Martin D. Levine

Bio: Martin D. Levine is an academic researcher from McGill University. The author has contributed to research in topics: Image segmentation & 3D single-object recognition. The author has an hindex of 43, co-authored 185 publications receiving 9090 citations. Previous affiliations of Martin D. Levine include University of Oklahoma Health Sciences Center & Jet Propulsion Laboratory.

Tracking deformable objects in the plane using an active contour model

Abstract: The problems of segmenting a noisy intensity image and tracking a nonrigid object in the plane are discussed. In evaluating these problems, a technique based on an active contour model commonly called a snake is examined. The technique is applied to cell locomotion and tracking studies. The snake permits both the segmentation and tracking problems to be simultaneously solved in constrained cases. A detailed analysis of the snake model, emphasizing its limitations and shortcomings, is presented, and improvements to the original description of the model are proposed. Problems of convergence of the optimization scheme are considered. In particular, an improved terminating criterion for the optimization scheme that is based on topographic features of the graph of the intensity image is proposed. Hierarchical filtering methods, as well as a continuation method based on a discrete sale-space representation, are discussed. Results for both segmentation and tracking are presented. Possible failures of the method are discussed. >

Visual Saliency Based on Scale-Space Analysis in the Frequency Domain

Abstract: We address the issue of visual saliency from three perspectives. First, we consider saliency detection as a frequency domain analysis problem. Second, we achieve this by employing the concept of nonsaliency. Third, we simultaneously consider the detection of salient regions of different size. The paper proposes a new bottom-up paradigm for detecting visual saliency, characterized by a scale-space analysis of the amplitude spectrum of natural images. We show that the convolution of the image amplitude spectrum with a low-pass Gaussian kernel of an appropriate scale is equivalent to an image saliency detector. The saliency map is obtained by reconstructing the 2D signal using the original phase and the amplitude spectrum, filtered at a scale selected by minimizing saliency map entropy. A Hypercomplex Fourier Transform performs the analysis in the frequency domain. Using available databases, we demonstrate experimentally that the proposed model can predict human fixation data. We also introduce a new image database and use it to show that the saliency detector can highlight both small and large salient regions, as well as inhibit repeated distractors in cluttered images. In addition, we show that it is able to predict salient regions on which people focus their attention.

Registering multiview range data to create 3D computer objects

Abstract: Concerns the problem of range image registration for the purpose of building surface models of 3D objects. The registration task involves finding the translation and rotation parameters which properly align overlapping views of the object so as to reconstruct from these partial surfaces, an integrated surface representation of the object. The registration task is expressed as an optimization problem. We define a function which measures the quality of the alignment between the partial surfaces contained in two range images as produced by a set of motion parameters. This function computes a sum of Euclidean distances from control points on one surfaces to corresponding points on the other. The strength of this approach is in the method used to determine point correspondences. It reverses the rangefinder calibration process, resulting in equations which can be used to directly compute the location of a point in a range image corresponding to an arbitrary point in 3D space. A stochastic optimization technique, very fast simulated reannealing (VFSR), is used to minimize the cost function. Dual-view registration experiments yielded excellent results in very reasonable time. A multiview registration experiment took a long time. A complete surface model was then constructed from the integration of multiple partial views. The effectiveness with which registration of range images can be accomplished makes this method attractive for many practical applications where surface models of 3D objects must be constructed. >

Face Recognition Using the Discrete Cosine Transform

Abstract: An accurate and robust face recognition system was developed and tested. This system exploits the feature extraction capabilities of the discrete cosine transform (DCT) and invokes certain normalization techniques that increase its robustness to variations in facial geometry and illumination. The method was tested on a variety of available face databases, including one collected at McGill University. The system was shown to perform very well when compared to other approaches.

Pattern Recognition with Fuzzy Objective Function Algorithms

Abstract: New updated! The latest book from a very famous author finally comes out. Book of pattern recognition with fuzzy objective function algorithms, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Planning Algorithms: Introductory Material

Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters

Abstract: Two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space. In both cases, the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the least squared error criterion function. In the first case, the range of T consists largely of ordinary (i.e. non-fuzzy) partitions of X and the associated iteration scheme is essentially the well known ISODATA process of Ball and Hall. However, in the second case, the range of T consists mainly of fuzzy partitions and the associated algorithm is new; when X consists of k compact well separated (CWS) clusters, Xi , this algorithm generates a limiting partition with membership functions which closely approximate the characteristic functions of the clusters Xi . However, when X is not the union of k CWS clusters, the limi...

Lung volumes and forced ventilatory flows

Abstract: Lung volumes are subdivided into static and dynamic lung volumes. Static lung volumes are measured by methods which are based on the completeness of respiratory manoeuvres, so that the velocity of the manoeuvres should be adjusted accordingly. The measurements taken during fast breathing movements are described as dynamic lung volumes and as forced inspiratory and expiratory flows. ### 1.1 Static lung volumes and capacities The volume of gas in the lung and intrathoracic airways is determined by the properties of lung parenchyma and surrounding organs and tissues, surface tension, the force exerted by respiratory muscles, by lung reflexes and by the properties of airways. The gas volumes of thorax and lung are the same except in the case of a pneumothorax. If two or more subdivisions of the total lung capacity are taken together, the sum of the constituent volumes is described as a lung capacity. Lung volumes and capacities are described in more detail in § 2. #### 1.1.1 Determinants Factors which determine the size of the normal lung include stature, age, sex, body mass, posture, habitus, ethnic group, reflex factors and daily activity pattern. The level of maximal inspiration (total lung capacity, TLC) is influenced by the force developed by the inspiratory muscles (disorders include e.g. muscular dystrophy), the elastic recoil of the lung (disorders include e.g. pulmonary fibrosis and emphysema) and the elastic properties of the thorax and adjacent structures (disorders include e.g. ankylosis of joints). The level of maximal expiration (residual volume, RV) is determined by the force exerted by respiratory muscles (disorders include e.g. muscle paralysis), obstruction, occlusion and compression of small airways (disorders include e.g. emphysema) and by the mechanical properties of lung and thorax (disorders include diffuse fibrosis, kyphoscoliosis). Assessing the total lung capacity is indispensable in establishing a restrictive ventilatory defect or in diagnosing abnormal lung distensibility, as may occur in patients …