Investigation of new feature descriptors for image search and classification

Content-based image search, classification and retrieval is an active and important research area due to its broad applications as well as the complexity of the problem. Understanding the semantics and contents of images for recognition remains one of the most difficult and prevailing problems in the machine intelligence and computer vision community. With large variations in size, pose, illumination and occlusions, image classification is a very challenging task. A good classification framework should address the key issues of discriminatory feature extraction as well as efficient and accurate classification. Towards that end, this dissertation focuses on exploring new image descriptors by incorporating cues from the human visual system, and integrating local, texture, shape as well as color information to construct robust and effective feature representations for advancing content-based image search and classification. Based on the Gabor wavelet transformation, whose kernels are similar to the 2D receptive field profiles of the mammalian cortical simple cells, a series of new image descriptors is developed. Specifically, first, a new color Gabor-HOG (GHOG) descriptor is introduced by concatenating the Histograms of Oriented Gradients (HOG) of the component images produced by applying Gabor filters in multiple scales and orientations to encode shape information. Second, the GHOG descriptor is analyzed in six different color spaces and grayscale to propose different color GHOG descriptors, which are further combined to present a new Fused Color GHOG (FC-GHOG) descriptor. Third, a novel GaborPHOG (GPHOG) descriptor is proposed which improves upon the Pyramid Histograms of Oriented Gradients (PHOG) descriptor, and subsequently a new FC-GPHOG descriptor is constructed by combining the multiple color GPHOG descriptors and employing the Principal Component Analysis (PCA). Next, the Gabor-LBP (GLBP) is derived by accumulating the Local Binary Patterns (LBP) histograms of the local Gabor filtered images to encode texture and local information of an image. Furthermore, a novel Gabor-LBPPHOG (GLP) image descriptor is proposed which integrates the GLBP and the GPHOG descriptors as a feature set and an innovative Fused Color Gabor-LBP-PHOG (FC-GLP) is constructed by fusing the GLP from multiple color spaces. Subsequently, The GLBP and the GHOG descriptors are then combined to produce the Gabor-LBP-HOG (GLH) feature vector which performs well on different object and scene image categories. The six color GLH vectors are further concatenated to form the Fused Color GLH (FC-GLH) descriptor. Finally, the Wigner based Local Binary Patterns (WLBP) descriptor is proposed that combines multi-neighborhood LBP, Pseudo-Wigner distribution of images and the popular bag of words model to effectively classify scene images. To assess the feasibility of the proposed new image descriptors, two classification methods are used: one method applies the PCA and the Enhanced Fisher Model (EFM) for feature extraction and the nearest neighbor rule for classification, while the other method employs the Support Vector Machine (SVM). The classification performance of the proposed descriptors is tested on several publicly available popular image datasets. The experimental results show that the proposed new image descriptors achieve image search and classification results better than or at par with other popular image descriptors, such as the Scale Invariant Feature Transform (SIFT), the Pyramid Histograms of visual Words (PHOW), the Pyramid Histograms of Oriented Gradients (PHOG), the Spatial Envelope (SE), the Color SIFT four Concentric Circles (C4CC), the Object Bank (OB), the Context Aware Topic Model (CA-TM), the Hierarchical Matching Pursuit (HMP), the Kernel Spatial Pyramid Matching (KSPM), the SIFT Sparse-coded Spatial Pyramid Matching (Sc-SPM), the Kernel Codebook (KC) and the LBP

Computational imaging and automated identification for aqueous environments

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2011Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods. Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classi fication with bag-of-words models and multi-stage boosting for rock sh detection. Methods for extracting images of sh from videos of longline operations are demonstrated. A prototype digital holographic imaging device is designed and tested for quantitative in situ microscale imaging. Theory to support the device is developed, including particle noise and the effects of motion. A Wigner-domain model provides optimal settings and optical limits for spherical and planar holographic references. Algorithms to extract the information from real-world digital holograms are created. Focus metrics are discussed, including a novel focus detector using local Zernike moments. Two methods for estimating lateral positions of objects in holograms without reconstruction are presented by extending a summation kernel to spherical references and using a local frequency signature from a Riesz transform. A new metric for quickly estimating object depths without reconstruction is proposed and tested. An example application, quantifying oil droplet size distributions in an underwater plume, demonstrates the efficacy of the prototype and algorithms.Funding was provided by NOAA Grant #5710002014, NOAA NMFS Grant #NA17RJ1223, NSF Grant #OCE-0925284, and NOAA Grant #NA10OAR417008

Adaptivna tehnika obrade slike za kontrolu kvalitete u proizvodnji keramičkih pločica

