Integrated Multiple Features for Tumor Image Retrieval Using Classifier and Feedback Methods

AbstractThe content based image retrieval method greatly assists in retrieving medical images close to the query image from a large database basing on their visual features. This paper presents an effective approach in which the region of the object is extracted with the help of multiple features ignoring the background of the object by employing edge following segmentation method followed by extracting texture and shape characteristics of the images. The former is extracted with the help of Steerable filter at different orientations and radial Chebyshev moments are used for extracting the later. Initially the images similar to the query image are extracted from a large group of medical images. Then the search is by accelerating the retrieval process with the help of Support Vector Machine (SVM) classifier. The performance of the retrieval system is enhanced by adapting the subjective feedback method. The experimental results show that the proposed region based multiple features and integrated with classifier and subjective feedback method yields better results than classical retrieval systems

Hand-draw sketching for image retrieval through fuzzy clustering techniques

Nowadays, the growing of digital media such as images represents an important issue for niultimedia mining applications. Since the traditional information retrieval techniques developed for textual documents do not support adequately these media, new approaches for indexing and retrieval of images are needed. In this paper, we propose an approach for retrieving image by hand-drawn object sketch. For this purpose. we address the classification of images based on shape recognition. The classification is based on the combined use of geometrical and moments features extracted by a given collection of images and achieves shape-based classification through fuzzy clustering techniques. Then, the retrieval is obtained using a hand-draw shape that becomes a query to submit to the system and get ranked similar images

ROCA: Robust CAD Model Retrieval and Alignment from a Single Image

We present ROCA, a novel end-to-end approach that retrieves and aligns 3D CAD models from a shape database to a single input image. This enables 3D perception of an observed scene from a 2D RGB observation, characterized as a lightweight, compact, clean CAD representation. Core to our approach is our differentiable alignment optimization based on dense 2D-3D object correspondences and Procrustes alignment. ROCA can thus provide a robust CAD alignment while simultaneously informing CAD retrieval by leveraging the 2D-3D correspondences to learn geometrically similar CAD models. Experiments on challenging, real-world imagery from ScanNet show that ROCA significantly improves on state of the art, from 9.5% to 17.6% in retrieval-aware CAD alignment accuracy

Phase retrieval beyond the homogeneous object assumption for X-ray in-line holographic imaging

X-ray near field holography has proven to be a powerful 2D and 3D imaging technique with applications ranging from biomedical research to material sciences. To reconstruct meaningful and quantitative images from the measurement intensities, however, it relies on computational phase retrieval which in many cases assumes the phase-shift and attenuation coefficient of the sample to be proportional. Here, we demonstrate an efficient phase retrieval algorithm that does not rely on this homogeneous-object assumption and is a generalization of the well-established contrast-transfer-function (CTF) approach. We then investigate its stability and present an experimental study comparing the proposed algorithm with established methods. The algorithm shows superior reconstruction quality compared to the established CTF-based method at similar computational cost. Our analysis provides a deeper fundamental understanding of the homogeneous object assumption and the proposed algorithm will help improve the image quality for near-field holography in biomedical application

Semantic localisation via globally unique instance segmentation

In this work we propose a novel approach to semantic localisation. Our work is motivated by the need for environment perception techniques which not only perform self-localisation within a map but also simultaneously recognise surrounding objects. Such capabilities are crucial for computer vision applications which interact with the environment: autonomous driving, augmented reality or robotics. In order to achieve this goal we propose a solution which consists of three key steps. Firstly, a database of panoramic RGB images and corresponding globally unique, per-pixel object instance labels is built for the desired environment where we typically consider objects from static categories such as "building" or "tree". Secondly, a semantic segmentation network capable of predicting more than 3000 labels is trained on the collected data. Finally, for a given panoramic query image, the corresponding instance label image predicted by the network is used for semantic matching within the database. The matching is performed in two stages: (i) a fast retrieval of a small subset of database images (~100) with highly overlapping instance label histograms, followed by (ii) an explicit approximate 3 DoF (yaw, pitch, roll) alignment of the selected subset of images and the query image. We evaluate our approach in challenging indoor and outdoor navigation scenarios, achieving better or similar performance when compared to state-of-the-art image retrieval-based localisation approaches using key-point matching and image level embedding. Our contribution includes: (i) a description of a novel semantic localisation approach using globally unique instance segmentation, (ii) corresponding quantitative and qualitative analysis and (iii) a novel CamVid-360 dataset containing 986 labelled instances of buildings, trees, road signs and poles

Similarity Measurement of Breast Cancer Mammographic Images Using Combination of Mesh Distance Fourier Transform and Global Features

Similarity measurement in breast cancer is an important aspect of determining the vulnerability of detected masses based on the previous cases. It is used to retrieve the most similar image for a given mammographic query image from a collection of previously archived images. By analyzing these results, doctors and radiologists can more accurately diagnose early-stage breast cancer and determine the best treatment. The direct result is better prognoses for breast cancer patients. Similarity measurement in images has always been a challenging task in the field of pattern recognition. A widely-adopted strategy in Content-Based Image Retrieval (CBIR) is comparison of local shape-based features of images. Contours summarize the orientations and sizes images, allowing for heuristic approach in measuring similarity between images. Similarly, global features of an image have the ability to generalize the entire object with a single vector which is also an important aspect of CBIR. The main objective of this paper is to enhance the similarity measurement between query images and database images so that the best match is chosen from the database for a particular query image, thus decreasing the chance of false positives. In this paper, a method has been proposed which compares both local and global features of images to determine their similarity. Three image filters are applied to make this comparison. First, we filter using the mesh distance Fourier descriptor (MDFD), which is based on the calculation of local features of the mammographic image. After this filter is applied, we retrieve the five most similar images from the database. Two additional filters are applied to the resulting image set to determine the best match. Experiments show that this proposed method overcomes shortcomings of existing methods, increasing accuracy of matches from 68% to 88%

