Feature-based Image Comparison and Its Application in Wireless Visual Sensor Networks

This dissertation studies the feature-based image comparison method and its application in Wireless Visual Sensor Networks. Wireless Visual Sensor Networks (WVSNs), formed by a large number of low-cost, small-size visual sensor nodes, represent a new trend in surveillance and monitoring practices. Although each single sensor has very limited capability in sensing, processing and transmission, by working together they can achieve various high level tasks. Sensor collaboration is essential to WVSNs and normally performed among sensors having similar measurements, which are called neighbor sensors. The directional sensing characteristics of imagers and the presence of visual occlusion present unique challenges to neighborhood formation, as geographically-close neighbors might not monitor similar scenes. Besides, the energy resource on the WVSNs is also very tight, with wireless communication and complicated computation consuming most energy in WVSNs. Therefore the feature-based image comparison method has been proposed, which directly compares the captured image from each visual sensor in an economical way in terms of both the computational cost and the transmission overhead. The feature-based image comparison method compares different images and aims to find similar image pairs using a set of local features from each image. The image feature is a numerical representation of the raw image and can be more compact in terms of the data volume than the raw image. The feature-based image comparison contains three steps: feature detection, descriptor calculation and feature comparison. For the step of feature detection, the dissertation proposes two computationally efficient corner detectors. The first detector is based on the Discrete Wavelet Transform that provides multi-scale corner point detection and the scale selection is achieved efficiently through a Gaussian convolution approach. The second detector is based on a linear unmixing model, which treats a corner point as the intersection of two or three “line” bases in a 3 by 3 region. The line bases are extracted through a constrained Nonnegative Matrix Factorization (NMF) approach and the corner detection is accomplished through counting the number of contributing bases in the linear mixture. For the step of descriptor calculation, the dissertation proposes an effective dimensionality reduction algorithm for the high dimensional Scale Invariant Feature Transform (SIFT) descriptors. A set of 40 SIFT descriptor bases are extracted through constrained NMF from a large training set and all SIFT descriptors are then projected onto the space spanned by these bases, achieving dimensionality reduction. The efficiency of the proposed corner detectors have been proven through theoretical analysis. In addition, the effectiveness of the proposed corner detectors and the dimensionality reduction approach has been validated through extensive comparison with several state-of-the-art feature detector/descriptor combinations

PhD forum: correlation coefficient based template matching for indoor people tracking

Abstract—One of the most popular methods to extract information from an image sequence is template matching. The principle of template matching is tracking a certain feature or target over time based on the comparison of the content of each frame with a simple template. In this article, we propose an correlation coefficient based template matching which is invariant to linear intensity distortions to do correction or verification of our existing indoor people tracking system

Adversarial nets with perceptual losses for text-to-image synthesis

Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text. Despite the overall fair quality, the generated images often expose visible flaws that lack structural definition for an object of interest. In this paper, we aim to extend state of the art for GAN-based text-to-image synthesis by improving perceptual quality of generated images. Differentiated from previous work, our synthetic image generator optimizes on perceptual loss functions that measure pixel, feature activation, and texture differences against a natural image. We present visually more compelling synthetic images of birds and flowers generated from text descriptions in comparison to some of the most prominent existing work

Report of subpanel on feature extraction

The state of knowledge in feature extraction for Earth resource observation systems is reviewed and research tasks are proposed. Issues in the subpixel feature estimation problem are defined as: (1) the identification of image models which adequately describe the data and the sensor it is using; (2) the construction of local feature models based on those image models; and (3) the problem of trying to understand these effects of preprocessing on the entire process. The development of ground control point (GCP) libraries for automated selection presents two concerns. One is the organization of these GCP libraries for rectification problems, i.e., the problems of automatically selecting by computer the specific GCP's for particular registration tasks. Second is the importance of integrating ground control patterns in a data base management system, allowing interface to a large number of sensor image types with an automatic selection system. The development of data validation criteria for the comparison of different extraction techniques is also discussed

MIRACLE at ImageCLEFannot 2008: Classification of Image Features for Medical Image Annotation

This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2008. A lot of effort was invested this year to develop our own image analysis system, based on MATLAB, to be used in our experiments. This system extracts a variety of global and local features including histogram, image statistics, Gabor features, fractal dimension, DCT and DWT coefficients, Tamura features and coocurrency matrix statistics. Then a k-Nearest Neighbour algorithm analyzes the extracted image feature vectors to determine the IRMA code associated to a given image. The focus of our experiments is mainly to test and evaluate this system in-depth and to make a comparison among diverse configuration parameters such as number of images for the relevance feedback to use in the classification module