Automation of the visual inspection for quality control in production of materials with textures (tiles, textile, leather, etc.) is not widely implemented. A sophisticated system for image acquisition, as well as a fast and efficient procedure for texture analysis is needed for this purpose. In this paper the Surface Failure Detection (SFD) algorithm for quality control in ceramic tiles production is presented. It is based on Discrete Wavelet Transform (DWT) and Probabilistic Neural Networks (PNN) with radial basis. DWT provides a multi-resolution analysis, which mimics behavior of a human visual system and it extracts from the tile image the features important for failure detection. Neural networks are used for classification of the tiles with respect to presence of defects. Classification efficiency mainly depends on the proper choice of the training vectors for neural networks. For neural networks preparation we propose an automated adaptive technique based on statistics of the tiles defects textures. This technique enables fast adaptation of the SFD algorithm to different textures, which is important for automated visual inspection in the production of a new tile type.Automatizacija vizualne provjere za kontrolu kvalitete u proizvodnji materijala s teksturama (pločice, tekstil, kože, itd.) nije široko primijenjena u praksi. Za ovu namjenu potreban je sofisticirani sustav za snimanje slika, kao i brza i efikasna procedura za analizu tekstura. U ovom je radu predstavljen algoritam za detekciju površinskih oštećenja (SFD) u proizvodnji keramičkih pločica. Temelji se na diskretnoj valićnoj transformaciji (DWT) i probabilističkim neuronskim mrežama (PNN) s radijalnim bazama. DWT omogućava više-rezolucijsku analizu koja oponaša ljudski vizualni sustav i izdvaja iz slike pločice značajne za detekciju oštećenja. Neuronske mreže se koriste za klasifikaciju pločica ovisno o postojanju oštećenja. Efikasnost klasifikacije najviše ovisi o odgovarajućem odabiru vektora za učenje neuronskih mreža. Za pripremu neuronskih mreža predlažemo automatiziranu adaptivnu tehniku koja se temelji na statistici tekstura oštećenja na pločicama. Ova tehnika omogućava brzu adaptaciju SFD algoritma na različite teksture, što je posebno važno za automatiziranu vizualnu provjeru u proizvodnji novog tipa pločica

Adaptivna tehnika obrade slike za kontrolu kvalitete u proizvodnji keramičkih pločica

Automation of the visual inspection for quality control in production of materials with textures (tiles, textile, leather, etc.) is not widely implemented. A sophisticated system for image acquisition, as well as a fast and efficient procedure for texture analysis is needed for this purpose. In this paper the Surface Failure Detection (SFD) algorithm for quality control in ceramic tiles production is presented. It is based on Discrete Wavelet Transform (DWT) and Probabilistic Neural Networks (PNN) with radial basis. DWT provides a multi-resolution analysis, which mimics behavior of a human visual system and it extracts from the tile image the features important for failure detection. Neural networks are used for classification of the tiles with respect to presence of defects. Classification efficiency mainly depends on the proper choice of the training vectors for neural networks. For neural networks preparation we propose an automated adaptive technique based on statistics of the tiles defects textures. This technique enables fast adaptation of the SFD algorithm to different textures, which is important for automated visual inspection in the production of a new tile type.Automatizacija vizualne provjere za kontrolu kvalitete u proizvodnji materijala s teksturama (pločice, tekstil, kože, itd.) nije široko primijenjena u praksi. Za ovu namjenu potreban je sofisticirani sustav za snimanje slika, kao i brza i efikasna procedura za analizu tekstura. U ovom je radu predstavljen algoritam za detekciju površinskih oštećenja (SFD) u proizvodnji keramičkih pločica. Temelji se na diskretnoj valićnoj transformaciji (DWT) i probabilističkim neuronskim mrežama (PNN) s radijalnim bazama. DWT omogućava više-rezolucijsku analizu koja oponaša ljudski vizualni sustav i izdvaja iz slike pločice značajne za detekciju oštećenja. Neuronske mreže se koriste za klasifikaciju pločica ovisno o postojanju oštećenja. Efikasnost klasifikacije najviše ovisi o odgovarajućem odabiru vektora za učenje neuronskih mreža. Za pripremu neuronskih mreža predlažemo automatiziranu adaptivnu tehniku koja se temelji na statistici tekstura oštećenja na pločicama. Ova tehnika omogućava brzu adaptaciju SFD algoritma na različite teksture, što je posebno važno za automatiziranu vizualnu provjeru u proizvodnji novog tipa pločica

