Geometrical Image Transforms

Cílem bakalářské práce je nastudování základů zpracování obrazu, především pak lineárních transformací obrazu a interpolací obrazových bodů. Za účelem bližšího seznámení je vhodné provést implementaci základních geometrických transformací a alespoň několika druhů různých interpolačních metod.This bachelor's thesis is about introducing to basics of image processing, mostly with linear image transformations and interpolation. For the purpose of the closer understanding is properly to implement some basic geometrical image transformations and some different interpolation methods.

Salient Regions for Query by Image Content

Much previous work on image retrieval has used global features such as colour and texture to describe the content of the image. However, these global features are insufficient to accurately describe the image content when different parts of the image have different characteristics. This paper discusses how this problem can be circumvented by using salient interest points and compares and contrasts an extension to previous work in which the concept of scale is incorporated into the selection of salient regions to select the areas of the image that are most interesting and generate local descriptors to describe the image characteristics in that region. The paper describes and contrasts two such salient region descriptors and compares them through their repeatability rate under a range of common image transforms. Finally, the paper goes on to investigate the performance of one of the salient region detectors in an image retrieval situation

Advanced Feature Learning and Representation in Image Processing for Anomaly Detection

Techniques for improving the information quality present in imagery for feature extraction are proposed in this thesis. Specifically, two methods are presented: soft feature extraction and improved Evolution-COnstructed (iECO) features. Soft features comprise the extraction of image-space knowledge by performing a per-pixel weighting based on an importance map. Through soft features, one is able to extract features relevant to identifying a given object versus its background. Next, the iECO features framework is presented. The iECO features framework uses evolutionary computation algorithms to learn an optimal series of image transforms, specific to a given feature descriptor, to best extract discriminative information. That is, a composition of image transforms are learned from training data to present a given feature descriptor with the best opportunity to extract its information for the application at hand. The proposed techniques are applied to an automatic explosive hazard detection application and significant results are achieved

Performance analysis of Image transforms.

