The framework of image recognition based on modified freeman chain code

Image recognition of line drawing involves feature extraction and feature comparison; works on the extraction required the representation of the image to be compared and analysed. Combining these two requirements, a framework that implements a new extraction algorithm of a chain code representation is presented. In addition, new corner detection is presented as pre-processing to the line drawing input in order to derive the chain code. This paper presents a new framework that consists of five steps namely pre-processing and image processing, new corner detection algorithm, chain code generator, feature extraction algorithm, and recognition process. Heuristic approach that is applied in the corner detection algorithm accepts thinned binary image as input and produces a modified thinned binary image containing J characters to represent corners in the image. Using the modified thinned binary image, a new chain code scheme that is based on Freeman chain code is proposed and an algorithm is developed to generate a single chain code series that is representing the line drawing input. The feature extraction algorithm is then extracts the three pre-defined features of the chain code for recognition purpose. The features are corner properties, distance between corners, and angle from a corner to the connected corner. The explanation of steps in the framework is supported with two line drawings. The results show that the framework successfully recognizes line drawing into five categories namely not similar line drawing, and four other categories that are similar but with attributes of rotation angle and scaling ratio

Object Shape Recognition

Objects around us make our environment; in day to day life we tend to classify each of the objects visible to us. We tend to classify each object like a ball is spherical; a notebook is rectangular and so on using our senses. A machine like computer does not have senses to recognize or even detect an object. We have to train or develop an algorithm for a machine like computer to do so. The approach here is to classify some of the common objects around us and decide whether they belong to any geometric shape or not. The shape of the objects can be represented by some feature space which may be used for recognizing shape of the objects. We use the corner detection method, signature method and chain code method to achieve a good recognition. The corner detection method is based on detecting corners on the boundary and then deriving the feature vector from the distance between the corners. The signature method is based on the distance of the boundary points from the centre of the object and all those distances from the feature vector. The chain code method is based on finding the chain code of the object and then finds the histogram of it, which forms the feature vector. The purpose of this thesis is to use all the three methods of recognition and visualize their performances

Improved clustering approach for junction detection of multiple edges with modified freeman chain code

Image processing framework of two-dimensional line drawing involves three phases that are detecting junction and corner that exist in the drawing, representing the lines, and extracting features to be used in recognizing the line drawing based on the representation scheme used. As an alternative to the existing frameworks, this thesis proposed a framework that consists of improvement in the clustering approach for junction detection of multiple edges, modified Freeman chain code scheme and provide new features and its extraction, and recognition algorithm. This thesis concerns with problem in clustering line drawing for junction detection of multiple edges in the first phase. Major problems in cluster analysis such as time taken and particularly number of accurate clusters contained in the line drawing when performing junction detection are crucial to be addressed. Two clustering approaches are used to compare with the result obtained from the proposed algorithm: self-organising map (SOM) and affinity propagation (AP). These approaches are chosen based on their similarity as unsupervised learning class and do not require initial cluster count to execute. In the second phase, a new chain code scheme is proposed to be used in representing the direction of lines and it consists of series of directional codes and corner labels found in the drawing. In the third phase, namely feature extraction algorithm, three features proposed are length of lines, angle of corners, and number of branches at each corner. These features are then used in the proposed recognition algorithm to match the line drawing, involving only mean and variance in the calculation. Comparison with SOM and AP clustering approaches resulting in up to 31% reduction for cluster count and 57 times faster. The results on corner detection algorithm shows that it is capable to detect junction and corner of the given thinned binary image by producing a new thinned binary image containing markers at their locations

Low-level processing for real-time image analysis

A system that detects object outlines in television images in real time is described. A high-speed pipeline processor transforms the raw image into an edge map and a microprocessor, which is integrated into the system, clusters the edges, and represents them as chain codes. Image statistics, useful for higher level tasks such as pattern recognition, are computed by the microprocessor. Peak intensity and peak gradient values are extracted within a programmable window and are used for iris and focus control. The algorithms implemented in hardware and the pipeline processor architecture are described. The strategy for partitioning functions in the pipeline was chosen to make the implementation modular. The microprocessor interface allows flexible and adaptive control of the feature extraction process. The software algorithms for clustering edge segments, creating chain codes, and computing image statistics are also discussed. A strategy for real time image analysis that uses this system is given

Analytical modelling in Dynamo

BIM is applied as modern database for civil engineering. Its recent development allows to preserve both structure geometrical and analytical information. The analytical model described in the paper is derived directly from BIM model of a structure automatically but in most cases it requires manual improvements before being sent to FEM software. Dynamo visual programming language was used to handle the analytical data. Authors developed a program which corrects faulty analytical model obtained from BIM geometry, thus providing better automation for preparing FEM model. Program logic is explained and test cases shown