Motion detection, noise reduction, texture suppression, and contour enhancement by spatiotemporal Gabor filters with surround inhibition

Petkov, Nicolai; Subramanian, Easwar

doi:10.1007/s00422-007-0182-0

Cited by 64 publications

(46 citation statements)

References 78 publications

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“…In most cases, this aspect of the filter design is not mentioned. Image processing literature documents two options for Gabor filter normalization, namely the normalization by division [34], [35], [36] and zero integral methods [20], [37]. This study seeks to demonstrate that Gabor filter normalization is essential to robust vessel enhancement and that it affects the effectiveness of automatic thresholding.…”

Section: Literature Surveymentioning

confidence: 99%

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Segmentation of retinal blood vessels using normalized Gabor filters and automatic thresholding

Gwetu¹,

Tapamo²,

Viriri³

2014

SACJ

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Although computerized retinal image blood vessel segmentation has been extensively researched, there is still room for improvement in the quality of the segmented images. Since retinal image analysis is still widely used in the diagnosis of diabetic retinopathy, efficient and accurate image characterization techniques are required. Previous work has mainly focused on improving segmentation accuracy rates with little regard to the false positives that are produced by illumination variation. This research work presents a hybrid approach towards the segmentation of retinal blood vessels. New approaches towards the reduction of background illumination variation are proposed using normalized Gabor filtering. These are the base-offset encoding and a modified version of an existing zero-integral kernel technique. The valley emphasis automatic thresholding scheme is used to segment the Gabor response images. Experiments are conducted on the DRIVE and STARE retinal image data sets. Accuracy rates of up to 94% are achieved through the zero-integral and base offset methods. This is comparable with results from literature, where the same data sets are segmented using other classification techniques. The median-offset method is found to most effectively reduce background illumination variation.

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Section: Literature Surveymentioning

confidence: 99%

“…It divides a pixel's GFR by a factor that is derived from its neighborhood [36]. In [35], this factor is the average gray level in a pixel window.…”

Section: Gabor Filter Normalization By Divisionmentioning

confidence: 99%

Segmentation of retinal blood vessels using normalized Gabor filters and automatic thresholding

Gwetu¹,

Tapamo²,

Viriri³

2014

SACJ

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“…In seminal work, [38] suggested that a two-dimensional spatial pattern Adaboost-HMM Classifier for Phonemes Adaboost-HMM Classifier for Visemes gaudio gvisual gaudio_visual moving at a given velocity corresponds to a three-dimensional spatiotemporal pattern of a given orientation which can be detected with an appropriately oriented 3D spatiotemporal filter, such as a 3D Gabor filter. In [39] Nikolai Petkov , et al, model the spatio temporal receptive field profile of simple cells as a family of Gabor filter function denoted by g v,, (x, y, t) where (x,y,t)  R 3 which is centered in the origin (0, 0, 0) as given in Eq. (1) in Appendix.Stimuli motion is three-dimensional motion.…”

Section: Customized Spatiotemporal Gabor Filtermentioning

confidence: 99%

Emotion based Contextual Semantic Relevance Feedback in Multimedia Information Retrieval

Singh¹,

Tripathi²

2012

IJCA

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Every query issued by a user to find some relevant information,contains the semantic and its associated contexts, but, indentifying and conveying these semantic and context (present in the query) to MIR system is a major challenge and still needs to be tackled effectively. Thus, exploiting the plausibility of context associated with semantic concept for the purpose of enhancement in retrieval of the possible relevant information, we propose Emotion Based Contextual Semantic Relevance Feedback(ECSRF) to learn, refine, discriminate and identify the current context present in a query. We will further investigate: (1) whether multimedia attributes(audio, speech along with visual) can be purposefully used to work out a current context of user's query and will be useful inreduction of search space and retrieval time; (2) whether increasing the Affective features (spoken emotional word(s)with facial expression(s)) in identifying, discriminating emotions would increase the overall retrieval performance in terms of Precision, Recall and retrieval time;(3)whether increasing the discriminating power of classifier algorithm in query perfection would increase the search accuracy with less retrieval time.We introduce an Emotion Recognition Unit(ERU) that comprises of a customized 3D spatiotemporal Gabor filter to capture spontaneous facial expression, and emotional word recognition system (combination of phonemes and visemes) to recognize the spoken emotional words. Integration of classifier algorithms GMM, SVM and CQPSB are compared in ECSRF framework to study the effect of increasing the discriminating power of classifier on retrieval performance.Observations suggest that prediction of contextual semantic relevance is feasible, and ECSRF model can benefit from incorporating such increased affective features and classifier to increase a MIR system's retrieval efficiency and contextual perceptions.

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“…In particular, two-dimensional Gabor functions were proposed for computational modelling of these cells [11,30]. Gabor functions were then widely applied in diverse computer vision tasks, including edge detection [32,37], texture analysis [9,17,23,29,49,50], image coding and compression [12], person identification based on iris pattern analysis [13], image enhancement [10], face recognition [36], motion analysis [42], and retrieval from image databases [54]. Further refinements of these models, include non-classical receptive field inhibition [43], also called surround suppression, and the filters that deploy this mechanism were shown to be effective detectors of object contours [21,22].…”

Section: Introductionmentioning

confidence: 99%

COSFIRE: A Brain-Inspired Approach to Visual Pattern Recognition

Azzopardi

Petkov

2014

Lecture Notes in Computer Science

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Abstract. The primate visual system has an impressive ability to generalize and to discriminate between numerous objects and it is robust to many geometrical transformations as well as lighting conditions. The study of the visual system has been an active reasearch field in neuropysiology for more than half a century. The construction of computational models of visual neurons can help us gain insight in the processing of information in visual cortex which we can use to provide more robust solutions to computer vision applications. Here, we demonstrate how inspiration from the functions of shape-selective V4 neurons can be used to design trainable filters for visual pattern recognition. We call this approach COSFIRE, which stands for Combination of Shifted Filter Responses. We illustrate how a COSFIRE filter can be configured to be selective for the spatial arrangement of lines and/or edges that form the shape of a given prototype pattern. Finally, we demonstrate the effectiveness of the COSFIRE approach in three applications: the detection of vascular bifurcations in retinal fundus images, the localization and recognition of traffic signs in complex scenes and the recognition of handwritten digits. This work is a further step in understanding how visual information is processed in the brain and how information on pixel intensities is converted into information about objects. We demonstrate how this understanding can be used for the design of effective computer vision algorithms.

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Motion detection, noise reduction, texture suppression, and contour enhancement by spatiotemporal Gabor filters with surround inhibition

Cited by 64 publications

References 78 publications

Segmentation of retinal blood vessels using normalized Gabor filters and automatic thresholding

Segmentation of retinal blood vessels using normalized Gabor filters and automatic thresholding

Emotion based Contextual Semantic Relevance Feedback in Multimedia Information Retrieval

COSFIRE: A Brain-Inspired Approach to Visual Pattern Recognition

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