Image databases are now currently utilized in a wide range of different areas, in particular, the development and application of remote sensing platforms result in the production of huge amounts of image data.
Though advanced image compression technology has solved part of the storage problem, searching and locating through such a database is still a difficult task. In the 90's Content-based Image Retrieval (CBIR) has gained increasing popularity among researchers, however, how to retrieve the content of an image efficiently and effectively still lacks of common recognition. This is because the low level features of an image including color, shape, texture, etc., which could be easily analyzed do not coincide with the high level concepts of an image.
Another major problem in the practical implementation of a CBIR for remotely sensed images is that the content-based indexing and searching process always requires extremely high computational power. On the other hand, the content-based image retrieval algorithms are very suitable for parallel computation as the algorithms can be broken into several data independent processes for running on a parallel computer.
In this paper, we discuss the porting problem of a sequential application of remote sensed image retrieval in a parallel environment using the new paradigm of programming introduced by born of a new structured program languages (Assist 1.2) and evaluate several skeletons composition to optimize the performance of our application.
Video abstraction and summarization, being request in several applications, has address a number of researches to automatic video analysis techniques. The processes for automatic video analysis are based on the recognition of short sequences of contiguous frames that describe the same scene, shots, and key frames representing the salient content of the shot. Since effective shot boundary detection techniques exist in the literature, in this paper we will focus our attention on key frames extraction techniques to identify the low level visual features of the frames that better represent the shot content. To evaluate the features performance, key frame automatically extracted using these features, are compared to human operator video annotations.
Automated annotation and analysis of video sequences requires efficient methods to abstract video information. The identification of shots in video sequences is an important step for summarizing the content of the video. In general, video shots need to be clustered to form more semantically significant units, such as scenes and sequences. In this paper, we describe a neural network based technique for automatic clustering of video frame signatures. The proposed technique utilizes Self Organizing Map (SOM) and/or Parallel Collision Control Network (PCC) to automatically produce a set of prototype vectors useful in the following process of scene segmentation. Results presented in this paper show that the SOM network perform efficiently, operating without requiring `a priori' knowledge about the number of shots present in the video. When we require the segmentation of a video composed by similar shots, the PCC network is suitable for its capability to preserve the acquired information.
Resizing techniques are commonly used in graphic applications and window environments to change image size and adapt it to the video resolution. Different algorithms have been proposed to perform image reduction with different effects on the image content. In this paper we present a proposal for a graphic coprocessor, based on TMS320C80, optimized for line drawing low-loss reduction and suitable for VDT presentations. Based on DCT and local thresholding, the algorithm is characterized by fine granularity, task independence and consequently good matching with parallel processing. In the proposed system, the line drawing is spilt into 32*32 pixels windows. In order to perform the image reduction, each window is subject to DCT computation and inverse transformation of the only low-frequency coefficients. The reduced image is thresholded to a value function of the black pixel percentage in the original image and the reduction ratio. The coprocessor is able to reduce an A0 line drawing acquired at 300 dpi in less than 0.2 seconds, by a reduction ratio f 1/32, producing good information preservation in the new image suitable for VDT presentation.
This paper presents a model of image coding and delivering for multimedia and images browsing system based on a multiresolution format. The multiresolution format coding is suitable for evaluations either on server performance or on the effect on the image content, in terms of semantic and syntactic degradation. Multimedia and image browsing systems are used in image based service (IBS). Pictorial, technical, medical, geographic information management, such as home shopping and WWW service, are based on images organised in databases. In this system a large amount of resources are enslaved to image coding, transmission, decoding and showing. Considering that not every image retrieved corresponds to the user needs, a non negligible resource is unwisely used. The need for full-resolution image retrieval involves high time consumption for data retrieval and for image viewing. Likewise in a window system, the user wants to be able to resize the image frame flexibly. More techniques are available1 with different performance in terms of content maintenance and complexity, but the network load is not reduced if the resizing is realised by the client. A simple solution to guarantee the client independent service, in terms of client image-resolution, is in the storing of the images in the server in more files with different resolution of the same image, Multi File Coding (MFC), but with the image information degraded as a function ofthe downsized images and the algorithm used. It is well known that an image can be represented in mathematical form as a continuous functionf of two variables x and y. Using x and y as coordinates of the point on the screen,f is an attribute of the point (like luminance, HSV component etc.). Assuming that the information contained in one image is localized in the points where the functionfis defmed, in a picture the information is uniformly distributed over a large part of the image. On the other hand, in technical images the functionf is defined only in a small area (the plotting area),while the remaining area represents the background and is devoid of information. Resizing operations are characterized by the reduction of the image pixels (understood as a basic element of the image), likewise the information located in the image decreases as a function of the lost pixels or as a function ofthe reduction ratio2. This situation involves a degradation of the image. In the case of pictorial images the information loss is uniformly distributed and usually counterbalanced by human reasoning-driven mechanisms. So in the reduction of pictorial images the visual information loss is less than the pixel loss. In the resizing of technical images the use of symbols, thin lines and types localized in small areas involves the loss of information content.
