Delineation of line patterns in images using B-COSFIRE filters

Delineation of line patterns in images is a basic step required in various applications such as blood vessel detection in medical images, segmentation of rivers or roads in aerial images, detection of cracks in walls or pavements, etc. In this paper we present trainable B-COSFIRE filters, which are a model of some neurons in area V1 of the primary visual cortex, and apply it to the delineation of line patterns in different kinds of images. B-COSFIRE filters are trainable as their selectivity is determined in an automatic configuration process given a prototype pattern of interest. They are configurable to detect any preferred line structure (e.g. segments, corners, cross-overs, etc.), so usable for automatic data representation learning. We carried out experiments on two data sets, namely a line-network data set from INRIA and a data set of retinal fundus images named IOSTAR. The results that we achieved confirm the robustness of the proposed approach and its effectiveness in the delineation of line structures in different kinds of images.Comment: International Work Conference on Bioinspired Intelligence, July 10-13, 201

Pattern recognition employing spatially variant unconstrained correlation filters

A spatial domain Optimal Trade-off Maximum Average Correlation Height (SPOT-MACH) filter is proposed in this thesis. The proposed technique uses a pre-defined fixed size kernel rather than using estimation techniques. The spatial domain implementation of OT-MACH offers the advantage that it does not have shift invariance imposed on it as the kernel can be modified depending upon its position within the input image. This allows normalization of the kernel and allows inclusion of a space domain non-linearity to improve performance. The proposed SPOT-MACH filter can be used to maximize the height of the correlation peak in the presence of distortions of the training object and provide resistance to background clutter. One of the major characteristics of the SPOT-MACH filter is that it can be tuned to maximize the height and sharpness of the correlation peak by using trade-offs between distortion tolerance, peak sharpness and the ability to suppress clutter noise. A number of non-parametric local regression techniques offer a simplified approach to pattern recognition problems which employ linear filtering using low pass filters designed using moving window local approximations. In most of these cases the algorithms search for a region of interest near the point of estimation for various prevailing conditions which fit the required criteria. These estimates are calculated for a defined window size which is determined as being the largest area within which the estimators do not widely vary from the criteria. The only drawback in this approach is that the window size is directly proportional to the required computational resources and would adversely affect the performance of the system if the moving window size is not proportionate to the resources. The proposed filter employs an optimization technique using low-pass filtering to highlight the potential region of interests in the image and then restricts the movement of the kernel to these regions to allow target identification and to use less computational resources. Also another optimization technique is also proposed which is based on an entropy filter which measures the degree of randomness between two changing scenes and would return the area where change has occurred i.e. the target object might be present. This approach gives a more accurate region of interest than the low-pass filtering approach. Apart from the software based optimization approaches two hardware based enhancement techniques have also been proposed in this thesis. One of the approaches employs Field Programmable Gate Array (FPGA) to perform correlation process employing the inbuilt multipliers and look up tables and the other one uses Graphical Processing Unit (GPU) to do parallel processing of the input scene. Also in this thesis a detailed analysis of SPOT-MACH has been carried out by comparing with popular feature based techniques like Scale Invariant Feature Transform (SIFT) and a comparison matrix has been created. The proposed filter uses a two-staged approach using speed optimizations and then detection of targets from input scenes. Both visible and Forward Looking Infrared (FLIR) imagery data sets have been used to test the performance of filter

Advances in Pattern Recognition Algorithms, Architectures, and Devices

Over the last decade, tremendous advances have been made in the general area of pattern recognition techniques, devices, and algorithms. We have had the distinct pleasure of witnessing this remarkable growth as evidenced through their dissemination in the previous Optical Engineering special sections we have jointly edited— January 1998, March 1998, May 2000, and January 2002. Twenty-six papers were finally accepted for this latest special section, encompassing the recent trends and advancements made in many different areas of pattern recognition techniques utilizing algorithms, architectures, implementations, and devices. These techniques include matched spatial filter based recognition, hit-miss transforms, invariant pattern recognition, joint transform correlator JTC based recognition, morphological processing based recognition, neural network based recognition, wavelet based recognition, fingerprint and face recognition, data fusion based recognition, and target tracking, as well as other techniques. These papers summarize the work of 70 researchers from eight countries