This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

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Distinctive Image Features from Scale-Invariant Keypoints

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

/pdf/data-mining-concepts-and-techniques-4dtvdfkvmi.pdf

Data Mining: Concepts and Techniques

Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.

/pdf/slic-superpixels-compared-to-state-of-the-art-superpixel-5d8sq04am9.pdf

SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

The k-means method is a widely used clustering technique that seeks to minimize the average squared distance between points in the same cluster. Although it offers no accuracy guarantees, its simplicity and speed are very appealing in practice. By augmenting k-means with a very simple, randomized seeding technique, we obtain an algorithm that is Θ(logk)-competitive with the optimal clustering. Preliminary experiments show that our augmentation improves both the speed and the accuracy of k-means, often quite dramatically.

/pdf/k-means-the-advantages-of-careful-seeding-4p8vluc5ni.pdf

k-means++: the advantages of careful seeding

http://library.utia.cas.cz/prace/20030125.pdf

Image registration methods: a survey

Given a set P of n points in R d , a fundamental problem in computational geometry is concerned with nding the smallest enclosing \range" of P. Well known instances of this problem include nding, e.g., the smallest enclosing box 10], smallest enclosing simplex 11], minimum volume ball 17, 20], (2-D) smallest enclosing ellipsoid 16, 20, 2, 14], and minimum volume annulus 1, 8, 5]. In this paper, we consider the following generic variant: Given a set of n points in R d , nd the smallest range in question that contains (at least) a certain quantile (up to 50%) of the data. Although this variant has been studied to some extent in computational geometry | instances include enclosing k points by a circle 7, 4] and nding the smallest axis parallel rectangle enclosing k points 15] | the algorithms proposed deal mainly with speciic cases and their running times are relatively high (O(n 2 log n) and O(n 3), respectively, for the minimum enclosing circle and axis parallel rectangle in the plane). stems from the growing need for eecient data analysis techniques that are robust to outlying/noisy observations. While such data can be handled successfully due to robust statistical estimators, the eecient computation of these estimators continues to pose a formidable algorithmic challenge. The basic measure of the robustness of an estima-tor is its breakdown point, that is, the fraction (up to 50%) of outlying data that can corrupt the estimator (see, e.g., 13] for an exact deenition). For example , Rousseeuw's least median-of-squares (LMS) regression estimator 12] (which is equivalent to nding the narrowest hyperstrip contanining at least 50% of the data) is among the best known 50% breakdown-point estimators. The best exact algorithm known for this problem runs in O(n d+2 log n) time 19]. Because of this high running time, a simple O(n logn) Monte Carlo algorithm (for xed d) is commonly used 13]. However, the latter provides no guarantee of accuracy (even probabilistic) unless the data set satisses certain assumptions. 1 Other robust estimators considered in this paper are the minimum volume disk (e.g., the minimum volume ellipsoid (MVE)) and the minimum volume annulus (MVA) enclosing (at least) half of the points in P. As in the case of LMS, algorithms known for these estimators have either high complexity or they provide no guarantee of accuracy due to their Monte Carlo nature 13]. In an …

Quantile approximation for robust statistical estimation.

The conventional ordinary least squares (OLS) method of fitting a, line to a set of data points is notoriously unreliable when the amount of random noise in the input (such as an image) is significant compared with the amount of data that is correlated with the line itself. Although points which lie far away from the line (i.e., outliers) are usually only to noise, they contribute the most to the squared distances, thereby skewing the line estimate from its correct position. In this paper we present an analytic method of separating the data of interest from the outliers. We assume that the overall data (i.e., the line data plus the noise) can be modeled as a mixture of two statistical distributions. Applying a variant of the method of moments (MOM) to the assumed model yields an analytic estimate of the desired line.

http://ieeexplore.ieee.org/iel2/4428/12512/00576962.pdf

Removal in Line Fitting

This paper demonstrates the use of genetic algorithms for evolving a grandmaster-level evaluation function for a chess program. This is achieved by combining supervised and unsupervised learning. In the supervised learning phase the organisms are evolved to mimic the behavior of human grandmasters, and in the unsupervised learning phase these evolved organisms are further improved upon by means of coevolution. 
While past attempts succeeded in creating a grandmaster-level program by mimicking the behavior of existing computer chess programs, this paper presents the first successful attempt at evolving a state-of-the-art evaluation function by learning only from databases of games played by humans. Our results demonstrate that the evolved program outperforms a two-time World Computer Chess Champion.

Simulating Human Grandmasters: Evolution and Coevolution of Evaluation Functions

Pairwise compatibility measure (CM) is a key component in solving the jigsaw puzzle problem (JPP) and many of its recently proposed variants. With the rapid rise of deep neural networks (DNNs), a trade-off between performance ( i.e. , accuracy) and computational efﬁciency has become a very signiﬁcant issue. Whereas an end-to-end DNN-based CM model exhibits high performance, it becomes virtually infeasible on very large puzzles, due to its highly intensive computation. On the other hand, exploiting the concept of embeddings to alleviate signiﬁcantly the computational efﬁciency, has resulted in degraded performance, according to recent studies. This paper derives an advanced CM model (based on modiﬁed embeddings and a new loss function, called hard batch triplet loss ) for closing the above gap between speed and accuracy; namely a CM model that achieves SOTA results in terms of performance and efﬁciency combined. We evaluated our newly derived CM on three commonly used datasets, and obtained a reconstruction improvement of 5.8% and 19.5% for so-called Type-1 and Type-2 problem variants, respectively, compared to best known results due to previous CMs.

Edge2Vec: A High Quality Embedding for the Jigsaw Puzzle Problem

X-ray imaging is a fundamental clinical tool for screening and diagnosing various diseases. However, the spatial resolution of radiographs is often limited, making it challenging to diagnose small image details and leading to difficulties in identifying vertebrae anomalies at an early stage in chest radiographs. To address this limitation, we propose a novel and robust approach to significantly improve the quality of X-ray images by iteratively training a deep neural network. Our framework includes an embedded internal guidance layer that enhances the fine structures of spinal vertebrae in chest X-ray images through fully unsupervised training, utilizing an iterative procedure that employs the same network architecture in each enhancement phase. Additionally, we have designed an optimized loss function that accurately identifies object boundaries and enhances spinal features, thereby further enhancing the quality of the images. Experimental results demonstrate that our proposed method surpasses existing detail enhancement methods in terms of BRISQUE scores, and is comparable in terms of LPC-SI. Furthermore, our approach exhibits superior performance in restoring hidden fine structures, as evidenced by our qualitative results. This innovative approach has the potential to significantly enhance the diagnostic accuracy and early detection of diseases, making it a promising advancement in X-ray imaging technology.

Nathan S. Netanyahu

Papers

Quantile approximation for robust statistical estimation.

Removal in Line Fitting

Simulating Human Grandmasters: Evolution and Coevolution of Evaluation Functions

Edge2Vec: A High Quality Embedding for the Jigsaw Puzzle Problem

Unsupervised Iterative U-Net with an Internal Guidance Layer for Vertebrae Contrast Enhancement in Chest X-Ray Images