by ultra-wideband (UWB) testbed. Performance evaluation is performed on two video sequences of 30 minutes. The experimental results show that maximum likelihood estimation based tracker outperforms the state-of-the-art on low resolution cameras. © 2014 IEEE.

关键词： Maximum likelihood estimation

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An approach to large scale interactive retrieval of cultural heritage

An approach to large scale interactive retrieval of cultural...

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2014 Eurographics Workshop on Graphics and Cultural Heritage, GCH 2014

作者： Takami, Masato Bell, Peter Ommer, Bjrn Heidelberg Collaboratory for Image Processing IWR University of Heidelberg Germany Robert Bosch GmbH Corporate Research Computer Vision Research Lab Hildesheim Germany

ISBN: (纸本)9783905674637

Large scale digitization campaigns are simplifying the accessibility of a rapidly increasing number of images from cultural heritage. However, digitization alone is not sufficient to effectively open up these valuable resources. Retrieval and analysis within these datasets is currently mainly based on manual annotation and laborious preprocessing. This is not only a tedious task, which rapidly becomes infeasible due to the enormous data load. We also risk to be biased to only see what an annotator beforehand has focused on. Thus a lot of potential is being wasted. One of the most prevalent tasks is that of discovering similar objects in a dataset to find relations therein. The majority of existing systems for this task are detecting similar objects using visual feature keypoints. While having a low processing time, these methods are limited to detect only close duplicates due to their keypoint based representation. In this work we propose a search method which can detect similar objects even if they exhibit considerable variability. Our procedure learns models of the appearance of objects and trains a classifier to find related *** address a central problem of such learning-based methods, the need for appropriate negative and positive training samples. To avoid a highly complicated hard negative mining stage we propose a pooling procedure for gathering generic negatives. Moreover, a bootstrap approach is presented to aggregate positive training samples. Comparison of existing search methods in cultural heritage benchmark problems demonstrates that our approach yields significantly improved detection performance. Moreover, we show examples of searching across different types of datasets, e.g., drafts and photographs. © 2014 All rights reserved.

关键词： Benchmarking

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Human object classification in daubechies complex wavelet domain 2nd

Human object classification in daubechies complex wavelet do...

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2nd International Conference on Context-Aware Systems and Applications, ICCASA 2013

作者： Khare, Manish Srivastava, Rajneesh Kumar Khare, Ashish Binh, Nguyen Thanh Dien, Tran Anh Image Processing and Computer Vision Lab Department of Electronics and Communication University of Allahabad Allahabad India Ho Chi Minh City University of Technology Ho Chi Minh Viet Nam

ISBN: (纸本)9783319059389

Human object classification is an important problem for smart video surveillance applications. In this paper we have proposed a method for human object classification, which classify the objects into two classes: human and non-human. The proposed method uses Daubechies complex wavelet transform coefficients as a feature of object. Daubechies complex wavelet transform is used due to its better edge representation and approximate shift-invariant property as compared to real valued wavelet transform. We have used Adaboost as a classifier for classification of objects. The proposed method has been tested on standard datasets like, INRIA dataset. Quantitative experimental evaluation results show that the proposed method is better than other state-of-the-art methods and gives better performance. © Springer International Publishing Switzerland 2014.

关键词： Adaptive boosting

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A low resolution multi-camera system for person tracking

A low resolution multi-camera system for person tracking

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IEEE International Conference on image processing

作者： Mohamed Eldib Nyan Bo Bo Francis Deboeverie Jorge Nino Junzhi Guan Samuel Van de Velde Heidi Steendam Hamid Aghajan Wilfried Philips Image Processing and Interpretation-Vision Systems Department of Telecommunications and Information Processing Ghent University-iMinds Belgium Ambient Intelligence Research Lab Stanford University USA

关键词： Cameras image resolution Correlation Maximum likelihood detection Maximum likelihood estimation Video sequences Tracking

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Maximum likelihood thresholding algorithm based on four-parameter gamma distributions

Maximum likelihood thresholding algorithm based on four-para...

