Morphological hit-or-miss transformation for shape recognition

In this paper, the morphological hit-or-miss transformation is analyzed for use in the recognition of both perfect shapes and imperfect shapes. Shape recognition is achieved by locating the objects to be recognized within the image. The shape recognition problem is discussed in the following two aspects. First, a theorem is presented in which the hit-or-miss operations employ the structuring elements [thetav]A and [thetav](W [subset of] Ac), instead of using sets A and (W [subset of] Ac). Here A is the boundary of A, and [thetav](Q [subset of] Ac) is the containing A, [thetav]A is the boundary of A, and [thetav](W [subset of] Ac) is the boundary of (W [subset of] Ac). Second, the recognition of imperfect shapes due to indeterminate variation of an object shape is studied here. Our method employs a priori known shape information as a basis for structuring elements and constructs structuring elements which are then used in a hit-or-miss transformation to find the location of the shape to be recognized. Each occurrence of a target shape is represented either by one point or by a small cluster of points within a calculated range according to the associated structuring elements. Finally, we present the hit-or-miss operation without window restrictions as a technique for recognizing both perfect and imperfect shapes, thereby making the method more flexible.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29148/1/0000190.pd

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

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

Matemaattisen morfologian käyttö geometrisessa musiikinhaussa

The usual task in music information retrieval (MIR) is to find occurrences of a monophonic query pattern within a music database, which can contain both monophonic and polyphonic content. The so-called query-by-humming systems are a famous instance of content-based MIR. In such a system, the user's hummed query is converted into symbolic form to perform search operations in a similarly encoded database. The symbolic representation (e.g., textual, MIDI or vector data) is typically a quantized and simplified version of the sampled audio data, yielding to faster search algorithms and space requirements that can be met in real-life situations. In this thesis, we investigate geometric approaches to MIR. We first study some musicological properties often needed in MIR algorithms, and then give a literature review on traditional (e.g., string-matching-based) MIR algorithms and novel techniques based on geometry. We also introduce some concepts from digital image processing, namely the mathematical morphology, which we will use to develop and implement four algorithms for geometric music retrieval. The symbolic representation in the case of our algorithms is a binary 2-D image. We use various morphological pre- and post-processing operations on the query and the database images to perform template matching / pattern recognition for the images. The algorithms are basically extensions to classic image correlation and hit-or-miss transformation techniques used widely in template matching applications. They aim to be a future extension to the retrieval engine of C-BRAHMS, which is a research project of the Department of Computer Science at University of Helsinki

A Modified Neural Network system based on Morphological operations for detection of images with variation in Gray level intensity and facial expressions

We introduce an algorithm based on the morphological shared-weight neural network. Which extract the features and then classify them. This type of network can work effectively, even if the gray level intensity and facial expression of the images are varied. The images are processed by a morphological shared weight neural network to detect and extract the features of face images. For the detection of the edges of the image we are using sobel operator. We are using back propagation algorithm for the purpose of learning and training of the neural network system. Being nonlinear and translation-invariant, the morphological operations can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The recognition efficiency of this modified network is about 98%

On morphological hierarchical representations for image processing and spatial data clustering

Hierarchical data representations in the context of classi cation and data clustering were put forward during the fties. Recently, hierarchical image representations have gained renewed interest for segmentation purposes. In this paper, we briefly survey fundamental results on hierarchical clustering and then detail recent paradigms developed for the hierarchical representation of images in the framework of mathematical morphology: constrained connectivity and ultrametric watersheds. Constrained connectivity can be viewed as a way to constrain an initial hierarchy in such a way that a set of desired constraints are satis ed. The framework of ultrametric watersheds provides a generic scheme for computing any hierarchical connected clustering, in particular when such a hierarchy is constrained. The suitability of this framework for solving practical problems is illustrated with applications in remote sensing

Modeling of evolving textures using granulometries

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