Recognition of Architectural and Electrical Symbols by COSFIRE Filters with Inhibition

Abstract

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.

Automatic Generation of 3D Building Models Based on Line Segment Vectorization

“…Both N and i are positive integers, and N < N + i, which indicates that P N is in front of P N+i after sorting. Consider Figure 12 as an example, the corresponding (N, N + i) of the 4 lower left wall-corners (formed by red right angles) are (1, 3), (4, 5), (11,13), and (20,22).…”

“…e most common research focused on recognition methods or algorithms [19][20][21][22]: statistical grouping method for partitioning line drawings into shapes, those shapes which represent meaningful parts of the symbols that constitute the line drawings [19]; a new rotation invariant corner detection method for architectural line drawing images [20]; a generic method for floor plan analysis and interpretation [21], in which basic building blocks, i.e., walls, doors, and windows are first detected using a statistical patch-based segmentation approach, and structural pattern recognition techniques are second applied to further locate the main entities, i.e., rooms of the building; and a novel approach to automatically recognize architectural and electrical symbols [22].…”

Fusion of CNN- and COSFIRE-Based Features with Application to Gender Recognition from Face Images

“…The other approach that we are concerned with, namely, Combination of Shifted Filter Responses (COSFIRE), is also a brain-inspired visual pattern recognition method, which has been effectively applied in various applications, including traffic sign recognition [36], handwritten digit recognition [37], architectural symbol recognition [38], quality visual inspection [39], contour detection [40], delineation of curvilinear structures [41], such as the blood vessels in retinal fundus images and cracks in pavements [42], butterfly recognition [36], person identification from retinal images [43], and gender recognition from face images [44,45]. COSFIRE is a filtering approach whose selectivity is determined in a configuration stage by the automatic analysis of given prototype patterns.…”

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

“…We compute the mean value of the intensities of all the pixels in an image. For the images that have a mean intensity value of at least 90 % of the maximum, we apply the morphological operations proposed in [19]. First, we dilate the images by six line-shaped structuring elements of 6-pixel length with different orientations ({0, Then, we perform a thinning operation followed by six dilations using line-shaped structuring elements of 4-pixel length with equidistant orientations.…”

“…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].…”