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International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： Peter De-Ford Geovanni Martinez Image Processing and Computer Vision Research Laboratory (IPCV-LAB) Universidad de Costa Rica San José Costa Rica

In this contribution, we present a segmentation algorithm based on thresholding to subdivide an intensity image in the regions of object and background. The optimal threshold is found by maximizing a likelihood function derived from a novel intensity probability density function model, which consists of the sum of two weighted four-parameter gamma distributions, as a more flexible alternative to currently used models consisting of the sum of two weighted two-parameter Gaussian distributions. According to our experiments with 132 images, the proposed algorithm is in average slightly better than the best found in the scientific literature, performing particularly good in low contrast images. The additional parameters and complexity of its likelihood function resulted in an increase of the processing time by a factor of 3, from 0.003 sec/image to 0.009 sec/image.

关键词： image segmentation Probability density function Approximation algorithms Gaussian distribution Histograms Shape Pattern recognition

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Offline learning of prototypical negatives for efficient online Exemplar SVM

Offline learning of prototypical negatives for efficient onl...

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IEEE Workshop on Applications of Computer vision (WACV)

作者： Masato Takami Peter Bell Björn Ommer Robert Bosch GmbH Computer Vision Research Lab Hildesheim Germany Heidelberg Collaboratory for Image Processing & IWR University of Heidelberg Germany

Online searches in big image databases require sufficient results in feasible time. Digitization campaigns have simplified the access to a huge number of images in the field of art history, which can be analyzed by detecting duplicates and similar objects in the dataset. A high recall is essential for the evaluation and therefore the search method has to be robust against minor changes due to smearing or aging effects of the documents. At the same time the computational time has to be short to allow a practical use of the online search. By using an Exemplar SVM based classifier [12] a high recall can be achieved, but the mining of negatives and the multiple rounds of retraining for every search makes the method too time-consuming. An even bigger problem is that by allowing arbitrary query regions, it is not possible to provide a training set, which would be necessary to create a classifier. To solve this, we created a pool of general negatives offline in advance, which can be used by any arbitrary input in the online search step and requires only one short training round without the time-consuming mining. In a second step, this classifier is improved by using positive detections in an additional training round. This results in a classifier for the online search in unlabeled datasets, which provides high recall in short calculation time respectively.

关键词： Training Support vector machines Art Feature extraction Search problems Visualization image databases

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Semi-automatic Fisher-Tippett Guided Active Contour for Lumbar Multifidus Muscle Segmentation

Semi-automatic Fisher-Tippett Guided Active Contour for Lumb...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Dorothy Lui Christian Scharfenberger Diana E. De Carvalho Jack P. Callaghan Alexander Wong Faculty of Systems Design Engineering Vision and Image Processing Lab University of Waterloo Waterloo Canada The Department of Kinesiology University of Waterloo Waterloo Canada

Rehabilitative Ultrasound Imaging or diagnostic ultrasound is used to measure geometric properties of the lumbar multifidus muscle to infer muscle strength or degeneration for back pain therapy. For this purpose, a novel semi-automatic approach (FTS: Fisher-Tippett Segmentation) based upon the Decoupled Active Contour is proposed to reliably and quickly segment the lumbar multifidus muscle in diagnostic ultrasound. To overcome speckle or hardly visible region boundaries in ultrasound images, we first propose a novel external energy functional to explicitly consider the underlying Fisher-Tippett distribution of ultrasound data. We then introduce a user-guided Hidden Markov Model trellis formation for improved segmentation of weakly-defined regions. Extensive experiments have shown that our approach not only improves the segmentation performance when compared to existing methods, but also does not rely on sub-specialized knowledge for segmentation.

关键词： Muscles Ultrasonic imaging image segmentation Hidden Markov models Back Medical treatment Active contours

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