Vision systems for a mobile robot based on line detection using the Hough Transform and artificial neural networks.

This project contributes to the problem of mobile robot self-navigation within a rectilinear framework based on visual data. It proposes a number of vision systems based on detection of straight lines in images captured by a robot using the Hough transform and artificial neural networks as core algorithms. The Hough transform is a robust method for detection of basic features (Boyce et al 1987). However, it is so computationally demanding that it is not commonly used in real time applications and applications which utilise anything but small images (Song and Lyu 2005). (Dempsey and McVey 1992) have suggested that this problem might be resolved if the Hough transform were implemented with artificial neural networks. This project investigates the feasibility of systems using these core algorithms, and systems that are hybrids of them. Prior to application of the core algorithms to a captured image, various stages of pre-processing are carried out including resizing for optimum results, edgedetection, and edge thinning using an adaptation of the thinning method of (Park, 2000) proposed by this work. An analysis of the costs and benefits of thinning as part of pre-processing has also been performed. The Hough transform based system, which has been largely successful, has involved a number of new approaches. These include a peak detection scheme; post-processing schemes which find valid sub-lines of lines found by the peak detection process, and establish which high-level features these sub-lines represent; and an appropriate navigation scheme. Two artificial neural network systems were designed based on lines detection and sub-lines detection respectively. The first was able to detect long lines, but not shorter (even though navigationally important) lines, and so was aborted. The second system has two major stages. Networks of stage 1 developed to detect sub-lines in sub-images derived by breaking down the original images, did so passibly well. A network in stage 2 designed to use the results of stage 1 to guide the robots motion did not do so well for most test images. The networks of stage 1, however, have been helpful with development of a hybrid vision system. Suggestions have been made on how this work can be furthered

Vanishing Point Detection with Direct and Transposed Fast Hough Transform inside the neural network

In this paper, we suggest a new neural network architecture for vanishing point detection in images. The key element is the use of the direct and transposed Fast Hough Transforms separated by convolutional layer blocks with standard activation functions. It allows us to get the answer in the coordinates of the input image at the output of the network and thus to calculate the coordinates of the vanishing point by simply selecting the maximum. Besides, it was proved that calculation of the transposed Fast Hough Transform can be performed using the direct one. The use of integral operators enables the neural network to rely on global rectilinear features in the image, and so it is ideal for detecting vanishing points. To demonstrate the effectiveness of the proposed architecture, we use a set of images from a DVR and show its superiority over existing methods. Note, in addition, that the proposed neural network architecture essentially repeats the process of direct and back projection used, for example, in computed tomography.Comment: 9 pages, 9 figures, submitted to "Computer Optics"; extra experiment added, new theorem proof added, references added; typos correcte

A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks

Deep LSTM is an ideal candidate for text recognition. However text recognition involves some initial image processing steps like segmentation of lines and words which can induce error to the recognition system. Without segmentation, learning very long range context is difficult and becomes computationally intractable. Therefore, alternative soft decisions are needed at the pre-processing level. This paper proposes a hybrid text recognizer using a deep recurrent neural network with multiple layers of abstraction and long range context along with a language model to verify the performance of the deep neural network. In this paper we construct a multi-hypotheses tree architecture with candidate segments of line sequences from different segmentation algorithms at its different branches. The deep neural network is trained on perfectly segmented data and tests each of the candidate segments, generating unicode sequences. In the verification step, these unicode sequences are validated using a sub-string match with the language model and best first search is used to find the best possible combination of alternative hypothesis from the tree structure. Thus the verification framework using language models eliminates wrong segmentation outputs and filters recognition errors

Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks

https://doi.org/10.1007/978-3-319-77712-2_62The detection of red blood cells in blood samples can be crucial for the disease detection in its early stages. The use of image processing techniques can accelerate and improve the effectiveness and efficiency of this detection. In this work, the use of the Circle Hough transform for cell detection and artificial neural networks for their identification as a red blood cell is proposed. Specifically, the application of neural networks (MLP) as a standard classification technique with (MLP) is compared with new proposals related to deep learning such as convolutional neural networks (CNNs). The different experiments carried out reveal the high classification ratio and show promising results after the application of the CNNs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech