Rotationally and Illumination Invariant Descriptor Based On Intensity Order

In this thesis, a novel method for local feature description where local features are grouped in normalized support regions with the intensity orders is proposed. Local features extracted using this kind of method are not only gives advantage of invariant to rotation and illumination changes, but also converts the image information into the descriptor. These features are calculated with different ways, one is based on gradient and other one is based on the intensity order. Local features calculated by the method of the gradient performs well in most of the cases such as blur, rotation and large illuminations and it overcome the problem of orientation estimation which is the major error source for false negatives in SIFT. In order to overcome mismatching problem, method of multiple support regions are introduced in the proposed method instead of using single support region which performs better than the single support region, even though single support region is better than SIFT. The idea of intensity order pooling is inherently rotational invariant without estimating a reference orientation. Experimental results show that the idea of intensity order pooling is efficient than the other descriptors, which are based on estimated reference orientation for rotational invariance

Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields

This work presents a first evaluation of using spatio-temporal receptive fields from a recently proposed time-causal spatio-temporal scale-space framework as primitives for video analysis. We propose a new family of video descriptors based on regional statistics of spatio-temporal receptive field responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain and from object recognition to dynamic texture recognition. The time-recursive formulation enables computationally efficient time-causal recognition. The experimental evaluation demonstrates competitive performance compared to state-of-the-art. Especially, it is shown that binary versions of our dynamic texture descriptors achieve improved performance compared to a large range of similar methods using different primitives either handcrafted or learned from data. Further, our qualitative and quantitative investigation into parameter choices and the use of different sets of receptive fields highlights the robustness and flexibility of our approach. Together, these results support the descriptive power of this family of time-causal spatio-temporal receptive fields, validate our approach for dynamic texture recognition and point towards the possibility of designing a range of video analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure

Using basic image features for texture classification

Representing texture images statistically as histograms over a discrete vocabulary of local features has proven widely effective for texture classification tasks. Images are described locally by vectors of, for example, responses to some filter bank; and a visual vocabulary is defined as a partition of this descriptor-response space, typically based on clustering. In this paper, we investigate the performance of an approach which represents textures as histograms over a visual vocabulary which is defined geometrically, based on the Basic Image Features of Griffin and Lillholm (Proc. SPIE 6492(09):1-11, 2007), rather than by clustering. BIFs provide a natural mathematical quantisation of a filter-response space into qualitatively distinct types of local image structure. We also extend our approach to deal with intra-class variations in scale. Our algorithm is simple: there is no need for a pre-training step to learn a visual dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different datasets. We have tested our implementation on three popular and challenging texture datasets and find that it produces consistently good classification results on each, including what we believe to be the best reported for the KTH-TIPS and equal best reported for the UIUCTex databases

Segmentation fusion for connectomics

We address the problem of automatic 3D segmentation of a stack of electron microscopy sections of brain tissue. Unlike previous efforts, where the reconstruction is usually done on a section-to-section basis, or by the agglomerative clustering of 2D segments, we leverage information from the entire volume to obtain a globally optimal 3D segmentation. To do this, we formulate the segmentation as the solution to a fusion problem. We first enumerate multiple possible 2D segmentations for each section in the stack, and a set of 3D links that may connect segments across consecutive sections. We then identify the fusion of segments and links that provide the most globally consistent segmentation of the stack. We show that this two-step approach of pre-enumeration and posterior fusion yields significant advantages and provides state-of-the-art reconstruction results. Finally, as part of this method, we also introduce a robust rotationally-invariant set of features that we use to learn and enumerate the above 2D segmentations. Our features outperform previous connectomic-specific descriptors without relying on a large set of heuristics or manually designed filter banks.Engineering and Applied Science