by Francis Fuk-sing Wu.Thesis (M.Phil.)--Chinese University of Hong Kong, 1991.Bibliography: leaves [90]-[94]LIST OF FIGURES --- p.viiLIST OF TABLES --- p.ixNOTATIONS --- p.xChapter 1 --- INTRODUCTIONChapter 1.1 --- Introduction --- p.1-1Chapter 1.2 --- Properties of Orthonormal Transforms --- p.1-4Chapter 1.3 --- Some Considerations of a Transform System --- p.1-5Chapter 1.4 --- Motivation of Work --- p.1-7Chapter 1.5 --- Organization of the Thesis --- p.1-9Chapter 2 --- COVARIANCE ESTIMATION SCHEMES AND PERFORMANCE COMPARISON OF THE KLT'SChapter 2.1 --- Introduction --- p.2-1Chapter 2.2 --- Statistics of an Image --- p.2-1Chapter 2.3 --- Mathematical Covariance Functions --- p.2-3Chapter 2.3.1 --- One-Dimensional : First-Order Markov Model --- p.2-3Chapter 2.3.2 --- Two-Dimensional : Separable and Non-Separable Isotropic Models --- p.2-3Chapter 2.4 --- Goviance Estimation Schemes --- p.2-5Chapter 2.4.1 --- Scheme 1 --- p.2-5Chapter 2.4.2 --- Scheme 2 --- p.2-7Chapter 2.4.3 --- Scheme 3 --- p.2-8Chapter 2.5 --- KLT's of Different Random Processes --- p.2-11Chapter 2.6 --- Transform Comparison Based on Real Images --- p.2-14Chapter 2.7 --- Concluding Remarks --- p.2-18Chapter 3 --- DC TRANSFORMED ENERGY PACKING ABILITY OF KLT AND DCTChapter 3.1 --- Introduction --- p.3-1Chapter 3.2 --- Analysis of DC Transformed Energy Using Mathematical Covariance Models --- p.3-1Chapter 3.2.1 --- First-Order Markov Process --- p.3-1Chapter 3.2.2 --- Separable Model --- p.3 -3Chapter 3.2.3 --- Non-Separable Isotropic Model --- p.3-4Chapter 3.2.4 --- Results --- p.3-5Chapter 3.3 --- Chen and Smith Method and Experimental Results --- p.3-7Chapter 3.4 --- Concluding Remarks --- p.3-15Chapter 4 --- COMPATIBILITY OF THE DCT AND ICTChapter 4.1 --- Introduction --- p.4-1Chapter 4.2 --- The Discrete Cosine Transform (DCT) --- p.4 -2Chapter 4.3 --- The Family of Interger Cosine Transforms (ICT) --- p.4 -3Chapter 4.4 --- Analysis of Error Due to Different Forward and Inverse Transforms --- p.4 -8Chapter 4.4.1 --- First-Order Markov Process --- p.4-8Chapter 4.4.2 --- Separable Model --- p.4-10Chapter 4.4.3 --- Non-Separable Isotropic Model --- p.4-12Chapter 4.5 --- Results --- p.4-14Chapter 4.6 --- Concluding Remarks --- p.4 -18Chapter 5 --- ERROR BEHAVIOUR OF THE DCT AND THE ICTChapter 5.1 --- Introduction --- p.5 -1Chapter 5.2 --- Problem Identification --- p.5 -1Chapter 5.2.1 --- Error Distribution and Energy Due to Rounding Operation --- p.5-2Chapter 5.2.2 --- Error Distribution and Energy Due to Linear Transformation --- p.5-3Chapter 5.2.3 --- Estimation of Residual Error Energy --- p.5-6Chapter 5.3 --- Error Energy in the One-Dimensional Order-8 DCT System --- p.5-8Chapter 5.4 --- Error Energy in the One-Dimensional Order-8 ICT(4) System --- p.5-11Chapter 5.5 --- Error Energy in the Two-Dimensional Order-8 DCT System --- p.5-13Chapter 5.6 --- Error Energy in the Two-Dimensional Order-8 ICT(4) System --- p.5-16Chapter 5.7 --- Error Energy in Other Transform Systems --- p.5-19Chapter 5.7.1 --- Error Energy in the Two-Dimensional Order-16 DCT System --- p.5-20Chapter 5.7.2 --- Error Energy in the One-Dimensional Order-16 ICT(7) System --- p.5-21Chapter 5.7.3 --- Error Energy in the Two-Dimensional Order-16 ICT(7) System --- p.5 -21Chapter 5.8 --- Results --- p.5 -22Chapter 5.9 --- Concluding Remarks --- p.5 -24Chapter 6 --- CONCLUSIONS --- p.6-1Chapter 6.1 --- Summary of Work --- p.6-1Chapter 6.2 --- Contribution of the Work --- p.6-2Chapter 6.3 --- Recommendation for Further Work --- p.6-3Chapter 7 --- REFERENCES --- p.7 -1Chapter 8 --- APPENDIXChapter A.1 --- Separability of KLTs --- p.A -1Chapter A.2 --- Derivation of DCT and KLT DC Transformed Energy --- p.A -2First-Order Maikov Process --- p.A -2Separable Model --- p.A -3Non-Separable Isotropic Model --- p.A -4RESULT

Visual Attention Consistency under Image Transforms for Multi-Label Image Classification

Human visual perception shows good consistency for many multi-label image classification tasks under certain spatial transforms, such as scaling, rotation, flipping and translation. This has motivated the data augmentation strategy widely used in CNN classifier training -- transformed images are included for training by assuming the same class labels as their original images. In this paper, we further propose the assumption of perceptual consistency of visual attention regions for classification under such transforms, i.e., the attention region for a classification follows the same transform if the input image is spatially transformed. While the attention regions of CNN classifiers can be derived as an attention heatmap in middle layers of the network, we find that their consistency under many transforms are not preserved. To address this problem, we propose a two-branch network with an original image and its transformed image as inputs and introduce a new attention consistency loss that measures the attention heatmap consistency between two branches. This new loss is then combined with multi-label image classification loss for network training. Experiments on three datasets verify the superiority of the proposed network by achieving new state-of-the-art classification performance