A parallel windowing approach to the Hough transform for line segment detection

In the wide range of image processing and computer vision problems, line segment detection has always been among the most critical headlines. Detection of primitives such as linear features and straight edges has diverse applications in many image understanding and perception tasks. The research presented in this dissertation is a contribution to the detection of straight-line segments by identifying the location of their endpoints within a two-dimensional digital image. The proposed method is based on a unique domain-crossing approach that takes both image and parameter domain information into consideration. First, the straight-line parameters, i.e. location and orientation, have been identified using an advanced Fourier-based Hough transform. As well as producing more accurate and robust detection of straight-lines, this method has been proven to have better efficiency in terms of computational time in comparison with the standard Hough transform. Second, for each straight-line a window-of-interest is designed in the image domain and the disturbance caused by the other neighbouring segments is removed to capture the Hough transform buttery of the target segment. In this way, for each straight-line a separate buttery is constructed. The boundary of the buttery wings are further smoothed and approximated by a curve fitting approach. Finally, segments endpoints were identified using buttery boundary points and the Hough transform peak. Experimental results on synthetic and real images have shown that the proposed method enjoys a superior performance compared with the existing similar representative works

Image feature analysis using the Multiresolution Fourier Transform

The problem of identifying boundary contours or line structures is widely recognised as an important component in many applications of image analysis and computer vision. Typical solutions to the problem employ some form of edge detection followed by line following or, more commonly in recent years, Hough transforms. Because of the processing requirements of such methods and to try to improve the robustness of the algorithms, a number of authors have explored the use of multiresolution approaches to the problem. Non-parametric, iterative approaches such as relaxation labelling and "Snakes" have also been used. This thesis presents a boundary detection algorithm based on a multiresolution image representation, the Multiresolution Fourier Transform (MFT), which represents an image over a range of spatial/spatial-frequency resolutions. A quadtree based image model is described in which each leaf is a region which can be modelled using one of a set of feature classes. Consideration is given to using linear and circular arc features for this modelling, and frequency domain models are developed for them. A general model based decision process is presented and shown to be applicable to detecting local image features, selecting the most appropriate scale for modelling each region of the image and linking the local features into the region boundary structures of the image. The use of a consistent inference process for all of the subtasks used in the boundary detection represents a significant improvement over the adhoc assemblies of estimation and detection that have been common in previous work. Although the process is applied using a restricted set of local features, the framework presented allows for expansion of the number of boundary feature models and the possible inclusion of models of region properties. Results are presented demonstrating the effective application of these procedures to a number of synthetic and natural images

Implementation of a real time Hough transform using FPGA technology

This thesis is concerned with the modelling, design and implementation of efficient architectures for performing the Hough Transform (HT) on mega-pixel resolution real-time images using Field Programmable Gate Array (FPGA) technology. Although the HT has been around for many years and a number of algorithms have been developed it still remains a significant bottleneck in many image processing applications. Even though, the basic idea of the HT is to locate curves in an image that can be parameterized: e.g. straight lines, polynomials or circles, in a suitable parameter space, the research presented in this thesis will focus only on location of straight lines on binary images. The HT algorithm uses an accumulator array (accumulator bins) to detect the existence of a straight line on an image. As the image needs to be binarized, a novel generic synchronization circuit for windowing operations was designed to perform edge detection. An edge detection method of special interest, the canny method, is used and the design and implementation of it in hardware is achieved in this thesis. As each image pixel can be implemented independently, parallel processing can be performed. However, the main disadvantage of the HT is the large storage and computational requirements. This thesis presents new and state-of-the-art hardware implementations for the minimization of the computational cost, using the Hybrid-Logarithmic Number System (Hybrid-LNS) for calculating the HT for fixed bit-width architectures. It is shown that using the Hybrid-LNS the computational cost is minimized, while the precision of the HT algorithm is maintained. Advances in FPGA technology now make it possible to implement functions as the HT in reconfigurable fabrics. Methods for storing large arrays on FPGA’s are presented, where data from a 1024 x 1024 pixel camera at a rate of up to 25 frames per second are processed

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

Real Time Extraction of Human Gait Features for Recognition

Human motion analysis has received a great attention from researchers in the last decade due to its potential use in different applications such as automated visual surveillance. This field of research focuses on human activities, including people identification. Human gait is a new biometric indicator in visual surveillance system. It can recognize individuals as the way they walk. In the walking process, the human body shows regular periodic variation, such as upper and lower limbs, knee point, thigh point, stride parameters (stride length, Cadence, gait cycle), height, etc. This reflects the individual’s unique movement pattern. In gait recognition, detection of moving people from a video is important for feature extraction. Height is one of the important features from the several gait features which is not influenced by the camera performance, distance and clothing style of the subject. Detection of people in video streams is the first relevant step of information and background subtraction is a very popular approach for foreground segmentation. In this thesis, different background subtraction methods have been simulated to overcome the problem of illumination variation, repetitive motions from background clutter, shadows, long term scene changes and camouflage. But background subtraction lacks capability to remove shadows. So different shadows detection methods have been tried out using RGB, YCbCr, and HSV color components to suppress shadows. These methods have been simulated and quantitative performance evaluated on different indoor video sequence. Then the research on shadow model has been extended to optimize the threshold values of HSV color space for shadow suppression with respect to the average intensity of local shadow region. A mathematical model is developed between the average intensity and the threshold values.Further a new method is proposed here to calculate the variation of height during walking. The measurement of height of a person is not affected by his clothing style as well as the distance from the camera. At any distance the height can be measured, but for that camera calibration is essential. DLT method is used to find the height of a moving person for each frame using intrinsic as well as extrinsic parameters. Another parameter known as stride, function of height, is extracted using bounding box technique. As human walking style is periodic so the accumulation of height and stride parameter will give a periodic signal. Human identification is done by using theses parameters. The height variation and stride variation signals are sampled to get further analyzed using DCT (Discrete Cosine Transformation), DFT (Discrete Fourier Transformation), and DHT (Discrete Heartily Transformation) techniques. N - harmonics are selected from the transformation coefficients. These coefficients are known as feature vectors which are stored in the database. Euclidian distance and MSE are calculated on these feature vectors. When feature vectors of same subject are compared, then a maximum value of MSE is selected, known as Self-Recognition Threshold (SRT). Its value is different for different transformation techniques. It is used to identify individuals. Again we have discussed on Model based method to detect the thigh angle. But thigh angle of one leg can’t be detected over a period of walking. Because one leg is occluded by the other leg. So stride parameter is used to estimate the thigh angle