Fast 4D Ultrasound Registration for Image Guided Liver Interventions

Liver problems are a serious health issue. The common liver problems are hepatitis, fatty liver, liver cancer and liver damage caused by alcohol abuse. Continuous, long term disease may cause a condition of the liver known as the Liver Cirrhosis. Liver cirrhosis makes the liver scarred and hardened up causing portal hypertension. In such a situation the collateral vessels try to bypass the liver as blood cannot freely flow through the liver; causing internal bleeding. One of the treatments of portal hypertension is Transjugular intrahepatic portosystemic shunt (TIPS). In a TIPS procedure a tract in the liver is created that shortcuts two veins in the liver, reducing the portal hypertension. Radiofrequency ablation (RFA) is use for the treatment of liver cancer. In RFA, a needle electrode is placed through the skin into the liver tumor. High-frequency electrical currents are passed through the electrode, creating heat that destroys the cancer cells, without damaging the surrounding liver tissues. TIPS and RFA are minimally invasive procedures, where small incisions are made to perform the surgery and are alternative to open surgery. A minimally invasive alternative has large potential in reducing complication rates, minimizing surgical trauma and reducing hospital stay. However, in these procedures, due to lack of direct eyesight, three-dimensional imaging information about the anatomy and instruments during the intervention is required. The most difficult part of these procedures is the interpretation and selection of oblique views for needle/instrument insertion and target visualization. In our work we develop and evaluate techniques that enable the effective use of 3D ultrasound for image guided interventions. Ultrasound is low cost, mobile and unlike CT and X-rays does not use any harmful radiation in the imaging process. During these procedures, breathing shifts the region of interest and makes it difficult to constantly focus on a region of interest. We provide an approach to correct for the motion due to breathing. Additionally, we propose a method for image fusion of interventional ultrasound and preoperative imaging modalities such as CT for cases where the lesions are visible in CT but not visible in ultrasound. Incorporating CT data during intervention additionally adds greater definition and precision to the ultrasound based navigation system. Concluding, in this thesis, we presented methods and evaluated their accuracies that demonstrate the use of real-time 3D US and its fusion with CT in potentially improving image guidance in minimally invasive US guided liver interventions

Joint Adaptive Median Binary Patterns for texture classification

a b s t r a c t This paper addresses the challenging problem of the recognition and classification of textured surfaces given a single instance acquired under unknown pose, scale and illumination conditions. We propose a novel texture descriptor, the Adaptive Median Binary Pattern (AMBP) based on an adaptive analysis window of local patterns. The principal idea of the AMBP is to convert a small local image patch to a binary pattern using adaptive threshold selection that switches between the central pixel value as used in the Local Binary Pattern (LBP) and the median as in Median Binary Pattern (MBP), but within a variable sized analysis window depending on the local microstructure of the texture. The variability of the local adaptive window is included as joint information to increase the discriminative properties. A new multiscale scheme is also proposed in this paper to handle the texture resolution problem. AMBP is evaluated in relation to other recent binary pattern techniques and many other texture analysis methods on three large texture corpora with and without noise added, CUReT, Outex_TC00012 and KTH_TIPS2. Generally, the proposed method performs better than the best state-of-the-art techniques in the noiseless case and significantly outperforms all of them in the presence of impulse noise

Adaptive Median Binary Patterns for Texture Classification

Abstract-This paper addresses the challenging problem of recognition and classification of textured surfaces under illumination variation, geometric transformations and noisy sensor measurements. We propose a new texture operator, Adaptive Median Binary Patterns (AMBP) that extends our previous Median Binary Patterns (MBP) texture feature. The principal idea of AMBP is to hash small local image patches into a binary pattern texton by fusing MBP and Local Binary Patterns (LBP) operators combined with using self-adaptive analysis window sizes to better capture invariant microstructure information while providing robustness to noise. The AMBP scheme is shown to be an effective mechanism for non-parametric learning of spatially varying image texture statistics. The local distribution of rotation invariant and uniform binary pattern subsets extended with more global joint information are used as the descriptors for robust texture classification. The AMBP is shown to outperform recent binary pattern and filtering-based texture analysis methods on two large texture corpora (CUReT and KTH TIPS2-b) with and without additive noise. The AMBP method is slightly superior to the best techniques in the noiseless case but significantly outperforms other methods in the presence of impulse noise

Segmentation fusion for connectomics

We address the problem of automatic 3D segmentation of a stack of electron microscopy sections of brain tissue. Unlike previous efforts, where the reconstruction is usually done on a section-to-section basis, or by the agglomerative clustering of 2D segments, we leverage information from the entire volume to obtain a globally optimal 3D segmen-tation. To do this, we formulate the segmentation as the so-lution to a fusion problem. We first enumerate multiple pos-sible 2D segmentations for each section in the stack, and a set of 3D links that may connect segments across con-secutive sections. We then identify the fusion of segments and links that provide the most globally consistent segmen-tation of the stack. We show that this two-step approach of pre-enumeration and posterior fusion yields significant advantages and provides state-of-the-art reconstruction re-sults. Finally, as part of this method, we also introduce a robust rotationally-invariant set of features that we use to learn and enumerate the above 2D segmentations. Our fea-tures outperform previous connectomic-specific descriptors without relying on a large set of heuristics or manually de-signed filter banks. 1