Application of the Wigner distribution to monitoring cutting tool condition

This thesis is about the application of the Wigner distribution to cutting tool monitoring and control. After reviewing traditional methods, a new method is proposed. This is to regard the surface texture and geometric error of form of a machined workpiece as the fingerprint of a cutting process, to analyse it, and to extract cutting tool vibration information from it, which can then be used for cutting tool monitoring. In order to analyse the surface texture effectively, three analysing tools, i.e. the Fourier transform, the ambiguity function, the Wigner distribution (WD), are examined and compared with each other, and it is concluded that the WD is best able to analyse both stationary and nonstationary signals. Furthermore, computer simulation of both chirp signals and frequency modulated signals is then carried out, and it is shown that the WD can be used to extract useful parameters successively. In order to demonstrate the suitability of the WD for machine tool condi- tion monitoring, first cutting tool vibration are measured directly by two linear variable differential transformers mounted on the cutting tool, and then these measured data about vibration are used to verify those parameters extracted from the surface of the machined workpiece by the WD. It is found that • the extracted frequencies in both horizontal and vertical direction are within 10% of those measured, • the extracted amplitudes in both horizontal and vertical direction are highly correlated with those measured. This result confirms the feasibility of this technique. In spite of being an off-line process, this technique is simple, reliable, and can reveal the direct effect of cutting processes

Optophone design: optical-to-auditory vision substitution for the blind

An optophone is a device that turns light into sound for the benefit of blind people. The present project is intended to produce a general-purpose optophone to be worn on the head about the house and in the street, to give the wearer a detailed description in sound of the'scene he is facing. The device will therefore consist'of an'electronic camera, some signal-processing electronics, earphones`, and a battery. The two major problems are the derivation of (a) the most suitable mapping from images to sounds, and (b) an algorithm to perform the mapping in real'time on existing electronic components. This thesis concerns problem (a). Chapter 2 goes into the general scene-to-sound mapping problem in some detail'and presents the work of earlier investigators. Chapter 3 1- discusses the design of tests to evaluate the performance of candidate mappings. A theoretical performance test (TPT) is derived. Chapter 4 applies the TPT to the most obvious mapping, the cartesian piano transform. Chapter 5 applies the TPT to a mapping based on the cosine transform. Chapter 6 attempts to derive a mapping by principal component analysis, using the inaccuracies of human sight and hearing and the statistical properties of real scenes and sounds. Chapter 7 presents a complete scheme, implemented in software, for representing digitised colour scenes by audible digitised stereo sound. Chapter 8 tries to decide how'many numbers are required to specify a steady spectrum with no noticeable degradation. Chapter 9 looks'at a scheme designed to produce more natural-sounding sounds related to more meaningful portions of the scene. This scheme maps windows in the scene to steady spectral patterns of short duration, the location of the window being conveyed by simulated free-field listening. Chapter 10 gives detailed recommendations as to further work

Computational imaging and automated identification for aqueous environments

Thesis (Ph. D.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2011."June 2011." Cataloged from PDF version of thesis.Includes bibliographical references (p. 253-293).Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods. Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classification with bag-of-words models and multi-stage boosting for rock sh detection. Methods for extracting images of sh from videos of long-line operations are demonstrated. A prototype digital holographic imaging device is designed and tested for quantitative in situ microscale imaging. Theory to support the device is developed, including particle noise and the effects of motion. A Wigner-domain model provides optimal settings and optical limits for spherical and planar holographic references. Algorithms to extract the information from real-world digital holograms are created. Focus metrics are discussed, including a novel focus detector using local Zernike moments. Two methods for estimating lateral positions of objects in holograms without reconstruction are presented by extending a summation kernel to spherical references and using a local frequency signature from a Riesz transform. A new metric for quickly estimating object depths without reconstruction is proposed and tested. An example application, quantifying oil droplet size distributions in an underwater plume, demonstrates the efficacy of the prototype and algorithms.by Nicholas C. Loomis.Ph.D

Feature point classification and matching

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2007.Thesis (Master's) -- Bilkent University, 2007.Includes bibliographical references leaves 85-105.A feature point is a salient point which can be separated from its neighborhood. Widely used definitions assume that feature points are corners. However, some non-feature points also satisfy this assumption. Hence, non-feature points, which are highly undesired, are usually detected as feature points. Texture properties around detected points can be used to eliminate non-feature points by determining the distinctiveness of the detected points within their neighborhoods. There are many texture description methods, such as autoregressive models, Gibbs/Markov random field models, time-frequency transforms, etc. To increase the performance of feature point related applications, two new feature point descriptors are proposed, and used in non-feature point elimination and feature point sorting-matching. To have a computationally feasible descriptor algorithm, a single image resolution scale is selected for analyzing the texture properties around the detected points. To create a scale-space, wavelet decomposition is applied to the given images and neighborhood scale-spaces are formed for every detected point. The analysis scale of a point is selected according to the changes in the kurtosis values of histograms which are extracted from the neighborhood scale-space. By using descriptors, the detected non-feature points are eliminated, feature points are sorted and with inclusion of conventional descriptors feature points are matched. According to the scores obtained in the experiments, the proposed detection-matching scheme performs more reliable than the Harris detector gray-level patch matching scheme. However, SIFT detection-matching scheme performs better than the proposed scheme.Ay, Avşar PolatM.S