Coordinates in the 3D space of elements in a SAR image can be obtained by the combination of along-track, slant-range and interferometric fringe measurements. In order to evaluate the elevation of a pixel with respect to a slant- range reference plane, its absolute interferometric phase is required and this is typically derived unwrapping a 2D interferometric fringe pattern. Phase inconsistencies in SAR interferograms due to noise and topography determine unwrapping errors which appear as discontinuities in the computed absolute phase field. Phase aliasing arising from rapid phase variations from topography generates 2D unwrapping inconsistencies characterized by phase patterns statistically different from those induced by noise. In this paper, the spatial configurations of the phase field around residues is utilized in the phase unwrapping procedure. The feasibility of a neural network approach for classifying residual complex geometric phase patterns requiring different corrective measures is also presented. In addition, a method based on pseudo-differential interferometry to resolve residual inconsistencies as noise- or topography-generated is explored.
Neural networks have potential advantages such as real-time operation and robustness based on their parallel structure, self-organization, fuzziness, and particularly their adaptive learning ability. A single neural network is useful for identification of objects. To carry out identifying complex objects, however, it is necessary to consider hybrid architectures of two or more networks, which offer some degrees of improvement in performances. In this paper, neural learning techniques, the self-organizing feature mapping (SOFM), and learning vector quantization (LVQ2) have been applied to the automatic target recognition problem in the presence of a satellite object with high level noises. SOFM, unsupervised learning captures the homogeneity within-class characteristics; whereas LVQ2, supervised learning captures the heterogeneity of between-class.
In this paper, several approaches including K-means, fuzzy K-means (FKM), fuzzy adaptive resonance theory (ART2) and fuzzy Kohonen self-organizing feature mapping (SOFM) are adapted to segment the texture image. In our tests five features, energy, entropy, correlation, homogeneity, and inertia, are used in texture analysis. The K-means algorithm has the following disadvantages: (1) supervised learning mode, (2) slow real-time ability, (3) instability. The FKM algorithm has improved the performance of the instability by means of the introduction of fuzzy distribution functions. The fuzzy ART2 has advantages, such as unsupervised training, high computation rates, and a great degree of fault tolerance (stability/plasticity). Fuzzy operator and mapping functions are added in the network to improve the generality. The fuzzy SOFM integrates the FKM algorithm into fuzzy membership value as a learning rate and updates stratifies of the Kohonen network. This yields automatic adjustment of both the learning rate distribution and update neighborhood, and has an optimization problem related to FKM. Therefore, the fuzzy SOFM is independent of the sequence of feed of input patterns whereas final weight vectors by the Kohonen method depend on the sequence. The fuzzy SOFM is `self-organizing' since the `size' of the update neighborhood and learning rate are automatically adjusted during learning. Clustering errors are reduced by fuzzy SOFM as well as better convergence. The numerical results show that fuzzy ART2 and fuzzy SOFM are better than K-means algorithms. The images segmented by the algorithms are given to prove their performances.
Neural filters with multilayer backpropagation network have been proved to be able to define mostly all linear or non-linear filters. Because of the slowness of the networks' convergency, however, the applicable fields have been limited. In this paper, fuzzy logic is introduced to adjust learning rate and momentum parameter depending upon output errors and training times. This makes the convergency of the network greatly improved. Test curves are shown to prove the fast filters' performance.