Sparse Models for Computer Vision

Perrinet, Laurent

doi:10.1002/9783527680863.ch14

Cited by 8 publications

(6 citation statements)

References 90 publications

(139 reference statements)

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“…Orientations are measured as an undirected angle in radians, in the range from 0 to π (but not including π). Tests with a range of different numbers of orientations and scales yielded similar results [15] This transform is linear and can be performed by a simple convolution repeated for every edge type. Following [3], convolutions were performed in the Fourier (frequency) domain for computational efficiency.…”

Section: Biologically-inspired Sparse Codingsupporting

confidence: 49%

“…As a matter of fact, a normative explanation for the coding of images into activity in the neural tissue is the optimal representation of visual information [1]. Inspired by the representation in V1, a popular method requires the efficient modeling of images as the sparsest combination of edges and such a coding strategy indeed resembles the neural activity (that is, the spike patterns) observed in V1 [15]. Using such a method, it was previously shown that any image from a database of static, grayscale natural images may be coded using solely a relatively few number of coefficients (See Figure 2 and SI Section 5 for a full description of the algorithm).…”

Section: Motivationmentioning

confidence: 99%

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Biologically-inspired characterization of sparseness in natural images

Perrinet

2016

2016 6th European Workshop on Visual Information Processing (EUVIP)

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Natural images follow statistics inherited by the structure of our physical (visual) environment. In particular, a prominent facet of this structure is that images can be described by a relatively sparse number of features. We designed a sparse coding algorithm biologically-inspired by the architecture of the primary visual cortex. We show here that coefficients of this representation exhibit a heavy-tailed distribution. For each image, the parameters of this distribution characterize sparseness and vary from image to image. To investigate the role of this sparseness, we designed a new class of random textured stimuli with a controlled sparseness value inspired by our measurements on natural images. Then, we provide with a method to synthesize random textures images with a given statistics for sparseness that matches that of some given class of natural images and provide perspectives for their use in neurophysiology.

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Section: Biologically-inspired Sparse Codingsupporting

confidence: 49%

Section: Motivationmentioning

confidence: 99%

Biologically-inspired characterization of sparseness in natural images

Perrinet

2016

2016 6th European Workshop on Visual Information Processing (EUVIP)

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“…` Sparse linear regression [58] This model used to approximate the regression and noise repeatedly. It used mean residual square for this noise reduction.…”

Section: Review About the Object Detection Methods Using Ml/dl Algorithmsmentioning

confidence: 99%

Study Of Deep Learning Techniques For Differently Abled Applications And Detection Techniques

Anandh¹

2021

TURCOMAT

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In worldwide, Visually Impaired Persons (VIP) are facing several issue related to visual impairment and blindness and they are assisted with technical inventions. Based on the survey from World Health Organization (WHO), around 2.2 billion peoples are suffered from visual impairment and among them 1 million peoples are suffered by blindness. Vision is the major sensing organ of the human and to assist the VIP regarding this, there are various digital vision products are in the market which is based on digital technologies and advanced algorithms. This will transform the VIP’s vision world into audio to get to know about their surroundings includes objects, motion, obstacles and spatial locations. The objective of this paper is to provide the detailed survey about the existing object recognition, face recognition and text to voice recognition methods proposed to assist VIP. Due to the increase development of machine learning and deep learning algorithms, the digital image recognition and object recognition are become more efficient. These advanced technologies can make sure to assist the VIP to detect and recognize the people face and objects in front of them in the form of audio that are practiced by them on daily basis.

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“…A simplistic formalism to quantify this sparseness is to consider the coding of natural images using a bank of filters resembling the Mexican-hat profiles observed in the retina of most mammals (See Supplementary Figure 6). Then, it is possible to determine the efficiency of a near-to-optimal coding formalism [22] to show that any image from a database of static, grayscale natural images may be coded solely by a few coefficients (See Figure 1 ; see SI Section 6.1 for a full description of the algorithm). Moreover, we observed that on this database, some images were sparser than others but that globally they all fitted well a similar power-law probability distribution function.…”

Section: Motivationmentioning

confidence: 99%

Differential response of the retinal neural code with respect to the sparseness of natural images

Ravello,

Escobar,

Palacios

et al. 2016

Preprint

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Natural images follow statistics inherited by the structure of our physical (visual) environment. In particular, a prominent facet of this structure is that images can be described by a relatively sparse number of features. To investigate the role of this sparseness in the efficiency of the neural code, we designed a new class of random textured stimuli with a controlled sparseness value inspired by measurements of natural images. Then, we tested the impact of this sparseness parameter on the firing pattern observed in a population of retinal ganglion cells recorded ex vivo in the retina of a rodent, the Octodon degus. These recordings showed in particular that the reliability of spike timings varies with respect to the sparseness with globally a similar trend than the distribution of sparseness statistics observed in natural images. These results suggest that the code represented in the spike pattern of ganglion cells may adapt to this aspect of the statistics of natural images. * See http://invibe.net/LaurentPerrinet.

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Sparse Models for Computer Vision

Abstract: BibTex entryThis chapter appeared as :

Cited by 8 publications

References 90 publications

Biologically-inspired characterization of sparseness in natural images

Biologically-inspired characterization of sparseness in natural images

Study Of Deep Learning Techniques For Differently Abled Applications And Detection Techniques

Differential response of the retinal neural code with respect to the sparseness of natural images

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