Understanding image structure from a new multi-scale representation of higher order derivative filters

Ghosh, Kuntal; Sarkar, Sandip; Bhaumik, Kamales

doi:10.1016/j.imavis.2006.07.022

Cited by 25 publications

(16 citation statements)

References 33 publications

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“…There are other similar models based on higher order derivatives of Gaussians (Ghosh et al, 2007). Some researchers have attempted to explain some of the geometrical illusion patterns investigated here and some brightness illusion patterns by using high level visual models, such as the perceptual inferences and fill in models proposed by Grossberg and Todorovic (1988), as well as Gestalt grouping and junction analysis (Gilchrist et al, 1999).…”

Section: Alternate Explanationsmentioning

confidence: 98%

“…Classical receptive field (CRFs) models mainly emphasize the contrast sensitivity of the retinal ganglion cells and are modelled based on the circular centre and surround antagonism using differences and second differences of Gaussians (DoG) or Laplacian of Gaussian (LoG) (Ghosh et al, 2007) to reveal the edge information.…”

Section: Classical Receptive Field Modelsmentioning

confidence: 99%

“…The extracted edge map is an essential and primitive task in most image processing applications, but edge map alone cannot provide any information about the shades, lights, and also three dimensional structure of the image (Ghosh et al, 2007). Therefore according to Marr's "raw primal sketch", there is a need for further processing to get the "full primal sketch" for a 3D view of the world (Marr & Hildreth, 1980).…”

Section: Classical Receptive Field Modelsmentioning

confidence: 99%

“…Young (1985Young ( , 1987 introduced modelling of the retinal and cortical RFs of many neurons based on linear combination of Gaussians and higher derivatives of Gaussian. In a recent study, Ghosh et al, (2007) used the 4 th and 6 th order derivatives of Gaussians to extract the shade information next to the edges. Still there is no biological evidence on the structure of these functions.…”

Section: Classical Receptive Field Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Bioplausible Multiscale Filtering in Retinal to Cortical Processing as a Model of Computer Vision

Nematzadeh

Lewis

Powers

2015

Proceedings of the International Conference on Agents and Artificial Intelligence

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Visual illusions emerge as an attractive field of research with the discovery over the last century of a variety of deep and mysterious mechanisms of visual information processing in the human visual system. Among many classes of visual illusion relating to shape, brightness, colour and motion, "geometrical illusions" are essentially based on the misperception of orientation, size, and position. The main focus of this paper is on illusions of orientation, sometimes referred to as "tilt illusions", where parallel lines appear not to be parallel, a straight line is perceived as a curved line, or angles where lines intersect appear larger or smaller. Although some low level and high level explanations have been proposed for geometrical tilt illusions, a systematic explanation based on model predictions of both illusion magnitude and local tilt direction is still an open issue. Here a neurophysiological model is expounded based on Difference of Gaussians implementing a classical receptive field model of retinal processing that predicts tilt illusion effects.

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Section: Alternate Explanationsmentioning

confidence: 98%

Section: Classical Receptive Field Modelsmentioning

confidence: 99%

Section: Classical Receptive Field Modelsmentioning

confidence: 99%

Section: Classical Receptive Field Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Bioplausible Multiscale Filtering in Retinal to Cortical Processing as a Model of Computer Vision

Nematzadeh

Lewis

Powers

2015

Proceedings of the International Conference on Agents and Artificial Intelligence

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“…In accordance with neurons in the early visual areas, local feature description extracts image features over small local regions [50]. The first-order operator that extracts edges information [51] and high-order operator conveying more knowledge of texture regions [52] are combined to generate more sufficient accurate quality-aware features. The experimental results reveal that local feature description has several properties that favor its usage in detailed discriminative information extraction for image quality assessment.…”

Section: Performance Comparison On Different Databasesmentioning

confidence: 99%

Image Quality Assessment Based on Multi-Order Local Features Description, Modeling and Quantification

Ding

Zhao

Zhi

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYImage quality assessment (IQA) plays an important role in quality monitoring, evaluation and optimization for image processing systems. However, current quality-aware feature extraction methods for IQA can hardly balance accuracy and complexity. This paper introduces multi-order local description into image quality assessment for feature extraction. The first-order structure derivative and high-order discriminative information are integrated into local pattern representation to serve as the quality-aware features. Then joint distributions of the local pattern representation are modeled by spatially enhanced histogram. Finally, the image quality degradation is estimated by quantifying the divergence between such distributions of the reference image and those of the distorted image. Experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches in consideration of not only accuracy that is consistent with human subjective evaluation, but also robustness and stability across different distortion types and various public databases. It provides a promising choice for image quality assessment development. key words: image quality assessment, image quality degradation, local pattern representation, feature extraction, visual perception

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No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features

Oszust

Piórkowski

Obuchowicz

2020

Magnetic Resonance in Med

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Purpose: Subjective quality assessment of displayed magnetic resonance (MR) images plays a key role in diagnosis and the resultant treatment. Therefore, this study aims to introduce a new no-reference (NR) image quality assessment (IQA) method for the objective, automatic evaluation of MR images and compare its judgments with those of similar techniques. Methods: A novel NR-IQA method was developed. The method uses a sequence of scaled images filtered to enhance high-frequency components and preserve lowfrequency parts. Since the human visual system (HVS) is sensitive to local image variations and local features often mimic the attraction of the HVS to high-frequency image regions, they were detected in the filtered images and described. Then, the statistics of obtained descriptors were used to build a quality model via the Support Vector Regression method. Results: The method was compared with 21 state-of-the-art techniques for NR-IQA on a new dataset of 70 distorted MR images assessed by 31 experienced radiologists, using typical evaluation criteria for the comparison of NR measures. The introduced method significantly outperforms the compared approaches, in terms of the correlation with human judgments. Conclusions: It is demonstrated that the presented NR-IQA method for the assessment of MR images is superior to the state-of-the-art NR techniques. The method would be beneficial for a wide range of image processing applications, assessing their outputs and affecting the directions of their development. K E Y W O R D Shigh-boost filtering, image quality assessment, local features, magnetic resonance images, no-reference, subjective tests

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Understanding image structure from a new multi-scale representation of higher order derivative filters

Cited by 25 publications

References 33 publications

Bioplausible Multiscale Filtering in Retinal to Cortical Processing as a Model of Computer Vision

Bioplausible Multiscale Filtering in Retinal to Cortical Processing as a Model of Computer Vision

Image Quality Assessment Based on Multi-Order Local Features Description, Modeling and Quantification

No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features

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