New grayscale hit-miss operator

The morphological binary hit-miss operator has been used extensively to locate features within a binary image. We propose a grayscale hit-miss operator that detects signal shapes and is applicable to scalar-valued functions on one, two, or more dimensions. The hit and miss structuring elements define the lower and upper bounds of the signal: If a signal lies between the hit and miss templates, then the hit-miss operator will produce a one output; otherwise, it will respond with zero. We incorporate a fuzzy logic element to the hit-miss operator to indicate how strongly the signal matches the hit-miss templates

A new design tool for feature extraction in noisy images based on grayscale hit-or-miss transforms

The Hit-or-Miss transform (HMT) is a well known morphological transform capable of identifying features in digital images. When image features contain noise, texture or some other distortion, the HMT may fail. Various researchers have extended the HMT in different ways to make it more robust to noise. The most successful, and most recent extensions of the HMT for noise robustness, use rank order operators in place of standard morphological erosions and dilations. A major issue with the proposed methods is that no technique is provided for calculating the parameters that are introduced to generalize the HMT, and, in most cases, these parameters are determined empirically. We present here, a new conceptual interpretation of the HMT which uses a percentage occupancy (PO) function to implement the erosion and dilation operators in a single pass of the image. Further, we present a novel design tool, derived from this PO function that can be used to determine the only parameter for our routine and for other generalizations of the HMT proposed in the literature. We demonstrate the power of our technique using a set of very noisy images and draw a comparison between our method and the most recent extensions of the HMT

A fast method for computing the output of rank order filters within arbitrarily shaped windows

Rank order filters are used in a multitude of image processing tasks. Their application can range from simple preprocessing tasks which aim to reduce/remove noise, to more complex problems where such filters can be used to detect and segment image features. There is, therefore, a need to develop fast algorithms to compute the output of this class of filter. A number of methods for efficiently computing the output of specific rank order filters have been proposed [1]. For example, numerous fast algorithms exist that can be used for calculating the output of the median filter. Fast algorithms for calculating morphological erosions and dilations - which are also a special case of the more general rank order filter - have also been proposed. In this paper we present an extension of a recently introduced method for computing fast morphological operators to the more general case of rank order filters. Using our method, we are able to efficiently compute any rank, using any arbitrarily shaped window, such that it is possible to quickly compute the output of any rank order filter. We demonstrate the usefulness and efficiency of our technique by implementing a fast method for computing a recent generalisation of the morphological Hit-or-Miss Transform which makes it more robust in the presence of noise. We also compare the speed and efficiency of this routine with similar techniques that have been proposed in the literature

Hierarchical stack filtering : a bitplane-based algorithm for massively parallel processors

With the development of novel parallel architectures for image processing, the implementation of well-known image operators needs to be reformulated to take advantage of the so-called massive parallelism. In this work, we propose a general algorithm that implements a large class of nonlinear filters, called stack filters, with a 2D-array processor. The proposed method consists of decomposing an image into bitplanes with the bitwise decomposition, and then process every bitplane hierarchically. The filtered image is reconstructed by simply stacking the filtered bitplanes according to their order of significance. Owing to its hierarchical structure, our algorithm allows us to trade-off between image quality and processing time, and to significantly reduce the computation time of low-entropy images. Also, experimental tests show that the processing time of our method is substantially lower than that of classical methods when using large structuring elements. All these features are of interest to a variety of real-time applications based on morphological operations such as video segmentation and video enhancement

Digital implementation of the cellular sensor-computers

Two different kinds of cellular sensor-processor architectures are used nowadays in various applications. The first is the traditional sensor-processor architecture, where the sensor and the processor arrays are mapped into each other. The second is the foveal architecture, in which a small active fovea is navigating in a large sensor array. This second architecture is introduced and compared here. Both of these architectures can be implemented with analog and digital processor arrays. The efficiency of the different implementation types, depending on the used CMOS technology, is analyzed. It turned out, that the finer the technology is, the better to use digital implementation rather than analog

COASTLINE EXTRACTION IN VHR IMAGERY USING MATHEMATICAL MORPHOLOGY WITH SPATIAL AND SPECTRAL KNOWLEDGE

In this article, we are dealing with the problem of coastline extraction in Very High Resolution (VHR) multispectral images (Quickbird) on the Normandy Coast (France). Locating precisely the coastline is a crucial task in the context of coastal resource management and planning. In VHR imagery, some details on coastal zone become visible and the coastline definition depends on the geomorphologic context. According to the type of coastal units (sandy beach, wetlands, dune, cliff), several definitions for the coastline has to be used. So in this paper we propose a new approach in two steps based on morphological tools to extract coastline according to their context. More precisely, we first perform two detections of possible coastline pixels (respectively without false positive and without false negative). To do so, we apply a recent extension to multivariate images of the hit-or-miss transform, the morphological template matching tool, and rely on expert knowledge to define the sought templates. We then combine these two results through a double thresholding procedure followed by a final marker-based watershed to locate the exact coastline. In order to assess the performance and reliability of our method, results are compared with some ground-truth given by expert visual analysis. This comparison is made both visually and quantitatively. Results show the high performance of our method and its relevance to the problem under consideration

Configurable 3D-integrated focal-plane sensor-processor array architecture

A mixed-signal Cellular Visual Microprocessor architecture with digital processors is described. An ASIC implementation is also demonstrated. The architecture is composed of a regular sensor readout circuit array, prepared for 3D face-to-face type integration, and one or several cascaded array of mainly identical (SIMD) processing elements. The individual array elements derived from the same general HDL description and could be of different in size, aspect ratio, and computing resources

Target detection with morphological shared-weight neural network : different update approaches

Neural networks are widely used for image processing. Of these, the convolutional neural network (CNN) is one of the most popular. However, the CNN needs a large amount of training data to improve its accuracy. If training data is limited, a morphological shared-weight neural network (MSNN) can be a better choice. In this thesis, two different update approaches based on an evolutionary algorithm are proposed and compared to each other for target detection based on the MSNN. Another network training, based on back propagation, is used for comparisons in this thesis, which was proposed by Yongwan Won and applied by my colleague and fellow graduate student, Shuxian Shen and Anes Ouadou. Single-layer and multiple-layer MSNNs are both presented with different approaches. For a dataset, the author created part of a dataset for this thesis and used another dataset created by Shen to make comparisons with her network. Results of the MSNN are compared with CNN results to show the performance. Experiments show that for a single-layer MSNN, the performance of an evolutionary algorithm with partial backpropagation is the best. For a multiple layer MSNN, backpropagation performs better, although the MSNN still has a better performance than the CNN.Includes bibliographical reference