A Shape Descriptor Based on Trainable COSFIRE Filters for the Recognition of Handwritten Digits

Computer Analysis of Images and Patterns

et al. 2015

Self Cite

Abstract. The automatic recognition of symbols can be used to automatically convert scanned drawings into digital representations compatible with computer aided design software. We propose a novel approach to automatically recognize architectural and electrical symbols. The proposed method extends the existing trainable COSFIRE approach by adding an inhibition mechanism that is inspired by shape-selective TEO neurons in visual cortex. A COSFIRE filter with inhibition takes as input excitatory and inhibitory responses from line and edge detectors. The type (excitatory or inhibitory) and the spatial arrangement of low level features are determined in an automatic configuration step that analyzes two types of prototype pattern called positive and negative. Excitatory features are extracted from a positive pattern and inhibitory features are extracted from one or more negative patterns. In our experiments we use four subsets of images with different noise levels from the Graphics Recognition data set (GREC 2011) and demonstrate that the inhibition mechanism that we introduce improves the effectiveness of recognition substantially.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recognition of Architectural and Electrical Symbols by COSFIRE Filters with Inhibition

Guo

Shi

Computer Analysis of Images and Patterns

et al. 2015

Self Cite

“…In [6] we applied this shape descriptor to the recognition of handwritten digits, an application that has been extensively used for the evaluation of shape descriptors. We achieved a recognition rate of 99.52 % on the MNIST data set [34] of 70,000 (60,000 training and 10,000 test) western Arabic digits.…”

Section: Methodsmentioning

confidence: 99%

COSFIRE: A Brain-Inspired Approach to Visual Pattern Recognition

Lecture Notes in Computer Science

Petkov

2014

Self Cite

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.

“…The data set contains 60,000 training and 10,000 test digit images in gray scale of size 28 × 28 pixels. 4 For configuration, we randomly select 20 training images from each digit class. We select a random location as the point of interest to configure a COSFIRE filter in each image from the same digit class.…”

Section: Recognition Of Handwritten Digitsmentioning

confidence: 99%

“…A COSFIRE filter is configured to be selective for a given local pattern by extracting from that pattern characteristic properties of contour parts (such as orientation) and their geometrical arrangement. COSFIRE filters were demonstrated to be effective for detection of local patterns (keypoints) and recognition of objects and achieve very good performance in various applications [4,6,8,19,47,49,50]. They were also used in a multilayer hierarchical approach [6].…”

Section: Introductionmentioning

confidence: 99%

Inhibition-augmented trainable COSFIRE filters for keypoint detection and object recognition

Guo

Shi

Machine Vision and Applications

et al. 2016

Self Cite

The shape and meaning of an object can radically change with the addition of one or more contour parts. For instance, a T-junction can become a crossover. We extend the COSFIRE trainable filter approach which uses a positive prototype pattern for configuration by adding a set of negative prototype patterns. The configured filter responds to patterns that are similar to the positive prototype but not to any of the negative prototypes. The configuration of such a filter comprises selecting given channels of a bank of Gabor filters that provide excitatory or inhibitory input and determining certain blur and shift parameters. We compute the response of such a filter as the excitatory input minus a fraction of the maximum of inhibitory inputs. We use three applications to demonstrate the effectiveness of inhibition: the exclusive detection of vascular bifurcations (i.e., without crossovers) in retinal fundus images (DRIVE data set), the recognition of architectural and electrical symbols (GREC'11 data set) and the recognition of handwritten digits (MNIST data set).