Sum-box technique for fast linear filtering

Shen, Jun; Shen, Wei Feng; Castan, Serge

doi:10.1016/s0165-1684(02)00243-8

Cited by 15 publications

(13 citation statements)

References 16 publications

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“…The main computational costs of our methods are paid on low-pass filtering. There are many fast algorithms available for this purpose, such as Sum-box technique [15] or integral image [16]. We can simplify the feature extraction procedure to additions and subtractions.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Iris Recognition Based on Non-local Comparisons

Sun

Tan

Wang

2004

Advances in Biometric Person Authentication

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Abstract. Iris recognition provides a reliable method for personal identification. Inspired by recent achievements in the field of visual neuroscience, we encode the non-local image comparisons qualitatively for iris recognition. In this scheme, each bit iris code corresponds to the sign of an inequality across several distant image regions. Compared with local ordinal measures, the relationships of dissociated multi-pole are more informative and robust against intraclass variations. Thus non-local ordinal measures are more suited for iris recognition. In our early work, we have built a general framework "robust encoding of local ordinal measures" to unify several top iris recognition algorithms. Therefore the results reported in this paper improve state-of-the-art iris recognition performance essentially as well as evolve the framework from pair-wise local ordinal relationship to non-local ordinal feature of multiple regions. Our ideas are proved on CASIA iris image database.

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Section: Discussion and Future Workmentioning

confidence: 99%

Iris Recognition Based on Non-local Comparisons

Sun

Tan

Wang

2004

Advances in Biometric Person Authentication

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“…Filtered image I f can be regarded as a convolution between original image I 0 and kernel K [14]: I f = K ⊗ I 0 . The filter kernels, typically a matrix of size M*M, represents the number of pixels nearby taken into account.…”

Section: Methodsmentioning

confidence: 99%

Adipose segmentation in small animals at 7T: a preliminary study

et al. 2010

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BackgroundSmall animal MRI at 7 Tesla (T) provides a useful tool for adiposity research. For adiposity researchers, separation of fat from surrounding tissues and its subsequent quantitative or semi- quantitative analysis is a key task. This is a relatively new field and a priori it cannot be known which specific biological questions related to fat deposition will be relevant in a specific study. Thus it is impossible to predict what accuracy and what spatial resolution will be required in all cases and even difficult what accuracy and resolution will be useful in most cases. However the pragmatic time constraints and the practical resolution ranges are known for small animal imaging at 7T. Thus we have used known practical constraints to develop a method for fat volume analysis based on an optimized image acquisition and image post processing pair.MethodsWe designed a fat segmentation method based on optimizing a variety of factors relevant to small animal imaging at 7T. In contrast to most previously described MRI methods based on signal intensity of T1 weighted image alone, we chose to use parametric images based on Multi-spin multi-echo (MSME) Bruker pulse sequence which has proven to be particularly robust in our laboratory over the last several years. The sequence was optimized on a T1 basis to emphasize the signal. T2 relaxation times can be calculated from the multi echo data and we have done so on a pixel by pixel basis for the initial step in the post processing methodology. The post processing consists of parallel paths. On one hand, the weighted image is precisely divided into different regions with optimized smoothing and segmentation methods; and on the other hand, a confidence image is deduced from the parametric image according to the distribution of relaxation time relationship of typical adipose. With the assistance of the confidence image, a useful software feature was implemented to which enhances the data and in the end results in a more reliable and flexible method for adipose evaluation.ResultsIn this paper, we describe how we arrived at our recommended procedures and key aspects of the post-processing steps. The feasibility of the proposed method is tested on both simulated and real data in this preliminary research. A research tool was created to help researchers segment out fat even when the anatomical information is of low quality making it difficult to distinguish between fat and non-fat. In addition, tool is designed to allow the operator to make adjustments to many of the key steps for comparison purposes and to quantitatively assess the difference these changes make. Ultimately our flexible software lets the researcher define key aspects of the fat segmentation and quantification.ConclusionsCombining the full T2 parametric information with the optimized first echo image information, the research tool enhances the reliability of the results while providing more flexible operations than previous methods. The innovation in the method is to pair an optimized and very specific image acqu...

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“…The proposed algorithm can be summarised in three main steps: i) extraction of a window of interest of a potential crackle (based on fractal dimension and box filtering techniques) 14,[19][20][21] ; ii) verification of the validity of the potential crackle considering CORSA established criteria 7,22 ; and iii) characterisation and extraction of crackle's parameters.…”

Section: Automatic Crackle Detectionmentioning

confidence: 99%

“…The extraction of a window of interest involved different signal processing steps: i) Savitzky-Golay (polynomial) finite impulse response (FIR) smoothing 24 ; ii) fractal dimension estimation 12,14,19 ; iii) box filtering 21,25 ; and iv) application of a threshold to extract the beginning and the end of a window of interest. A diagram summarising these steps is presented in figure 2.…”

Section: Extraction Of a Window Of Interestmentioning

confidence: 99%

Automatic Crackle Detection Algorithm Based on Fractal Dimension and Box Filtering

Pinho¹,

Oliveira²,

Jácome³

et al. 2015

Procedia Computer Science

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Crackles are adventitious respiratory sounds that provide valuable information on different respiratory conditions. Crackles automatic detection in a respiratory sound file is challenging, and thus different signal processing methodologies have been proposed. However, limited testing of such methodologies, namely in respiratory sound files collected in clinical settings, has been conducted. This study aimed to develop an algorithm for automatic crackle detection and characterisation and to evaluate its performance and accuracy against a multi-annotator gold standard. The algorithm is based on three main procedures: i) extraction of a window of interest of a potential crackle (based on fractal dimension and box filtering techniques); ii) verification of the validity of the potential crackle considering computerised respiratory sound analysis established criteria; and iii) characterisation and extraction of crackle parameters. Twenty four 10-second files, acquired in clinical settings, were selected from 10 patients with pneumonia and cystic fibrosis. The algorithm performance was assessed by comparing its results with gold standard annotations (obtained by the agreement among three experts). A set of 7 parameters was optimised. High levels of sensitivity (SE=89%), positive predictive value (PPV=95%) and overall performance (F index=92%) were achieved. This promising result highlights the potential of the algorithm for automatic crackle's detection/characterisation in respiratory sounds acquired in clinical settings.

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Sum-box technique for fast linear filtering

Cited by 15 publications

References 16 publications

Iris Recognition Based on Non-local Comparisons

Iris Recognition Based on Non-local Comparisons

Adipose segmentation in small animals at 7T: a preliminary study

Automatic Crackle Detection Algorithm Based on Fractal Dimension and Box Filtering

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