Mean-shift analysis for image and video applications

Cataloged from PDF version of article.In this thesis, image and video analysis algorithms are developed. Tracking moving objects in video have important applications ranging from CCTV (Closed Circuit Television Systems) to infrared cameras. In current CCTV systems, 80% of the time, it is impossible to recognize suspects from the recorded scenes. Therefore, it is very important to get a close shot of a person so that his or her face is recognizable. To take high-resolution pictures of moving objects, a pan-tiltzoom camera should automatically follow moving objects and record them. In this thesis, a mean-shift based moving object tracking algorithm is developed. In ordinary mean-shift tracking algorithm a color histogram or a probability density function (pdf) estimated from image pixels is used to represent the moving object. In our case, a joint-probability density function is used to represent the object. The joint-pdf is estimated from the object pixels and their wavelet transform coefficients. In this way, relations between neighboring pixels, edge and texture information of the moving object are also represented because wavelet coefficients are obtained after high-pass filtering. Due to this reason the new tracking algorithm is more robust than ordinary mean-shift tracking using only color information. A new content based image retrieval (CBIR) system is also developed in this thesis. The CBIR system is based on mean-shift analysis using a joint-pdf. In this system, the user selects a window in an image or an entire image and queries similar images stored in a database. The selected region is represented using a joint-pdf estimated from image pixels and their wavelet transform coefficients. The retrieval algorithm is more reliable compared to other CBIR systems using only color information or only edge or texture information because the jointpdf based approach represents both texture, edge and color information. The proposed method is also computationally efficient compared to sliding-window based retrieval systems because the joint-pdfs are compared in non-overlapping windows. Whenever there is a reasonable amount of match between the queried window and the original image window then a mean-shift analysis is started.Cüce, Halil İbrahimM.S

Medical Image Registration and 3D Object Matching

The great challenge in image registration and 3D object matching is to devise computationally efficient algorithms for aligning images so that their details overlap accurately and retrieving similar shapes from large databases of 3D models. The first problem addressed is this thesis is medical image registration, which we formulate as an optimization problem in the information-theoretic framework. We introduce a viable and practical image registration method by maximizing an entropic divergence measure using a modified simultaneous perturbation stochastic approximation algorithm. The feasibility of the proposed image registration approach is demonstrated through extensive experiments. The rest of the thesis is devoted to a joint exploitation of geometry and topology of 3D objects for as parsimonious as possible representation of models and its subsequent application in 3D object representation, matching, and retrieval problems. More precisely, we introduce a skeletal graph for topological 3D shape representation using Morse theory. The proposed skeletonization algorithm encodes a 3D shape into a topological Reeb graph using a normalized mixture distance function. We also propose a novel graph matching algorithm by comparing the relative shortest paths between the skeleton endpoints. Moreover, we describe a skeletal graph for 3D object matching and retrieval. This skeleton is constructed from the second eigenfunction of the Laplace-Beltrami operator defined on the surface of the 3D object. Using the generalized eigenvalue decomposition, a matrix computational framework based on the finite element method is presented to compute the spectrum of the Laplace-Beltrami operator. Illustrating experiments on two standard 3D shape benchmarks are provided to demonstrate the feasibility and the much improved performance of the proposed skeletal graphs as shape descriptors for 3D object matching and retrieval

Object Duplicate Detection

With the technological evolution of digital acquisition and storage technologies, millions of images and video sequences are captured every day and shared in online services. One way of exploring this huge volume of images and videos is through searching a particular object depicted in images or videos by making use of object duplicate detection. Therefore, need of research on object duplicate detection is validated by several image and video retrieval applications, such as tag propagation, augmented reality, surveillance, mobile visual search, and television statistic measurement. Object duplicate detection is detecting visually same or very similar object to a query. Input is not restricted to an image, it can be several images from an object or even it can be a video. This dissertation describes the author's contribution to solve problems on object duplicate detection in computer vision. A novel graph-based approach is introduced for 2D and 3D object duplicate detection in still images. Graph model is used to represent the 3D spatial information of the object based on the local features extracted from training images so that an explicit and complex 3D object modeling is avoided. Therefore, improved performance can be achieved in comparison to existing methods in terms of both robustness and computational complexity. Our method is shown to be robust in detecting the same objects even when images containing the objects are taken from very different viewpoints or distances. Furthermore, we apply our object duplicate detection method to video, where the training images are added iteratively to the video sequence in order to compensate for 3D view variations, illumination changes and partial occlusions. Finally, we show several mobile applications for object duplicate detection, such as object recognition based museum guide, money recognition or flower recognition. General object duplicate detection may fail to detection chess figures, however considering context, like chess board position and height of the chess figure, detection can be more accurate. We show that user interaction further improves image retrieval compared to pure content-based methods through a game, called Epitome