108 research outputs found
Contour detection by CORF operator
We propose a contour operator, called CORF, inspired by
the properties of simple cells in visual cortex. It combines, by a weighted
geometric mean, the blurred responses of difference-of-Gaussian operators
that model cells in the lateral geniculate nucleus (LGN). An operator
that has gained particular popularity as a computational model of a simple
cell is based on a family of Gabor Functions (GFs). However, the GF
operator short-cuts the LGN, and its effectiveness in contour detection
tasks, which is assumed to be the primary biological role of simple cells,
has never been compared with the effectiveness of alternative operators.
We compare the performances of the CORF and the GF operators using
the RuG and the Berkeley data sets of natural scenes with associated
ground truths. The proposed CORF operator outperforms the GF operator
(RuG: t(39)=4.39, p<10−4 and Berkeley: t(499)=4.95, p<10−6).peer-reviewe
A robust contour detection operator with combined push-pull inhibition and surround suppression
Contour detection is a salient operation in many computer vision applications as it extracts features that are important for distinguishing objects in scenes. It is believed to be a primary role of simple cells in visual cortex of the mammalian brain. Many of such cells receive push-pull inhibition or surround suppression. We propose a computational model that exhibits a combination of these two phenomena. It is based on two existing models, which have been proven to be very effective for contour detection. In particular, we introduce a brain-inspired contour operator that combines push-pull and surround inhibition. It turns out that this combination results in a more effective contour detector, which suppresses texture while keeping the strongest responses to lines and edges, when compared to existing models. The proposed model consists of a Combination of Receptive Field (or CORF) model with push-pull inhibition, extended with surround suppression. We demonstrate the effectiveness of the proposed approach on the RuG and Berkeley benchmark data sets of 40 and 500 images, respectively. The proposed push-pull CORF operator with surround suppression outperforms the one without suppression with high statistical significance
A Push-Pull CORF Model of a Simple Cell with Antiphase Inhibition Improves SNR and Contour Detection
We propose a computational model of a simple cell with push-pull inhibition, a property that is observed in many real simple cells. It is based on an existing model called Combination of Receptive Fields or CORF for brevity. A CORF model uses as afferent inputs the responses of model LGN cells with appropriately aligned center-surround receptive fields, and combines their output with a weighted geometric mean. The output of the proposed model simple cell with push-pull inhibition, which we call push-pull CORF, is computed as the response of a CORF model cell that is selective for a stimulus with preferred orientation and preferred contrast minus a fraction of the response of a CORF model cell that responds to the same stimulus but of opposite contrast. We demonstrate that the proposed push-pull CORF model improves signal-to-noise ratio (SNR) and achieves further properties that are observed in real simple cells, namely separability of spatial frequency and orientation as well as contrast-dependent changes in spatial frequency tuning. We also demonstrate the effectiveness of the proposed push-pull CORF model in contour detection, which is believed to be the primary biological role of simple cells. We use the RuG (40 images) and Berkeley (500 images) benchmark data sets of images with natural scenes and show that the proposed model outperforms, with very high statistical significance, the basic CORF model without inhibition, Gabor-based models with isotropic surround inhibition, and the Canny edge detector. The push-pull CORF model that we propose is a contribution to a better understanding of how visual information is processed in the brain as it provides the ability to reproduce a wider range of properties exhibited by real simple cells. As a result of push-pull inhibition a CORF model exhibits an improved SNR, which is the reason for a more effective contour detection.</p
Brain-Inspired Algorithms for Processing of Visual Data
The study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compose it. These findings inspired researchers in image processing and computer vision to deploy such models to solve problems of visual data processing. In this paper, we review approaches for image processing and computer vision, the design of which is based on neuro-scientific findings about the functions of some neurons in the visual cortex. Furthermore, we analyze the connection between the hierarchical organization of the visual system of the brain and the structure of Convolutional Networks (ConvNets). We pay particular attention to the mechanisms of inhibition of the responses of some neurons, which provide the visual system with improved stability to changing input stimuli, and discuss their implementation in image processing operators and in ConvNets.</p
CORF3D contour maps with application to Holstein cattle recognition using RGB and thermal images
Livestock management involves the monitoring of farm animals by tracking certain physiological and phenotypical characteristics over time. In the dairy industry, for instance, cattle are typically equipped with RFID ear tags. The corresponding data (e.g. milk properties) can then be automatically assigned to the respective cow when they enter the milking station. In order to move towards a more scalable, affordable, and welfare-friendly approach, automatic non-invasive solutions are more desirable. Thus, a non-invasive approach is proposed in this paper for the automatic identification of individual Holstein cattle from the side view while exiting a milking station. It considers input images from a thermal-RGB camera. The thermal images are used to delineate the cow from the background. Subsequently, any occluding rods from the milking station are removed and inpainted with the fast marching algorithm. Then, it extracts the RGB map of the segmented cattle along with a novel CORF3D contour map. The latter contains three contour maps extracted by the Combination of Receptive Fields (CORF) model with different strengths of push-pull inhibition. This mechanism suppresses noise in the form of grain type texture. The effectiveness of the proposed approach is demonstrated by means of experiments using a 5-fold and a leave-one day-out cross-validation on a new data set of 3694 images of 383 cows collected from the Dairy Campus in Leeuwarden (the Netherlands) over 9 days. In particular, when combining RGB and CORF3D maps by late fusion, an average accuracy of was obtained for the 5-fold cross validation and for the leave–one day–out experiment. The two maps were combined by first learning two ConvNet classification models, one for each type of map. The feature vectors in the two FC layers obtained from training images were then concatenated and used to learn a linear SVM classification model. In principle, the proposed approach with the novel CORF3D contour maps is suitable for various image classification applications, especially where grain type texture is a confounding variable
Convolutional Neural Networks Exploiting Attributes of Biological Neurons
In this era of artificial intelligence, deep neural networks like
Convolutional Neural Networks (CNNs) have emerged as front-runners, often
surpassing human capabilities. These deep networks are often perceived as the
panacea for all challenges. Unfortunately, a common downside of these networks
is their ''black-box'' character, which does not necessarily mirror the
operation of biological neural systems. Some even have millions/billions of
learnable (tunable) parameters, and their training demands extensive data and
time.
Here, we integrate the principles of biological neurons in certain layer(s)
of CNNs. Specifically, we explore the use of neuro-science-inspired
computational models of the Lateral Geniculate Nucleus (LGN) and simple cells
of the primary visual cortex. By leveraging such models, we aim to extract
image features to use as input to CNNs, hoping to enhance training efficiency
and achieve better accuracy. We aspire to enable shallow networks with a
Push-Pull Combination of Receptive Fields (PP-CORF) model of simple cells as
the foundation layer of CNNs to enhance their learning process and performance.
To achieve this, we propose a two-tower CNN, one shallow tower and the other as
ResNet 18. Rather than extracting the features blindly, it seeks to mimic how
the brain perceives and extracts features. The proposed system exhibits a
noticeable improvement in the performance (on an average of ) on
CIFAR-10, CIFAR-100, and ImageNet-100 datasets compared to ResNet-18. We also
check the efficiency of only the Push-Pull tower of the network.Comment: 20 pages, 6 figure
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