Quality assessment by region in spot images fused by means dual-tree complex wavelet transform

This work is motivated in providing and evaluating a fusion algorithm of remotely sensed images, i.e. the fusion of a high spatial resolution panchromatic image with a multi-spectral image (also known as pansharpening) using the dual-tree complex wavelet transform (DT-CWT), an effective approach for conducting an analytic and oversampled wavelet transform to reduce aliasing, and in turn reduce shift dependence of the wavelet transform. The proposed scheme includes the definition of a model to establish how information will be extracted from the PAN band and how that information will be injected into the MS bands with low spatial resolution. The approach was applied to Spot 5 images where there are bands falling outside PAN’s spectrum. We propose an optional step in the quality evaluation protocol, which is to study the quality of the merger by regions, where each region represents a specific feature of the image. The results show that DT-CWT based approach offers good spatial quality while retaining the spectral information of original images, case SPOT 5. The additional step facilitates the identification of the most affected regions by the fusion process

Multi-Oriented Multi-Resolution Edge Detection

In order to build an edge detector that provides information on the degree of importance spatial features represent in the visual field, I used the wavelet transform applied to two-dimensional signals and performed a multi-resolution multi-oriented edge detection. The wavelets are functions well-localized in spatial domain and in frequency domain. Thus the wavelet decomposition of a signal or an image provides outputs in which you can still extract spatial features and not only frequency components. In order to detect edges the wavelet I chose is the first derivative of a smoothing function. I decompose the images as many times as I have directions of detection. I decided to work for the moment on the X-direction and the Y-direction only. Each step of the decomposition corresponds to a different scale. I use a discrete scale s = 2j (dyadic wavelet) and a finite number of decomposed images. Instead of scaling the filters at each step I sample the image by 2 (gain in processing time). Then, I extract the extrema, track and link them from the coarsest scale to the finest one. I build a symbolic image in which the edge-pixels are not only localized but labelled too, according to the number of appearances in the different scales and according to the contrast range of the edge. Without any arbitrary threshold I can subsequently classify the edges according to their physical properties in the scene and their degree of importance. This process is subsequently intended to be part of more general perceptual learning procedures. The context should be: none or as little as possible a priori knowledge, and the ultimate goal is to integrate this detector in a feedback system dealing with color information, texture and smooth surfaces extraction. Then decisions must be taken on symbolic levels in order to make new interpretation or even new edge detection on ambiguous areas of the visual field

3D Object Recognition Based On Constrained 2D Views

The aim of the present work was to build a novel 3D object recognition system capable of classifying man-made and natural objects based on single 2D views. The approach to this problem has been one motivated by recent theories on biological vision and multiresolution analysis. The project's objectives were the implementation of a system that is able to deal with simple 3D scenes and constitutes an engineering solution to the problem of 3D object recognition, allowing the proposed recognition system to operate in a practically acceptable time frame. The developed system takes further the work on automatic classification of marine phytoplank- (ons, carried out at the Centre for Intelligent Systems, University of Plymouth. The thesis discusses the main theoretical issues that prompted the fundamental system design options. The principles and the implementation of the coarse data channels used in the system are described. A new multiresolution representation of 2D views is presented, which provides the classifier module of the system with coarse-coded descriptions of the scale-space distribution of potentially interesting features. A multiresolution analysis-based mechanism is proposed, which directs the system's attention towards potentially salient features. Unsupervised similarity-based feature grouping is introduced, which is used in coarse data channels to yield feature signatures that are not spatially coherent and provide the classifier module with salient descriptions of object views. A simple texture descriptor is described, which is based on properties of a special wavelet transform. The system has been tested on computer-generated and natural image data sets, in conditions where the inter-object similarity was monitored and quantitatively assessed by human subjects, or the analysed objects were very similar and their discrimination constituted a difficult task even for human experts. The validity of the above described approaches has been proven. The studies conducted with various statistical and artificial neural network-based classifiers have shown that the system is able to perform well in all of the above mentioned situations. These investigations also made possible to take further and generalise a number of important conclusions drawn during previous work carried out in the field of 2D shape (plankton) recognition, regarding the behaviour of multiple coarse data channels-based pattern recognition systems and various classifier architectures. The system possesses the ability of dealing with difficult field-collected images of objects and the techniques employed by its component modules make possible its extension to the domain of complex multiple-object 3D scene recognition. The system is expected to find immediate applicability in the field of marine biota classification

Motion detection algorithm using wavelet transform

This thesis presents an algorithm that estimates motion in image sequence using wavelet transform. The motion detection is performed under unfavorable conditions of background movement, change of brightness, and noise. The algorithm is tolerant to brightness changes, noise, and small movement in the background. The false alarm rate of motion detection is reduced as compared to standard techniques. Using wavelet transform is numerically efficient and the storage requirements are significantly reduced. Also, a more accurate motion detection is achieved. Tests performed on real images show the effectiveness of this algorithm. Practical results of motion detection are presented

Basic spline wavelet transform and pitch detection of a speech signal

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 125-133).by Ken Wenchian Lee.M.S

Map online system using internet-based image catalogue

Digital maps carry along its geodata information such as coordinate that is important in one particular topographic and thematic map. These geodatas are meaningful especially in military field. Since the maps carry along this information, its makes the size of the images is too big. The bigger size, the bigger storage is required to allocate the image file. It also can cause longer loading time. These conditions make it did not suitable to be applied in image catalogue approach via internet environment. With compression techniques, the image size can be reduced and the quality of the image is still guaranteed without much changes. This report is paying attention to one of the image compression technique using wavelet technology. Wavelet technology is much batter than any other image compression technique nowadays. As a result, the compressed images applied to a system called Map Online that used Internet-based Image Catalogue approach. This system allowed user to buy map online. User also can download the maps that had been bought besides using the searching the map. Map searching is based on several meaningful keywords. As a result, this system is expected to be used by Jabatan Ukur dan Pemetaan Malaysia (JUPEM) in order to make the organization vision is implemented